project report on a study on supply chain analytics at milma by ...

PROJECT REPORT

A STUDY ON SUPPLY CHAIN ANALYTICS AT MILMA

SITARA K

1NZ19MBA53

Submitted to

DEPARTMENT OF MANAGEMENT STUDIES

NEW HORIZON COLLEGE OF ENGINEERING,

OUTER RING ROAD, MARATHALLI,

BENGALURU

In partial fulfilment of the requirements for the award of the degree of

MASTER OF BUSINESS ADMINISTRATION

Under the guidance of

Dr. PRIYAMEET KAUR KEER

ASSOCIATE PROFESSOR

2019 - 2021

CERTIFICATE

This is to certify that SITARA K bearing USN 1NZ19MBA53, is a bonafide student of Master

of Business Administration course of the Institute 2019-21, autonomous program, affiliated to

Visvesvaraya Technological University, Belgaum. The project report on “A Study on Supply

Chain Analytics at Milma” is prepared by her under the guidance of Dr. PRIYAMEET

KAUR KEER, in partial fulfilment of requirements for the award of the degree of Master of

Business Administration of Visvesvaraya Technological University, Belgaum Karnataka.

Signature of Internal Guide Signature of HOD Signature of principal

Name of the Examiners with affiliation Signature with date

1. External Examiner

2. Internal Examiner

Date: 19th April 2021

TO WHOM SO EVER IT MAY CONCERN

This is to certify that Ms. SITARA K (1NZ19MBA53), a student of MBA, New Horizon

College of Engineering, Bangalore has successfully completed 2 months (22 Feb 2021 to 16

April 2021) internship program at Milma.

The project that she worked was on “A STUDY ON SUPPLY CHAIN ANALYTICS AT

MILMA”. During her internship, Ms. SITARA K has been meticulous, diligent, inquisitive

and has shown leadership qualities.

We wish her all the best in her future endeavors.

Sincerely,

Shalini Jose

Business Associate

MILMA Kerala Co-operative Milk Marketing Federation Ltd., "Milma Bhavan", Pattom Palace P.O, Thiruvananthapuram-695 004. Kerala – India Email: milma@milma.com

Phone: +91-471- 2786400-448

DECLARATION

I, SITARA K, hereby declare that the project report on “A Study on Supply Chain Analytics at

Milma” prepared by me under the guidance of Dr. PRIYAMEET KAUR KEER, faculty of

M.B.A Department, New Horizon College of Engineering.

I also declare that this project report is towards the partial fulfilment of the university

regulations for the award of the degree of Master of Business Administration by Visvesvaraya

Technological University, Belgaum.

I have undergone an industry project for a period of Twelve weeks. I further declare that this

report is based on the original study undertaken by me and has not been submitted for the award

of a degree/diploma from any other University / Institution.

Signature of Student

Place:

ACKNOWLEDGEMENT

The successful completion of the project would not have been possible without the guidance

and support of many people. I express my sincere gratitude to SHALINI, BUSINESS

ASSOCIATE, MILMA for allowing to do my project at MILMA.

I thank the staff of MILMA, Kerala for their support and guidance and helping me in

completion of the report.

I am thankful to my internal guide Dr. PRIYAMEET KAUR KEER, for her constant support

and inspiration throughout the project and invaluable suggestions, guidance and also for

providing valuable information.

Finally, I express my gratitude towards my parents and family for their continuous support

during the study.

SITARA K

1NZ19MBA53

TABLE OF CONTENTS

SL. NUMBER CONTENTS PAGE

NUMBERS

Executive summary 1

Chapter 1- Theoretical Background of The

Study 2 - 29

Chapter 2- Industry Profile & Company Profile 30 - 50

Chapter 3- Research Methodology 51 - 56

Chapter 4- Data Analysis and Interpretation 57 - 73

Chapter 5 - Summary of Findings, Suggestion

and Conclusion

74 - 90

Bibliography

Annexure

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Executive Summary

This report is a study of Supply Chain Analytics at Milma based on a project that I have

successfully completed as a Business Analyst from 22.02.2021 to 16.04.2021.

The objective of the research is the applicability of supply chain analytics to the logistics at

Milma and using the data from it to create simple and dynamic dashboards to help the company

take necessary steps needed to improve the efficiencies in logistics. Thus, leading to faster and

leaner logistics processes at Milma. It also improves the trackability and clarity related to the

movement of the products.

Supply chain data was analyzed to produce powerful dashboards for procurement and sales at

Milma using Power BI. The dashboards consist of procurement and sales information of Milma

in the three main regions. Milma can make use of this model to find data points where sales

are less, find solutions for it using better marketing strategies and methods to increase sales in

that particular region. Milma can ensure that the products are tracked in a timely manner by

utilizing this model extensively, with live updates reflected on the dashboards.

This project work gave me an opportunity to learn Power BI and other aspects related to

logistics. The models created can be used for data visualization by offering highly interactive

and personalized views according to the needs of Milma.

Some of the study's recommendations include training Milma employees through workshops

on how to handle the newly implemented technologies with ease, making it easier for them to

understand the actual working and troubleshoot problems efficiently. Right management of the

dashboards that are created can be used to take rational decisions, to redesign the chain to

increase the value in logistics and to improve the overall supply chain at Milma, which in turn

can increase the profitability.

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Chapter 1

Theoretical Background of the Study

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Introduction

A supply chain is said to be a system of entities, events, services, people, and technology

associated with the manufacture and distribution of a good. Anything from the shipment of raw

materials from a supplier to the manufacturer to the final delivery to the end consumer is

included in a supply chain. The supply chain sets out all facets of the manufacturing process,

such as the tasks that take place at each point, knowledge that is shared, raw materials that are

converted into useful products, personnel, and other factors that go into the end product.

Analytics refers to processing data to draw important conclusions, to better make decisions.

The information age consists of data that is well provided for and aligned to our needs. About

all that a business does today is determined in one way or another by data and analytics.

