DISSERTATION o Attribution

COPYRIGHT AND CITATION CONSIDERATIONS FOR THIS THESIS/ DISSERTATION

o Attribution — You must give appropriate credit, provide a link to the license, and indicate ifchanges were made. You may do so in any reasonable manner, but not in any way thatsuggests the licensor endorses you or your use.

o NonCommercial — You may not use the material for commercial purposes.

o ShareAlike — If you remix, transform, or build upon the material, you must distribute yourcontributions under the same license as the original.

How to cite this thesis

Surname, Initial(s). (2012) Title of the thesis or dissertation. PhD. (Chemistry)/ M.Sc. (Physics)/ M.A. (Philosophy)/M.Com. (Finance) etc. [Unpublished]: University of Johannesburg. Retrieved from: https://ujcontent.uj.ac.za/vital/access/manager/Index?site_name=Research%20Output (Accessed: Date).

http://www.uj.ac.za/

https://ujdigispace.uj.ac.za/

POINT-OF-SALE DATA IN DEMAND PLANNING IN

CLOTHING INDUSTRY SUPPLY CHAINS

by

DOUGLAS NJABULO RAZA 201134897

DISSERTATION

Submitted in fulfilment of the requirements for the degree

of

Magister commercii

in

Logistics Management

Faculty of Management

UNIVERSITY OF JOHANNESBURG

Supervisor: Dr P.J. Kilbourn

November 2016

i

DECLARATION

DECLARATION

I certify that the minor dissertation/dissertation/thesis submitted by me for the degree Master’s

of Commerce (Logistics Management) at the University of Johannesburg is my independent

work and has not been submitted by me for a degree at another university.

DOUGLAS NJABULO RAZA

November 2016

ii

ACKNOWLEDGEMENTS My first acknowledgement goes to Jehovah God Almighty the giver and ‘sustainer’ of

strength and life. All glory and honour be to Him.

I thank my family, friends and colleagues who have been supportive at the various

stages of this study and I pray for blessings for each of you who had any form of

contribution to this research.

I also thank Linda Nontokozo Ndlovu who helped in the transcription of some of the

recorded interviews and who was also supportive and motivating at different points of

this study.

I would also like to thank my research supervisor, Dr Peter J. Kilbourn, who has been a

mentor and has guided me throughout the course of this study. With his support and

encouragement, I managed to overcome challenges and obstacles at various points of

this research.

My gratitude also goes to the Department of Transport and Supply Chain Management

at the University of Johannesburg for providing the space, personnel and resources to

ensure the continual progress and success of this project.

This study is dedicated to my late mother, Edith Tembi Tshuma, who was a pillar of

strength in all my academic endeavours.

‘True education means more than the pursual of a certain course of study. It means

more than a preparation for the life that now is. It has to do with the whole being and

with the whole period of existence possible to man.’

- E.G. White

iii

ABSTRACT In modern days’ dynamic and ever-changing consumer markets that are characterised

by the ‘empowered consumer’ and shorter product life cycles, supply chains need to be

value driven and consumer oriented. The South African clothing industry is one of the

industries that has a consumer market typified by this trend. A valuable feature in

consumer-oriented supply chains is an understanding of what the consumer needs are.

This is essential as consumers have become an increasingly fundamental part of the

value creation processes. Demand planning as a supply chain task provides the supply

chain members with an opportunity to better understand the nature of consumer

demand.

From the literature review it is evident that demand planning is a supply chain activity

concerned with effective management of demand and requires improving forecasts,

reducing costs, minimising risk, and increasing sales and profit. Demand planning is

fundamental in supply chain management (SCM) as it allows the supply chain members

to focus on the consumer and effectively and efficiently create optimal value. This

understanding of consumer demand ensures that the supply response is suited for the

demand thereof. In demand planning, point-of-sale (POS) data is an essential and

highly valuable input to the process thereof; however, literature suggests that POS-

based demand planning is one of the least utilised and often overlooked demand

planning approaches.

This study focused on the South African clothing retail industry; an industry that

accounted for 21% of South Africa’s total retail sales in 2014 and reported for having

experienced continual growth in the past few years. Effective SCM has also become a

critical issue in the clothing industry because high standards of customer service must

be maintained throughout the supply chain. Clothing manufacturers and retailers also

need to achieve a balance between their demand and supply processes and therefore

effective demand planning is an important activity in this industry.

The main purpose of the study was to determine the extent to which businesses in the

South African clothing retail industry use POS data in demand planning. Furthermore,

iv

the study sought to determine the nature of the demand planning process and the

systems employed for the collection and dissemination of POS data in the South African

clothing retail industry. Another purpose of the study was to determine the way POS

data is used in the demand planning process within this industry.

This study followed the grounded theory approach based on the collection of qualitative

data through interviews with employees of sampled companies in the South African

clothing retail industry. The respondents to these interviews were people who work as

demand planners in the respective companies. The study discusses the various issues

of the demand planning process performed by clothing retailers in South Africa and

more specifically the usage of POS data in the process thereof.

The overall findings were that companies within the South African clothing retail industry

make use of POS data and it plays a fundamental role in the demand planning process

and that POS data is considered an important input factor in the demand planning

process. However, the results of the study indicate that POS data cannot be applied in

the planning for all types of clothing products, and as much as POS data is a

fundamental factor in the demand planning process, there are other variables that also

form a critical part of the demand planning process.

v

TABLE OF CONTENTS DECLARATION ............................................................................................................... i ACKNOWLEDGEMENTS ............................................................................................... ii ABSTRACT ................................................................................................................... iii TABLE OF CONTENTS .................................................................................................. v

LIST OF FIGURES.......................................................................................................... x

LIST OF TABLES ......................................................................................................... xii LIST OF ACRONYMS .................................................................................................. xiii

CHAPTER 1: INTRODUCTION ...................................................................................... 1

1.1 Background ............................................................................................................ 1

1.1.1 Consumer focus and collaborative planning .................................................... 2

1.1.2 Demand forecasting and demand planning ..................................................... 3

1.1.3 Point-of-sale data in demand planning ............................................................ 5

1.1.4 The South African clothing industry ................................................................. 5

1.2 Definition of the problem statement ....................................................................... 6

1.3 Motivation and delineation of the study .................................................................. 8

1.4 Research questions ............................................................................................... 9

1.4.1 Main question .................................................................................................. 9

1.4.2 Investigative questions .................................................................................... 9

1.5 Objectives of the study ........................................................................................... 9

1.5.1 Primary objective ............................................................................................. 9

1.5.2 Secondary objectives ....................................................................................... 9

1.6 Limitations of the study ........................................................................................ 10

1.7 Research methodology ........................................................................................ 10

1.8 Study outline ........................................................................................................ 11

1.8.1 Chapter 2: Demand planning and the significance of POS data .................... 11

1.8.2 Chapter 3: The South African clothing retail industry ..................................... 11

1.8.3 Chapter 4: Research methodology ................................................................ 11

vi

1.8.4 Chapter 5: Research findings ........................................................................ 11

1.8.5 Chapter 6: Interpretation, conclusions and recommendations ....................... 12

CHAPTER 2: DEMAND PLANNING AND THE SIGNIFICANCE OF POS DATA ....... 14

2.1 Introduction .......................................................................................................... 14

2.2 The concept of demand planning ......................................................................... 14

2.3 Demand planning in supply chains ...................................................................... 16

2.3.1 The need for demand planning ...................................................................... 16

2.3.2 Collaboration and information sharing in demand planning ........................... 20

2.4 The process of demand planning ......................................................................... 24

2.5 The case for POS data in demand planning ........................................................ 26

2.5.1 POS data defined .......................................................................................... 27

2.5.2 The case for POS data-based demand planning ........................................... 30

2.5.3 Shortfalls of POS data ................................................................................... 33

2.6 Frameworks for the application POS data-based demand planning .................... 34

2.6.1 Multi-tiered causal analysis (MTCA) .............................................................. 34

2.6.2 POS data as a performance enhancing technique (PET) .............................. 35

2.7 Conclusion ........................................................................................................... 37

CHAPTER 3: THE SOUTH AFRICAN CLOTHING RETAIL INDUSTRY ..................... 40

3.1 Introduction .......................................................................................................... 40

3.2 The clothing industry ............................................................................................ 40

3.3 The global clothing supply chains ........................................................................ 41

3.3.1 Clothing industry liberalisation ....................................................................... 42

3.3.2 Global clothing industry supply chains ........................................................... 42

3.3.3 Retailing in the clothing industry .................................................................... 44

3.4 South African clothing supply chains ................................................................... 45

3.4.1 Historical background .................................................................................... 45

vii

3.4.2 The imports threat .......................................................................................... 46

3.5 The South African clothing retail industry ............................................................. 47

3.5.1 Retailing in South Africa................................................................................. 47

3.5.2 Clothing retailing ............................................................................................ 49

3.5.3 Clothing retail market contribution ................................................................. 50

3.5.4 Category performance ................................................................................... 51

3.5.5 Industry growth .............................................................................................. 52

3.5.6 Key industry players ...................................................................................... 53

3.6 Demand planning in the clothing retail industry .................................................... 55

3.6.1 POS data in clothing retail ............................................................................. 55

3.6.2 POS data collection and transmission technologies ...................................... 57

3.6.3 POS data usage in the South African clothing retail industry ......................... 57

3.7 Conclusion ........................................................................................................... 58

CHAPTER 4: RESEARCH METHODOLOGY .............................................................. 61

4.1 Introduction .......................................................................................................... 61

4.2 Research approach .............................................................................................. 62

4.3 Research strategy ................................................................................................ 63

4.3.1 Grounded theory ............................................................................................ 64

4.3.2 Case study approach ..................................................................................... 64

4.3.3 Literature review ............................................................................................ 65

4.4 Data collection: Interviews ................................................................................... 65

4.4.1 Semi-structured interviews ............................................................................. 67

4.4.2 Recording interview data ............................................................................... 67

4.5 Sampling .............................................................................................................. 68

4.5.1 Non-probability sampling ............................................................................... 69

4.5.2 Purposive sampling ....................................................................................... 70

4.5.3 Sampling frame .............................................................................................. 70

4.5.4 Sampling unit ................................................................................................. 71

4.6 Data analysis ....................................................................................................... 71

viii

4.6.1 Transcribing ................................................................................................... 72

4.6.2 Data analysis ................................................................................................. 72

4.7 Ethical considerations .......................................................................................... 73

4.8 Conclusion ........................................................................................................... 74

CHAPTER 5: DATA ANALYSIS AND PRESENTATION OF RESEARCH FINDINGS 77

5.1 Introduction .......................................................................................................... 77

5.2 Data capture and organisation ............................................................................. 77

5.3 Data analysis ....................................................................................................... 77

5.3.1 Approach to analytical rationale ..................................................................... 77

5.3.2 Analytical procedure: The grounded theory ................................................... 78

5.4 Computer-assisted qualitative data analysis software (CAQDAS) ....................... 79

5.4.1 Coding and data categorisation ..................................................................... 80

5.5 FINDINGS ............................................................................................................ 82

5.5.1 The positioning of a demand planner within the SA clothing retail companies ................................................................................................................................ 82

5.5.2 The role of a demand planner within the SA clothing retail companies .......... 84

5.5.3 Demand planning process in the SA clothing retail industry .......................... 85

5.5.4 Structure of SA clothing industry supply chains ............................................. 86

5.5.5 Nature of products sold within the SA clothing retail industry ........................ 87

5.5.6 The collection and dissemination of POS data .............................................. 88

5.5.7 The importance of demand planning in clothing retail .................................... 91

5.5.8 The usage of POS data ................................................................................. 92

5.5.9 The usage of POS data in demand planning ................................................. 94

5.5.10 Other organisational functions involved in the process ................................ 97

5.5.11 Problems in POS-based demand planning .................................................. 99

5.5.12 The future of POS data in demand planning in the SA clothing retail industry .............................................................................................................................. 100

5.6 CONCLUSION ................................................................................................... 101

ix

CHAPTER 6: CONCLUSION, INTERPRETATION AND RECOMMENDATIONS ..... 104

6.1 Introduction ........................................................................................................ 104

6.2 Literature review theoretical constructs .............................................................. 104

6.2.1 Demand planning and the role of POS data ................................................ 105

6.2.2 The South African clothing retail industry .................................................... 107

6.3 Research methodology ...................................................................................... 109

6.4 Interpretation of the research findings ................................................................ 109

6.4.1 Secondary objective 1 ................................................................................. 110




6.5 Recommendations ............................................................................................. 113

6.6 Future research .................................................................................................. 117

6.7 Conclusion ......................................................................................................... 118

LIST OF SOURCES .................................................................................................... 120

ANNEXURE A: Letter of Non-disclosure ................................................................. 128

ANNEXURE B: Interview questionnaire .................................................................. 129

x

LIST OF FIGURES Figure 2.1: Relationship of supply chain, logistics and demand planning 15

Figure 2.2: Bullwhip effect – increased variability of orders up the supply chain 18

Figure 2.3: Collaborative demand planning process 22

Figure 2.4: CPFR model 23

Figure 2.5: Demand planning process 25

Figure 2.6: Supply chain relationships and POS data point of capture 27

Figure 2.7: Discrepancies between orders placed and actual sales 31

Figure 3.1: Apparel global value chain 43

Figure 3.2: The supply chain in the textile and clothing sector 43

Figure 3.3: Real annual percentage growth in retail sales (2003–2013) 47

Figure 3.4: Retail trade sales in South Africa: 2009–2014 49

Figure 3.5: Retail market shares: general dealers and CFT retailers 50

Figure 3.6: Retail trade sales for 2014 51

Figure 3.7: South African apparel retail industry category segmentation:

percentage share by value for 2014 52

Figure 3.8: Retail annual sales in 2014 by type of retailer 52

Figure 3.9: South African apparel retail industry value: $ billion, 2010–2014 53

Figure 4.1 Steps in the research process 61

Figure 4.2: The ‘research onion’ 62

Figure 4.3: Classification of qualitative research methods 66

xi

Figure 4.4: Sampling methods 69

Figure 5.1: Grounded theory analysis process 80

Figure 5.2: Various titles used for demand planners in clothing retail companies 83

Figure 5.3: Other roles of demand planners 85

Figure 5.4: Product groupings as experienced by planners in terms of level of

Volatility 87

Figure 5.5: Technologies used within demand planning in the industry 90

Figure 5.6: Summary of the responses on the importance of demand planning in

Retail 92

Figure 5.7: Other input factors in demand planning 97

Figure 5.8: The organisational functions involved in demand planning 98

xii

LIST OF TABLES Table 2.1: Sources of POS data 28

Table 2.2: Strengths and weaknesses of direct POS and syndicated POS 29

Table 2.3: Calculating expected demand from a POS plan 36

Table 3.1: Top ten retail companies in Africa 48

Table 4.1: Distinctions between quantitative and qualitative data 63

Table 4.2: Advantages and disadvantages of audio recording the interview 77

Table 4.3: Summary of the research methodology 75

xiii

LIST OF ACRONYMS ATC: Agreement on Textiles and Clothing

APICS: American Production and Inventory Control Society

ARP: Automatic Replenishment Programs

BOH: Beginning Inventory on Hand

CAQDAS: Computer-Assisted Qualitative Data Analysis Software

CFT: Clothing, Footwear and Textiles

CGCSA: Consumer Goods Council of South Africa

CPFR: Collaborative Planning, Forecasting and Replenishment

CPG: Consumer Packaged Goods

CSCMP: Council of Supply Chain Management Professionals

CTFL: Clothing, Textiles, Footwear and Leather

DF: Demand Forecasters

DC: Distribution Centre

EDI: Electronic Data Interchange

EOH: Ending Inventory on Hand

ERP: Enterprise Resource Planning

GDP: Gross Domestic Product

GVC: Global Value Chain

IRI: Information Resource Inc

KPI: Key Performance Indicators

MRP: Materials Requirement Planning

xiv

MTCA: Multi-Tiered Causal Analysis

NCRF: National Clothing Retail Federation of South Africa

PET: Performance Enhancing Technique

POS: Point of Sale

POPI: Protection of Personal Information

RDF: Replenishment and Demand Forecasting

SA: South Africa

SCM: Supply Chain Management

SIC: Standard Industrial Classification

SKU: Stock Keeping Units

ST: Sell Through

UPC: Universal Product Code

VMI: Vendor Managed Inventory

W&R Seta Wholesale and Retail Sector Education and Training Authority

WTO: World Trade Organisation

YDE: Young Designers Emporium

YTD: Year-To-Date

1

CHAPTER 1: INTRODUCTION

1.1 Background

Supply chain management (SCM) as a business concept has gained prominence in

the business world over recent years. Introduced in early 1980, SCM has grown in

importance since the early 1990s (Habib, 2011). Furthermore, there has been a

notable transition in literature from the usage of the term ‘supply chain’ to the term

‘value chain’ (Jüttner, Christopher & Baker, 2007). This shift to ‘value chain’ signifies

a change from a supply focus to a demand focus whereby the supply chain is viewed

as a series of points where value for the consumer is being added. ‘Today, the value

orientation is more prevalent than ever before,’ according to Jüttner et al. (2007).

Thus, a global business environment in which consumers ‘have become an

increasingly fundamental part of the value creation processes requires a new

dominant logic for marketing’ (Tiu Wright, Denegri-Knott, Zwick & Schroeder,

2006:965).

This focus on consumer demand and value addition has made supply chain

members desire an understanding of consumer buying behaviour and consumer

demand patterns. This has not been an easy task for supply chain managers. It has,

as a result, pointed to the importance of demand planning as a supply chain activity.

Responding to volatility in market conditions, and the resulting customer demand

patterns, has proved to be the biggest challenge for supply chain executives and, not

surprisingly, become a key investment area (Butner, 2010:6).

The business context of our day is characterised by consumers with high demands

and who are highly discriminating. With consumer demand driving the markets and

its resulting effect on the sales and profitability of firms, there is a far greater need to

understand the nature of demand as well as the behaviour thereof. Consumer power

has become a cliché of modern consumer culture. This is evidenced in the

consumers’ ability to ignore, resist and adapt even the sleekest and most costly

multi-media assault, and this has resulted in the reiteration of statements such as

‘customers are too smart to be fooled’, ‘consumers see through bad marketing’, ‘the

customer is king’ or ‘the customer is always right’ (Tiu Wright et al., 2006:950).

2

Supply chains often fail because of a lack of understanding of the makeup and

nature of consumer demand. This failure to understand demand leads to

mismatched supply chain design (Blecker, Kersten & Meyer, 2009). Failure to match

supply and demand has several negative implications, which include the following:

Excess inventory due to low product sales and high product supply

Stock-outs due to low product supply and high demand

Wastages resulting in a mismatch between consumer preferences and supply

High amount of back orders which are more costly

High reverse logistics costs due to market rejected products.

The volatility of the demand requires quicker reaction and response from the supply

chain as a whole; this necessitates more swiftness on the supplier’s side and thus

the emergence of agile supply chains. ‘Agile supply chains are designed for

flexibility, emphasising the supply chain’s ability to respond rapidly to demand

changes, both in terms of volume and variety’ (Jüttner et al., 2007:6). The concept of

agile supply chains is even more important in the clothing retail industry, which will

be the focus of this study. ‘Clothing is increasingly considered as a perishable good

where time to market matters. This will render producers in more remote locations at

a disadvantage, particularly in the fashion segments of the clothing industry’

(Nordås, 2004:1).

This study seeks to discuss and explore the concept of demand planning and the

use of point-of-sale data in the clothing retail industry in South Africa – one of the

major retail sectors in South Africa. In addition to cost effectiveness, the competitive

advantage of firms in the clothing market segment is related to the ability to produce

designs that capture tastes and preferences, and even better, influence such tastes

and preferences (Nordås, 2004). This sustains the point that understanding

consumer demand and behaviour has become an indispensable tool to gaining

competitive advantage.

1.1.1 Consumer focus and collaborative planning

Consumer focus within supply chains has led businesses to focus on value creation

and this has resulted in business functions being more integrated with the mission of

3

collaboratively creating the product with the optimal value for the consumer. The

success or failure of supply chains is ultimately determined in the marketplace by the

end consumer (Christopher & Towill, 2001). Improved customer satisfaction can be

achieved in supply chains through market understanding. Businesses have the

objective of achieving the best performance from their supply chains through

different tools and strategies, which include accurate demand forecasting, inventory

and agile supply chain as the major components (You & Grossmann, 2008).

The value of collaboration in the business forecasting process cannot be understated

(Mael, 2011). The central focus of intra-firm and inter-firm collaboration is to achieve

the optimal service for the customer. This necessitates the sharing of information

and achieves full advantage of information sharing; as a result companies may need

to redesign their processes and facilities (Bowersox, Closs & Stank, 2000). A smooth

information flow from the front-end customer interaction back into production is

necessary, but on its own is insufficient. The required response action needs to be

determined and executed well to match the needs identified from the shared

information (Jüttner et al., 2007).

Collaboration to create optimal customer value has crossed business boundaries

resulting in inter-firm collaboration within the same supply chain, the product of this

being ‘integrated supply chains’. A key feature that has developed over time is the

idea that it is supply chains that compete, not companies (Christopher & Towill,

2001). The growing trend towards integrated supply chains shows the leveraged

benefits of businesses collaborating to attain the same goals (Bowersox et al.,

2000:4). Cooperative planning between trading partners facilitates better matching of

supply and demand. Partners within supply chains have moved from the tendency of

independently determining forecast to a practice of collaborating to develop realistic,

informed and detailed estimates that can be used to guide business operations

(Stank, Daugherty & Autry, 1999).

1.1.2 Demand forecasting and demand planning

Demand forecasting can be seen as a series of steps to establish the amount of

product and related information that consumers will need in the future, be it in the

short or long term (Pienaar & Vogt, 2012).The forecasting of how much future

4

demand there will be is a necessary activity within supply chains, and provides

vendors and manufacturers with the idea of how much inventory they should pre-

stock. Demand forecast establishes the volume of products, place and time horizon

in which they will be needed (Vlckova & Patak, 2010).

Demand forecasting and the subsequent setting of inventory levels is a complex task

due to the effect of promotions, changing demand patterns and competitive

pressures (Stank et al., 1999:76). There are therefore many factors to consider when

carrying out the process of forecasting and this includes both qualitative and

quantitative factors. Changes in market dynamics have brought many changes to the

forecasting strategy, one particular way being that the increased power of the

consumer has led the process of forecasting to become more demand driven and

less supply driven. Focus is more on demand pull rather than demand push

(Christopher & Towill, 2001).

A more specific activity in business collaborative planning, which is a development of

demand forecasting, is demand planning. Demand planning represents a set of

methodologies and information technologies for the use of demand forecasts in the

process of planning (Vlckova & Patak, 2010:1119). Vlckova and Patak (2010:1121)

state that different steps can be followed in demand planning and the demand

planning method of the company can be divided into six steps:

Understand essential forecast principles

Integrate systems for forecasting and planning

Identify key factors influencing the demand level

Identify and understand customer segments

Select appropriate forecasting techniques

Build a system for measuring performance and error rate of forecasts.

Demand planning is the key driver of the supply chain. Without knowledge of

demand, manufacturing has very little on which to develop production and inventory

plans for products among different warehouses and customers (Pienaar & Vogt,

2012). Overall, a demand-oriented approach to planning can greatly enhance

demand planning and improve overall costs and customer service efforts. Accurate

demand planning provides several benefits, for example, it allows manufacturing to

5

delay production of anticipatory stock. Furthermore, it can result in shorter, more

predictable order cycles for the retailer (Stank et al., 1999:82).