Advanced analytics methods are used by businesses to expand limits of operations, reduce

costs, find various advancement opportunities, and devise competent budgets. Analytics uses

data and mathematics to address business questions, to identify associations, to forecast

unpredictable effects, and to simplify decision making. Based on applied mathematics,

analytics, statistical modelling, and machine learning methods, this broad field of computer

science is used to discover concrete data correlations and uncover new insights.

Supply Chain

Supply chain consists of the tasks that is needed by an enterprise to distribute services and

products to the customers. It concentrates on the functional areas within a company necessary

to transform unprocessed resources to end products and services.

Supply chain can be a product-based supply chain or service-based supply chain. When the

supply chain of a company links with the supply chain of vendors and distributors it leads to

the formation of a supply chain network. This network helps to get a better understand and

monitor the movement of resources and expertise.

The need for a company to figure out its supply chain is necessary during the external review

in the pre planning stage. It helps the company to understand its business and where it would

like to see itself in future.

The basic steps of supply chain are procurement of raw materials, conversion into primary

portions, assembling the parts to create the final product and delivery of the goods to the

customer.

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Supply Chain Challenges

Insufficient clarity

The supply chain is often complex and tough to comprehend. Thus, making it difficult

to record and arrange for the shifting of products. Without achieving clarity in this the

demand forecasting will be problematic. Excess of products produced leads to

increased amounts of waste and deficit of products produced causes failure to meet the

demands.

Lag or misplacement of products

Lack of proper follow up and traceability of the products leads to unnecessary lags and

misplacement of products. Thus, in turn affecting the delivery of the products to the

end user. Leading to unhappy customers due to badly maintained relationships with

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Optimization of Supply Chain

Optimization of supply chain increases the ability and efficacy of the supply chain network

using the technical knowledge from Artificial Intelligence, Blockchain and Internet of Things.

Supply chain is an important aspect in any business as it helps to attain good customer relations.

Optimization can be achieved in 3 steps:

Designing of SC

In this step the main job is to develop an efficient supply chain network which involves

deciding the locations at which the warehouses can be so that the logistics process

works smoothly.

Planning of SC

It involves creation of an effective supply chain routing protocol, stock storage planning

and easy flow of information between all parties involved in the supply chain so as to

achieve a symmetry between supply and demand.

Execution of SC

It involves the administration of the warehouses, procurement centers and timely

delivery of the products so as to satisfy the customer needs and wants.

Importance of SC Optimization

Due to the ever-changing customer needs and demands, to match a competitor’s level of

production or to deal with supply disturbances, there is a need for the supply chain to be agile

to keep up to such requirements.

Competitive Edge

Optimization of supply chain acts as one of the main aspects to achieve competitive edge as it

helps to align many processes which helps in making the overall process of supply chain easier.

Sustainability of SC

As customers are becoming more cautious about the environment and the impacts of supply

chain on the environment. The customers are interested to know if the product are produced in

a vital and ethical manner.

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Supply Chain Management

Supply chain management is the management of movement of products or services beginning

from the procurement of raw materials to the distribution to the customer. A business has a grid

of contacts which include providers of raw resources to those who sell the products directly to

the end consumers. A good supply chain decreases producers and vendors excess stock pile up

and it also results in reduction of cost in terms loss due to excess produce, warehousing costs

and so on. An effective supply chain can be achieved by implementing lean processes. The

time involved in the supply chain process can be reduced by taking necessary steps to make or

buy products, or by outsourcing certain tasks to professional companies.

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Segments of SCM

Planning the process

The first step in supply chain management is planning, it is considered as one of the most

crucial steps in supply chain management. A company needs to decide the number of resources

that would be needed to meet the demands. For a company to have a good supply chain it has

to do proper planning so as to meet the objectives.

Sourcing of raw resources

Businesses need to establish relationships with the providers of raw resources which are needed

to make the end product or service that has to be provided. This step involves many actions

like placing order for the materials, obtaining the materials, managing the stock and handling

the financial transactions with these vendors.

Making the end product

During this step the products are produced from the raw materials. The goods are then put

thought various quality checks to make sure it satisfies all the quality standards that are

mentioned. On successful completion of the checks the products are packaged and sent out for

delivery.

Delivery of the goods

The delivery of the finished goods is usually carried out by the logistics section of supply chain

management. The main duties include organizing the goods that should be heading out for

deliveries, sending out the products, handing out of bill of sale to customers and collecting the

payments for the goods sold. Delivery of products can also be outsourced to other professional

logistics companies. Different means of transport can be used for delivery of goods.

Return of products

This in step the product is sent back to the company from the customers end due to some errors,

imperfections which leads to the customer not being satisfied with the received product. A

company should have a good back logistics network for the smooth working of the return

process.

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Analytics life cycle

The first prerequisite for any analytics project is data, no matter what you want to do with

analytics. Next, you need to evaluate the data until you have information. And then you need

to execute the findings of the research to guide decision making. Each process is ready for a

change in the analytics process. The quicker companies can progress across the analytical life

cycle, the quicker their investments in analytics can acquire real profit.

The steps of the analytics life cycle are: Data, Discovery and Deployment

Data today is large, agile and complicated. Analytics applications, including conventional

standardized data and new technologies, such as streaming sensor information, pictures and

videos, have to process data of any type. Data management technique is required to access,

plan, clean and control the data. An advanced analytics framework streamlines data processing

with native access engines, built-in data consistency and data preparation tools that simplify

time-consuming activities with Artificial Intelligence. Data governance means that the data can

be reliable, since you know the origins and content and can track the accuracy of the data. It is

also possible to safeguard information where it is necessary.

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Discovery

Discovery is about experimentation, visualization, and the creation of models. Finding a

suitable algorithm is always a trial-and-error operation. Selecting the perfect algorithm is based

on a number of facts like data size, business requirements, training period, variables, data

points, and more. Even the most professional data scientists, before testing with several

methods, cannot tell you which algorithm would work the best. In reality, comparing various

models coded in a different programming language with different data properties is normal in

the discovery process. This collaboration method works well where data scientists with diverse

skill sets are able to write programs in the language of their choice and non-programmers can

use a simple point-and-click interface to test the effects of various analytics approaches.