1.1.3 Point-of-sale data in demand planning

The usefulness of demand planning depends largely on the data and information

flows used in forecasting and planning (Andres, 2008). The key data needed for

supply chain planning at the retail level are the actual sales to consumers at each

retail outlet, which is called point of sale (POS) data. Of the three data streams,

namely shipment data, customer order and POS, POS is the best; this is because it

is free from order and demand variability and inventory decisions. Inventory

decisions can hide the underlying sales trend (Borgos, 2008).

POS data are the data of sales to end consumers – sale transactions recorded or

captured at the checkout counter. When a customer purchases a product, the teller

scans the product and that generates a till slip but beyond that, information is

recorded into the database. The recorded information contains the detail such as

place of purchase, product price, date and time of purchase (Andres, 2008). With the

present-day use of electronic cash registers, it has become possible to capture data

on individual retail transactions and store them in databases for subsequent analysis

(Borgos, 2008).

1.1.4 The South African clothing industry

The South African clothing industry is not an exception to the global phenomenon of

high competition; it has a complex set of dynamic linkages of value chains between

the producers and the retail outlets. ‘The South African domestic clothing market is

also similar in that it is dominated by a powerful retail sector whose market power

effectively subordinates the producers’ (Nattrass & Seekings, 2012). ‘The strong

impact of globalisation and delocalisation in the organisation of work is pressurising

the industry in terms of its competitiveness. Global competitiveness in terms of

quality, price and supply chain management are reducing the viability of the industry’

(Kruger & Ramdass, 2011:2562).

The South African clothing industry has large retail outlets. ‘Their products are

obtained from a range of sources such as China, low-wage producers in non-metro

6

South Africa and neighbouring countries’ (Nattrass & Seekings, 2012:20). Business

Partners (2014) claim that ‘In 2013, the Clothing, Textiles, Footwear and Leather

(CTFL) industry accounted for about 14% of manufacturing employment and

represented South Africa’s second largest source of tax revenue. The industry

contributes around 8% to the country’s gross domestic product (GDP)’.

For retail clothing business, present market changes and volatility require more

product variety, which generates demand uncertainty and supplier variability.

Managers are faced with great challenges with respect to supplier management,

product forecasting, inventory management, timely distribution and customer

satisfaction (Wu, 2004).

With the clothing industry making a significant contribution to GDP and employment

in South Africa, the topics of efficiency and customer service effectiveness become

more critical. This is substantiated by the fact that customers are the drivers of this

market and its profitability. As the industry is faced with high levels of demand

uncertainty, it is therefore necessary to delve into the subject of improving demand

plans and forecasts through the usage of POS data.

1.2 Definition of the problem statement

With high levels of demand volatility and difficult demand predictability experienced

in various consumer markets, the purpose and value of demand planning comes into

question. Because of this high level of volatility, figures of past performance in sales

quickly become obsolete and thus are not viable for use in forecasting future

demand. Businesses are faced with different inconsistencies that disturb customer

demand patterns and these inconsistencies range from financial to physical

constraints. As a result, the challenge of the transformation of historical sales

performance into a demand forecast for future activity is not sufficient to forecast

future events (Mael, 2011).

Supply chains today are complex and characterised by a high level of member

interdependencies which necessitates collaboration to reduce pressure from the

market. Businesses are facing pressures such as on-time-to-market, mass

customisation, lead-time extensions, capacity inflexibilities, and pressures on

7

working capital (Fay, 2010:32). It is therefore important for business to monitor the

factors that cause demand volatility and act proactively as volatility can completely

destabilise the supply chain (Mael, 2011).

Demand is a function of different factors; demand planning seeks to put these factors

in perspective and use them to determine the most likely demand patterns. These

factors are numerous and of these, some can be quantified, for example the inflation

rate, and some cannot, for example seasonality. This demonstrates that the process

of demand planning is not an easy task. Beyond predicting and estimating future

demand, demand planning seeks to influence demand with the intention of meeting

the sales and profit objectives. This presents a greater challenge as it may involve

influencing the factors that affect demand.

POS data is said to present the best reflection of customer demand over other types

of information like shipment data or orders received from retailers. These other types

of information tend to perpetuate backorders since stock-outs distort the shipment

history. Furthermore, visibility into true consumer demand is lost as there is a time

lag between shipments to the store and when POS occur. There is need, therefore,

for businesses to collect and use POS in their process of demand planning (Tolbert,

2008).

In the context of this study, the clothing retail industry can be said to be one of the

most complex markets for the demand planner due to its nature and the ever-

changing tastes, preferences and fashion trends. The attempt to align demand to

meet sales objectives is further complicated by the abstract definition of fashion

trends and the fact that one firm has little leverage in influencing market trends. POS

data is one tool in particular that retailers in this industry can potentially leverage in

order to deal with the predicament of high demand uncertainty.

The problem is that amid the ever-increasing importance and need for accuracy in

demand planning and the complexity of consumer markets, there is lack of

information and uncertainty about the use of POS data by South African clothing

retail firms in demand planning.

8

1.3 Motivation and delineation of the study

Timely information is one of the most valuable assets that firms possess, hence the

term knowledge asset management. The study focuses on POS data, which is one

of the types of information most valued by firms within consumer-driven supply

chains. The study should be able to assist supply chain members to leverage this

type of data to predict consumer demand and market trends, and so potentially gain

competitive advantage. Beyond this, the study will also compare the South African

demand planning methods and information management tools with those used by

the rest of the global competitors and recommend any necessary changes and

developments that can be made.

In the present day of complex economies and volatile markets such as pertain to the

clothing industry, information sharing has become inevitable among different supply

chain members. The study investigates the multi-directional flow of information,

which is upstream and downstream in supply chains, which should allow the

identification of the various information needs at each level of the supply chain and

how the information flows can be made more effective.

The study investigates current demand planning tools as practically used in the

supply chain. This investigation of the various methods allows deductions to be

made on the most useful tools in modern consumer markets. Furthermore, the study

identifies the information requirements for the effective use of these demand

planning tools. This allows all members in the respective supply chains to have the

same prioritisation in information disclosure and provision.

Research findings add value to the body of knowledge, no matter the how minute the

discovery is. In the same way, the findings of this study add more information to the

body of knowledge specifically in the field of SCM and demand planning.

The results of the study assist in providing further insight into the usefulness of POS

data in demand planning and bring to light the extent of the use of POS data in

demand planning by South African retail companies. From the literature review, it

appears that little research has been done on the usage of point of sale (POS) data

in demand planning within the South African clothing retail industry. This study seeks

to solve this problem.

9

1.4 Research questions

1.4.1 Main question

• To what extent do firms in the South African clothing industry use point-of-sale

data in demand planning and how can the use of point-of-sale data be

increased?

1.4.2 Investigative questions

• How is demand planning performed in the South African clothing retail

industry?

• What systems are employed for the collection and dissemination of point-of-

sale data in the South African clothing industry?

• How is point-of-sale data used in the demand planning process in the South

African clothing industry?

• How can the use of point-of-sale data be improved or enhance in demand

planning?

• What challenges and problems are faced by South African clothing retailers in

the collection, dissemination and usage of point-of-sale data in demand

planning?

1.5 Objectives of the study

The objectives of the study are as follows

1.5.1 Primary objective

• To determine the extent to which businesses in the South African clothing

retail industry use point-of-sale data in demand planning

1.5.2 Secondary objectives

• To determine the nature of the demand planning process as performed in the

South African clothing retail industry

10

• To determine the systems employed for the collection and dissemination of

point-of-sale data in the South African clothing industry

• To determine the way point-of-sale data is used in the demand planning

process within the South African clothing industry

• To determine ways improving the usage of point-of-sale data in demand

planning in a South African clothing retail context

• To find out the challenges and problems that are faced by South African

clothing retailers in the collection, dissemination and usage of point-of-sale

data in demand planning

1.6 Limitations of the study

The research findings and conclusions drawn will be based on the data collected

from the samples. The limitation associated with purposive sampling in this case is

that the sample may not be the best representation of the population thus results

cannot be generalised. However, the study is based on major clothing retailers and

consequently various industry players can benefit from the study by benchmarking

themselves to described practices and drawing valuable lessons.

Identification of businesses that form part of the sampling frame will be from memos

from associations under which these businesses are registered, for example, the

National Clothing Retail Federation of South Africa (NCRF). This could be a

limitation as not all companies are registered under these associations. However, the

companies registered with these associations form a major part of the South African

clothing retail industry.

1.7 Research methodology

A further literature review was done to define and describe the theoretical constructs

as well as to determine existing research on the subject matter to enable the

identification of the research gaps. Different types of literature were used including

journal articles, government reports, company documents and textbooks.

Qualitative research was done using face-to-face interviews to collect information on

the practice of demand planning as performed by demand planners in the South

African clothing retail industry, and the extent to which POS data is used in the

11

demand planning process. The collected information was thematically analysed

following the grounded theory approach to draw conclusions and recommendations.

1.8 Study outline

The study is set out as follows:

1.8.1 Chapter 2: Demand planning and the significance of POS data

This chapter describes the fundamental concepts that form part of the subject under

study. Existing views by other authors and researchers are presented and described.

It specifically addresses the process of demand planning and the POS data concept.

Covered in this is the role of demand planning in supply chains, the role of POS in

demand planning, the clothing industry and its contribution to effective demand

planning, and the South African clothing industry.

1.8.2 Chapter 3: The South African clothing retail industry

This chapter describes the South African clothing retail industry in detail. The nature,

structure and performance of the industry is discussed. A comparison is made

between local clothing supply chains and global clothing supply chains. Also

discussed is this chapter is the importance of effective SCM and effective demand

planning in this type of industry.

1.8.3 Chapter 4: Research methodology

The various aspects of the chosen research methodology including the research

approach, research design, sampling, and the data collection tool is discussed in this

chapter. The selection of the interview respondents is also discussed.

1.8.4 Chapter 5: Research findings

This chapter presents, describes and analyses the findings that are be made from

the primary data collected through the interviews. The data analysis procedure and

the results are discussed.

12

1.8.5 Chapter 6: Interpretation, conclusions and recommendations

This chapter makes interpretations, draws conclusions and makes recommendations

based on the findings made in Chapter 5. A discussion follows to show achievement

of the research objectives and to identify the areas for potential future research.

14

CHAPTER 2: DEMAND PLANNING AND THE SIGNIFICANCE OF POS DATA

2.1 Introduction

This chapter serves to describe and explain various concepts and issues

relevant to the study. Existing definitions, descriptions and claims from different

literature sources on the subject under study are discussed. The discussion

deals with the concept and role of demand planning in supply chains, the role of

POS within demand planning and its contribution to effective demand planning in

the South African clothing retail industry.

2.2 The concept of demand planning

There are several activities and processes that constitute supply chain management.

These SCM activities fall under three groupings, namely sourcing and procurement,

conversion, and logistics management (Pienaar & Vogt, 2012). The focus of this

study is on demand planning which falls under the grouping of logistics

management. Demand planning as an SCM activity is the process of using demand

forecasts to plan the supply chain response to meet the expected demand (Vlckova

& Patak, 2010).

‘Demand planning is a supply chain activity that uses sales forecast as one of

multiple inputs to create a demand plan aligned with financial goals and

inventory plans’ (PWC, 2012:2).

Viewing the process of demand planning as part of SCM emanates from the

cascading downwards of SCM-related processes. According to Pienaar and Vogt

(2012), demand planning is a logistics management activity; and logistics

management is by nature a part of the SCM concept. The descriptions by the

Council of Supply Chain Management Professionals (CSCMP) of supply chain

management and logistics management show how logistics management is related

to supply chain management and how demand planning is a logistics activity.

15

‘Supply chain management encompasses the planning and management of

all activities involved in sourcing and procurement, conversion, and all

logistics management activities’ (CSCMP, 2015).

‘Logistics management activities typically include inbound and outbound

transportation management, fleet management, warehousing, materials

handling, order fulfilment, logistics network design, inventory management,

supply/demand planning, and management of third party logistics services

providers’ (CSCMP, 2015).

From the descriptions above, the relationship between SCM, logistics management

and demand planning can be illustrated by Figure 2.1 below:

Figure 2.1: Relationship of supply chain, logistics and demand planning Source: Researcher’s illustration

A concept closely linked to demand planning is demand forecasting. For most

businesses demand forecasting is based on the mathematical extrapolation of prior

demand values into the future on the supposition that future demand will follow the

same patterns as in the past (Thomopoulos, 2015). While demand forecasting is

concerned with determining the quantities of a product that consumers will require in

the future, demand planning goes beyond this objective and challenges forecasts,

and seeks chances to grow customer demand through marketing events and

promotions to influence the forecasts to conform to the company strategy and

objectives (Blanchard, 2010).

Demand planning requires information input and insight contributions from different

functions of the business. The demand planning process is not performed on a silo

Supply Chain Management

Logistics Management

Demand Planning

16

basis or in isolation but involves a considerable amount of collaboration with other

departments including supply chain, sales, finance, marketing, operations and

consumer insights (Gallucci, 2015).

The output of demand planning has an impact on the various functions of the

business and thus it is expedient to involve these functions from the planning phase.

Representatives from the different organisational functions should be involved in the

demand planning process. To make the forecasts objective, the practice requires an

integration of numerous unbiased experts for obtaining required forecasts (Vlckova &

Patak, 2010:1121).

2.3 Demand planning in supply chains

This section is aimed at illustrating the need for effective demand planning and the

benefits that a supply chain can gain from its successful performance.

2.3.1 The need for demand planning

Businesses need to make a conscientious effort to understand the demand patterns

and consumer behaviour for their products or services; this will allow the design of

the appropriate response in supply to meet the expected demand. Consumer

demand presents multiple characteristics that businesses need to monitor and

understand, including seasonality, trend and degree of volatility. A white paper by

Hitachi Consulting (2009:1) identifies demand planning as one of the six key trends

causing a great impact on and transformation of the design of the supply chain and

the performance thereof.

Being able to predict and meet customer and consumer demand is a prerequisite for

any successful profitable business or supply chain. Companies therefore devote

substantial time, funds and effort in their pursuit of projecting demand so that

products can be available when and where they are needed (Lawless, 2014). The

complexity of present day consumer markets and the volatility of consumer demand

should be a great motivation for consumer-focused supply chains. Demand planning

presents a good opportunity for supply chain to focus on the consumer and create

optimal value. Having a demand-driven approach could assist members of the

supply chain to develop a customer-focused mind-set (Hitachi Consulting, 2009).

17

Supply chains are designed to suit the needs of the consumers. When the supply

chain is aligned to the consumer’s needs, every supply process works towards and

contributes to the same objective, thus creating optimal value for the consumer.

Bursa (2008:28) states that ‘the demand driven supply chain is a powerful weapon

for businesses of all sizes’, as companies are faced with dynamic market swings,

volatile fuel prices, unpredictable consumers, and high levels of global competition.

Demand planning allows businesses to understand the nature of consumer demand

and ensures that the supply response is suited to this demand. Failure to understand

the nature of demand for the business’s products results in a mismatch between

demand and supply; this could be an oversupply or a product shortage. The

mismatch causes the following efficiency and effectiveness problems:

Excess inventory due to low demand for the supplied product


1. Excess inventory due to low demand for the supplied product

The lack of visibility of consumer demand or the existence of inaccurate demand

forecasts may result in overstocking at different points of the supply chain.

Inaccurate demand forecasts and lack of visibility in the supply chain leads to

overstocking as orders are amplified to guard against stock-outs. This continued

amplification of orders to prevent against stock-out results in high order and demand

variability. The increase in demand variability as one moves upstream of the supply

chain results in a phenomenon termed the bullwhip effect. Figure 2.2 below

illustrates this phenomenon.

18

Figure 2.2: Bullwhip effect – increased variability of orders up the supply chain Source: Lee et al. (2004:1888)

According to Lee, Padmanabhan and Whang (2004:1887), ‘the bullwhip effect is the

amplification of demand variability from a downstream site to an upstream site’. The

bullwhip effect leads to tremendous supply chain inefficiencies that include undue

inventory investment, misguided capacity plans, poor transport plans, and missed

production schedules (Lee et al., 2004). It is important to note that excess inventory

is a considerable problem within any supply chain because of the associated high

costs. Durlinger (2012) describes the following inventory costs:

a. Capital costs: Funds are needed to finance the inventory and inventory can be

seen as money tied up that could otherwise result in a certain level of return if

invested elsewhere.

b. Handling and storage cost: These are all the costs linked to the handling and

storage of inventory.

c. Cost of risk: These are the costs associated with having the inventory in hand,

for example, obsolescence.

19

d. Reverse logistics costs owing to market rejected products: Unsold stock might

have to be moved back upstream in the supply chain to open shelf space for

other products.

2. Stock-outs due to low product supply and high demand

The other side of the coin to excess inventory is a stock-out. A stock-out results

when the ordered product or demand is greater than the available product or supply.

Like in the case of excess inventory, inaccurate demand forecasts and lack of

demand visibility may lead to inventory stock-outs. Understated demand forecasts

result in inventory shortage. Stock-outs are also associated certain costs, including:

a. Back order costs

When an order is placed and the available stock is not able to meet the order

requirements, the supplier will have to fulfil the unfulfilled part of the order at a

later time – this termed a back order. The cost of a back order would therefore

include the extra expense of handling and expediting orders that cannot be

fulfilled from the available stock (Pienaar & Vogt, 2012).

b. Cost of a lost sale

When an order cannot be readily met by a supplier, the customer may source

the product from another supplier, resulting in a lost sale. Pienaar and Vogt

(2012:215) state that ‘these costs can be measured in terms of the

contribution that is lost on that particular sale’.

c. Cost of a lost customer

Failure to meet the customer’s orders, especially if it persists, often results in

a customer permanently sourcing the product from other suppliers – the cost

of a lost customer. Congruent to the case of a lost sale would be the profit or

contribution that would have been gained from the orders that the customer

was going to place in the future (Pienaar & Vogt, 2012).

As a way of avoiding these costs, supply chain members have over time adopted

collaboration as a solution. Through different collaboration approaches and

initiatives, supply chain members aggressively work together to manage inventory.

‘The value of collaborative demand and supply planning between partners can be

understood through improved operational, financial benefits, process and

20

relationship benefits’ (Shepard, 2012:6). Instead of independently trying to estimate

demand patterns, supply chain members share information ahead of time and

collaborate to develop realistic, more accurate, informed and detailed estimates that

can be a guide to supply chain operations (Shepard, 2012).

2.3.2 Collaboration and information sharing in demand planning

Demand forecasts and demand plans have a direct impact on the objectives,

operations and tasks performed in the supply chain, from the sourcing and

procurement function to the final product distribution function. These different parts

of the supply chain are affected in the sense that the output from the process of

demand planning forms the basis of their objectives, which ultimately is to satisfy the

expected or planned demand.

In today’s volatile and complex state of consumer markets, if supply chains move

forward without a clear insight and a collaborative set of objectives and directions,

they will be faced with a high level of customer dissatisfaction, increased cost and

obsolescence, and ultimately a loss of revenue and market share (Fay 2010:32).

Consensus forecasting becomes necessary to be able to overlay judgement over the

statistical forecasts. Figure 2.3 illustrates the collaborative demand planning process.

Figure 2.3: Collaborative demand planning process Source: Crum and Palmatier (2003:29)

21

The impact of these demand plans on different business functions brings into focus

the need to involve them from the beginning. Submitting arbitrary demand plans to

different members of the supply chain without involving them in the actual planning

process or without requesting their input can be catastrophic. This could be due to

one of the following major reasons:

Demand is dependent on many causal factors and thus a failure to integrate

these factors into the demand plan results in uninformed and highly

inaccurate forecasts and demand plans (Stank, Daugherty & Autry, 1999:76).

To improve forecasts, there is a need for cross-functional collaboration

because no one has all the information.

The motivation to adopt and implement demand plans by the rest of the

supply chain members largely depends on their involvement in the

development of these plans. Thus, plans that are developed by only one

member of the supply chain will not be quickly adopted by the rest of the

supply chain members (Shepard, 2012).

Based on available historical data, demand planners can mathematically determine

the demand forecast by using different forecasting techniques that already exist.

These statistical forecasts provide a solid foundation with which to work; however,

these forecasts will be highly inaccurate if other demand causal factors are not

factored in. Forecasts will be more valuable after the overlaying of knowledge that

systems cannot possibly know (Blanchard, 2010). Businesses in the supply chain

need to ‘deploy internal collaboration before external collaboration, recognising that

the closer you get to the true demand signal, the better the forecast will be’

(Blanchard, 2010:54).

Collaboration needs to be between the supply chain members as well as between

the different functions of the businesses within the supply chain. This will ensure that

the benefits of collaboration realised within the supply chain are also realised within

each supply partner’s organisation. Demand planning should be viewed as a sub-

activity to the overall sales and operations planning instead of a rather than a

separate unconnected activity. Businesses in the supply chain should “build an

integrated business plan that is a cross-company activity and which directs the rest

22

of the business forward for profitably meeting customer demand” (Blanchard,

2010:54).

Information sharing within the supply chain is a prerequisite to ensuring demand

visibility and ensuring effective demand planning. Demand visibility entails being able

to see undistorted and accurate demand such that there will be enough time to

respond to it. Schrieber (2005) contends that, the more visible the demand, the

better the chance of accurate demand forecasts. Visibility does not only imply the

ability to track inventories and materials in the supply chain but also that information

regarding available resources can be effectively evaluated and managed.

Different information sharing models exist and these include product information

sharing, process information sharing, resource information sharing and inventory

information sharing (Ma, Wang, Che, Huang & Xu, 2013). Ma et al. (2013) further

state that, despite the information-sharing model used, information sharing generally

results in a reduction of the magnitude of the bullwhip effect as there will be reduced

information distortion.

Different forms of collaborative initiatives exist and these can be implemented to

ensure that all the relevant members of the supply chain are involved in demand

planning. The following section discusses how some of these initiatives can facilitate

collaboration in demand planning:

Collaborative planning, forecasting, and replenishment (CPFR)

Vendor managed inventory (VMI)

Automatic replenishment programs (ARP)

1. Collaborative planning, forecasting, and replenishment (CPFR)

CPFR is, as the name says, a collaboration strategy where all supply chain members

are involved in the planning, forecasting and replenishment of materials and

information. CPFR is formally defined by The American Production and Inventory

Control Society (APICS) as:

23

‘A collaboration process whereby supply chain trading partners can jointly

plan key supply chain activities from production and delivery of raw materials

to production and delivery of final products to end customers.’

Dong, Huang, Sinha and Xu (2014) provide a CPFR model, illustrated in Figure 2.4,

which shows the involvement of different supply chain members in the planning and

replenishment processes.

Figure 2.4: CPFR model Source: Dong, Huang, Sinha and Xu (2014:248)

Forecasting demand is a challenging task as a result of the influence of promotions,

volatile consumer buying patterns, and competitive pressures (Shepard, 2012).

Different members of the supply chain possess different sets of information, each of

which is vital to the process of demand planning. Each partner has knowledge of the

causal factors contributing to demand levels. As such CPFR allows supply chain

members to have access and visibility to a single forecast value and also ensures

that all supply chain links can contribute their knowledge of causal factors (Ireland &

Crum, 2005). Over and above the improved visibility and forecast accuracy, CPFR

also yields the following benefits:

Supply chain relationships are strengthened

Problems can be identified and eliminated proactively

24

Further collaboration on future plans is promoted

Key performance metrics can be analysed

It allows supply chain members to better understand the consumer buying

behaviour (Ireland & Crum, 2005).

2. Vendor managed inventory (VMI)

This is a collaborative strategy that requires a great deal of downstream supply chain

visibility and information sharing between the retailer and the supplier of the

concerned products. VMI is a technique that was developed in the mid-1980s, and

rests the responsibility of inventory policy management as well as the replenishment

process on the product supplier (Barratt & Oliveira, 2001). This collaborative strategy

is only appropriate when there is trust and extensive information sharing between the

retailer and the supplier (Barratt & Oliveira, 2001). An effective VMI program will

enable the supplier to move products to the customer when the POS stock level

prompts a replenishment order (Tolbert, 2008).

3. Automatic replenishment programs (ARP)

This collaborative initiative is closely linked to the VMI initiative with regard to the

extent of downstream supply chain visibility. With ARP, the actual product needs at

the retailer’s point triggers the inventory restocking rather depending on long-term

forecast and layers of safety stock (Stank, Daugherty & Autry, 1999).

2.4 The process of demand planning

Different authors and researchers propose a variety of methodologies to be followed

in the process of demand planning; however, a recent approach proposed by Loretto

(2014) suggests that, to achieve effective demand planning, the steps depicted in

Figure 2.5 should be adopted.

25

Figure 2.5: Demand planning process Source: Loretto (2014:6)

The steps of the demand planning process proposed by Loretto (2014) are described

below:

1. Maintain and understand data – At this stage, all concerned parties collect

data relevant to their function that will be provided as input for demand

planning. Here the objective is to leverage expertise on system or data and

prioritise efficiency.

2. Gather information and document assumptions – Here, all the information

from the relevant functions of the business and members of the supply chain

is gathered and the objective is to leverage functional expertise and as well as

to gather local market insight.

3. Develop statistical forecast – This stage involves the use of different statistical

tools and models such as trend regression, exponential smoothing and

moving average to develop demand forecast.

26

4. Modify forecast – After developing statistical forecasts, it is always necessary

to modify the forecast through judgement overlay. This involves the factoring-

in of the non-quantifiable factors that influence demand, then making the

necessary modifications.

5. Gain consensus – Having modified the initial statistical forecast, the parties

involved need to reach consensus on the final demand forecast. Different

insights are gained from the parties and the demand forecast is finalised.

In demand planning, there are two elements that comprise a consensus plan,

and these are base demand and activities or circumstances where the

baseline demand is the expected demand volume of a product if there are no

promotions and no outstanding situations that affect sales. On the other hand,

‘activities’ are generated internally through marketing and sales and are

related to total trend spend.

6. Communicate forecast – The final demand forecast is communicated to the

different functions of the business as well as the supply chain members so

that they can all plan and design their respective activities with a common

objective.

7. Manage demand – This involves exerting influence on demand to meet profit

objectives. It entails utilising local market knowledge to adapt to market

nuances.

8. Measure and track performance – Based on the adopted demand forecast

and related objectives, the performance of the supply chain members is

controlled to ensure that the set goals are met. This can be done by using key

performance indicators (KPIs).

2.5 The case for POS data in demand planning

The effectiveness of demand planning can be enhanced in many ways and this

section deals with one of them that can be used to achieve this objective, namely the

use of point-of-sale (POS) data. POS-based demand planning is one of the least

utilised and often overlooked forecasting approaches yet it is highly valuable, readily

available and a good source of forecasting data and insight (Borgos, 2008). Of the

several methods at the disposal of supply chain members to improve the planning

27

processes, POS data is one of the most effective. Leveraging POS data entails

integrating it as a fundamental component rather than as extra input data (Trepte &

Narayanaswamy, 2009).

2.5.1 POS data defined

Point-of-sale data is the information that is collected at the point where a product is

bought by the final consumer. POS data measures the last part of the supply chain,

namely the amount of product that the customer buys (Simon, 2008). Figure 2.6

shows the relationship of suppliers, customers and consumers and the point of sale

where POS data is captured.

Figure 2.6: Supply chain relationships and POS data point of capture Source: Simon (2008:5)

POS data is available to supply chain members either directly from the retailers

themselves or in syndicated data from syndicated data vendors like AC Nielsen and

Information Resource Inc (IRI) who are third parties in these information

transactions. Table 2.1 below shows the differences between POS data directly from

retailers and syndicated POS data.

28

Table 2.1 Sources of POS data

POS Data (Direct from retailer)

Syndicated Data (From third party vendor)

Who supplies it? Individual retailers Nielsen, IRI, SPINS, IMS, etc.

Types of data available Typical: Volume Sometimes: Distribution, price

Volume, distribution, price, and merchandising

Geographies available Individual retailers Sometimes: Store level

National, channel, region, market, and chain

Products available Typically supplier’s own SKU only

All SKU in a product category, regardless of supplier (so includes competitors)

Periods available Varies from daily to quarterly, may contain comparisons to year ago period

Weekly, monthly, quarterly, annual, and year-to-date (YTD), typically for three years of rolling data

Coverage All stores All stores of most participating retailers, sample for others

Delivery lag time Varies between real time and one quarter

Varies between about 10 days and one month

Processing required Varies by retailer Minimal Cost Varies from $0 to

thousands of dollars Varies from thousands to millions of dollars depending on product, geography, period, and types of data purchased, delivery time, and software tools used

Source: Simon (2009:5)

There are strengths and weaknesses associated with using POS data collected

either directly from the retailer or sourced from vendors of syndicated POS. Table

2.2 below summarises the weaknesses and strengths associated with each.

29

Table 2.2: Strengths and weaknesses of direct POS and syndicated POS

Direct (retailer) POS Syndicated data

Strengths

Minimal cost

Easy to access

Provides stock keeping unit (SKU)

level data

Strengths

Data is standardised

Data for all categories is available,

even competitors’

Data provided requires minimal

processing by the manufacturer

Provides more than just POS data

Weaknesses

Different standards and data fields

from different retailers

Data must be cleaned and

processed

Does not include competitors’

POS data

Weaknesses

Costly

Some customers do not share

their data with syndicated data

providers

Available in category level

Source: Researcher’s illustration

The capture of POS data today is much easier that it was 20 years back when

retailers used manual cash registers. Collecting POS data was cumbersome and

expensive. The implementation and availability of technologies that capture and

analyse POS data has enhanced its incorporation into the process of demand

planning (Bursa, 2008). With the use of electronic cash registers, businesses can

now collect POS data on each retail transaction and keep them in databases for

future analysis (Borgos, 2008).

POS data collected by the retailer has information about a particular transaction as

its basic element. This element of information generally contains the universal

product code (UPC), product price and the number of items per transaction. The fact

that the UPC is scanned from the product itself ensures that the accuracy of each

transaction is very high (Andres, 2008).

30

2.5.2 The case for POS data-based demand planning

Many researchers and industry specialists believe the potential advantage of POS

data is not being utilised to the full potential within the processes of forecasting and

demand planning (Tolbert, 2008). Although retailers are becoming increasingly

demanding, requiring improved performance from their suppliers and are willing to

share POS information, ‘only a few consumer goods manufacturers are taking full

advantage of it to improve their supply chain’ (Tolbert, 2008:33). The following

section discusses the reason supply chain members need to have POS data as an

essential input to the process of demand planning.

2.5.2.1 POS data is the best reflection of demand

Demand planners use different sets of information as inputs to their forecasts,

including previous shipments from the manufacturer’s warehouse or distribution

centre to the retailer; orders placed by customers (retailers); and POS data. Although

shipments made by the supplier are readily available in most enterprise resource

planning (ERP) systems, shipment data only reflect the supplier’s capability to

provide product shipment, not the exact retailer demand. Because of the shorter time

lag, POS data quickly reacts to fluctuations in causal factors, such as price or

weather, and thus allows the ‘determination of the parameters of the causal factors

with a high level of statistical significance’ (Andres, 2008:30).

Forecasting based on shipment figures perpetuates back orders ‘since stock-outs

distort the shipment history that is used to generate the sales forecasts’ (Tolbert,

2008:34). Suppliers need a closer monitoring of consumer demand, and leveraging

POS data is an excellent way of achieving this (Bursa: 2008). In addition, forecasting

based on orders placed by retailers to the supplier makes consumption changes

harder to detect because of the 'bullwhip effect’ that causes increased variability in

upstream order patterns (Lapide, 2005). Figure 2.7 demonstrates the differences

between orders placed and actual sales.

31

Figure 2.7: The discrepancies between orders placed and actual sales Source: Lee, et al. (2004:1889)

When compared to the analysis of purchase order, shipments or other demand

signals, POS has the closest alignment with real demand. Keifer (2010) suggests

that the usage of POS data shields supply chain members from the misinterpretation

and magnification of downstream demand signals created by the following practices:

1. Forward buying – This is a phenomenon where a retailer orders more

inventory than is required in order to take advantage of low prices or to avoid

an expected price increase. The excess inventory maybe held for later sale or

diverted to other locations.

2. Duplicate ordering – This is when retailers ‘play around’ with suppliers by

placing multiple duplicate orders as a way of avoiding a supply shortage.

Once the orders are confirmed by the distributors, the redundant orders are

cancelled.

3. Batch ordering – This is when the orders that are made are not related to

the replenishment needs but are for non-demand related reasons such as

economic order quantities or materials requirement planning (MRP) needs.

The use of orders placed or shipments made results in lost visibility into the true level

of consumer demand as a result of the time difference between the placing of the

32

order or shipments to the store and when points of sale occur (Lapide, 2005). In

comparison, the usage of POS data speeds up the reaction time to actual shifts in

consumer preferences (Tolbert, 2008:36). POS data is the closest representative of

the true consumer consumption of a product as it suffers minimal distortion caused

by consumer-level promotions and discounts (Lapide, 2005).

2.5.2.2 POS data improves forecast accuracy

The scanning and usage of the UPC significantly increases the accuracy of captured

information and thus the POS-based forecasts will also be more accurate. POS data

improves accuracy because scanning is far more precise than keying in numbers

from a pricing label. POS data gives a quick, near real-time look at the products

moving through a specific retail channel (Bursa, 2008). According to Bursa (2008),

supply chain members that have successfully integrated POS data into their demand

planning process have experienced increased accuracy in their demand projections,

lower inventory shortages and reduced overall costs.

2.5.2.3 POS data enhances the implementation of collaborative strategies

The usage of POS data in demand planning entails a high level of information and

coordination between members of the supply chain. It encourages the use of CPFR

and VMI programs as, with POS data, the manufacturer is no longer left predicting

when the retailer will place an order (Tolbert, 2008). Beyond providing the foundation

for the future and rationale for demand forecast, ‘POS data also helps to bring order

to the chaos in the demand planning process’ (Andres, 2008:38).

2.5.2.4 POS data is free from inventory decisions

The main strength of POS data above shipment data and/or purchase orders is that

it is not affected of inventory policies and decisions – unlike retailers, consumers

seldom make a conscious decision to grow or finish their home stock of a specific

product, ‘thus inventory decisions can hide the underlying sales trend’ (Andres,

2008:30). POS data is said to give a more accurate picture of future demand values

as it is unencumbered by inventory decisions and policies like batch sizing, shipping

quantities and lead times (Bursa 2008).

33

2.5.3 Shortfalls of POS data

Despite all the above-mentioned advantages that can be achieved by the usage of

POS data in demand planning, there are also drawbacks that are associated with it.

Some supply chains have not moved to the usage of POS data in demand planning

as their traditional planning systems are not well suited to support the use of such

data (Trepte & Narayanaswamy, 2009). Other than the issue of suitability, shortfalls

associated with using POS data in demand planning are listed below.

2.5.3.1 POS data is historical

As much as POS data provides the best signal of consumer demand when

compared to other sets of data, it is still historic in nature. Owing to this inherent

historic nature, POS data is therefore not necessarily indicative of what will occur in

the future. Demand patterns from the past will not guarantee an accurate

representation of future trends (Keifer, 2010).

2.5.3.2 POS cannot be used for new products

Naturally, there are no previous sales records for new products and as a result POS

data-based demand planning is impossible. As much as POS data provides good

reliability for post-launch replenishment cycles, it is not available to be used before

the launch when performing demand forecasts (Keifer, 2010). Nonetheless POS

data can provide an early indicator of what and how much of a product is selling; this

is important information for newly introduced products or short shelf-life products

(Bursa, 2008).

2.5.3.3 There is no single POS data format

Different retailers have different formats for their POS data and this can pose a

challenge to the supplier or manufacturer as there will be a need to process the data

to ensure it has the same format before being used in demand forecasting. Bursa

(2008), states that the format in which data is available can create problems for

demand planners as there is no single standard format, including EDI (electronic

data interchange), calendars, and selling horizons on the data from major retail

chains.

34

2.6 Frameworks for the application POS data-based demand planning

With all the above-mentioned benefits related to the integration of POS data into the

demand planning process, this last section describes two frameworks discussed by

Chase (2015) and Tribou (2010), namely the multi-tiered causal analysis framework

and the performance enhancing technique framework, respectively. These

frameworks propose the bases on how businesses can implement POS data-based

demand planning.

2.6.1 Multi-tiered causal analysis (MTCA)

MTCA is a forecasting technique that seeks to link consumer demand, which is

downstream data, to supply, which is upstream data, using a process of nesting

advanced analytical models (Chase, 2015). The downstream data would include

POS data collected directly from retailers or from syndicated data vendors like AC

Nielsen and Information Resource Inc. (IRI). Although MTCA is not a new concept,

its practice is still fairly new; technological advancements and improvements in data

collection, storage and processing all have played a huge role in its practical

adoption. The application of MTCA results in one forecast for both demand and

shipments unlike the traditional forecasting that has separate forecasts for both

(Chase, 2015).

The application of MTCA entails the execution of various ‘what-if’ scenarios in order

to influence and determine future consumer demand. MTCA links demand

(POS/syndicated scanner data) to supply (sales orders or shipments) by the use of

data and analytics (Chase, 2015). Chase (2015) also proposes a six-step process to

execute an effective MTCA procedure. This procedure shows how to apply MTCA to

sense, shape and predict demand and supply by the use of POS data or syndicated

data as well as shipment data at a large consumer packaged goods (CPG)

manufacturer. The brand manager, demand analyst and demand planner combine

their efforts in the following way: The responsibility of the brand manager is to

contribute through his knowledge of the market and its dynamics; the demand

analyst is to factor in his statistical experience and knowledge to use suitable

quantitative methods; and the demand planner is to bring in the overview and

expertise of demand and supply planning.

35

Chase (2015) proposed the steps summarised below to be followed in MTCA

process:

Step 1: Gather all the pertinent data for consumer demand and shipments.

This data includes POS data, marketing plans and periodic supply volumes.

Step 2: Build the consumer demand model using downstream data to sense

demand. Here, the demand analyst uses available POS data to arrive at

statistical consumer demand forecasts.

Step 3: Test the predictive ability of the model. This step entails

communication between the demand analyst and the brand manager to

determine the reasons for periods that lie outside the required statistical

confidence levels. Other causal variables are identified and factored into the

model.

Step 4: Refit the model using all the data.

Step 5: Run what-if scenarios to shape future demand. Here, the product

manager can shape demand by performing different ‘what-if’ simulations

using varying future values of different explanatory variables that can be

controlled.

Step 6: Link the consumer demand forecast to supply. Here the demand

analyst links the first tier (demand) to the second tier of the supply chain

(supply) through integrating consumer demand as one of the causal

(predictor) variables in the supply model.

2.6.2 POS data as a performance enhancing technique (PET)

PET is a methodology proposed by Tribou (2010) and he suggests that the use of

traditional methods in forecasting and demand planning, where forecasters use

historic order information to forecast demand, are not suitable for present-day

product characteristics. Products today are characterised by shorter life cycles; there

are now more frequent innovations, and the launch of new products. When

traditional forecasting is used on products with these new dynamic characteristics,

larger and larger forecasting errors occur (Tribou, 2010).

36

With this challenge of forecasting errors, being able to analyse inventory levels using

POS data from retailers or syndicated POS data vendors offers demand forecasters

a powerful platform for success. PET does not revolutionise the process of demand

planning but it is an evolution of the process (Tribou, 2010). The advantage of using

POS as a PET is that it is not a complex process and does not require the

installation of expensive forecasting software (Tribou, 2010).

The use of POS in demand planning forces the demand planner to understand the

sale patterns and why the actual demand varied from the plan (Tribou, 2010). Table

2.3 shows a spreadsheet of how POS data and inventory levels can be used to

determine expected demand. Each time period starts with inventory on hand

(Planned BOH); subtract from it sales, and add planned receipts to calculate the

inventory at the end of the period (Planned EOH), (which becomes the following

period's beginning on hand).

Table 2.3 Calculating expected demand from a POS plan

Source: Tribou (2010:21)

37

2.7 Conclusion

Demand planning is one of the SCM tasks; it involves the effective management of

demand and entails improving forecasts, reducing costs, minimising risk and

increasing sales profit. Demand planning includes but is not limited to forecasting.

Forecasting only has the objective of predicting future demand with the best possible

accuracy but is not concerned with the business strategy or sales objectives.

In a world where customers are the drivers of supply chains, the need to understand

customer demand has grown. This customer orientation of supply chains has led to

the increased importance of demand planning. Demand planning presents a good

opportunity for supply chains to focus on the consumer and create the optimal value.

Failure to understand and failure to predict demand can have various negative

impacts, the most serious being:

Excess inventory due to low demand for the supplied product


Effective demand planning is not carried out as a silo process or function but it

involves all the relevant parties in the company and members of the supply chain.

The involvement of the different parties in this process ensures that all the factors or

variables that will potentially influence demand are considered and factored in

appropriately. Another motivation for this collaborative process is that supply chain

members are more likely to cooperate with the plans if they are part of their

development. Consensus demand plans are the result of this collaborative planning

process.

Authors differ on the steps to be followed in the demand planning process. One of

these (Loretto: 2014) has been discussed in this chapter and is viewed as ideal as it

includes the essentials of demand planning, which are the forecasting stage,

collaboration and information sharing.

The usage of POS data is one the proposed ways that can improve supply chain

effectiveness. POS data is the data collected at the purchase point in stores; it can

also be sourced from third-party syndicated data vendors. Various motivations exist

for the usage of POS data in demand planning, including:

38

POS data is the best reflection of demand.

POS data improves forecast accuracy.

POS data enhances the implementation of collaborative strategies.

POS data is free from inventory decisions.

Various frameworks have been developed to implement POS data-based demand

planning and in this chapter two of these frameworks were discussed namely multi-

tiered causal analysis and the performance enhancing technique.

Guided by the concepts described in this chapter, this study will aim to evaluate the

extent to which companies in the South African clothing retail industry make use of

POS data in their demand planning processes. In the next chapter, the South African

clothing retail industry is described and analysed in detail.

40

CHAPTER 3: THE SOUTH AFRICAN CLOTHING RETAIL INDUSTRY

3.1 Introduction

This chapter describes the industry relevant to this study, namely the clothing retail

industry and more specifically the South African clothing retail industry. The nature,

structure and performance of the industry is discussed. A comparison is made

between South African clothing supply chains and global clothing supply chains.

Lastly the chapter discusses the importance of effective supply chain management

and effective demand planning in this type of industry.

3.2 The clothing industry

Over the two past centuries, the clothing industry has made a major contribution to

the industrialisation process and it continues to play a crucial role in the economic

and social development of most countries (Palpacuer, Gibbon & Thomsen, 2005).

The focus of this study, which concerns POS data-based demand planning, is

relevant in this industry because it plays an important role in the supply chain. In this

regard, Caniato, Caridi, Castelli and Luca (2008:65) state that:

‘Supply chain management has become a critical issue in fashion markets

because high standards can and must be maintained throughout the supply

chain, from production, to the distribution, to the retail stores.’

The clothing or apparel industry includes businesses that design and sell clothing,

footwear and accessories. Over and above the manufacturers, the industry used to

have intermediaries who would design and source clothing items which would then

be sold to retailers (Moisanen, 2014). However, in the present day, the distinguishing

between wholesalers and retailers in this sector has become more difficult as more

firms in the sector own both froms of operations (Moisanen, 2014). In this study, the

clothing Industry will also be referred to as the apparel industry or fashion industry.

The main objective of this chapter is to describe and analyse the nature and makeup

of the South African clothing retail industry.

41

The global clothing industry is dynamic and has been characterised by major

changes and reconstructions in the past decade. Most of these changes were

brought about by the promulgation of the Agreement on Textiles and Clothing (ATC)

in 2005 by the World Trade Organisation (WTO) (Lu, Karpova & Fiore, 2011). The

ATC led to the abolishment of quota restrictions and protectionism that was in place

in most countries and led to a move towards liberalisation of the industry.

3.3 The global clothing supply chains

Over the years, globalisation has led to the growth and development of international

markets and the clothing industry has not been an exception to this phenomenon.

Over time, the clothing industry has become one of the most globalised industries.

This sector employs millions of workers around the world, particularly in low-income

countries such as Bangladesh and India (Fernandez-Stark, Frederick & Gereffi,

2011). Clothing production is generally a significant catalyst in national development,

and is usually one of the foundational industries for countries engaged in export-

oriented industrialisation as it is characterised by low fixed costs and labour-intensive

production (Fernandez-Stark, et al., 2011).

A report by the United Nations Labour Organisation claimed that three-quarters of

the world clothing exports are from low-income countries, and the industry is vital to

their employment creation and economic development (Fernandez-Stark, et al.,

2011). On the other hand, the biggest part of clothing industry output is consumed in

the developed countries. This phenomenon has been increasing as an effect of the

adoption and implementation of the ATC by the WTO. Spending in the international

clothing industry is dominated by three main regions: the United States, the

European Union and Japan (Gereffi & Frederick, 2010).

Global buyers within the global clothing industry supply chains have networks that

cut across a great number of countries and regions; apparel sales in developed

countries is largely provided by imports from developing countries (Palpacuer, et al.,

2005). Developing countries have competed and survived in the clothing value chain

because of their inherent characteristics which include the access to and availability

of low-cost labour, advantageous trade agreements, and closeness to consumers

(Fernandez-Stark, et al., 2011).

42

3.3.1 Clothing industry liberalisation

The international expansion of the clothing industry had previously been propelled by

trade policy; however, the ATC in 2005 by the WTO led to the phasing out of most

quotas that had been historically set up for the regulation of the industry (Fernandez-

Stark, et al., 2011). The biggest implication of the ATC implementation was that

retailers and other buyers had the freedom to source and purchase textiles and

apparel of any quantity from anywhere while subject only to tariff systems (Gereffi &

Frederick, 2010).

Protectionism within the clothing industry has continuously been on a decline in the

past several years, with more apparel importing countries eliminating their previously

stringent barriers to the clothing trade (Gereffi & Frederick, 2010). This liberalisation

of the clothing and textile industry through the ATC has been a cause for controversy

because this industry contributes to employment in developed countries, mainly in

regions where it might be difficult to find alternative jobs. On the other hand,

developing countries stand to gain from this trade liberalisation and hence they

easily accepted the ATC (Nordås, 2004).

3.3.2 Global clothing industry supply chains

International clothing supply chains comprise four elements, namely: textile materials

supply (component networks); manufacturing of finished products (production

networks); transportation services and logistics (export networks); and marketing

(marketing networks) (Lu et al., 2011). The interaction and cascading down of the

elements form what is termed the global value chain (GVC). The diagram in Figure

3.1 below, adapted from Gereffi and Frederick (2010), is an illustration the interaction

of these elements.

43

Figure 3.1: Apparel global value chain Source: Gereffi & Frederick (2010:5)

A more generic illustration of the supply chain for the textile and clothing industry is

made by Nordås (2004:4) – see Figure 3.2. Dotted lines show information flow and

the solid lines show the flow of goods. The direction of the arrows indicates a

demand-pull-driven system where production is done according to the demands

made downstream of the supply chain.