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Deployment

Analytics efforts will be fruitful only if you execute and use the results of your observations.

Machine learning and other models are intended to be used accurately to get the right market

value. But the process of deployment is where most companies are suffering the most.

Designing a single model or thousands, it takes model management to switch from choosing

models to deploying models. Model management allows you to register, test, and maintain your

models centrally. The aim should be to develop models and deploy them everywhere, to

executive dashboards, straight into operating systems, or embedded into other software using

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Classification of Analytics

Descriptive

Descriptive analytics is a category of analysis focused on capturing and summarizing raw data

for simple understanding. In general, historical data is the subject of descriptive analytics,

including the framework which is important for interpreting facts and statistics. The area is

applied in a multitude of sectors, and uses, from inventory management to benchmarking

annual earnings and sales, and can serve a wide range of purposes. In the business intelligence

method, the field typically serves as a preliminary stage, providing a framework, for further

study and understanding. Descriptive analytics usually finds answers to what occurred, without

doing the more complicated analysis involved in diagnostics and predictive models.

Descriptive analytics is typically the first step in business intelligence, which can result in

visualizations such as pie charts, line diagrams, bar charts, and other simplified graphical

displays.

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Predictive

Predictive analytics is a type of analytics that predicts events, actions, and patterns using both

recent and historical data. It requires the application of data sets of mathematical analysis

methods, descriptive queries, and automatic machine learning algorithms to develop models

that are predictive which tells the feasibility of the happening of an event by giving a

quantitative value. It has large search engines and online service providers which is widely

adopted by marketing, financial services, and insurance companies. Industries such as

healthcare, retail, and manufacturing also commonly use predictive analytics. Uses of

predictive analytics include analyzing online advertisements, monitoring consumer behaviour

to assess purchase habits, flagging potentially irregular financial purchases, recognizing

patients at risk of contracting specific medical problems, and predicting imminent system

defects before they occur in industrial machinery.

Prescriptive

Beyond the past insights of descriptive analytics and the probable future results of predictive

analytics, prescriptive analytics offer suggestions for the next steps that can be taken. Based on

the effects that come from simulating multiple future situations, businesses will analyze and

settle on a variety of alternatives. It takes the predictive analytics predictions and possibilities

a step forward by developing informed strategies that will still comply with the primary

elements of the company’s objective. Every enterprise wants to be efficient and prescriptive

analytics provides the potential to seize resources and have an upper hand in successful

decision making. It offers greater insights into whether such events can arise such that as new

steps are taken, associated causes can be tracked.

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Business Analytics

Data related to organizations are gathered, organized, stored and studied about by utilizing

repetitive methods and calculation systems to convert the information into useful content for

businesses, this is what business analytics deals with. To increase effectiveness, determine

which information has value, find ways to resolve problems and so on can be considered as the

purposes of business analytics. Business intelligence is a part of analytics that is associated

with the need to find suitable content.

Business analysts all use precise data, technical analyses, and statistical modelling to create

solutions for data-driven challenges. Analytics, science that deals with calculations, content

models and transactional study are used by deep learning, composite group of data and artificial

intelligence to find out the tendencies that data follows. In order to reliably forecast potential

events linked to customer action or industry patterns, the accessible data and trends can be

leveraged to suggest measures that will direct consumers towards the desired target.

Components of Business Analytics

Data Aggregation

In order to prevent replication, data must be collected, organized, and cleaned until it can be

analyzed, and screened to delete incorrect, missing, and obsolete information. Sales,

transportation, finance and other data of business comes under business records. Data collected

from the customers or mediators directly through hard copies or soft copies, this is known as

volunteer information. Thus data is accumulated using volunteer and business records.

Data Mining

Sequence and flow that was not identifiable before can be found by drilling through data using

models. Data mining utilizes many computational methods like classification, regression, and

clustering. Classification is a process used to sort and group data when parameters like

demographics are known. Regression is a feature used, based on interpreting historical trends,

to forecast continuous statistical values. Clustering is a system in which unlabeled data is used,

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and it is the technique of grouping together identical objects and then creating groups with the

clustered data.

Association and Sequence

Consumers execute identical practices at the same time or sequentially perform similar

activities. Patterns such as association and sequencing may be revealed by data.

Text Mining

Companies accumulate textual content to derive useful relationship metrics from community

channels, journal put ups and written information from tech support teams. This knowledge

will be used to produce new goods on demand, enhance customer service and experience, and

review the success of competitors. This method of obtaining data is called text mining.

Forecasting

Through evaluating processes that occur over a given time or season, a prediction of potential

events or activities based on historical data may be generated.

Predictive Analytics

Predictive business analytics employs a number of computational approaches to construct

predictive models that collect data from databases, detect trends, and offer a predictive value

for a spectrum of operational performance.

Optimization

When patterns have been recognized and forecasts have been developed, organizations will use

modelling tools to assess best-case scenarios.

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Data Visualization

For simple and fast data processing, it provides visual representations such as charts and

graphs. These elements of data visualization empower companies to utilize their data to educate

and achieve future business priorities, improve sales, and deepen customer relationships.

Challenges Presented by Business Analytics

Managerial Distrust

It can be hard to convince everyone in upper management to join in for Business Analytics

introduction. While most companies have accepted a type of Business Intelligence and are able

to successfully handle data warehousing, analytics is still an environment regarded with

skepticism by higher authorities. People should be certain about it to get the best results.

Providing analytics along with the present existing business methods will assist to gain

confidence by the ones who are skeptical about it.

Lacking Collaboration

The assessment and execution of analytics-driven programs can be crippled by the inability to

achieve coordination within a cross-section of departments. For an analytics plan to thrive,

business and IT workers must be in harmony. Lack of cooperation poses the possibility that the

details offered will not be generated by analytics, leading to more mistrust and possible

abandonment of valuable advancement in technology.