Figure 3.2: The supply chain in the textile and clothing sector Source: Nordås (2004:4)

44

From the diagram above, the customer is the point where information flow begins

and this reflects how consumers form the basis of what is being produced and when

it is to be produced within clothing supply chains (Nordås, 2004).

The clothing industry is a typical buyer-driven value chain. Buyer-driven value chains

are characteristic of industries that are labour intensive, such as apparel and toy

production, “where retailers and branded marketers entirely outsource production to

less integrated and internally competing networks” (Gereffi & Memedovic, 2010:3). In

the buying process, consumers are influenced by issues like fashion and the

perception of clothing as a sign of social class. Demand patterns are affected and

subject to marketing activities like branding and advertising, which tends to lead to a

weakened consumer power (Euromonitor 2015).

Buyer-driven value chains, in contrast to producer-driven chains, are composed of

highly competitive and internationally decentralised factory systems with low barriers

to entry (Gereffi & Memedovic, 2010). The clothing industry is also characterised by

a rapid rate of change in the product preferences of individual buyers. Due to this

characteristic, industry players tend to attract customers through the creation of

resilient brands and by conducting intensive marketing campaigns. All this is done to

gain and maintain market share in this highly competitive market (Euromonitor

2015).

3.3.3 Retailing in the clothing industry

From a global perspective, the retailing sector has, over the years, gone through

major restructuring, especially in developed countries like the United States. Large

organisations that are specialised by product (for example a store selling only one

item such as shoes) and those specialised by price (that is, high-volume, low-cost

discount stores) are dominating global retailing. These changes within global

retailing are more visible in the apparel sector (Gereffi & Frederick, 2010). Retailing

within the clothing GVCs has become more concentrated, and multinational retailers

have gained more market power. These multinationals not only possess market

power within consumer markets but also possess buying power (Nordås, 2004).

45

Retailers and other downstream members of the global apparel supply chains are

gaining increasing power (Lu et al., 2011). Within the clothing industry supply chains,

‘lead firms’ are usually the marketers and merchandisers at the design and retail

ends of the chain (Lu et al., 2011). The clothing industry is one of the industries that

are demand focused, and it is highly consumer driven. Retailers have closer

interaction and interface with the consumer and thus have greater influence than

suppliers in this consumer-driven supply chain as they are in possession of valuable

information. Retailers within the clothing GVC are gaining prominence, especially

those in the low- to middle-priced markets (Nordås, 2004).

Furthermore and specifically with regard to the clothing industry, retailers and

marketers have the important design skills, information, bountiful budgets and

promotional campaigns needed to establish sustainable international brands to

appeal to the consumer (Lu et al., 2011). International retailers determine what is to

be produced, where it will be produced, by which manufacturer and the price thereof.

Often these lead firms to subcontract the production to an international network of

contract manufacturers located in developing countries as they have highly

competitive rates (Fernandez-Stark, et al. 2011).

Another global trend related to clothing retailing is that brand owners and marketers

are now resorting to in-house retailing and opening their own retail outlets.

Furthermore, brands which already have retail operations are likely to prioritise and

concentrate supply on their own retail outlets rather than meeting the needs of

external retail firms (Gereffi & Frederick, 2010). ‘Retailers used to be garment

manufacturers’ main customers, but now are often their competitors’ (Gereffi &

Memedovic, 2003:7).

3.4 South African clothing supply chains

3.4.1 Historical background

Historically, before the democratisation of South Africa, when the country was

disconnected from the world’s trading system, its clothing and textiles industry was

focused on supplying the domestic market and import substitution. There was a high

level of protectionism for the industry and inefficiencies were experienced. The result

46

of these inefficiencies was that the industry could not compete internationally and it

failed to develop significant export capacity. The industry was also characterised by

a concentration of the manufacture of low value-added products (Jauch & Traub-

Merz, 2006).

After the apartheid era, South Africa joined the World Trade Organisation (WTO) and

opened its industry to global trade. This change, as well as the depreciation of the

rand in the late 1990s and early 2000s, ensured that South Africa’s clothing industry

could compete globally. Exports increased but they were concentrated in low value-

added products (Jauch & Traub-Merz, 2006).

In relative terms, South Africa’s clothing industry is developed and provides a huge

amount of the country’s employment, forming part of South Africa’s top ten sources

of employment (PWC Report, 2012). However, the adoption over time of the

liberalisation of trade, especially through the WTO’s ATC, has led to a very rapid and

continued rise in imports, particularly from China (Jauch & Traub-Merz, 2006).

According to the PWC Report (2012), in 2012 about 90% of the clothing sold in the

country was imported.

3.4.2 The imports threat

Clothing manufacturers in South Africa compete with and are threatened by

international suppliers. In this clothing industry, some products are locally sourced

but the sector largely relies on imported products (PWC Report, 2012). The clothing

wholesale and manufacturing sectors in South Africa are now somewhat disjointed.

This phenomenon is caused by the fact that retailers are able to source their clothing

products directly from foreign suppliers. The liberalisation of international trade,

specifically through ATC by the WTO referred to above, resulted in the decrease of

local supplier power in the industry through competition exerted by producers located

in low-wage regions, most notably China and India (Euromonitor, 2015)

The textile industry in South Africa depends specifically on imports from China which

constituted 70% of the imports in 2012 (PWC Report, 2012:27). The increasing and

sustained surge in imports has caused an unparalleled crisis within the industry and

has led a great amount of lost employment (Jauch & Traub-Merz, 2006). To combat

this occurrence, the industry regulators implemented the ‘Proudly South African’

47

label in order to persuade shoppers to buy goods produced in South Africa. A further

regulation to promote this ‘buy local’ objective is that all textiles have to have labels

stating the country of origin (Ramdass, 2007).

3.5 The South African clothing retail industry

3.5.1 Retailing in South Africa

South Africa’s retail industry is strongly affected by global trends and the global

economy (Prinsloo, 2010). The clothing retail industry in South Africa is a part of a

growing national retail sector whose growth in the previous years had been

facilitated by a growth in the availability of retail space. The proliferation of shopping

centres in the country has had a major impact on the industry.

The South African retail sector is composed of formal or organised retailers as well

as informal retailers (Purushottam, 2011). The retail sector of the local economy

contributes about 14% to South Africa’s total gross domestic product (GDP)

(Prinsloo 2010:1). In 2012, the South African retail industry had an average growth of

3 per cent over the past eight years (Gauteng Treasury Quarterly Bulletin, 2012).

The diagram in Figure 3.3 below by StatsSA (2015) shows the retail trade sales

between the years 2003 and 2013.

Figure 3.3: Real annual percentage growth in retail sales (2003–2013) Source: StatsSA (2015)

48

According to StatsSA (2015), the retail industry in South Africa cascades down into

seven clusters, as follows:

General dealers

Retailers of food, beverages and tobacco in specialised stores

Retailers in pharmaceutical and medical goods, cosmetics and toiletries

Retailers in textiles, clothing, footwear and leather goods

Retailers in household furniture, appliances and equipment

Retailers in hardware, paint and glass

All other retailers

The South African economy is increasingly becoming consumer driven. As the

primary and secondary sectors of the economy are on the decline, industry

expansion falls into the hands of retailers (Gauteng Treasury Quarterly Bulletin,

2012). South Africa’s retail industry generally competes well at regional and

international levels. This is shown by the fact that South Africa’s top ten retailers

were also ranked top ten in Africa in 2015 (Supermarket and Retailer, 2015). Table

3.1 shows the detail of these companies.

Table 3.1 Top ten retail companies in Africa

Retail revenue rank

Name of company

Country of origin

2013 retail revenue (US$m)

1 Shoprite Holdings South

Africa

9

825 2 Massmart Holdings South

Africa

7

530 3 Pick n Pay Stores South

Africa

6

343 4 The Spar Group South

Africa

5

167 5 Woolworths Holdings South

Africa

3

828 6 The Foschini Group South

Africa

1

594 7 Mr Price Group South

Africa

1

558 8 Clicks Group South

Africa

1

350 9 JD Group (Steinhoff International

Holdings)

South

Africa

1

141 10 Truworths International South

Africa

1

008 Source: Supermarket and Retailer (2015)

49

Figure 3.4, adapted from StatsSA (2015), depicts the increasing retail sales in South

Africa.

Figure 3.4: Retail trade sales in South Africa: 2009–2014 Source: StatsSA (2015)

3.5.2 Clothing retailing

The South African clothing retail industry is characterised by low barriers to entry and

has comparatively low capital requirement. The result of this phenomenon is a high

probability of new entrants into the clothing market. Substitutes for mainstream

clothing retail include customised tailoring, factory shops, homemade clothing, and

second-hand clothing; however, these present an insignificant threat to the

mainstream retailing market players (Euromonitor, 2015).

Global value chains are faced with the phenomenon where retailers possess the

greatest bargaining power of all participants. South African supply chains are not

excluded from this phenomenon. South Africa’s clothing and textile retailers possess

considerable value chain power and have influence and control over the what is

produced as well as the timing thereof. For example, in 2006 the top five retailers

accounted for over 70% of South Africa’s formal clothing sales (Jauch & Traub-Merz,

2006:228).

In the official formal classifications of the Standard Industrial Classification (SIC)

from Statistics South Africa (StatsSA), the clothing retail sector is termed clothing,

50

footwear and textiles (CFT). A report by StatsSA on 2014 retail trade sales shows

that CFT retail had the second highest sales revenue after general dealers.

The general dealer and CFT retailer markets’ shares trends have been on the

increase particularly after the 2008 recession phase (StatsSA, 2015). This reflects a

direct relationship between economic growth and consumer spending on clothing

items. Figures 3.5 shows the retail market shares of these two largest types of

retailers for the period 2005–2013.

Figure 3.5: Retail market shares: General dealers and CFT retailers Source: StatsSA, BMR calculations (2013)

3.5.3 Clothing retail market contribution

Figure 3.6 by StatsSA (2015) shows the share of South Africa’s retail sales for the

year 2014. The figure depicts that CFT retail still comes second to general dealers,

having 21 per cent of the total retail revenue.

51

Figure 3.6: Retail trade sales for 2014 Source: StatsSA (2015)

3.5.4 Category performance

Women’s clothing has always generated the highest revenue in sales within South

Africa’s apparel industry. In 2014, the women’s clothing category was the industry’s

most profitable segment, with an overall sales value of $3.8 billion, equivalent to 51.4

per cent of the sector’s overall value. The men’s clothing category contributed a

sales value of $2.1 billion in 2014, equating to 28.2 per cent of the industry’s

aggregate value (Euromonitor, 2015). Figure 3.7 depicts these statistics.

52

Figure 3.7: South African apparel retail industry category segmentation: percentage share by value for 2014 Source: South African Apparel Retail (2015)

3.5.5 Industry growth

South Africa’s clothing retail industry underwent strong growth during the period

2010–2014. ‘This trend is projected to continue over the forecast period through to

2019’ (Euromonitor, 2015:7). According to StatsSA (2015), South Africa’s clothing

retail sales grew by 3.5 per cent in 2014, and Figure 3.8 below shows that textiles

and clothing retail sales had the biggest growth compared to all the other types of

retailers.

Figure 3.8: Retail annual sales in 2014 by type of retailer Source: StatsSA (2015)

53

Figure 3.9 by the South African Apparel Retail (2015) shows the value of sales for

South Africa’s clothing retail between the years 2010 and 2014. The figure depicts a

yearly increase in sales value within this industry.

Figure 3.9: South African apparel retail industry value: $ billion, 2010–2014 Source: South African Apparel Retail (2015)

3.5.6 Key industry players

The South African clothing retail sector has experienced a healthy growth in recent

years, thus attracting new entrants. As already mentioned, the industry has low

barriers to entry. The start-up capital requirements are at a level where it is fairly

easy for individual companies to enter. Nonetheless, a small number of large

companies, such as Truworths and Edcon Holdings, account for the biggest share of

total market revenues. These large companies benefit from scale economies that

permits these companies to establish brands in various retail stores, and afford them

greater buying power when negotiating with suppliers (Euromonitor, 2015). This

buying power advantage allows these companies to create competitive advantage

based more on price.

The economic structure of the industry can therefore be labelled an oligopoly

(Euromonitor, 2015). The South African formal clothing retail industry is principally

made up of a few large retailing organisations as evidenced by the fact already

stated that in 2006 the top five retailers accounted for over 70% of South Africa’s

54

clothing sales (Gauteng Treasury Quarterly Bulletin, 2012). The following section

describes some prominent players in the clothing retail industry in South Africa,

namely Edcon, Truworths and Mr Price.

3.6.6.1 Edcon Group

Edcon Pty (Ltd) is the largest clothing, footwear and textiles (CFT) retailing group in

South Africa (Gauteng Treasury Quarterly Bulletin, 2012). In the year 2012, Edcon

was estimated to have 31 per cent market share of the CFT retailing group (Gauteng

Treasury Quarterly Bulletin, 2012). According to Edcon (2015), ‘Edcon is the largest

non-food retailer in South Africa. We have been in operation for more than 80 years

and have expanded our footprint to include over 1,400 stores through nine different

store formats.’

Edcon conducts its business through three business divisions, namely: department

stores, discount stores, and credit and financial services. The department stores

division is made up the following retail chains: Boardmans, CNA, Edgars, Jet, Jet

Mart, Jet Shoes, Blacksnow, Jet Home, Discom, Legit, Prato, Red Square and

Temptations (Euromonitor, 2015:21). Notably Edgars operates full-line department

stores and is ‘one of the leading retailers of clothing, footwear, textiles and

accessories in South Africa’. ‘It is also one of the leading national distributors of top

global brands, supplemented by a value-for-money range of core merchandise,

including sportswear and other commodity products relevant to its customer

segment’ (Euromonitor, 2015:21).

3.5.6.2 Truworths International

‘Truworths International is an investment holding company that retails apparel and

other related merchandise through a network of around 650 stores in South Africa

and five franchise operations in other African countries’ (Euromonitor 2015:22). Truworths operates its business via two categories, namely, Truworths and Young

Designers Emporium (YDE). The Truworths category includes all the retailing

activities conducted by the company, through which it retails apparel such as

clothing, footwear and other clothing products to women, men and children

(Euromonitor 2015:22). The YDE category includes the agency activities through

55

which the company retails clothing, footwear and related products (Euromonitor

2015:22).

3.5.6.3 Mr Price

The Mr Price Group is involved in the operation of retail chains. It includes six retail

chains namely; Mr Price Apparel, Mr Price Home, Mr Price Sport, Miladys, Sheet

Street, and Mr Price Money (Euromonitor, 2015:22). The Mr Price Group conducts

its business via three categories, namely, apparel, home and central services. Mr

Price’s apparel segment is engaged in retail clothing, sportswear, footwear, sporting

equipment and accessories. The company also offers sports-related products

through Mr Price Sport (Euromonitor, 2015).

3.6 Demand planning in the clothing retail industry

With high levels of competition in the retail environment, suppliers are faced with a

great amount of pressure to comply with high customer service level requirements

while achieving minimal costs. There is a need therefore for clothing manufacturers

and retailers to achieve a balance between their demand and supply processes.

Effective demand planning helps in the achievement of this balance (Waller &

Williams, 2011).

As retail environments have changed overtime, demand forecasting has become

even more critical part in effective demand management (Williams & Waller, 2011).

However, forecast accuracy has dropped overtime; ‘this drop is attributed, in part, to

the increased lack of familiarity with forecasting techniques and increased complexity

due to product proliferation’ (Williams & Waller, 2011). Caniato et al., (2008) state

that within supply chains, the retail point is gaining an important role in the demand

management process because it is the unique point of interface between a firm and

its customer.

3.6.1 POS data in clothing retail

Obtaining accurate sales forecasts is difficult in the clothing industry and is generally

considered to be difficult due to demand volatility in clothing products (Moisanen,

2014). The demand for clothing items is easily affected by several variables that

include seasonal weather changes, actions of opinion leaders and other changes like

variations within the state if the economy (Moisanen, 2014).

56

Thomassey (2014:9) holds that the clothing industry is ‘a fascinating sector for the

sales forecasters; the long time-to-market characteristic of the clothing industry

contrasts with the short life cycle of products, thus complicating the forecasting

process’. Achieving high customer service levels while incurring minimal costs

requires accurate demand forecasts, but the achievement of these forecasts is not

an easy task (Williams & Waller, 2011:17). Despite the above-mentioned difficulties

associated with predicting sales, accurate sales forecasting in the clothing industry is

considered important ‘due to the long lead times caused by the relatively recent

trend of sourcing production out to other countries’ (Moisanen, 2014:1). The South

African clothing industry is characterised by this observable fact. The usage of POS

data in demand planning and forecasting greatly improves the accuracy of these

forecasts. In a volatile clothing industry with complex consumers, POS data

becomes an indispensable tool (Williams & Waller, 2011).

To enable suppliers to provide frequent supplies and achieve minimal inventories,

retailers need to share POS data. This requires ‘frequent communication between

retailers and their suppliers’ (Nordas, 2004:5). Particularly in the context of this

study, South Africa has increasingly competitive markets. As a result, this has

necessitated supply chain members to focus on core competencies, and gain

competitive advantage by the implementation of effective supplier relationship

management programs (Dube, Muyengwa & Battle, 2012).

The accuracy of order forecasts made by retail stores to the retail company’s

distribution centres (DCs) can be improved by the collection and usage of POS data.

The forecasting of DC orders to the supplier is more difficult due to increased

variability upstream of the supply chain (Williams & Waller, 2010). ‘Shared POS data

can reduce the variability introduced into the demand forecast process’ (Williams &

Waller, 2011). ‘The development in data technology has made it possible for any

retail company to collect, supply and analyse POS data. Access to POS data makes

it possible to react to changes in demand considerably faster’ (Nordas, 2004:6).

Williams and Waller (2011:17) state that ‘recent literature suggests that suppliers use

shared point-of-sale (POS) data to reduce demand forecast error and subsequently

improve demand management processes’.

57

3.6.2 POS data collection and transmission technologies

Retailers have the barcoding system as well as complementary equipment at their

disposal for the collection of POS data in real time. This technology enables retailing

companies to monitor the products sold on a continuous basis. The equipment

ensures accuracy and precision in the capture of POS data and also enables

retailers to keep track of inventories (Nordas, 2004). Within clothing supply chains,

the development and implementation of the barcoding system and POS scanning

capabilities enabled the capture and provision of accurate real-time information on

product sales (Gereffi & Memedovic, 2003). Furthermore, the sharing of POS data

by retailers using electronic data interchange (EDI) systems and other data transfer

technologies has gained prominence (Dube, Muyengwa & Battle, 2012).

Technology, including the EDI used by the retailer to restock, ‘enabled a model of

frequent shipments by suppliers to fill ongoing replenishment orders by retailers,

based on real-time sales information collected at the retailer’s stores on a daily basis’

(Gereffi & Memedovic, 2003:4).

3.6.3 POS data usage in the South African clothing retail industry

Retailer stores replenish their inventory either directly from the supplier or the

retailer’s own DC. Clothing retailers in South Africa generally replenish their retail

stores from their own DC and, as described in Chapter 2, the effective usage of POS

data would benefit these supply chains through replenishment- and inventory-related

efficiencies (Dube, Muyengwa & Battle, 2012).

The concept of predicting sales, more commonly referred to as demand or sales

forecasting, in the rapidly changing apparel industry has been greatly researched

during the past two decades (Moisanen, 2014:2). Despite this reference to

substantial research, the author of this study could not find any evidence of research

done on the process of demand planning and the usage of POS data within the

South African clothing industry. It is therefore the objective of this study to research

and document the usage of POS data in the process of demand planning within the

South African clothing retail industry.

58

3.7 Conclusion

The clothing industry has historical importance through its role in the global

industrialisation process and continues to be of importance to many developing

countries by being a major source of employment. The global industry has gone

through several changes over time with the major being the liberalisation brought

about by the implementation of the Agreement on Textiles and Clothing (ATC) by the

World Trade Organisation in 2005. This liberalisation led to the abolishment of

protectionism policies by many countries and led to a more integrated global clothing

industry. The global clothing industry is characterised by greater production in low-

income countries while consumption is greatest in developed countries. The clothing

industry is a highly competitive and buyer-driven industry that consists mainly of raw

material producers, textile producers, apparel manufacturers and retailers.

The South African clothing industry is also a contributor to the global clothing

industry and was opened to the international markets at the end of the apartheid era.

South Africa largely plays a consumer role and this is seen in dependence on

imports of materials as well as finished products, especially from China. About 90%

of items sold in South Africa in 2012 were imported, with 70% of those sourced from

China. South African clothing retail is a part of a growing retail sector and accounts

for the second biggest share in retailing after general dealers. The structure of the

industry is oligopolistic in nature where five of the biggest retailers account for about

70% of market share. However, many small-scale retailers exist forming a smaller

part of the market share. Women’s clothing contributes the largest revenue in this

sector, accounting for 54% of the industry’s value.

Demand planning and sales forecasting are important activities of clothing industry

supply chains owing to the high levels of competition, the long lead times caused by

the trend of sourcing production and operations to other countries, and the cost

minimisation objective. The retail point is gaining an important role in the demand

management process because it is most often the unique point of interface between

a firm and its customer. However, there is a challenge in the development of these

forecasts owing to the volatility of demand for apparel products. The accuracy of

these sales predictions within the process of demand planning can be improved by

using POS data to determine these future sales. Several technologies, like the

59

barcoding system and EDI, have been developed to facilitate the implementation of

POS data-based demand planning within the industry.

In the next chapter, the research methodology used in the study to collect data and

analyse data is described.

61

CHAPTER 4: RESEARCH METHODOLOGY

4.1 Introduction

The purpose of this chapter is to describe the research methodology used in the

study to collect and analyse data. The research approach, design, and the data

collection methods and tools are described and explained in the context of this

investigation. The study seeks to investigate the extent of the usage of POS data in

demand planning by companies in the South African clothing retail industry. The

research methodology was used in a manner aimed at meeting the research

objectives and ensuring the main and investigative questions were answered.

Different authors present different steps in conducting research. One of the simplest

and clearest research processes is presented by Sreejesh, Mohapatra and Anusree

(2014:14). They present a seven-step process of conducting research, as illustrated

in Figure 4.1.

Figure 4.1 Steps in the research process Source: Sreejesh, Mohapatra and Anusree (2014:14)

Saunders, Lewis and Thornhill (2009:108) make another vivid description of the

research process and the different stages the process should go through. The term

62

used for their illustration of the research process is the ‘research onion’ as depicted

in Figure 4.2.

Figure 4.2: The ‘research onion’ Source: Saunders, Lewis and Thornhill (2009:108)

4.2 Research approach

A qualitative research approach is used in this study. According to Zikmund, Babin,

Carr and Griffin (2012), a qualitative research approach seeks to meet research

objectives by the use of tools and methods that allow the researcher to interpret

phenomena without depending on numerical measurement. The qualitative approach

is therefore the most suitable approach for this study as the objective is to gain great

detail and in-depth understanding of the usage of POS data in demand planning. In

many cases, qualitative research is said to provide richer information than that of

quantitative approaches (Zikmund et al., 2012).

A qualitative approach in research results in qualitative data while a quantitative

approach results in quantitative data. In this context, Table 4.1 below by Saunders et

al. (2009:482) summarises the differences between qualitative data and quantitative

data.

63

Table 4.1 Distinctions between quantitative and qualitative data

Source: Saunders, et al. (2009:482)

A qualitative approach ‘enables the simplification and management of data without

destroying complexity and context’ (Atieno, 2009:16). Though a qualitative approach

is the most suitable for this particular study due to the exploratory nature of the

study, there are some drawbacks which can be encountered when following this

approach. First, the findings from a qualitative approach cannot be extended to wider

populations with the same degree of certainty that quantitative analyses can (Atieno,

2009:17). Furthermore, Saunders et al. (2009:483) state that analysing qualitative

data is a demanding process and should not be considered an ‘easy option’.