Poor Commitment

While several analytics product bundles are advertised as a simple to use pre-engineered

service, prices would be high and the returns are gradual. During the beginning half of analytics

it requires a lot of effort as many people get worked up and give up the thought. Managers must

provide an environment that supports the process and conduct regular performance checks. The

models develop during the course of time and provide insights for the future.

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Gradual Content Advancement

Owing to the unavailability or poor quality of usable data, Business Analytics implementations

frequently fail. An evaluation of maturity can often be carried out on the information

architecture and databases of the organization based on analytical specifications. Analysis,

clearing and obtaining data is done during the improvement phase. The condition of

categorized and practical data should be rated, the integration framework must have the

capacity to handle the recent updates of content that comes up.

Trends in Business Analytics

Artificial Intelligence

Artificial Intelligence is becoming more resourceful by tutoring itself and is being introduced

in many industries like investment, fabrication and trade.

Big Data

In every field of life with a growing focus on digitization, databases grow at an exponential

pace. More knowledge implies more future observations, but the sheer volume can be daunting.

This growth is both an advantage and a drawback.

Deep Learning

Deep learning processes huge amount information that can be used to figure out trends and

outputs that were difficult to analyze before. Machine learning is usually followed by deep

learning.

Micro-Segmentation

Companies categorize their customers into small groups based on the data related to their

buying behavior, this helps the company to better understand their focus group better and make

improvements to satisfy customers based on their specific needs.

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Neural Networks

Accurate forecasts are given by this method using significant past trends by proper processing

and classification of data. Neural networks are trained so as to have the ability to analyze

information like how an individual would.

Power Business Intelligence (BI)

Power BI is an application software that is used to create data visualization reports of different

types of data like organized, disorganized and mixed data. Power BI is preferred over Excel

spreadsheets as it can handle enormous amount of data as compared to Excel. The reports that

are created using Power BI are dynamic and can be easily sent to others.

Desktop version of Power BI

It is an unpaid software that assists to transform and picturize information by running it on your

systems. It has the ability to associate information from many places and collate them into an

information system. Dynamic reports with visuals that can be easily understood by clients or

end users who is in need of the report which can be created using this software. End users who

use business knowledge can take the help of this software to build reports.

Some of the applications are as follows:

Link to data

To build a data model, transform and clean the data,

Build visuals that include graphical representations of the results, such as charts or

graphs.

Develop reports on one or more report sections, which are visual databases.

Share reports using the Power BI service with others

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Data researchers or business intelligence experts (report creators) are sometimes considered

the individuals most commonly responsible for the above activities. Many individuals who do

not consider themselves to be an analyst or a report maker, however, use Power BI Desktop to

produce persuasive reports or to collect data from multiple sources and construct data models

so they can exchange these reports with their colleagues and organizations.

In the Power BI Desktop, there are three views:

Report: Reports and graphics are produced in this view, a majority portion of the

development phase is used up for this.

Data: This vision displays the information used in the report-associated information

system, and converts the data into the report format for best use.

Model: You see and control the relationships between tables in your data model in this

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Supply Chain Analytics

Supply chain analytics is the application of analytics to supply chain. It helps to run through

large amount of data which help a company to take better decisions, enhances the forecasting

capabilities increasing the profitability for the company. Supply chain analytics leads to a good

return of invest for a business that applies it. It helps to figure out the risks prior so as to take

corrective actions to prevent the risks from occurring. Use of analytics on supply chain data

helps to take better planning actions so as to reduce the wastes that are caused due to over

estimation of productions of goods. Lena processes are implemented by the use of supply chain

analytics. Supply chain analytics helps to understand the sequence and tendency that data

would follow based on the previous analysis of similar data, this helps to better predict what

would happen in future and take appropriate actions to reduce the errors that can occur.

Kinds of Supply Chain Analytics

Descriptive SC analytics

It compares supply chain old data and current data to understand the operations that is currently

being followed by the company.

For example, it helps to see what are the similar trends and patterns that is occurring currently

and that has happened before in the past records of the company.

Predictive SC analytics

It deals with understanding what can be done to prepare and prevent faults that can happen in

future before it actually affects the working of the company.

For example, based on the sequence that the data follows actions can be taken as precautionary

measures.

Diagnostic SC analytics

It helps to understand the errors that have occurred in past and what were the reasons that

caused them to occur.

For example, analysis of the data is conducted to figure out why the anomalies occurred in the

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Perspective SC analytics

It helps businesses resolve issues and boost enterprise value. Assists to reduce the disturbances

in operations that can occur by joining hands with right partners.

For example, a company coming up with best strategies to improve on its overall operations to

increase the profitability.

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Five C’s of SC Analytics

The five C’s of SC analytics can be used for optimization of the supply chain which helps to

maintain the company’s position and turns out feasible for a long period.

The five C’s are as follows:

Connections of data

The capability of supply chain analytics to retrieve of the same form

or of different forms of data from social communication platforms

or online network of things using business resource planning and

enterprise to enterprise assimilation tools.

Collaborations with providers

To boost better collaborations with providers it is necessary to adopt

cloud-based business connections to allow numerous business

collaborations and pacts.

Cyber awareness in supply chain

One of the main responsibilities of supply chain analytics is to make sure

that all the components within the network are secure from cyber

intervention and meddling which would affect the efficient working of

all operations with the supply chain.

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Cognitively enabled supply chain

The activities and judgements of the recent supply chains are regulated

by Artificial Intelligence platforms, this helps to make many supply

chains programmed and self-automated to carry out the necessary tasks

with the supervision of humans.

Comprehensible information from SC analytics

The information received from supply chain analytics must be easy to

understand, so that the implementations can happen fast by reducing the

latency occurring during the various supply chain operations.

Supply Chain

Analytics

Collaboartive

Connected

CognitiveCyber aware

Comprehensible

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Emanating trends in SC analytics

Optimization of Inventory in SC analytics

The use of supply chain analytics helps to plan properly before purchasing materials so that

there is no over purchasing which would require a lot of inventory space which will lead to

unnecessary increase in the overall cost involved. Thus with the help of proper planning the

inventories can be made simple with minimum storage of material.

Analytics of Demand in SC analytics

Supply chain analytics helps to understand the patterns and trends to be able to judge when the

demand would be more or less. A proper understanding of demand analysis has a lot of benefits

like overproduction can be minimized, only required quantity of raw materials can be acquired,

more man power can be hired based on the increase of demand and so on.

Inventory Optimization

Demand Analytics

Replenishment Planning Analytics

SC Metrics Measurement and Reporting

Networking Planning and Optimization

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Analysis of Replenishment Decision in SC analytics

Supply chain analytics can be useful to decide the right amount that is needed to be purchased

for the restocking of raw materials required by the business for the production of the end

products.

Network Decision and Enhancement in SC analytics

Supply chain analytics helps to make sure the network in which the operations are carried out

is planned properly so that there is not much time wasted in travelling long distances between

the components of the chain. This help to maintain the customer relationships by timely

satisfaction of demands.

Supply chain parameter analysis and documentation in SC analytics

All operations of the supply chain are assessed and if any deviations are found from the normal

then corrective actions are taken. These actions are taken based on the “SMART” goals. The

changes that are made need to be documented properly so that it can be useful for future

references.

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Advantages of Supply Chain Analytics

Improved demand forecasting

Demand forecasting is considered as one of the most important aspects of supply chain as

incorrect forecasting can lead to a lot of problems involving cost and time. Supply chain

analytics helps to better understand the demand by analyzing the previous data and predicting

similar demands of products.

Hike in Return on Investment

Supply chain analytics helps to analysis data better and to take the right decisions. These steps

lead to a better return on investment than enterprises which fail to perform analysis on the data

they possess.

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Improved receptiveness

Supply chain analytics helps to track the needs and requirements on regular basis this helps to

comprehend the need for more or less materials to be acquired so as to avoid unnecessary

disruption in the flow of goods. Thus, supply chain analytics allows the chain to be agile.

Increased clarity across the Supply Chain

Supply chain analytics allows real time tracking of orders with the use of technologies like

“Blockchain, IoT and Artificial Intelligence”. This helps to increase the clarity of the supply

chain and to take necessary actions. Without the application of analytics, it makes it very

difficult to track the orders and understand the reasons for delay.

Improved rapport with providers

Supply chain analytics has a very well-connected network of partners, providers and

distributors this helps to improve the relationship among them as there is constant

communication within the about the requirements.

Improved Disruption Handling

Supply chain analytics helps to handle disruptions that can occur in future by providing signs

and warnings of what can tend to occur thus giving the supply chain managers time in advance

the tackle the situation than waiting till the last minute when happens.

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Blockchain

Blockchain is a form of structure which is used to store collection of data. In blockchain as the

name suggests the data is stored in blocks and when a block reaches its maximum capacity the

data is then carried over to the next block. The blocks are connected like a chain so that the

order remains intact.

Blockchain technology has 3 parts:

Blocks

It is a structure that is used to store data.

In blockchain the data is not all stored at one place or controlled by just one person. The data

in blockchain is distributed at many places known as nodes. Each node in the network contains

copies of the data on the blockchain. Any actions taking place on the chain can be easily tracked

cause of the transparency property that blockchain possess.

Miners

It is used to make new blocks on the existing chain, this phenomenon is known as mining in

blockchain.

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Advantages

Security of data

It is very difficult to hamper the data that is stored in a blockchain. This is because each block

on the blockchain has a unique hash code to itself. If there is any attempt to hamper data only

that block is changed which causes the has code of that block also to change, but the hash codes

of the other blocks remain the same.

Transparency of data

The data in blockchain is distributed at many places known as nodes. Each node in the network

contains copies of the data on the blockchain. Any actions taking place on the chain can be

easily tracked cause of the transparency property that blockchain possess.

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Reliable Business

In blockchain the all parties involved in a network are properly verified and only authentic ones

are allowed to remain in the network. So, all the businesses that take place are reliable and safe.

Decrement in money involved

The cost associated in blockchain is reduced as the presence of third parties is eliminated, most

of the documentation involved in the trade is carried out online thus reducing the paperwork

tremendously and leaner processes replace the old processes which involve a lot of money.

Live tracking of products

Due to the transparency in the processes, it allows live tracking of the products using

blockchain. This helps to take corrective actions when there are delays in the delivery of

consignment. Live tracking also helps improve relationships with customers as they are given

all information about the products status and not keeping them in the dark, this in turn increases

profitability of the enterprise.

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Chapter 2

Industry and Company Profile

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Dairy Industry in India

India is known as one of the biggest producers of milk on a global scale. After liberalization

this industry has grown swiftly. The main purpose of this industry is to increase the production

of milk and promote the use of technologies for processing milk. Most of India’s milk yield

comes from rural areas who are small scale producers. Farmers who are into farming are usually

into the dairy business also because crop farming is seasonal whereas dairy production happens

all through the year.

The Indian Dairy Industry structure consists of two main categories:

Organised Sector

This category is made up of co - operatives and companies that are run privately.

Unorganised Sector

This category is made up of local dealers, milkmen and milk that is used for self-consumption.

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The main movement in this industry in India started off in a small village in Gujarat where the

farmers protested against the unethical trade methods that were followed. The solution to this

problem was to take control of acquirement, transformation and commerce by removing the

intermediators. This was adopted by few villages in Gujarat under the leadership of Dr.

Verghese Kurien. This grew to become one of the best dairy producers in the country what we

today know as AMUL.

This led to the setup of National Dairy Development Board with the main goal of duplicating

the Amul model nationwide. The Amul model consists of three levels which are as follows:

Cooperative dairy societies at village level

Milk unions at district level

Member unions at state level

33 | P a g e

In this model it can be seen that the villagers themselves control the dairies. The farmer groups

use the state milk federation to directly sell the products the end users. Thus, the intermediators

are eliminated which helps the farmers to get a good price. The involvement of intermediators

leads to huge amounts being taken away by them and the farmers hardly getting anything in

return. This model helps to poor farmers to get good profitability even when the business in

34 | P a g e

Milma is one of the famous organizations in the dairy industry in Kerala. It was founded in the

year 1980. Milma deals with the supply and delivery of milk and milk-based products

throughout Kerala. The main aim of Milma is to produce ample amount of milk for the whole

state. According to “Operation Flood” which was a dairy project started by “National Dairy

Development Board” whose main goal was to connect the milk producers to satisfy the

demands of urban regions. It was during the second phone of this project that Kerala was

included in it. Thus, the birth of Milma took place and its head office is in Thiruvananthapuram.