4.3 Research strategy

Research design or research strategy is the framework or blueprint for carrying out a

research project in an efficient manner and provides the actions necessary for

collection, measurement and analysis of data (Sreejesh et al., 2014:14). Related to

this description, Zikmund et al. (2012:64) state that research design is ‘a master plan

that specifies the methods and procedures for collecting and analysing the needed

information’. Maree (2007) discusses six types of qualitative research design,

namely:

Conceptual studies

Historical research

Action research

Case study research

Ethnography

Grounded theory

The grounded theory is relevant to the study in hand and will be covered in detail.

64

4.3.1 Grounded theory

The research strategy or design for this study is the grounded theory. Saldana

(2011) states that the grounded theory is a strategy whereby the researcher

continually compares collected data units through a series of cumulative coding

cycles in order to determine emerging themes and categories. As described by

Stake (2010), the grounded theory research design is used when the main aim is to

build theories and draw conclusions about the population. The inductive nature of

this study therefore makes the grounded theory the most suitable research design or

strategy. However, like any research approach, grounded theory research has

advantages and disadvantages, as listed below.

Advantages

‘Grounded theory is a respected qualitative way of moving from individual

knowledge to collective knowledge’ (Stake, 2010:17).

The grounded theory approach gives priority to the data and the field under

study over theoretical assumptions (Flick, 2011).

The use of this procedure overcomes the main shortfall associated with the

deductive procedure. The latter has already been cited above as being likely

to introduce an untimely conclusion on the issues under investigation, with

the potential of the theoretical constructs departing widely from the views of

participants in a social setting (Saunders et al., 2011)

Disadvantages

‘The grounded theory strategy is an interpretive process, not a logico-

deductive one and researcher should treat it as a highly creative one’

(Saunders et al., 2009:149).

The grounded theory design is not easy; it requires that the researcher

obtains a tacit knowledge of, or feel for, their data (Saunders et al., 2009).

4.3.2 Case study approach

Another strategy that also could have been used is the case study strategy.

According to Zikmund et al. (2012), case studies involve the documentation of the

history of a particular person, group, organisation or event. A case study can also be

defined as a systemic enquiry into an event or a set of related events which aims to

65

describe and explain the occurrence of interest (Maree, 2007:75). The use of case

studies in the research design presents certain advantages and disadvantages

including the following:

Advantage

In comparison to other methods, ‘case studies have an advantage with

respect to the “depth” of the analysis, where depth can be understood as

empirical completeness and natural wholeness or as conceptual richness and

theoretical consistency’ (Given, 2008:69).

Disadvantage

Case studies can be narrow in scope and it becomes difficult to make

generalisations about the population (Adams, Khan, Raeside & White, 2007).

4.3.3 Literature review

As part of the research methodology, secondary research was done through a

literature study. Through this literature study, the research gap was identified and the

critical constructs for the study were defined. The following are the main motivations

for conducting a literature review:

To provide a sound theoretical overview of the existing research findings,

theories and models in terms of the specific research model

To situate and locate the research project and outline its context (Fox &

Bayat, 2008)

The literature review was restricted to formal documents, papers and books in the

discipline of commerce or business to ensure the relevance of the information

collected.

4.4 Data collection: Interviews

For this study, interviews were used to collect data. Saunders et al. (2013) simply

describe interviews as a purposeful discussion or conversation between two or more

people. Maree (2007) provides a broader definition: interviews entail a two-way

conversation whereby the interviewer asks the participants questions. These

questions are asked in order to gather data and to learn about an individual or an

66

organisation’s culture, views, opinions and behaviours. Interviews form part of a set

of qualitative research data collection methods. Figure 4.3, adapted from Sreejesh et

al. (2014:47), shows the relation of interviewing to other qualitative research data

collection methods.

Figure 4.3: Classification of qualitative research methods Source: Sreejesh et al. (2014:47)

Interviews fall into the continuum of being highly formalised and structured to being

informal and unstructured conversations (Saunders et al., 2013). There are different

types of interviews that are used in research and the common forms are briefly

described below:

Unstructured/Open-ended interview – This is a conversation whereby the

researcher has the objective to explore the perceptions as well as the

opinions of the participant about a phenomenon (Maree, 2007).

Semi-structured interview – This is a two-way conversation which has the

objective to address specific themes of a study without following a strict order

or set of questions (Saunders et al., 2013:320). This form of interviewing

allows the adaptation of the questions to the context of the organisation or

individual under study.

Structured interviews – ‘In this case, the questionnaire contains a set of

sequential ordered, carefully worded, open-ended questions’ (Sreejesh et al.,

2014:49). The form of interviewing limits the variation in the questions asked

67

and promotes consistency. Despite this advantage, this choice limits flexibility

that could be needed to probe into participant differences.

4.4.1 Semi-structured interviews

For purposes of this study, semi-structured interviews were used. As already

mentioned a semi-structured interview is a two-way conversation without a strict set

of questions. In this context, the strengths and limitations for using semi-structured

interviews are described below:

Strengths

Since the structure and order of questions is not strictly adhered to, this form

of interviewing allows the researcher to address more specific issues

(Zikmund et al., 2012:151).

The use of this technique allows some amount of flexibility to the interview,

but still ensures that the interviewer keeps the interview limited to the topics

that are essential to the research (Sreejesh et al., 2014).

Zikmund et al. (2010) state that the responses from semi-structured interviews

are usually easier to interpret than other qualitative approaches.

Limitations

The effectiveness of semi-structured interviews depends on the skill of the

interviewer as there is a need to apply probing techniques (Sreejesh et al.,

2014:48).

‘The technique does not permit the interviewer to probe into unanticipated

issues cropping up during the interaction, which were not a part of the basic

checklist’ (Sreejesh et al., 2014:48).

4.4.2 Recording interview data

Table 4.2 below, adopted from Saunders et al. (2013:341), summarises the

advantages and disadvantages of audio-recording interviews.

68

Table 4.2: Advantages and disadvantages of audio-recording the interview

Source: Saunders et al. (2013:341)

4.5 Sampling

Due to the constraints associated with performing a study on the total population,

sampling is done; in this way, time and cost restrictions can be overcome. Sampling

can be defined as the process or technique of selecting an appropriate sample with

the intention of finding out issues or characteristics of the whole population (Adams

et al., 2007:87). The two main classes of sampling are probability sampling and non-

probability sampling. Non-probability sampling will be used in this this study. Maree

(2007:172) makes a distinction between these two classes of sampling, where

‘probability methods are based on the principles of randomness and probability

theory, and non-probability methods are not’.

Figure 4.4 by Saunders (2009:213) shows a summary of the sampling classes as

well as the respective methods under each.

69

Figure 4.4: Sampling methods Source: Saunders (2009:213)

4.5.1 Non-probability sampling

Non-probability sampling can be described as sampling where the researcher cannot

predict the representation of each element in the population in the sample (Leedy &

Ormrod, 2014). With non-probability sampling, the probability of each case being

selected from the total population is not known (Adams et al., 2007; Saunders,

2009). The following characteristics of non-probability sampling make it suitable for

this study:

1. Unavailability of sampling frame

Adams et al. (2007) state that when a sampling frame is unavailable, non-probability

sampling methods are suitable to serve the objectives of the study. In this particular

study, an ideal sampling frame for the whole industry could not be obtained and thus

non-probability sampling is well suited.

2. Cost and time effectiveness

Other ‘reasons for choosing non-probability over probability sampling are cost and

time factors’ (Adams et al., 2007:89). ‘Non-probability sampling is less costly and can

be performed over a comparatively shorter period’ (Adams et al., 2007:89).

70

4.5.2 Purposive sampling

The different methods under non-probability sampling are depicted in Figure 4.3 and

of these, purposive sampling will be used. In purposive sampling, sampling units are

chosen for a particular purpose (Leedy & Ormrod, 2014:221). ‘Purposive sampling

allows the researcher to use his or her judgement in selecting cases that will best

enable him or her to answer the research questions as well as satisfy the objectives’

(Saunders, 2009:237).

Purposive sampling is also motivated by the idea that who a person is (in the context

of this study it will be a company) and where that person is located within a group is

important (Given, 2008:697). However, when using purposive sampling, it is required

that the researcher provides justification for the selection of particular sample

elements (Leedy & Ormrod, 2014:221). Furthermore, this sampling is said to be

appropriate if the researcher wishes to select cases that are particularly informative

(Saunders, 2009:237). The advantages as well as the disadvantages of using

purposive sampling are discussed below.

Advantages

Purposive sampling recognises the fact that some ‘well-placed articulate

informants will often advance the research far better than any randomly

chosen sample’ (Given, 2008:697).

Though to a limited extent, purposive sampling ‘can provide the researcher with the

justification to make generalisations from the sample that is being studied, whether

such generalisations are theoretical, analytical and/or logical in nature’ (Saunders,

2009:237).

Disadvantages

Samples created through purposive sampling cannot be considered as

statistically representative of the total population (Saunders, 2009:213).

Purposive sampling is highly prone to researcher bias (Given, 2008).

4.5.3 Sampling frame

A sampling frame is the list of elements from which the sample may be drawn

(Adams et al., 2007:88). An ideal sampling frame for this study would have been a

list with all South African clothing retail companies depicting their sales volume, thus

71

helping in the major or non-major demarcation. Such a list could not be obtained

because of the following reasons:

Protection of Personal Information (POPI) Act – Compliance with this Act

prevented some organisations like the Wholesale and Retail Sector Education

and Training Authority (W&R Seta), which could have provided this ‘ideal list’,

from disclosing their registered members and their related information.

Diversified business operations – Some organisations, like the Consumer

Goods Council of South Africa (CGCSA), could not provide the suitable list as

their classification of the different registered companies cut across and

beyond clothing retailing.

Saunders et al., (2009) suggests that “where no suitable list exists the researcher will

have to compile their own sampling frame, perhaps drawing upon existing lists”.

Owing to the above mentioned challenge of getting a complete list to be used as a

sampling frame, the sampling frame used for this study is a combination of list

obtained from the National Clothing Retail Federation of South Africa (NCRF) and

Silo. According to the NCFR website (2014), their organisation ‘represents the

interests of retailers who are the favoured destinations of millions of men, women

and children when choosing their clothing, footwear, fashion accessories, cosmetics

and related items’. The organisation claims its members account for more than R50

billion in sales revenue annually. This endorses the organisation’s membership as

being a good basis for the sampling frame. According to Silo website (2015), “Silo is

South Africa’s leading digital brand content provider and source of unified retail

intelligence, retail analytics and product analytics”.

4.5.4 Sampling unit

The ‘sampling unit can be described as a single element or group of elements

subject to selection in the sample’ (Adams et al., 2007:88). In the context of this

study, the sampling units are the major clothing retail companies in South Africa.

4.6 Data analysis

This section describes the data analysis process, that is, the transcription step

followed by the actual analysis to draw findings and interpretation.

72

4.6.1 Transcribing

During and after the interviewing stage, the audio-recorded interview was

transcribed. As defined by Saunders et al. (2009), transcribing is reproducing an

audio-recorded interview as a written (Word-processed) account using the actual

words. Transcriptions were made into Word files and the transcription was done

verbatim to overcome the following drawbacks associated with partial transcription

cited by Saunders et al. (2009):

The researcher will still have to listen to the entire recording carefully and the

filtering process can be cumbersome.

The researcher might miss certain things, and this means having to go back

to the audio-recording. This allows room for subjectivity in selecting important

parts.

4.6.2 Data analysis

After the captured data has been transcribed, the data is analysed. Qualitative data

can be analysed in different ways depending on the collection method and of the

type of data collected. In this study, grounded theory analysis was used. According

to Saldana (2011), grounded theory analysis is an analytical process whereby the

researcher continually compares small data units through a series of cumulative

coding cycles in order to determine a range of dimensions to the emerging themes

and categories.

One of the most important steps in grounded theory analysis is coding. According to

Yin (2011), the objective in coding the transcribed information is to move towards a

higher conceptual level. Coding is the process of reading carefully through

transcribed data, line by line, and dividing it into meaningful analytical units (Maree,

2007:105). Items that seem to be essentially similar are assigned the same code;

however, the uniqueness of each set of responses should not be ignored (Yin,

2011). As a supplement to the manual coding of data, computer-assisted qualitative

data analysis (CAQDA) was used.

The next chapter (Chapter 5) focuses on dealing with the research findings and

describes how the analysis process described above was practically implemented.

73

4.7 Ethical considerations

In any form of research performed, it is necessary to take ethical considerations into

account to ensure the application of moral behaviour during the research process

(Zikmund et al., 2013). Saunders et al. (2009:84) state ‘that ethics refers to the

appropriateness of your behaviour in relation to the rights of those who are the

subject of your work, or are affected by it’. Below is a discussion of some of these

issues and how the researcher complied with the requirements thereof.

Informed consent

Consent is given when research participants know and understand what the

researcher requires of them and agree to the research study (Zikmund et al., 2013).

For this study, the researcher ensured that the interview respondents gave their full

consent to participate in the interviews. Communication was made with potential

respondents, disclosing what the research is about and giving them the chance to

voluntarily give their consent. The communication included an interview consent

letter that clarified the nature of the interview and the related information. This letter

is shown in Annexure A).

Confidentiality and respondent anonymity

Another ethical issue considered was confidentiality. Confidentiality, as suggested by

Flick (2011), ensures that people who read the report should not be able to tell which

organisation or individual took part in the research. For this purpose, the specific

details were encrypted. The level of confidentiality observed in this study was

expressed in the interview consent letter, which clearly stated that ‘the information

gathered in the study will be treated with the strictest confidence’.

Anonymity is specifically about is ensuring that the information about the

respondents or companies is only used in a way which makes it impossible for other

persons to identify the participants, or for any institution to use it against the interests

of the participant (Flick, 2011). In this study, the researcher did not disclose any

information classified as sensitive personal data. By not disclosing the identity of the

respondents and sensitive personal data, the respondents are protected from any

issues such as victimisation or discrimination. When the respondent remains

anonymous, subjectivity by the respondent is also minimised (Saunders et al., 2012).

74

Scientific quality

Flick (2011) also lists the scientific quality of a study as an ethical issue, suggesting

that if a study does not add to the body of knowledge or the study is a duplication of

or is identical to a study done previously, then the study is unethical. To abide with

this ethical code, this study was decided on after a thorough literature review had

been done to identify a research gap in order to ensure that no similar study existed

on the same topic.

Objectivity

Objectivity is related to avoiding issues such as selective data collection, data

manipulation and data fabrication. Lack of objectivity can render the findings of a

study invalid and unreliable. Lack of objectivity in collection also affects the analysis

of the data; as Saunders et al. (2012:194) state, ‘Without objectively collected data,

your ability to analyse and report your work accurately will also be impaired.’

4.8 Conclusion

The different aspects of the research methodology described in this chapter were

selected through a series of steps in cognisance of the fact that there is no research

design, method or instrument that is suitable for every research circumstance. Thus,

choices made at each level of the research methodology were made after

consideration of the various strengths and limitations they present to the particular

circumstance. The choices made were those the researcher perceived as having the

best advantage to the research circumstance.

The research was done through a combination of primary and secondary research.

The secondary research was performed by consulting different forms of literature

primarily for identifying research gaps and defining the critical constructs of the

research. The primary research was done with the purpose of answering questions

that emanated from the literature study. The summary of the research methodology

for this primary research is depicted in Table 4.3.

75

Table 4.3 Summary of the research methodology

Selection made

Research approach Qualitative study

Research design Case study

Data collection method Interview – Semi-structured

Sampling method Non-probability sampling – Purposive

Data analysis Thematic content analysis


Though the chosen design and the respective instruments have inherent limitations,

it is expected that their strengths will satisfy the research objectives and answer the

questions accordingly.

The following chapter describes and analyses the findings from the fieldwork done

following the methodology discussed in this chapter.

77

CHAPTER 5: DATA ANALYSIS AND PRESENTATION OF RESEARCH FINDINGS

5.1 Introduction

This chapter serves to describe and present the findings that emanate from the data

collected through primary research. The previous chapter described the

methodology followed in research design, sampling and the actual data collection.

This chapter starts with a description of the analysis performed to reach the findings

thereof.

5.2 Data capture and organisation

As stated in the previous chapter, the data was collected through semi-structured

interviews. The respondents, who work as demand planners in the companies that

form part of the sample, responded to the questions that are shown as an annexure

(Annexure B) to this study. All the interviews were digitally voice recorded as they

were being performed. These voice recordings were transcribed into text files

verbatim. The transcribed data was then analysed. The analysis followed is

described below.

5.3 Data analysis

Saldana (2011:89) states that the ‘purpose and outcome of analysing data is to

reveal to others, through fresh insights, that which has been observed and

discovered about a particular condition’. It can therefore be said that the process of

data analysis answers the question: what does the collected data say? For this study

the purpose was to reveal the role as well as the usage of point-of-sale data in

demand planning by major clothing retail companies in South Africa.

5.3.1 Approach to analytical rationale

Saunders et al. (2011) suggest that two possible approaches can be followed as

analytical rationales, namely the deductive approach and the inductive approach.

The deductive analytical rationale applies when the research objectives and the

related research questions stem or have been developed from already existing

theories. Deduction is what one generally draws and concludes from established

78

facts and evidence (Saldana: 2011). This approach has, however, been criticised as

there is the likelihood of introducing an untimely conclusion on the issues under

investigation and the potential of the theoretical constructs varying from the views of

participants in a social setting (Saunders et al., 2009).

On the other hand, the inductive analytical rationale, which is the analysis approach

followed in this study, applies when data is collected and explored in order to

observe the themes or issues that emanate from the data, and to discover which

issues to follow up and concentrate on (Saunders et al., 2011). The inductive

approach involves exploring and making inferences, transferring them from the

particular to the general, grounded on scrutiny and investigation of the collected

information (Saldana, 2011). This inductive analytical rationale is also referred to as

a grounded approach ‘because of the nature of the theory or explanation that

emerges as a result of the research’ (Saunders et al., 2009:490).

‘An inductive approach may also combine some elements of a deductive approach

as one seeks to develop a theoretical position and then test its applicability through

subsequent data collection and analysis’ (Saunders et al., 2009:490). Similarly, in

this study, as much as the analysis performed is oriented towards the inductive

analytical rationale, it will not be purely inductive. Reference will also be made to pre-

existing theoretical constructs. The following section discusses the grounded theory

procedure, which is the procedure to be followed under this inductive approach.

5.3.2 Analytical procedure: The grounded theory

The analytical procedure followed in the analysis of the collected data is termed the

grounded theory, which according to Saldana (2011) is an analytic process whereby

the researcher continually compares small data units through a series of cumulative

coding cycles in order to determine a range of dimensions to the emerging themes

and categories. ‘The classic grounded theory is often thought of as the best example

of the inductive approach’ (Saunders et al., 2011:148). The basic idea in this

procedure is, therefore, to look at the small detail from different sources of data in

order to get a bigger picture.

The use of this procedure overcomes the main shortfall associated with deductive

process which has was cited above as being the likelihood of introducing an untimely

79

conclusion on the issues under investigation and the potential of the theoretical

constructs departing widely from the views of participants in a social setting

(Saunders et al., 2011).

5.4 Computer-assisted qualitative data analysis software (CAQDAS)

The grounded theory analytical procedure was implemented with the help of

computer-assisted qualitative data analysis software (CAQDAS). Because the

analysis of qualitative data is a long, cumbersome and iterative process, analysis for

this study was facilitated by CAQDAS. CAQDAS has the following advantages, as

mentioned by Hesse-Biber (2010:23):

It is time effective and therefore analysis takes a shorter time.

It enhances the ability to work the data.

It assists in the development of an organising system.

It allows exploration of different possibilities of data analysis and

interpretation.

It facilitates secondary analysis of qualitative data sets.

Although the usage of CAQDAS has numerous advantages, it also has some

shortfalls, notably that the software may be difficult to use unless one has been

trained in using the particular software at one’s disposal. Furthermore, CAQDAS still

requires effort by the researcher in the interpretation of the coded information to

determine the emerging categories and themes.

The specific brand of CAQDAS that was nominated and used is ATLAS.ti. This

particular CAQDAS facilitates coding, theming and the creation of memos. Figure 5.1

illustrates the grounded theory analysis process followed using the CAQDAS

ATLAS.ti.

80

Figure 5.1: Grounded theory analysis process


5.4.1 Coding and data categorisation

5.4.1.1 Coding

According to Saldana (2011:95) ‘coding is a heuristic – a method of discovery – to

the meanings of individual sections of data’. Coding therefore involves the continual

comparison of phenomena, cases, concepts and so on, searching and identifying

patterns and proceeding themes leading to the development of theories through a

process of abstraction (Flick, 2009:307). More simply put, coding is used as a

method of finding patterns, classes and categories emanating from the given sets of

data (Saldana, 2011).

Various authors including Saldana (2011), Saunders (2011) and Flick (2009),

recognise the different types of levels of coding proposed by Strauss and Corbin

(1990), namely open coding, axial coding and selective coding, as discussed below.

Open coding – This is considered the first step in coding and aims at

expressing data and phenomena in the form of concepts; the concepts are

known as codes (Flick, 2009). In data analysis, a code is described as ‘a word

or short phrase that symbolically assigns a summative, salient, essence-

capturing, and/or evocative attribute for a portion of language-based or visual

data’ (Saldana, 2011).

Coding

Categorising

Theming

81

Axial coding – This is the second level of coding and involves searching for

relationships between the categories of data that have emerged from open

coding and shows a process of theoretical development (Saunders, 2011).

Flick (2009) states that at this stage categories emanating from open coding

are refined and differentiated; furthermore, the relations between these

categories are elaborated.

Selective coding – Selective coding is done to ensure that the data analysis

process is integrated. This is done through the identification of one of these

principal categories, termed the central or key category, in order to relate the

other categories (Saunders, 2011). In this stage the emphasis is placed on

recognising and developing the relationships between the principal categories

that have emerged from this grounded theory analytical process (Saunders,

2011:96).

5.4.1.2 Theming the data

Unlike codes, which are most often single words or short phrases that symbolise a

datum, themes are extended phrases or sentences that summarise the manifest

apparent and underlying meanings of data (Saldana, 2011).

In this study, the above-mentioned coding and theming procedure was followed and

used to analyse and make sense of the collected qualitative data. The findings of this

procedure are described in the following section and have been grouped according

to the research objectives. The discussion covers the themes that emanated from

the findings under each objective:

• To determine the nature of the demand planning process as performed in the South African clothing retail industry

• The positioning of a demand planner within the South African clothing

retail companies

• The role of a demand planner within the South African clothing retail

companies

• Demand planning process in the SA clothing retail industry

82

• To determine the systems employed in the collection and dissemination of POS data within the South African clothing supply chains

• The structure of South African clothing industry supply chains

• The nature of products sold within the South African clothing retail

industry

• The collection and dissemination of POS data

• To determine the way POS data is used in the demand planning process within this industry

• The importance of demand planning in clothing retail

• The usage of POS data

• The usage of POS data in demand planning

• To find out the challenges and problems that are faced by South African clothing retailers in the collection, dissemination and usage of POS data in demand planning

• Other organisational functions involved in the process

• Problems faced in POS-based demand planning

• The future of POS data in demand planning

• To determine ways of improving the usage of POS data in demand planning in a South African clothing retail context

5.5 FINDINGS

The analysis was done from eight interview transcriptions of respondents from the

sample retail companies; one interview was done on each of the companies. In order

to protect company and respondent privacy and confidentiality, the companies and

respondents were allocated numbers and are referred to as Company 1 (C1),

Respondent 1 (R1), Company 2 (C2), and Respondent 2 (R2) respectively.