It is an organization which consists of three levels. The three levels are:

At the village level is the cooperative dairy societies

At the district level is the area milk producing unions

At the state level is “Kerala Cooperative Milk Marketing Federation”

Milma has three main regional operation centers in Ernakulam, Malabar and

Thiruvananthapuram.

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Board of Directors

Mr. P A Balan Master (Chairman of KCMMF)

Mr. Rajeshkumar Singh IAS (Additional Secretary, Govt. of Kerala)

Mr. Kallada Ramesh (Chairman of TRCMPU)

Mr. John Theruvath (Chairman of ERCMPU)

Mr. Mani K S (Chairman of MRCMPU)

Mr. Karumadi Murali (Representative of TRCMPU)

Adv. S Gireesh Kumar (Representative of TRCMPU)

Mr. E M Paily (Representative of ERCMPU)

Mr. K K Johnson (Representative of ERCMPU)

Mrs. Lissy Xavier (Representative of ERCMPU)

Mr. P Sreenivasan (Representative of MRCMPU)

Mrs. K K Anitha (Representative of MRCMPU)

Mr. P P Narayanan (Representative of MRCMPU)

Mr. S Rajeev (Regional Head of National Dairy Development Board)

Mr. S Sreekumar (Director of Dairy Development Department)

Mr. O B Suresh Kumar (Deputy Secretary of Finance Department Government of Kerala)

Dr. Patil Suyog Subhash Rao IFS (Managing Director)

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Vision

To continuously seek to produce superior quality milk and milk-based items to the end

consumers with prime benchmarks. The items are produced by spirited and fixated employees

by making use of the best and latest technologies that is available leading to the growth and

profitability for the producers.

Mission

The mission of Milma is to assist in the growth of milk producers through the contentment

experienced by the end consumers on using the products made at Milma.

Objectives of Milma

To develop a feasible dairy corporation in Kerala.

To offer a steady retail place and even amount for the yield given by the milk producers.

To direct excess milk supply from smaller regions to larger regions where there is a

shortage of supply, this also helps the producers to get returns for their yield.

Performing tasks to boost manufacturing, acquisition, transforming and selling, milk

and other milk-based products that are produced by the farmers so as to help them grow

financially.

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Societies

Each of the three regional operating centers have societies within them.

In Thiruvananthapuram union there are four districts with operating societies under it:

Trivandrum which has 220 operating societies

Pathanamthitta which has 112 operating societies

Kollam which has 169 operating societies

Alapuzha which has 167 operating societies

In Ernakulam union there are four districts with operating societies under it:

Ernakulam which has 304 operating societies

Idduki which has 174 operating societies

Kottayam which has 203 operating societies

Thrissur which has 198 operating societies

In Malabar union there are six districts with operating societies under it:

Kannur which has 173 operating societies

Palakkad which has 316 operating societies

Kasaragod which has 129 operating societies

Malappuram which has 215 operating societies

Kozhikode which has 232 operating societies

Wayanad which has 57 operating societies

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Dairies

Each of the three regional operating centers have dairies within them.

In Thiruvananthapuram union there are three dairies:

Kollam Dairy

Pathanamthitta Dairy

Thiruvananthapuram Dairy

In Ernakulam union there are four dairies:

Kattapana Dairy

Kottayam Dairy

Thrissur Dairy

Ernakulam Dairy

In Malabar union there are seven dairies:

Kozhikode Dairy

Palakkad Dairy

Kannur Dairy

Wayanad Dairy

Kasaragod Dairy

Malayora Dairy

Naduvattam Central Product Dairy

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Kerala Co-operative Milk Marketing Federation units

KCMMF is considered as the main body of Milma that takes care and controls the working of

all the other bodies. The main goal of the body is to provide profitable revenue to the producers

and superior quality items for the end customers. It handles mainly three units under it.

Central Dairy Products center at Alappuzah

Center for Cattle Feed at Malapuzah, Palakkad

Center for Cattle Feed at Pattanakkad, Alappuzha

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Center for Cattle Feed at Malapuzah, Palakkad

This center was established by the Department of Animal Management, Kerala in the year

1972. After which it was transferred to KCMMF in 1982 by the National Dairy Development

Board and Government of Kerala for the operation of the second “Operation Flood” project in

the state. Feeds that are produced in this center are “Milma Gomathi Pellet” and “Milma

Gomathi Rich Pellet”.

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Center for Cattle Feed at Pattanakkad, Alappuzha

This center was set up in 1985, it was a small center with a competency of 100 metric tons per

day. In 1993 the center was expanded to provide a competency of 300 metric tons per day and

the plant was also renovated with latest equipment. The feed from this center is given to milk

producers in many districts. Feeds that are produced in this center are “Milma Gomathi Pellet’

and “Milma Gomathi Rich Pellet”. Recently “Milma Gomathi Gold Feed” was introduced.

Another variety of feed is the “Milma Bypro Feed” which is highly nutritional for milking

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Central Dairy Products center at Alappuzah

Central Dairy Products center is controlled by KCMMF. This center mainly caters to the other

products that are produced at Milma like “curd, ghee, flavoured milk, milk powder, fruit juices,

milkshakes, buttermilk and packaged drinking water”. This center has a Quality Checking

Laboratory where chemical and microbiology tests are carried out.

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Products

The main product of Milma company is milk. The company has now ventured out to producing

of different kinds of products which can be made with milk.

Milma Milk

Milma produces many kinds of milk to satisfy the needs of the end consumers. Milma produces

seven varieties of milk.

Milma Long Life Milk

Milma Homogenised Toned Milk

Milma Pasteurised Standardised Milk

Milma Pasteurised Homogenised Toned Milk

Milma Pasteurised Toned Special Milk

Milma Pasteurised Toned Milk

Milma Pasteurised Double Toned Milk

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Fermented Milk Products

Milma produces three varieties in fermented products.

Milma Skimmed Curd

Milma Sambharam

Milma Set Curd

Dairy Products which are Rich of Fat

Milma produces two products under this category.

Butter

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Other Milk Based Products

Milma has a wide range of milk-based products that it produces.

Frozen Milk Products

Milma Sip up

Milma Ice cream

Milma Kulfi

Energizing Drinks

Flavoured Milk

Mango Drink

Chocolate and Strawberry Milkshake

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Milk Products

Milma Milma Powder

Milma Paneer

Milma Milk based Sweets

Milk Peda

Palada Mix

Gulab Jamun

Chocolate

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SWOT Analysis of Milma

Strengths

Milma is one of the main dairy industries in Kerala with many societies and dairies

under it.

Milma has its own cattle feed production units also thus there is no need to relay on

external sources for feed.

Milma has a very well-connected network as the three main regional units work closely

with all the units that come under it.

Milma does not only focus on one range of product like milk which is its main product.

It has also come up with many milk-based products that have gained the interest of end

consumers.

Milma has gained the confidence of the milk producers by giving them the best returns

for their yield and by making sure their yield does not go waste by directing it to regions

where there are shortages.

Weakness

Lack of proper tracking of products.

Inadequacy to handle the supply of milk.

Opportunities

Incorporating of latest technologies like “AI, Blockchain and IoT” for overall

improvement of supply chain activities.

Creation of dynamic dashboards for better inventory control and demand forecasting

with the help of analytics.

Implementation of “Instant Integrated Management Information System” this will help

in better decision making.

Threats

With the increase in Veganism there may be a hit in the sales of product if more

customers convert to vegans.

Customers with lactose intolerance will go for substitute products which are not dairy

based.

Competitor companies who have better supply chain analytics.

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Porter’s Model of Five Force

Suppliers’ Potential to Bargain

The supplier’s potential to bargain is low as the milk producers who provide their yield to

Milma are given the best of profits or returns as the number of intermediators are reduced

cause of the structure followed at Milma.

Buyers’ Potential to Bargain

The buyers’ potential to bargain is low, as Milma is one of the most trusted brands when it

comes to dairy products in the state of Kerala. The organization has many loyal customers

who have been using their products from generations and so they have never felt the need

to bargain.

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Risk of New Entry

The risk of new entrants is quite low as the companies venturing into this field will know

the history of Milma and its connection to the people of the state. To break through all the

offerings given by Milma and trying to gain a customer base would be a task.

Risk of Alternative Products

The risk of alternative products is medium cause of the adaptation of veganism by some

people. Few customers with lactose intolerance also tend to look out for products that are

not diary based so as to satisfy their needs. This can turn out to be a bigger problem if it

converts many of Milma’s customers to non-dairy product consumers.

Conflicts Among Existing Rivals

The conflicts among rivals are less as Milma has been is this industry for the longest time

as compared to the other rivals. Milma has ventured out into making of other dairy based

products, showing that it’s not stagnant focusing on just one product but has been growing

by creating new products to satisfy the needs for their customers. Milma has its own quality

checking laboratories and its own cattle feed centers also thus giving it an upper hand

compared to the other players in this market.

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Future Growth and Prospects

Incorporating of latest technologies like “AI, Blockchain and IoT” for overall

improvement of supply chain activities.

Creation of dynamic dashboards for better inventory control and demand forecasting

with the help of analytics.

An email structure to make communication smoother with all the components in the

supply chain.

Implementation of “Instant Integrated Management Information System” this will help

in better decision making.

Usage of cloud technology to interchange information easily and make processes

happen faster by usage of latest technologies.

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Chapter 3

Research Methodology

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Literature Review

Incorporation of Blockchain in supply chain helps to better track the products that are being

shipped to different parts by providing better visibility. Blockchain helps to keep data

decentralized than all data stored at one place and controlled by one entity. With this technology

comes with a few challenges like extendibility, collaboration issues which has to be addressed

properly to get maximum results from blockchain. (Sohail Jabbar et al., 2020)

Blockchain usage in supply chain provides an immense degree of protection to important

reports, analysis assessments and remedial operation methods that are usually prone to being

exploited by people who want to get insights about the company. It also helps to reduce the

expenses involved which may arise by inappropriate charging made by callous providers, the

transparency in blockchain leads to decrease in such costs. It also aids to increase the

information exchanged among different units of a company by proper coordination with each

other thus leading to a reduction in the number of issues arising. (V.G. Venkatesh et al.,2020)

Supply Chain with analytics provides immense support when it comes decision making, it helps

to analyze the past data and present conditions to predict demand for the future, optimization

of resources involved in the operations and makes the organization agile. (Tino T. Herden et

al., 2020)

Food Supply Chain has many crucial functions as it needs to make sure all the perishable

products are delivered correctly to the end consumer. The has been a arise in technology

integrations in this industry. In a dairy industry the utmost importance is to maintain the quality

of the products. The integration of Blockchain technologies in dairy industry can help to

maintain authentic information about the quality of the products. (Joseph Kasten, 2019)

Technologies like blockchain helps to decrease deal costs by eliminating intermediators by

gaining partners confidence through proper clarity and security provided to all the information

that is exchanged. The two types of ambiguities that deals to deal costs are circumstantial and

observable. (C.G. Schmidt and S.M. Wagner, 2019)

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Blockchain can be used to provide flexibility in supply chain that assists to deal with

disturbances that occur suddenly in the operations which can be easily resolved by coordinating

efficiently with all the partners in the network to make sure the delay that has occurred can be

minimized as much as possible and not miss the deadline by a huge amount of time. (Hokey

Min, 2018)

Application of analytics to supply chain can lead to many benefits like choosing the best route

for transportation, sustainable logistics, shelf space optimization, reverse logistics process can

be made easier by proper tracking of the consignments as most of the time this process is

usually not handled in an efficient way. Analytics helps to automate logistics; self-arranging

logistics makes use of Radio Frequency IDs to track the items. (Anitha P and Malini M. Patil,

2018).