5.5.1 The positioning of a demand planner within the SA clothing retail companies

The term or title demand planner is not universally used in the South African clothing

retail supply chains. This was first experienced in the research process through the

difficulty faced in getting in contact with people performing the role of demand

83

planning. The titles used in these companies include demand planner, demand

forecaster, merchandise planner, retail planner and planner.

However, despite the differences in the titles, the role or work structure of the

individuals in these positions is largely the same. Some of the respondents used

different titles when referring to their role, switching from one name to another. For

the purposes of this description and narrative, the term ‘demand planner’ will be

used to refer to all the people working under these positions who were interviewed.

Figure 5.2 shows the various titles and implied titles of the people who perform the

demand planning role.

Figure 5.2: The various titles used for demand planners in clothing retail companies Source: Network illustration from analysis using Atlas.ti

For the majority of the clothing retail companies (six of the eight respondents), the

hierarchical position of demand planners lies on the middle management level. It

transpired that they report to strategic or executive level individuals. As an example,

the following statements were mentioned:

R4 – ‘It’s middle level. Demand planning is a middle management specialist role so

it’s performed at that level and then is aggregated upwards to the top line

executives and also spread downwards to your assistants.’

R1 – ‘It’s a tactical level, which falls under the middle management tier within the

company’s hierarchy.’

84

However, one of the respondents mentioned that the position occupied by their

demand planners is at an operational level, and this respondent was quoted saying:

R6 – ‘It’s a fairly junior position in the company. Very focused.’

5.5.2 The role of a demand planner within the SA clothing retail companies

The responsibilities or duties performed by the planners in the different companies

vary and the jurisdiction the planners have within their companies also varies. As

much as the tasks performed differ somewhat from company to company, there are

some common roles that most or all of the respondents said they perform. The role

that is notably common to all the demand planners is determining demand forecasts.

The following quotes of respondents show how forecasting is a common role

performed by demand planners:

R1 – ‘We look at data with the objective of optimising the forecasts going forward.’

R3 – ‘Our main function is to basically reforecast product for best potential sales in

the future season.’

R4 – ‘It entails forecasting for the replenishment of eligible products.’

R5 – ‘For those kind of programs, we do constant forecasting.’

R5 – ‘You review your forecasts on a frequent basis and amend as required

whether you are trading down or up.’

R7 – ‘To assist in creating a forecast that is reliable that is reflective of the current

trend.’

R8 – ‘We do forecasts and demand planning for the different buyers’ class

structures.’

Although forecasting forms the foundational role of the demand planners, their role is

not limited to that; demand planning is a broader concept. This distinction shows that

the role of the respondents in this study is not mere demand forecasting but demand

planning. This is because their roles go beyond determining forecasts in creating an

alignment between demand planning and company strategy.

Most of the respondents in this study displayed an understanding of the difference

between demand forecasting and demand planning. The quotes below show the

respondents’ understanding of the two concepts:

85

R1 – ‘Planners will sense the strategy for the product and take some of that into the

process and feed it into your forecast.’

R2 – ‘We forecast basic sales that we can achieve in the future season to maximise

growth and profit.’

R3 – ‘You review your forecasts on a frequent basis and amend as required whether

you are trading down or up.’

The statements above show that the demand planners are conscious of the fact that

the demand planning role goes beyond demand forecasting. Figure 5.3 shows the

various codes (in vivo codes) that were created from the descriptions made by the

respondents on their roles other than forecasting.

Figure 5.3: Other roles of demand planners Source: Network illustration from analysis using Atlas.ti

5.5.3 Demand planning process in the SA clothing retail industry

Literature discussed in Chapter 2 suggested that there are different models and

frameworks that are followed in the process of demand planning. Similar to this

sentiment, the analysis of the interviews done reveals that companies in the South

African clothing retail sector follow different steps in demand planning.

Despite the differences in the approaches followed by the different companies,

86

similarities were noted. First the process of demand planning is an iterative process

across most companies; it has a given series of steps where different input factors

are considered. Some respondents had this to say on the process that their

company follows in demand planning:

R6 – ‘So you review that constantly, is it up or down and the performance relevant to

the base and how you react according to it.’

R7 – ‘We have different inputs that make up or result in our forecast. So the steps in

the process include considering and using all your influencing inputs.’

Secondly, demand planning in the clothing industry depends on historical data, the

data that has been collected from previous sales. The past sales data gives a

general guide as to what the customer preferences are, and thus helps the demand

planning team to know which stock keeping units (SKUs) are selling and which are

not. As mentioned in Chapter 2, past sales data is the closest representative of the

true customer consumption of a product. The sales history would therefore be a

good starting point to understanding the customer demand and the replenishment

requirements thereof. Below are some of the comments made by the respondents

concerning sales history:

R1 – ‘We start off by understanding what needs to be on replenishment from a

business point of view and that is determined by the planners.’

R4 – ‘In demand planning, you start with your data, your history.’

R7 – ‘These inputs include previously collected information from past sales, which

serve as a guide and then we make various considerations, additions to this to

get our forecasts.’

5.5.4 Structure of SA clothing industry supply chains

Based on the information gathered, the South African clothing industry has a supply

chain that is characterised by much dependence on imported products. Notably, the

respondents mentioned China as the biggest contributor to the products that they

sell. These retail companies also source some of their products locally but the

quantity is said to be relatively much lower.

87

5.5.5 Nature of products sold within the SA clothing retail industry

Another issue of importance under enquiry was the level of demand volatility that the

demand planners have to consider in their demand forecasts. A common issue that

emanated from this enquiry was that the degree of volatility differs according to the

range of products for which forecasts are determined. Some products are more

stable and are easier to forecast for. On the other hand, some products are more

volatile and are characterised by a high level of uncertainty. One of the respondents

from Company 3 cited that the volatility of their products is quite high due to the high

influence of economic factors. Figure 5.4 shows the different terminology used by the

demand planners to describe these two product groupings.

Figure 5.4: Product groupings as experienced by planners in terms of level of volatility Source: Network illustration from analysis using Atlas.ti.

It is evident that demand planners make a distinction between these two broad

product categories. The first category comprises products with a more stable

demand and makes provision for products that are termed by the different

respondents as yearly products or replenishment items, or core products. Customers

demand these products throughout the year, thus the retailer should have them in

88

stock all year round. Some of the examples under this category include white or

black t-shirts, socks and underwear. One of the respondents described this category

of products in the following way:

R2 – ‘You have bread and butter lines, like basic shorts, basic t-shirts, basic polos

which we will continuously have in our store, so that’s easy peezy.’

The second category is the one that is more volatile and uncertain; the products

could be new to the market and therefore there is no historical reference point to

guide forecasts. Furthermore, these kinds of products could be what is termed

seasonal products or trend items or non-core. This category of products changes

over time and therefore is more volatile. One of the respondents described this

category of products in the following way:

R4 – ‘For the seasonal goods, we have minimal forecasting because that process

is generally led by fashions trends which are qualitative rather than

quantitative.’

5.5.6 The collection and dissemination of POS data

The POS data collection systems in all the respondents’ companies collect the

information of a particular sale at store level; the data scanning or collection systems

are linked to a central system where all the sales information from different stores is

aggregated and stored.

Although the general POS details such as the product item, price and size that are

collected at the point of purchase is similar from one company to the next, it was

noted that different companies have different output information on their systems. On

collection, the details are shown at SKU level, but when the planners pull data from

the system, the output would be aggregated in a way that is convenient to the user.

One respondent mentioned that the different departments can pull out the detail or

report that they need from the system. A respondent mentioned the following detail is

collected and contained by the database:

R4 – ‘It’s not only sales data that is captured. You have transfers, transfers in and

transfers out captured; you’ve got markdowns and promotions that are

89

captured. You’ve got obsolete stock write-offs as well as captured from the

POS system.’

In this collection, dissemination and usage of POS data, it is notable that technology

plays a fundamental role. Results suggest that modern-day demand planning

depends on the availability of suitable technology. The respondents cited the

following:

R1 – ‘Our processes depend on our systems, our technology, from software to

hardware. Technology is very important. Today we can do more electronically

compared to what could be done in the past manually.’

R4 – ‘Technology is crucial because, for example, the work that we do, it goes

across 525 stores, cross combinations that get to close to 2 million product

lines. So a human being cannot perform such a function to have 500 stores

having 2 million forecasts.’

R8 – ‘Technology and systems play a vital role from a data warehousing point-of-

view; the foundational kind of information that you can use as the basis for

any forecast or plan is what our database holds.’

Data analysis results suggest that the role of technology in the demand planning

process includes the following:

1. Data capturing – This is done when stock is received in stores and also when

a sale takes place. This is normally done through barcoding and the

complementary scanning system. The implementation of such systems can

ensure more accuracy.

2. Data dissemination – Technological systems have greatly improved the speed

of data transfer, for example, one of the respondents cited that they can see

the sales as they happen on a live system.

3. Data storage – The captured data is stored in a company database and can

be retrieved as and when needed. Clothing retail companies have thousands

of SKUs and it would be a very difficult task to store and retrieve this

information if the current technological systems were not in place.

90

4. Forecasting – Numerous algorithms are used in determining forecasts, and

performing this task without the help of software would be strenuous and time

consuming, as one responded cited:

R4 – ‘Technology allows you to disintegrate and aggregate forecasts. Allows

you to spread it. Technology takes half a second to plot a graph that

will take me a day to plot.’

5. Internal and external communications – Throughout the iterative process of

demand planning, the representatives of the different company departments

need to stay in close communication. Furthermore, once the future demand

figures are determined, communication with local and international suppliers

needs to be done. All this communication is more effective with the help of the

different technological systems like electronic data interchange (EDI) and the

various online interfaces.

Figure 5.5 shows a summary of the various technologies that are used within

demand planning in the South African clothing retail industry.

Figure 5.5: Technologies used within demand planning in the industry Source: Network illustration from analysis using Atlas.ti

91

5.5.7 The importance of demand planning in clothing retail

Of the many reasons supporting the importance of demand planning within these

clothing retail companies, inventory positioning and availability stood out.

Respondents expressed this reason in different ways as shown below, and Figure

5.6 shows a summary of these responses.

R2 – ‘It’s actually vital because we have people that provide the buying team with

all the data to make the correct decisions whether it will be colour analysis,

whether it will be size analysis.’

R3 – ‘It’s crucial, l mean it’s all about getting the right type of stock at the right store

at the right time and that is basically what we do on a strategic level while at

the same time managing our working capital; we don’t want to spend too

much money.’

R5 – ‘It’s vitally important. Like I said one of the goals as a planner, the

responsibility and accountability first of all is to ensure that you get

merchandise to the right locations within the right volumes so that you can

capitalise and maximise your turnover opportunity.’

R8 – ‘It plays a very important role. In order to meet your customer demand, what

you need to ensure from an inventory management perspective is that you

are never in a stock out situation. So, you always want to ensure that if the

sales potential is there you want the stock to be there to support it.’

92

Figure 5.6: Summary of the responses on the importance of demand planning in retail Source: Network illustration from analysis using Atlas.ti

5.5.8 The usage of POS data

All the interviewed respondents cited that their companies collect and use POS data

in one way or another. The concept of POS data is universally understood by the

respondents and they referred to it either as POS data or sales data in the

discussions during the interview. When asked about their company’s usage of POS

data, the respondents had a strong affirmation and confidence in their responses, for

example:

R1 – ‘Oh yes, It’s used, it’s used. Remember in replenishment and demand

forecasting (RDF) we have current data, old data, and recent data from the

previous week. So POS is reflective.’

R2 – ‘We have a rewards program which is a point-of-sale system and that smart

shopper analyses everything, right from what shop you like buying at, what

product you generally buy.’

All the respondents cited that they have systems that collect POS data at store level

and that this store level system is linked to some form of central system where

information from all the retail stores is consolidated and available for access to the

93

different company functions. All the respondents cited that they have access to their

company’s POS database. The demand planning and other functions that are part of

their company can pull out the specific data they want that is relevant for their

planning or reporting requirements.

In as much as all of the respondents have access, the time lag in terms of availability

of the POS data to the demand planners varies from one company to the next. In the

interviews done, the longest time lag is one week and the shortest being the one

where they have a live system. The one-week time lag means that the store’s data is

only available as a weekly sales report and a live system implies that the demand

planners can observe sales as they happen.

R3 – ‘On a weekly basis we receive aggregated data of the sale information and we

have access to all of the information relevant for our reports as well as

forecasting.’

R4 – ‘The sale is captured as a sale and because data is transmitted live.’

The implication of the lag time on POS availability concerns stock response time and

supply chain visibility. The quicker the access to POS the more visible the supply

chain; on the other hand, the delay in access or availability of POS data to the

company’s functions and suppliers hampers supply chain visibility. Nevertheless, the

actual measure of visibility would be relative and subjective. More than half of the

respondents cited that they have a one-day time lag access to POS data from the

time a sale happens. For example:

R2 – ‘We get sales reports once a week, so basically we would analyse weekly

sales but we can get sales back daily. So if I needed to look at something

specific at any point in time, I can have all that information up right now.’

The second impact of the time lag in POS data access and availability is on

response to replenishment requirements at stores. For example, a weekly time lag

means that a stock out for a product in a particular store can be seen by planners or

allocators seven days after it has occurred. This means that response to stock

requirements will take longer and the period that elapses with a store having a stock

out represents lost opportunities and costs.

94

5.5.9 The usage of POS data in demand planning

Of all the mentioned uses of POS data within South Africa’s clothing retail

companies, forecasting is the most common use in all the responses given. The

respondents cited that POS data is a major input factor to the determining of

forecasts. When questioned on the significance of POS data in the demand planning

process, all the respondents noted that POS data has a fundamental role. The

respondents provided the following statements to support this view:

R2 – ‘It is really important, without POS data, you plan blind; you don’t know what

to buy. You need to have direction. And your direction is given by your

customer through your POS system.’

R1 – ‘It is very important; I mean how do we even forecast better for the next buy

accurately? So it is very important.’

R4 – ‘It’s critical because that forms your base data, so it is that information you’ve

collected from the point of sale that you use to go forward in terms of

generating your forecasts (......) So it is critical. In fact, that is the mouth

piece.’

R8 – ‘POS data is vitally important because it is the foundation of every bit of

analysis that we do.’

The importance of POS data in demand planning described in Chapter 2 is

confirmed by these respondents. The most important reasons emanating from this

research that make POS data fundamental in demand planning are:

POS data is the best reflection of demand the demand planners can use

as a guide to determining future demand plans.

POS data improves forecast accuracy.

POS data shows the products currently out of stock and thus the planners

and allocators can send stock down to stores as per the need of each

store.

With the above-mentioned reasons on the importance of POS data, the next point of

enquiry was the way POS data is integrated in the process of demand planning. As

already stated, in clothing retail there is a dichotomy between replenishment items

95

versus trend items; this distinction also affects the application or integration of POS

data into demand planning. POS data shows historic sales information and is most

suitable for replenishment items which have a continual demand. In this case, POS

data can be used as a guide to how much each store requires of the item in the next

delivery.

On the other hand, trend items are products that change from season to season or

from one period to the next. In this case POS data may not be the best reflection of

what the future demand for trend items could be. POS data may also not be relevant

for a new product item that has no historic sales data, and other factors will have to

be used to guide demand planning for such products. Lastly, POS data may also be

irrelevant for products where the stock keeping levels are dictated by the supplier, as

suppliers of big brands have leverage over retailers and may have the onus of

dictating how much of a product to allocate to retailers.

In the light of this difference, below are some quotes by the respondents on the

relevance of POS data for each product type:

R1 – ‘Trends, as you know, change over time and are more volatile. Your

replenishment items are more stable. We mainly forecast for replenishment

items.’

R1 – ‘For newer products you won’t have any POS data but you will have to get

your cue from the POS of the related products and plan accordingly.’

R5 – ‘We work obviously with all the big brands internationally like Nike, Adidas and

Converse. There’s not so much control in terms of original concept and

development on that front.’

Another source of information that is said to be useful in the process of demand

planning is third-party-sourced sales data within the overall industry. These different

third party companies are involved in the trade of sales and trends information, and

they may provide direction to the different companies on their market performance.

Companies can thus proceed in line with the direction that the market is taking. From

this study, it seems the role of third-party-sourced data is of minimal significance;

96

respondents were either using it minimally or not at all. For example, some

respondents mentioned the following:

R1 – ‘Our forecasts are largely based on the information we collect within our

stores and I am not sure about other departments but as demand forecasters

(DFs) we don’t use any. If the other departments do, it’s probably minimal.’

R2 – ‘I think in terms of us making paradigm shifts of strategic decisions, that kind

of data think so big hey. Because what we do is, we kind of involved in the

market already so we are out there, we know what must be we buy.’

R4 – ‘It is negligible but still important because it gives you that bearing in terms of

direction but not in terms actual forecasting.’

Over and above the POS data which is said to be the key input in demand planning,

there are other factors that are also considered in determining demand figures.

Findings show that the involvement of the different organisational functions means

that there are different input factors that are considered before agreeing on the final

future demand figures. When probed on what other factors are considered in

determining forecasts, all respondents confidently stated that POS data is not the

only input factor:

R2 – ‘You mostly use and analyse sales data then the buying department, they

travel overseas quite frequently and generally see the overseas trends which

they then bring in.’

R4 – ‘So there are various issues to consider in your adjustment. But remember

your product when you forecast for it, you have already compartmentalised it.

So when you forecast for men’s jeans, you not going to apply the same

thinking as boys’ jeans.’

R5 – ‘Different factors, qualitative and quantitative factors, exist that are factored in

the determination of forecast. For example, one of the qualitative factors that

we look at is the competitive landscape.’

From the information gathered, it can be seen that demand planning is not a mere

forecast of future demand based on historical sales, but is a collaborative and

97

iterative process that requires input from all the relevant functions of the department.

Various factors were mentioned by the respondents, and Figure 5.7 shows a

summary of these issues that also become input factors.

Figure 5.7: Other input factors in demand planning Source: Network illustration from analysis using Atlas.ti

5.5.10 Other organisational functions involved in the process

The respondents that were interviewed for this research are the main people

responsible for the task of demand planning and as already stated, the titles used for

their position varies between companies. Although most of the responsibility lies with

these demand planners, they stated that the responsibility of determining future

demand figures is not solely their responsibility. As mentioned before, demand

planning is not a mere estimation of future demand levels but involves the planning

of demand to meet sales objectives and the company growth strategy. For this

reason, the role then requires input from different functions of the department.

Demand planning decisions have an impact on different parts of a supply chain and

thus the involvement of different parties in the process. This is linked to what was

mentioned in Chapter 2 – that different parts of the supply chain are affected by the

98

output of demand planning in the sense that the latter forms the basis of operational

objectives. Figure 5.8 below shows a summary of the different functions or

departments that are involved in this demand planning process.

Different companies have different structures with regard to who contributes to the

task of demand planning. However, one thing in common is that the demand

planning role is not a ‘one-man show’. Some of the respondents mentioned the

following in this collaborative context:

R1 – ‘Different departments are involved at different stages of the process.’

R3 – ‘l do the planning, and there is a bunch of other planners there as well. We

obviously report to a merchandise manager.’

R4 – ‘In demand planning, there is a lot of collaboration, sales, marketing,

operations and also top-line executives. You collaborate and you agree on a

forecast and you work on that forecast to go forward.’

R6 – ‘The demand planners work in collaboration with mainstream planners and

together they create the final plan.’

Figure 5.8 shows a summary of the responses given by the demand planner on the

question of the organisational functions involved in demand planning.

99

Figure 5.8: The organisational functions involved in demand planning Source: Network illustration from analysis using Atlas.ti

Demand planning thus requires a collaborative effort, whereby the various

department representatives give their input so that every demand function works

towards the same goal. This goes back to the argument by Fay (2010:32) mentioned

in Chapter 2 that, if supply chains move forward without a clear insight and a

collaborative set of objectives and direction, they will be faced with a high level of

customer dissatisfaction, increased cost and obsolescence, and ultimately a loss of

revenue and market share.

5.5.11 Problems in POS-based demand planning

The respondents cited that there are problems faced in the collection and usage of

POS data for demand planning purposes. Of the eight respondents, only one

claimed not to be facing any problems in this regard. The continuum of problems

differs from one company to another despite some commonalities.

A common problem cited in most companies is that of human intervention.

Respondents echoed that the collection of POS data is done and influenced by store

level staff.

R5 – ‘The risk of human influence … So in the event that you have sold items but

not acknowledge but you give to a customer without scanning, as a store

manager you can override it, you can say that wasn’t a sale, so I’m going to

“zerrorise” it.’

R7 – ‘Of course, there is human intervention.’

R8 – ‘Also the other problem that I think is not a unique problem with us but with

most retailers is the integrity of that information is only as good as the way it is

handled from the operational perspective in stores.’

Problems that could be linked to human interference include the inherent human

error; this may include among other things the double scanning of a product and the

mismatching of products and barcode tags. The mismatching of barcode tags is said

to happen when a product has a missing tag; in an attempt to solve this problem, a

100

staff member may decide to use the tag on another item with the same price. This

action may cause misleading data as a common price does not necessarily mean

common colour or size.

Another problem linked to human interference is the manipulation of data by store

level managers. This right of being able to manipulate store data can make the

system vulnerable to unethical tendencies and actions like fraud and pilferage. A

further problem faced at a demand planning level is the subjectivity and bias in the

interpretation of the collected information. Users of POS data must make sense of

the collected data by extrapolating various meanings of the data. One respondent

mentioned the following in this regard:

R2 – ‘Any data that you get is subjective and all dependent on interpretation. So if

you are an inexperienced planner you can misread your data and present

information that is incorrect.’

Respondents also mentioned system- and technology-related problems. The users

sometimes face technical problems associated with the operation of the systems and

technology. Some respondents mentioned the following on technological problems:

R4 – ‘Its transmission, downtimes, power outages, disconnection …’

R7 – ‘I’d be working and the system would freeze. So the capacity of the network or

computer or whatever should be that it can handle that kind of information …’

5.5.12 The future of POS data in demand planning in the SA clothing retail industry

Respondents highlighted some views on the future of the usage of POS data, in

terms of potential improvements as well as its future role in the process of demand

planning. One area of improvement, common to some respondents, is training and

education. There is need for training of the different personnel that deal with POS

data at any point, be it collection, analysis or usage. One of the respondents cited

that store level personnel need to be trained on the need to preserve the integrity of

POS data. There is also a need to train managers on the importance of POS-based

demand planning.

101

Another area with potential for improvement was that of the time lag between

collection and availability of POS data on the central system. Respondents cited that

sale figures in the POS data need to be readily accessible in a relatively shorter time

lag. Shorter time lags will allow quicker response to the stock requirements in the

respective stores, and will allow the demand planners to quickly sense any shifts in

consumer tastes and preferences as well as demand patterns for their products.

Shorter time lags take advantage of the real value of POS data, which is the fact that

POS data is as close to ‘actual demand’ figures that most companies can get.

Therefore, late access to this data defeats the purpose thereof. The quicker transfer

of POS data to the various members of the supply also enhances supply chain

visibility and integration.

The other area of possible improvement lies with the system and technology used to

collect and analyse POS data. First, it is suggested that the system should block

human manipulation of data on the system. This will ensure that the integrity of the

data is preserved. Furthermore, better systems are being developed and the old

systems are being upgraded. Companies within the clothing industry need to identify

systems, algorithms and software that are most suited for their needs so they can

better use POS data in their demand planning.

Improvement in the collection of customer-related information was also mentioned.

Although some of these companies already collect customer-related information

through their loyalty programmes, some of these retail companies do not. The

collection of customer-related information will help develop customer profiles and

store profiles in terms of the type of shoppers in a specific store, thereby assisting

planners to better forecast and improve the alignment of their plans.

5.6 CONCLUSION

This chapter has been dedicated to describing the analysis approach, the method of

analysis, and the steps followed in the analysis. It ended with a description of the

research findings.

An inductive analytical approach was followed since the theories, conclusions and

deductions were made from the data collected. The strengths and weaknesses of

this method were described. Because of this approach, coupled with the nature of

102

the collected data, the grounded theory analytical procedure was followed. This

theory approach meant that there was continual comparing of the data sets in order

to derive themes. To facilitate the application of the grounded theory procedure,

CAQDAS was used. The specific software used and referred to in the description of

the findings was Atlas.ti. With help of this software, grounded theory principles were

applied and implemented. Coding was done and these codes were used to identify

the commonalities which are the themes that have been described in the findings.

The research findings were structured in accordance with the research objectives

and research so that these objectives and questions could deliberately be addressed

by the findings emanating from the data collected. Many issues arose from the

analysis done and some of the notable ones include:

• The various roles of demand planners with the South African clothing retail

industry

• The demand planning process as performed in the South African clothing

retail industry

• The importance of demand planning

• The uses of POS data

• The fundamental role of POS data in demand planning

• The areas of potential improvements in the usage of POS data in demand

planning, for example technology and training of staff

As this chapter has provided a description of the findings, the next chapter is

dedicated to interpreting these results and checking whether the research objectives

have been achieved. The findings emanating from this chapter were further analysed

by following the grounded theory approach in order to draw conclusions, find

solutions and propose recommendations.

104

CHAPTER 6: CONCLUSION, INTERPRETATION AND RECOMMENDATIONS

6.1 Introduction

This study focuses on the usage of POS data in the demand planning process within

the South African clothing retail industry. Thus far, through literature review and

primary research, various issues relevant to demand planning have been discussed.

This chapter brings together the theoretical constructs and findings for interpreting

and drawing useful conclusions

Furthermore, recommendations are made on some of the problems as well as

opportunities identified from the study done.

6.2 Literature review theoretical constructs

In this study, various sources of information were consulted and referred to, including

journal articles, textbooks, websites and reports. The literature review culminated in

the identification and development of the theoretical constructs used in this study. A

relatively smaller amount of literature review was done in Chapter 1; however,

Chapters 2 and 3 had a more extensive literature review covering the usage of POS

data in demand planning and the nature of the South African clothing retail industry,

respectively.

Based on the literature review on demand planning and the usage of POS data

therein, a research gap or research problem was identified which focused on the lack

of information concerning the demand planning processes used by South African

retailers as well the influence of POS data within South African industries. With the

South African clothing retail industry as the focus area, the following research

objectives were developed:

Primary objective

• To determine the extent to which businesses in the South African clothing

retail industry use POS data in demand planning

Secondary objectives

105

• To determine the nature of the demand planning process as performed in the

South African clothing retail industry

• To determine the systems employed for the collection and dissemination of

POS data

• To determine the way POS data is used in the demand planning process

within this industry

• To determine ways improving the usage of POS data in demand planning in a

South African clothing retail context

• To find out the challenges and problems that are faced by South African

clothing retailers in the collection, dissemination and usage of POS data in

demand planning

The following section discusses the theoretical constructs that emanated from the

literature review; these constructs were used to guide and align the primary research

performed. First, the discussion focuses on the concept and sub-concepts of

demand planning and the role of POS data in demand planning. Furthermore, the

structure and characteristics of the global and South African clothing industry are

briefly discussed. The section also discusses the research methodology that was

followed to collect data to fulfil the research objectives.

6.2.1 Demand planning and the role of POS data

In Chapter 2, literature was reviewed with a focus on the demand planning process,

its framework as well as the use of POS data in the process. Through this review,

various issues came to the fore. First, demand planning was described as one of the

logistics or supply chain management activities concerned with future customer

demand issues such as forecasting, sales growth strategies, inventory efficiency and

related cost reductions. A distinction was made between demand planning and

forecasting where the latter is mostly concerned with mathematically extrapolating

prior demand values into the future, proposing that future demand will follow the

same patterns. Demand planning uses various input factors from the different

company departments; on the other hand, the output of demand planning also has

impact on the various functions.

Demand planning was said to be an important function within modern-day supply

chains and this is due to the following motivations:

106

Demand planning allows businesses to understand the nature of consumer

demand and ensures that the supply response is suited for this demand.

Demand planning presents a good opportunity for supply chains to focus on

the consumer and create the optimal value thereof.

Effective demand plans avoid the costs associated with a mismatch between

demand and supply, be it excess inventory costs or stock out costs.

Another issue discussed was the need for collaborative effort in demand planning.

Instead of independently trying to project demand patterns, supply chain members

and different organisational functions share information and work together in

determining future demand plans. Submitting arbitrary demand plans to different

members of the supply chain without involving them in the actual planning process is

considered bad demand planning practice. Collaboration between different functions

and supply chain members becomes necessary to overlay judgement over the

statistical forecasts. This involves the consideration of other qualitative and

quantitative factors that may not have been included in the primary forecasts but are

considered relevant in determining future plans. Various collaborative initiatives can

be followed in the demand planning process, including:

Collaborative planning, forecasting, and replenishment (CPFR)

Automatic replenishment programs (ARP)

Vendor managed inventory (VMI)

Seeing that this study is focused on POS-based demand planning, Chapter 2 also

discussed the role and importance of POS data in demand planning. POS data is

described as the information that is collected at the point where a product is bought

by the final consumer. The usage of POS in demand planning is said to improve the

effectiveness of the demand plans. Modern-day technologies have made the

collection of POS data much easier than before. POS data can either be sourced

directly from the point of purchase at store level or sourced from third-party vendors,

each of these having its own pros and cons. POS data contains details about a

particular sales transaction. The different transactions can be aggregated to show

the total sales quantities per product.

The suggested valuable role of POS data in the demand planning process lies in the

characteristics it possesses, including:

107

It is the best reflection of demand – POS data shields retailers and

suppliers from the misinterpretation and amplification of downstream

demand signals created by practices such as forward buying, duplicate

ordering and batch ordering (Keifer, 2010).

It improves forecast accuracy – POS data gives a quick, near real-time

look at the products moving through a specific retail channel.

It enhances the implementation of collaborative strategies – The usage of

POS data requires information sharing and coordination between

members of the supply chain and organisational functions; thus, it is said

to ‘help bring order to chaos’.

POS data is free from inventory decisions – Inventory decisions can hide

the underlying sales trend and thus cause misleading sales forecasts.

Despite these POS data characteristics which can enhance the demand planning

process, it is generally said that companies are not making full use of this valuable

data. Various ways were proposed as to how POS data can be integrated into the

demand planning process.

6.2.2 The South African clothing retail industry In Chapter 3, the focus was on describing the clothing industry, and more specifically

the South African clothing retail industry. Of note was that the global clothing supply

chains have been going through tremendous changes, especially after the

Agreement on Textiles and Clothing (ATC) in 2005. In short, the ATC led to the

abolishment of quota restrictions and protectionism that was in place in most

countries and led to a move towards liberalisation of the industry. This generally led

to a more open international clothing market. The clothing industry has a significant

economic influence in many countries especially in low-income Asian countries. It

contributes to employment creation and national development.

The structure of the global clothing supply chains is configured such that the major

production in this industry is done in low-income countries while the highest

consumption is in the developed countries. This concentration of production in

developing Asian countries is driven mainly by low labour cost. The clothing industry

is said to be a buyer-driven industry; it is highly competitive and has a fast pace of

108

change in the demands of individual buyers. To survive in this industry, competitors

create resilient brands and employ intensive marketing campaigns.

In the context of South Africa, the industry is largely dependent on imported

products, more specifically from China. Before South Africa joined the World Trade

Organisation (WTO), the industry was characterised by a high level of protectionism.

After South Africa joined the WTO, it opened its industry to global trade and now

provides a huge amount of the country’s employment. South Africa was also affected

by the ATC, which had a negative impact on local manufacturers as they now have

to compete with manufacturers located in low-wage regions, most notably China and

India. Some efforts to fight this phenomenon, such as the Proudly South African

campaign have not been effective enough in doing so.

This study focused on the retailing part of the clothing industry. The South African

retail sector is growing as evidenced by the proliferation of retail spaces through

shopping malls or centres. Clothing retailing is also a part of this growth. Of the

seven retail clusters, clothing retail is termed clothing, footwear and textiles (CFT)

and as of 2014 had the second highest share of total retail sales. It constituted 21%

of the total retail sales after general dealers. This CFT cluster also had the highest

growth rate, with women’s apparel dominating the cluster.

South African clothing retailing can be divided into mainstream retail and ‘alternative

retail’; the former includes the major formal clothing retailers like Edcon and Mr Price

Clothing, while the latter includes customised tailoring, factory shops, homemade

clothing, and second-hand clothing. The mainstream clothing retail sector in South

Africa can be considered an oligopoly as it is dominated by few big companies. On

the other hand, the alternative retail has many players that compete with each other

as well as with the mainstream retailing.

The need for effective demand planning in the clothing retail industry is justified and

motivated not only by the advantages that effective demand planning has but the

nature of the industry itself. The need to understand customer demand patterns in

this dynamic industry is the biggest motivation. In any industry, POS data can play a

huge role improving demand planning. It is said that in this volatile clothing industry

with complex consumers, POS data has become an indispensable tool. Thus, the

109

study focused on the role of POS data in demand planning within the South African

clothing retail industry.

6.3 Research methodology Chapter 4 concentrated on describing and justifying the research methodology

followed in this study. A summary of the research methodology in this research is

described in the following section.

This qualitative study on South Africa’s major clothing retail companies followed an

inductive approach. It was performed through a series of semi-structured interviews

based on a sample of eight major clothing retailers. The interviews were conducted

telephonically as well as face to face with the respondents who work as demand

planners in the companies that form the sample. The research design followed the

grounded theory strategy; this approach involves drawing conclusions and building

theories based on the collected data.

The collected data was then analysed following the inductive analytical rationale,

which is the method of drawing conclusions or developing theories based on the

collected information. In this rationale, the grounded theory approach was followed in

performing the actual analysis, which involved the comparison of various sets of data

then coding and capturing the themes that arose from this process.

Two levels of coding were performed, namely, open coding and axial coding. Coding

facilitated the searching and identification of patterns and proceeding themes that led

to the conclusions made. Performing the analysis process was facilitated by using a

CAQDAS product called Atlas.ti because of the various advantages it presents. The

themes and conclusions emanating from this analysis process were described in

Chapter 5 as research findings. The following section interprets the research findings

and shows how the findings met the research objectives.

6.4 Interpretation of the research findings This section interprets the research findings by showing how the research objectives

were met and by drawing conclusions and inferences. This section is categorised

according to the research objectives reported in Chapter 1. The achievement of

primary research objective is not covered separately owing to the fact that the

110

secondary objectives were determined so as to contribute to the achievement of the

primary objective.

6.4.1 Secondary objective 1 This objective had to do with determining the nature of the demand planning process

as performed in the South African clothing retail industry. The study fulfilled this

objective and identified various characteristics and issues on the demand planning

process followed in this industry.

First, the role of demand planners was determined. It was discovered that the people

who perform the role of demand planning within the clothing retail companies have

different titles in different companies. Nevertheless, the role and duties performed by

the people in this position is largely the same. For most of the companies, the role of

a demand planner lies within middle level management.

Of the many tasks that these demand planners perform, all them mentioned demand

forecasting as a pivotal task required of them. Most of the demand planners

mentioned that their position is understood and well defined, with clear reporting

lines; however, some mentioned the role of demand planning as a ‘floating role’

without definite executive management buy-in. Furthermore, the demand planners

showed that they have a clear understanding of the difference between forecasting

and demand planning.

Secondly, companies in the South African clothing retail sector follow different steps

or procedures in their demand planning process, just as the literature proposes.

Although the procedures followed differ from one company to the other, there are

some commonalities. First, the process is an iterative one involving the consideration

of different input factors and continuous revising of plans. Secondly, the historical

sales information plays a fundamental role in the demand planning process.

6.4.2 Secondary objective 2

This objective was concerned with determining the systems employed in the

collection and dissemination of POS data within South African clothing supply

chains. This objective was achieved, but first to understand this data dissemination

process, the structure of South African clothing industry supply chains and nature of

products sold within the industry were determined.

111

The South African clothing industry is largely dependent on imported products. The

literature review and the primary research done in this study point to the fact that

China is the largest contributor to these imports. Some of the products produced in

this industry are also manufactured locally; however, these constitute a small

percentage. Only one of the eight companies studied claimed to have an in-house

production capacity which is locally based.

The volatility of products affects the level of accuracy in forecasting – the more

volatile the product, the more difficult it is to forecast, and vice versa. The South

African clothing retail industry has two distinct types of products, namely,

replenishment and trend products. Trend products are more volatile while the

demand for replenishment items is more stable.

Concerning POS data collection, retail companies collect POS data of a particular

sale at store level. These data scanning or collection systems are linked to a central

system where all the sales information from different stores are aggregated and

stored. This central database is then made accessible to the various functions of the

company and they can pull reports and statistics as per their departmental needs.

Suppliers also have access to POS data but only for the products they supply.

Technology plays a vital role in POS data collection, storage and dissemination.


The third secondary objective was to determine the way POS data is used in the

demand planning process within the clothing retail industry. This objective was

achieved by determining the importance of the demand planning process and the

role of POS data in this process.

Demand planning is an important task within clothing retail companies in South

Africa. Of all the reasons supporting its importance, the most important is the impact

of demand planning in the positioning of inventory.

Clothing retail companies in South Africa collect POS data at store level and this

information is then stored in a central system. The different users can retrieve the

stored information as and when needed. The availability of POS data to the different

supply chain members has an impact on supply chain visibility. The quicker the

sharing of this POS data to the supply chain members, the better the visibility.

112

Data collected at the point of purchase contains various details about a specific sale.

This information can be used for various purposes by the different company

functions. Various technologies are used in the collection and dissemination of this

data. Another notable source of data is third-party-sourced POS data, which is sales

information about the performance of the industry or competitors. As much as this

information is considered in the planning process, it has minimal influence.

Furthermore, it was discovered that the collected POS data is used for different

purposes; more relevant to this study was the usage of POS data in demand

planning. POS data is said to play a fundamental role in demand forecasts where it

is considered a major input factor. Forecasting and demand planning are mostly

about determining the future demand in order to ensure optimal inventory levels at

various points of the supply chain. POS data is most suitable for this because of the

advantages that have already been mentioned in the preceding subheading.

However, there are some situations where POS data is not relevant and these

include the following:

Trend items which are influenced by fashion trends and have no direct link to

previous sales data

New product lines that have not been previously sold by the company, so

there is no previous history

Supplier-dictated products. Some suppliers of big brands dictate the range of

products as well as the quantities that retailers should stock.

Over and above the usage of POS data, demand planning also considers other input

factors. The involvement of other company departments means various issues are

brought to the table. These factors include:

Sales objectives and company strategy

Competition and competitor activities

Promotions and other marketing activities

The economy – inflation and consumer buying power

Lead times

Supplier constraints

Social factors such as lifestyle changes

113


The goal of the fourth secondary objective was identify the challenges and problems

faced by South African clothing retailers in the collection, dissemination and usage of

POS data in demand planning. This objective was achieved as the findings have

shown that companies in the clothing industry do face various challenges with POS

data in demand planning, as follows:

Human error

Human interference through the manipulation of information

Lack of training in the collection and capture of POS data

Technological and system failures in POS dissemination

Software issues in demand planning

Lack of management buy-in regarding the demand planning process


The fifth secondary objective was to determine ways of improving the usage of POS

data in demand planning in a South African clothing retail context. This objective was

achieved by probing the respondents on the issue and by reviewing suggestions

from the literature. The following section discusses some of these possible

improvements.

6.5 Recommendations

The first recommendation is related to the first objective of the demand planning

process. As literature suggested, demand planning has a crucial role in today’s

consumer-driven supply chains and as such should be well performed and

continually monitored and re-evaluated. Furthermore, there needs to be clear

management buy-in. Top management should recognise demand planning for its

importance and provide enough support for it to function properly.

Another recommendation related to the first objective is that, seeing that companies

have different business configurations, organisational structures, competitive

philosophies and product types, there cannot be a standard ideal demand planning

framework or procedure to suit all. Companies need to develop their demand

114

planning procedure based on the different variables relevant to their own operating

environment and strategy.

Despite the differences in the procedure followed, there are some principles that

should be applied to any procedure. These include the following:

Continuous revision – Different factors influence forecasts and plans in

diverse ways and in varying degrees and times; thus, the process of

demand planning for a particular period should not be a once-off but

iterative to ensure the timely adjustments to any changes in any of the

affecting variables or input factors.

Collaboration – The task and process of demand planning should not be a

silo process but should involve the various members of the supply chain

and well as organisational functions. Demand plans and forecasts are

more effective when collaboratively developed. Furthermore, the adoption

of the plans will be easier and accuracy will be improved when all the

issues and variables have been considered to develop plans based on

consensus.

Better use of POS data – As mentioned in literature and in the findings,

POS data has inherent characteristics that enhance the demand planning

process. Organisations that engage in the process of forecasting and

demand planning should therefore use POS data to improve the

effectiveness thereof.

Better use of technology – Modern-day supply chains deal with thousands

of SKUs. To better control and manage this product proliferation, various

technological systems can be utilised to ensure effective and efficient

forecasting and demand planning. Companies should put in place modern

and relevant hardware and software to achieve this advantage.

Another recommendation is related to the dichotomous nature of products

within this industry. As different products have different levels of volatility

and demand behaviour patterns, there is a need to develop parallel

forecasting procedures or demand planning strategies for the various

product types. In the case of this industry, for replenishment items, a

continuous forecasting and replenishment system could be developed.

115

Because the demand is more certain and ongoing, replenishment can be

done on a frequent basis, thus ensuring optimal inventory positioning. An

example of such a system is one implemented by Woolworths

supermarkets where the company uses an automated inventory

replenishment system that allows continuous replenishment based on the

needs of each particular store in their supply chain (Tung, 2009).

The successful implementation of such a system could greatly improve the efficiency

and effectiveness of the supply chain. However, such a system would require an

agile transport system which could possibly result in an increase in transport costs.

As such, there is a need to make the necessary trade-off calculations to ensure such

a system reduces overall costs rather than increases them.

Another example of a well-functioning system is the one used by Zara. Zara has an

efficient and effective supply chain. The clothing store keeps a significant amount of

its production in-house and makes sure that its own factories reserve 85 per cent of

their capacity for in-season adjustments (Lu, 2014). Seeing that supply

responsiveness is vital in this industry, in-house production allows the company to be

flexible in the amount, frequency and variety of new products to be launched (Lu,

2014).

It has become clear that demand planning needs to be performed and considered an

important function within companies and supply chains. The following sequential

issues associated with inventory positioning support this view:

With poor demand planning and poor forecasts, there would be a

mismatch between demand and supply.

If demand plans are ignored or the process is performed poorly, this

mismatch in demand and supply results in poor inventory positioning.

Poor inventory positioning will have various negative impacts on the entire

supply chain. These may include missed sales opportunities, excess

inventory levels, stock obsolescence and back orders.

Another recommendation is that supply chains should make more use of POS data

in optimising their response to customer demand. The usage of POS data in the

116

planning process by companies in vital. This is supported by the research findings

that show the advantages associated with POS data, as follows.

POS data show how the company is performing in terms of sales as well

as the performance of different SKUs.

POS data serve as the best cue or guide for future demand levels.

POS data form the foundational data used in various forecasting

algorithms.

POS data show the stock replenishment requirements of the different store

outlets at a given time.

Supply chain visibility has an influence on the effectiveness and efficiency of supply

chains, and in recognition of this fact, it is important that POS data be shared with all

the relevant supply chain members as quickly as possible to enhance visibility.

POS data should contain as much data as possible about a particular sale. Rich

sales data will give more information about the nature of demand and thus allow

better suited plans and supply. The importance of rich sales data can be seen in

some companies that have implemented customer loyalty programmes; this allows

the creation of a customer profile and thus supply or replenishment for different

stores can be tailored for their customers with better precision.

Based on the inherent value and benefits of POS data in demand planning, it is

important for demand planners to source POS data as soon as possible after it has

been generated and use it to determine forecasts that will better represent future

demand. However, these forecasts should not be limited to POS data but should

factor in all the variables that influence customer demand and buying behaviour.

Another issue to consider in the improvement of demand planning is collaboration.

Literature suggested the high need for collaboration in the demand planning process,

and the demand planning process in the South African clothing process is in line with

this. However, beyond this intra-business collaboration, there is need for

collaboration with suppliers that are in the same supply chain. The practice by large

clothing manufacturers of dictating stock-holding or replenishment decisions could

result in various problems associated with a mismatch in demand and supply.

117

Lastly, considering the import-dependency of the industry, there are challenges

associated with such a configuration:

Suppliers dictate trends and standards.

Long lead times result in slower replenishment and poor response to

change in demand patterns.

Lost employment opportunities occur, whereas if the manufacture of these

products was based locally, it would create employment opportunities for

South Africans.

Considering the above-mentioned issues, a possible solution to this predicament is

the indigenisation of clothing manufacture by using more local suppliers and by

increasing local production. The South African government and clothing industry

stakeholders should consider the various ways of promoting local production of

clothing products. This will, however, require expertise, modern technologies as well

as large amounts of investment.

6.6 Future research

This research has identified the following prospective future research opportunities

that may require investigation in the future:

Optimising the collection of POS data – The timing and accuracy of POS data

collection is important in the demand planning process and thus there is a

need to research further in order to determine how the collection and

dissemination of POS data can be improved.

The usage of qualitative variables in demand planning – This study mainly

looked at the usage of POS data in the demand planning process. POS data

is quantitative in nature and many algorithms exist to facilitate the usage of

quantitative data in determining forecast. An issue in need of enquiry is the

inclusion of qualitative factors in forecasting.

The potential for local sourcing in the South African clothing industry – The

clothing retailers in South Africa depend on imported products. Dependence

on exports has various macro and micro negatives, and as a result, there is a

need for a further enquiry into the possibility of developing local product

supply.

118

6.7 Conclusion

This study was incited, driven and directed by the research gap identified after a

literature study revealed that no specific research has been done on the usage of

POS data by South African companies in demand planning. This qualitative study on

South Africa’s major clothing retail companies was performed through a series of

semi-structured interviews based on a sample of eight major clothing retailers. The

research design followed the grounded theory approach. The captured data was

analysed following the grounded theory approach facilitated by a CAQDAS called

Atlas.ti. Through this process of analysis, findings were drawn on the subject under

study.

The findings of this research show that demand planning is an important part in

management supply chains, as the influence and impact of its output on the various

members of the supply chain became clear. Demand planning affects inventory

positioning, replenishment decisions, ordering and production decisions. Considering

all of this, companies need to ensure that the process of demand planning is carried

out effectively and that different functions collaborate in this process to factor in all

the issues that influence demand. A key issue in this study was the role POS data

played in the process of demand planning.

Outcomes of this research showed that companies collect POS data and use it for

various purposes including having it as an input to the demand planning process.

POS data plays a fundamental role in the process of demand planning as both

secondary and primary research show that it is the foundational input in this process.

Furthermore, its importance is substantiated by the inherent characteristics it has

that enhance forecasting accuracy and planning effectiveness. However, POS data

is not used in isolation; there are other qualitative and quantitative factors that need

to be considered as inputs in the demand planning process.

Lastly, the role of POS in demand planning is expected to grow as customers are

becoming increasingly demanding. Companies should therefore invest and

implement robust POS collection and dissemination systems and technologies to

ensure that they can leverage the advantages of POS data. A company’s ability to

119

match product supply to customer demand leads to high levels of efficiency and

effectiveness; POS data can play a fundamental role in enhancing this ability.

120

LIST OF SOURCES Adams, J., Khan, H. T., Raeside, R., & White, D. I. (2007). Research methods for graduate business and social science students. SAGE Publications India. Andres, F. (2008). Demand planning and forecasting with POS data: A case study. Journal of business forecasting methods and systems 27(4):29. Atieno, O.P. (2009). An analysis of the strengths and limitation of qualitative and quantitative research paradigms. Problems of Education in the 21st Century, 13(1), 13-38. Barratt, M. & Oliveira, A. (2001). Exploring the experiences of collaborative planning initiatives. International Journal of Physical Distribution & Logistics Management, 31(4), pp.266-89.

Blanchard, D. (2010). Supply chain management best practices. John Wiley & Sons, New Jersey. Blecker, T., Kersten, W. & Meyer, M. (2009). High performance logistics: Methods and technologies. Erich Schmidt Verlag GmbH & Co KG. Borgos, M. (2008). More power with point of sales data. The Journal of Business Forecasting, 27(4):19. Bowersox, D.J., Closs, D.J. & Stank, T.P. (2000). Ten mega trends that will revolutionize supply chain logistics. Bursa, K. (2008). How to effectively manage demand with demand sensing and shaping using point of sales data. The Journal of Business Forecasting, 27(4):26. Available: http://connection.ebscohost.com/c/articles/37299793/how-effectively-manage-demand-demand-sensing-shaping-using-point-sales-data [Accessed 04 August 2015]. Butner, K. (2010). The smarter supply chain of the future. Strategy & Leadership, 38(1):22-31. Available: http://www.emeraldinsight.com/doi/abs/10.1108/10878571011009859 [Accessed 05 August 2015]. Business Partners (2014). The South African textile and clothing industry – an overview. Business Partners. Available: http://www.businesspartners.co.za/knowledge-hub/manufacturing-fund/posts/south-african-textile-clothing-industry-overview-3252/ [Accessed 13 March 2015].

http://connection.ebscohost.com/c/articles/37299793/how-effectively-manage-demand-demand-sensing-shaping-using-point-sales-data

http://connection.ebscohost.com/c/articles/37299793/how-effectively-manage-demand-demand-sensing-shaping-using-point-sales-data

http://www.emeraldinsight.com/doi/abs/10.1108/10878571011009859

http://www.businesspartners.co.za/knowledge-hub/manufacturing-fund/posts/south-african-textile-clothing-industry-overview-3252/

http://www.businesspartners.co.za/knowledge-hub/manufacturing-fund/posts/south-african-textile-clothing-industry-overview-3252/

121

Caniato, F., Caridi, M., Castelli, C. & Luca, L. (2008). Demand and retail management in luxury fashion industries. Research Journal of Textile and Apparel, 12(3), pp.65-76. Available: http://www.emeraldinsight.com/doi/abs/10.1108/RJTA-12-03-2008-B007 [Accessed 16 September 2015]. Chase, C. (2015). Using multi-tiered causal analysis to synchronize demand and supply. SAS Institute Inc. Available: https://support.sas.com/resources/papers/proceedings15/SAS1388-2015.pdf [Accessed 05 August 2015]. Christopher, M. & Towill, D. (2001). An integrated model for the design of agile supply chains. International Journal of Physical Distribution & Logistics Management, 31(4):235-46. Available: http://www.emeraldinsight.com/doi/abs/10.1108/09600030110394914 [Accessed 13 March 2015]. Crum, C. & Palmatier, G.E. (2003). Demand management best practices: Process, principles, and collaboration. J. Ross Publishing. CSCMP (2015). Definitions. Council of Supply Chain Management Professionals. Available: https://cscmp.org/supply-chain-management-definitions [Accessed 05 August 2015]. Dong, Y., Huang, X., Sinha, K.K. & Xu, K. (2014). Collaborative demand forecasting: Toward the design of an exception-based forecasting mechanism. Journal of Management Information Systems, 31(2):245-84. Dube, P., Muyengwa, G., & Battle, K. (2012, June). Study of collaborative planning, forecasting and replenishments in South African retail industry. In Computers and Industrial Engineering 42. Edcon (2015). About us - find out more about our group. Edcon Group Available: http://www.edcon.co.za/ [Accessed 04 August 2015]. Euromonitor (2015). Retailing in South Africa. Euromonitor Available: http://www.euromonitor.com/retailing-in-south-africa/report [Accessed 03 August 2015]. Fay, G. (2010). Demand sensing in volatile times. The Journal of Business Forecasting, 29(3):31. Fernandez-Stark, K., Frederick, S. & Gereffi, G. (2011). The Apparel Global Value Chain. Duke Center on Globalization Governance and Competitiveness. Flick, U. (2011). Introducing research methodology: A beginner’s guide to doing a research project. Sage.

http://www.emeraldinsight.com/doi/abs/10.1108/RJTA-12-03-2008-B007

https://support.sas.com/resources/papers/proceedings15/SAS1388-2015.pdf

https://support.sas.com/resources/papers/proceedings15/SAS1388-2015.pdf


https://cscmp.org/supply-chain-management-definitions

http://www.edcon.co.za/

http://www.euromonitor.com/retailing-in-south-africa/report

122

Fox, W. & Bayat, M. S. (2008). A guide to managing research. Juta Publishers, Cape Town. Gallucci, J. (2015). 7 Behaviors of great demand planners. The Journal of Business Forecasting, 34(1):4. Available: https://www.questia.com/library/journal/1P3-3679188531/7-behaviors-of-great-demand-planners [Accessed 12 April 2015]. Gauteng Treasury Quarterly Bulletin, (2012). The Retail Industry on the Rise in South Africa. Available: http://www.treasury.gpg.gov.za/Documents/QB1%20The%20Retail%20Industry%20on%20the%20Rise.pdf [Accessed 16 September 2015]. Gereffi, G. & Frederick, S. (2010). The global apparel value chain, trade and the crisis: challenges and opportunities for developing countries. World Bank Policy Research Working Paper Series, Available: http://www19.iadb.org/intal/intalcdi/PE/2010/05413.pdf [Accessed 05 August 2015]. Gereffi, G., & Memedovic, O. (2003). The global apparel value chain: What prospects for upgrading by developing countries (pp. 1-40). Vienna: United Nations Industrial Development Organization. Available: http://www.ids.ac.uk/ids/global/pdfs/AppareF1.pdf [Accessed 13 March 2015]. Habib, M., 2011. Supply Chain Management (SCM): Theory and Evolution. INTECH Open Access Publisher. Available: http://cdn.intechopen.com/pdfs/17671/InTechSupply_chain_management_scm_theory_and_evolution.pdf [Accessed 12 March 2015]. Hesse-Biber, S. (2010). Qualitative approaches to mixed methods practice. Qualitative Inquiry, 16(6), 455-468. Hitachi Consulting (2009). Six key trends changing the supply chain management today. Hitachi Consulting. Available: http://www.sdcexec.com/news/10358095/six-key-trends-changing-the-supply-chain-management-today [Accessed 13 March 2015]. Ireland, R.K. & Crum, C., 2005. Supply chain collaboration: How to implement CPFR and other best collaborative practices. J. Ross Publishing. Jauch, H. & Traub-Merz, R. (Eds.). (2006). The future of the textile and clothing industry in Sub-Saharan Africa. Friedrich-Ebert-Stiftung, Division for International Development Cooperation, Africa Department.

https://www.questia.com/library/journal/1P3-3679188531/7-behaviors-of-great-demand-planners

https://www.questia.com/library/journal/1P3-3679188531/7-behaviors-of-great-demand-planners

http://www.treasury.gpg.gov.za/Documents/QB1%20The%20Retail%20Industry%20on%20the%20Rise.pdf

http://www.treasury.gpg.gov.za/Documents/QB1%20The%20Retail%20Industry%20on%20the%20Rise.pdf

http://www19.iadb.org/intal/intalcdi/PE/2010/05413.pdf

http://www.ids.ac.uk/ids/global/pdfs/AppareF1.pdf

http://cdn.intechopen.com/pdfs/17671/InTechSupply_chain_management_scm_theory_and_evolution.pdf

http://cdn.intechopen.com/pdfs/17671/InTechSupply_chain_management_scm_theory_and_evolution.pdf

http://www.sdcexec.com/news/10358095/six-key-trends-changing-the-supply-chain-management-today

http://www.sdcexec.com/news/10358095/six-key-trends-changing-the-supply-chain-management-today

123

Jüttner, U., Christopher, M. & Baker, S. (2007). Demand chain management integrating marketing and supply chain management. Industrial marketing management, 36(3):377-92. Available: http://citeseerx.ist.psu.edu/viewdoc/download;jsessionid=D6FBA19F43F06F051FD8D8123D573D54?doi=10.1.1.129.928&rep=rep1&type=pdf [Accessed 12 March 2015]. Keifer, S. (2010). Beyond point of sale: Leveraging demand signals for forecasting. The Journal of Business Forecasting, 29(2):14. Kruger, D. & Ramdass, K. (2010). 24th Annual SAIIE Conference Proceedings, South African Institute for Industrial Engineering. Lapide, L., 2005. Account level forecasting needs downstream data. The Journal of Business Forecasting, 24(2):17. Lawless, M. (2014). Predictive analytics: An opportunity for better demand planning and forecasting. The Journal of Business Forecasting, 33(4):44. Available: https://www.questia.com/library/journal/1P3-3601906371/predictive-analytics-an-opportunity-for-better-demand [Accessed 12 April 2015].

Lee, H.L., Padmanabhan, V. & Whang, S. (2004). Information distortion in a supply chain: The bullwhip effect. Management science, 50(12_supplement):1875-86.

Leedy, P.D. & Ormrod, J.E. (2014). 10th Ed. Practical Research, Planning and Design. Pearson: London. Loretto, R., 2014. A new approach to effective demand planning. Journal of Business Forecasting, 33(2). Lu, C. (2014) Zara supply chain analysis - the secret behind Zara's retail success. Available: https://www.tradegecko.com/blog/zara-supply-chain-its-secret-to-retail-success [Accessed 27 September 2016]. Lu, Y., Karpova, EE. & Fiore, A.M. (2011). Factors influencing international fashion retailers’ entry mode choice. Journal of Fashion Marketing and Management: An International Journal, 15(1), 58-75. Ma, Y., Wang, N., Che, A., Huang, Y. and Xu, J. (2013). The bullwhip effect under different information-sharing settings: a perspective on price-sensitive demand that incorporates price dynamics. International Journal of Production Research, 51(10):3085-16. Mael, C. (2011). The roller coaster ride of volatility. The Journal of Business Forecasting, 30(2):11.

http://citeseerx.ist.psu.edu/viewdoc/download;jsessionid=D6FBA19F43F06F051FD8D8123D573D54?doi=10.1.1.129.928&rep=rep1&type=pdf

http://citeseerx.ist.psu.edu/viewdoc/download;jsessionid=D6FBA19F43F06F051FD8D8123D573D54?doi=10.1.1.129.928&rep=rep1&type=pdf

https://www.questia.com/library/journal/1P3-3601906371/predictive-analytics-an-opportunity-for-better-demand

https://www.questia.com/library/journal/1P3-3601906371/predictive-analytics-an-opportunity-for-better-demand

https://www.tradegecko.com/blog/zara-supply-chain-its-secret-to-retail-success

https://www.tradegecko.com/blog/zara-supply-chain-its-secret-to-retail-success

124

Maree, K. (ed.) (2007). First steps in research. Van Schaik Publishers, Pretoria. Moisanen, J., (2014). Demand forecasting in the apparel industry. Available: http://urn.fi/URN:NBN:fi:amk-2014052910961 [Accessed 16 September 2015]. Nattrass, N. & Seekings, J. (2012). Differentiation within the South African clothing industry: Implications for wage setting and employment. Centre for Social Science Research. http://www.ncrfsa.org/ Accessed [13 March 2015]. NCRF (2015). NCRFSA - Home. Available: http://www.ncrfsa.org/ [Accessed 13 March 2015]. Nordås, H.K. (2004). The global textile and clothing industry post the agreement on textiles and clothing. World, 7(1,000). Available: https://www.wto.org/english/res_e/booksp_e/discussion_papers5_e.pdf [Accessed 13 March 2015] Palpacuer, F., Gibbon, P. & Thomsen, L. (2005). New challenges for developing country suppliers in global clothing chains: a comparative European perspective. World development, 33(3), pp.409-30. Available: http://www.sciencedirect.com/science/article/pii/S0305750X04002025 [Accessed 16 September 2015]. Pienaar, W.J. & Vogt, J.J. (2012). Business logistics management: A value chain perspective. Oxford University Press, London. Prinsloo, D.A. (2010). Classification and hierarchy of retail facilities in South Africa. Urban studies. Available: http://www.urbanstudies.co.za/wp-content/uploads/2016/07/New-Retail-Classification-2010.pdf [Accessed 22 September 2015]. Purushottam, N. (2011). Store attributes preference in selecting a store: a study of large scale retail stores in South Africa. Available: http://hdl.handle.net/10500/5654 [Accessed 22 September 2015]. PWC. (2012). Five steps to better demand plan clarity and accuracy. PWC Consulting Services. Available: https://www.pwc.com/us/en/operations-management/assets/demand-planning.pdf [Accessed 05 August 2015]. PWC Report, (2012). South African retail and consumer products outlook 2012-2016. PWC.

http://urn.fi/URN:NBN:fi:amk-2014052910961

http://www.ncrfsa.org/

https://www.wto.org/english/res_e/booksp_e/discussion_papers5_e.pdf

http://www.sciencedirect.com/science/article/pii/S0305750X04002025

http://www.urbanstudies.co.za/wp-content/uploads/2016/07/New-Retail-Classification-2010.pdf

http://www.urbanstudies.co.za/wp-content/uploads/2016/07/New-Retail-Classification-2010.pdf

http://hdl.handle.net/10500/5654

https://www.pwc.com/us/en/operations-management/assets/demand-planning.pdf

125

Available: https://www.pwc.co.za/en/assets/pdf/retail-and-consumer-products-outlook-2012-2016.pdf [Accessed 22 September 2015]. Ramdass, M.K. (2007). An engineering management framework for the SA clothing industry with a focus on KwaZulu-Natal (Doctoral dissertation, University of Johannesburg). Saldana, J. (2011). Fundamentals of Qualitative Research. New York: Oxford University Press. Saunders, M.N., Saunders, M., Lewis, P., et al. (2009). Research methods for business students, Pearson Education, India. Schrieber, J. (2005). Demand visibility improves demand forecasts. The Journal of Business Forecasting, 24(3):32. Available: http://search.proquest.com/openview/2cb89118bc1caab58a05f5e17dcf089c/1?pq-origsite=gscholar [Accessed 12 April 2015]. Shepard, D. (2012). Collaborative demand and supply planning between partners. Available: http://supplychainacuity.com/uploads/SCA_Collaboration_Monograph_February2012.pdf [Accessed 05 August 2015]. Silo (2015). Silo - Homepage. Available: http://www.silo.co.za/ [Accessed 13 March 2015]. Simon, R. (2008). The abcs of point of sales (POS) data. The Journal of Business Forecasting, 27(4):4. Sreejesh, S., Mohapatra, S. & Anusree, M.R. (2014). Business research methods. Springer International Publishing AG. Stake, R.E. (2010). Qualitative research: Studying how things work. Guilford Press. Stank, T. P., Daugherty, P.J. & Autry, C.W. (1999). Collaborative planning: Supporting automatic replenishment programs. Supply chain management: An international Journal, 4(2):75-85. Available: http://search.proquest.com/docview/216863102?accountid=13425 [Accessed 13 March 2015]. Statistics South Africa. (2015). Retail sector growth losing steam, Quarter 1 2015, Pretoria. Available: http://www.statssa.gov.za/?p=4163 [Accessed 22 September 2015] Strauss, A., & Corbin, J. (1990). Basics of qualitative research (Vol. 15). Newbury Park, CA: Sage.

https://www.pwc.co.za/en/assets/pdf/retail-and-consumer-products-outlook-2012-2016.pdf

https://www.pwc.co.za/en/assets/pdf/retail-and-consumer-products-outlook-2012-2016.pdf

http://search.proquest.com/openview/2cb89118bc1caab58a05f5e17dcf089c/1?pq-origsite=gscholar

http://search.proquest.com/openview/2cb89118bc1caab58a05f5e17dcf089c/1?pq-origsite=gscholar

http://supplychainacuity.com/uploads/SCA_Collaboration_Monograph_February2012.pdf

http://supplychainacuity.com/uploads/SCA_Collaboration_Monograph_February2012.pdf

http://www.silo.co.za/

http://search.proquest.com/docview/216863102?accountid=13425

http://www.statssa.gov.za/?p=4163

126

Supermarket and Retailer (2015).Top ten retailers in Africa. Supermarket and Retail. Available: http://www.supermarket.co.za/SR_Downloads/S&R%202015-12%20Africa%20focus.pdf [Accessed 12 March]. Thomassey, S. (2014). Sales forecasting in apparel and fashion industry: A review. In Intelligent fashion forecasting systems: Models and applications (pp. 9-27). Springer Berlin Heidelberg. Thomopoulos, N.T. (2015). Demand forecasting for inventory control. In Demand Forecasting for Inventory Control (pp. 1-10). Springer International Publishing. Tiu Wright, L., Denegri-Knott, J., Zwick, D. & Schroeder, J.E. (2006). Mapping consumer power: An integrative framework for marketing and consumer research. European journal of marketing, 40(9/10):950-71. Available: http://www.emeraldinsight.com/doi/abs/10.1108/03090560610680952 [Accessed 12 March 2015].

Tolbert, F. (2008). Why point of sales data matters for demand management. The Journal of Business Forecasting, 27(4):33.

Trepte, K. & Narayanaswamy, R. (2009). Forecasting consumer products using prediction markets (Doctoral dissertation, Massachusetts Institute of Technology). Tribou, S., 2010. POS Planning – The ‘pet’ of forecasting. The Journal of Business Forecasting, 29(1):20. Available: http://search.proquest.com/openview/016e91dccec02f7ded09bc32a5c6eb62/1?pq-origsite=gscholar [Accessed 05 August 2015]. Tung, L. (2009). AutoStockR hits Big W, Dick Smith. Zdnet. Available: http://www.zdnet.com/article/autostockr-hits-big-w-dick-smith/ [Accessed 27 September 2016] . Vlckova, V. & Patak, M. (2010). The 6th International Scientific Conference ‘Business and Management 2010’. Selected paper. Conference proceedings. Williams, B.D. & Waller, M.A. (2011). Top‐down versus bottom‐up demand forecasts: the value of shared point‐of‐sale data in the retail supply chain. Journal of Business Logistics, 32(1), 17-26. Wu, J. (2004). Supply chain management in selected clothing retail businesses (Doctoral dissertation, North-West University). Available: http://dspace.nwu.ac.za/handle/10394/647 [Accessed 13 April 2015]. You, F. & Grossmann, I.E. (2008). Design of responsive supply chains under demand uncertainty. Computers & Chemical Engineering, 32(12):3090-3111. Available: https://cscmp.org/about-us/supply-chain-management-definitions [Accessed 05 August 2015].

http://www.supermarket.co.za/SR_Downloads/S&R%202015-12%20Africa%20focus.pdf

http://www.supermarket.co.za/SR_Downloads/S&R%202015-12%20Africa%20focus.pdf


http://search.proquest.com/openview/016e91dccec02f7ded09bc32a5c6eb62/1?pq-origsite=gscholar

http://search.proquest.com/openview/016e91dccec02f7ded09bc32a5c6eb62/1?pq-origsite=gscholar

http://www.zdnet.com/article/autostockr-hits-big-w-dick-smith/

http://dspace.nwu.ac.za/handle/10394/647

https://cscmp.org/about-us/supply-chain-management-definitions

127

Zikmund, W., Babin, B., Carr, J., et al. (2012). Business research methods. Cengage Learning, UK.

128

ANNEXURE A: Letter of Non-disclosure

129

ANNEXURE B: Interview questionnaire Main Question:

To what extent do firms in the South African Clothing Retail Industry use Point of

sale (POS) data in demand planning and how the usage can be increased?

Investigative Questions

1. What steps are followed in demand planning?

2. What systems are used in the collection and dissemination of POS data?

3. How is POS data used?

4. How can companies in the clothing retail industry gain more leverage through

the use of POS data?

SUMMARY: INTERVIEW QUESTIONAAIRE SECTIONS.

1. • COMPANY/ DEPARTMENT BACKGROUND

2. • THE DEMAND PLANNING PROCESS

3. • THE USAGE OF POS DATA

4. • THE COLLECTION OF POS DATA

5. • THE SHARING AND DISSEMINATION OF POS DATA

6. • THE ROLE OF POS DATA IN DEMAND PLANNING

7 • IMPROVEMENTS/FUTURE OF POS DATA USAGE

130

Interview Questions

Date:

Time:

Place:

INTRODUCTION

Good Morning Sir/Madam. My name is Douglas Njabulo Raza and I am a student at

the University of Johannesburg. I am doing research for my Masters dissertation on

the use of Point of sale data (which I will be referring to as POS data) in demand

planning by companies in the Clothing retail companies in South Africa.

1. COMPANY/ DEPARTMENT BACKGROUND

1.1 What is your company and what type of business are you in?

1.2 How is your supply chain structured?

1.3 Which department are you in and what is its basic function?

1.4 Is demand planning a defined task with clear processes in your company?

1.5 How volatile is the overall demand for the products you sell?

2. DEMAND PLANNING PROCESS

2.1 How important is the demand planning task within your supply chain?

2.2 Who performs the demand planning role for the company and at what level of the

organization’s hierarchy is the task performed?

2.3 Which departments/functions of the company are involved in the demand

planning process?

2.4 What basic steps are followed in the process of demand planning?

2.5 Which parts of your supply chain are driven by POS data based forecasts?

131

3. USAGE OF POS DATA

3.1 Does the company use POS data in demand planning, if so how?

3.2 At what level of aggregation (in terms of product grouping) is POS based

demand forecasting done?

3.3 Is POS data used in long term or short forecasting?

3.4 Which type of products is most suitable for POS data based demand planning?

4. COLLECTION OF POS DATA

4.1 What technologies and systems are used to collect POS data?

4.2 What detail is contained in the data collected?

4.3 Do you use any 3rd party sourced POS data in your demand planning?

4.4 If so, how is it important, if not, why?

4.5 How visible (in terms of time span) are the POS transactions to the demand

planners?

5. SHARING AND DISSEMINATION OF POS DATA

5.1 How is POS data disseminated?

5.2 Which parts of the supply chain have access to or use POS data?

5.3 Do you sell or give access of your POS data to third party organisations?

6. THE ROLE OF POS DATA IN DEMAND PLANNING

6.1 How big is the role of POS in determining forecasts in demand planning?

6.2 How is POS data integrated in demand planning to influence forecasts?

6.3 Which other factors are of significance in planning demand?

132

7. IMPROVEMENTS/FUTURE OF POS DATA USAGE

7.1 Are there any problems you face in the collection or dissemination of POS data?

7.2 How can the use of POS data be improved?

7.3 What is the role of technology in the whole process of demand planning?

7.4 What is the future of POS data within your supply chain?

That will be all and thank you for your time and assistance in this research. I will

make contact for any further questions that I may have.

DISSERTATION o Attribution

Documents

Transcript of DISSERTATION o Attribution