Application of analytics in supply chain is being adapted at a fast pace by many companies.

Analytics on supply chain helps to better understand the data which makes to take better

decisions, predict scenarios, make forecasts about need for products. It leads to the growth of

the company, increase in the revenues earned and ability to stand out from other competitors.

(Prashanth R Nair, 2014)

Analytics application in supply chain has many benefits few of them being “in marketing it can

be used for sentiment analysis to know about the demand for new trends, in procurement it can

be used for supplier negotiations, in warehouse operations it can be used to manage inventory

space and predict demands and in transportation it can be used to optimize the routes for the

transport of products.” (Ivan V R and Benny T, 2014)

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Research Gap

There is a lot of research happening in the field of supply chain analytics to create efficient

supply chains for companies by the implementation of various technologies. According to

current research it can be found that much light has not been thrown in the direction related to

the use of data from these technologies being incorporated with data visualization tools to make

dashboard that would help an organization to make better decisions related to supply and need

of materials, transportation details and so on which in turn improves the overall supply chain

related processes.

Statement of the Issue

In any company we know that logistics is considered as the heart of the business. A huge

amount of world business is solely dependent on logistics. Supply chain is considered the most

intricate part of a business as in includes many entities of differing views and preferences. To

achieve an efficiency in the supply chain process it includes all entities functioning in a

synergic and harmonious manner so that the transaction of goods, messages and so on happen

smoothly. One of the major issues in supply chain is that there is not proper clarity about the

processes that are taking place, this turns out to be quite a problem for many businesses.

Cohesion of items and procedures in a multi entity chain is considered as a big challenge. These

problems make it hard to figure out at which stage of supply chain the item is at, thus leading

to many issues like delay in deliveries, cancellation costs, bad reputation and unsatisfied

customers.

Necessity for the Study

Application of Supply Chain Analytics at Milma helps to provide many benefits like:

By analyzing the previous data to better understand the sequence and tendency of risks

that has been occurring over the past years at Milma.

Proper analysis of need and supply can help make better decisions related to

procurement of materials and inventory management.

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Application of analytics to supply chain helps to give better insights related to the

decisions that are to be taken.

“Lean Supply Chain” helps to reduce the overall cost and risks related to supply chain,

therefore improving the whole process.

Objective of the Study

The objective of the research is the applicability of supply chain analytics to the logistics at

Milma and using the data from it to creative simple and dynamic dashboards to help the

company take necessary steps needed to improve the efficiencies in logistics. Thus, leading to

faster and leaner logistics processes at Milma. It also improves the trackability and clarity

related to the movement of the products.

Scope of the Study

Milma is a powerful and influential dairy company in Kerala which has many competitors. The

logistics of the company is considered as one of the important aspects of the business which

can be improved by incorporating new methods and technologies. This research helps to focus

on the benefits Milma can achieve with the implementation of supply chain analytics. The

proper analysis of data helps to predict the future scenarios and take better precautions to deal

with risks and reduce the wastes generated leading to a sustainable chain. Leveraging the

benefits from the dashboards created helps Milma to have a competitive edge.

Sample Size

The sample size used for this research is 110 responses. The participants who took part in this

research are employees who work at Milma.

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Sampling Technique

Sampling technique that is used for this study is simple random sampling. This method is

chosen as it is simple to implement, also it saves time and cost involved. It is a legitimate

sampling method that helps to reduce unfairness.

Data Collection tools

Data was collected for this study by conducting personal interviews with the employees who

are working at Milma to get a better understanding of the supply chain process which involves

planning and execution of tasks.

Another method that was used to collect data is an online questionnaire which acted as a survey

tool to get opinions of the employees related to the application of analytics to the logistics of

Milma.

Statistical Tools

The statistical tools used for this study are Microsoft Power BI to create dynamic dashboards.

Python and Microsoft Excel are used for data visualization.

Limitations of the Study

Some of the limitations of this study are as follows:

For a big company like Milma to implement analytics as a whole would require time

and it is not something that can happen overnight.

For technologies to be adapted also there must be people who can handle them and

work with the data that is generated from it, the employees must be well versed in the

latest technologies.

Climatic, financial, communal, legislative, constitutional and technological aspects can

influence the business.

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Chapter 4

Data Analysis and Interpretation

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Data Collection

No. "How

important

is logistics

at Milma?

taking

steps to

improv

logistic

"Should

logistics

be a part

of the

company

strategy

"How often

is logistics

critically

reviewed at

Milma?"

"What are the

quality metrics

used in logistics

at Milma? "

"Problems

influence

logistics at

Milma? "

1 Important Yes Yes Monthly Timely Delivery Tracking

6 Important Yes Yes Quarterly Timely Delivery Tracking

8 Indifferent Yes No Quarterly Timely Delivery Tracking

11 Important Yes Yes Yearly Cost Leadtime

12 Important Yes Yes Yearly Quality Reliability

15 Important Yes Yes Yearly Quality Tracking

16 Important Yes Yes Yearly Error - free Tracking

17 Important Yes Yes Yearly Timely Delivery Tracking

19 Indifferent Yes No Yearly Timely Delivery Tracking

21 Important No Yes More than a

Timely Delivery Tracking

22 Important No Yes Monthly Timely Delivery Tracking

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25 Important Yes Yes Monthly Timely Delivery Cost

26 Not

important

Yes Yes Monthly Timely Delivery Reliability

27 Important Yes No Quarterly Quality Reliability

28 Important Yes Yes Quarterly Quality Reliability

29 Important Yes Yes Quarterly Quality Tracking

39 Not

important

Yes Yes Yearly Timely Delivery Reliability

40 Important Yes No Yearly Cost Cost

42 Important Yes Yes More than a

Quality Reliability

43 Important No Yes Monthly Quality Tracking

44 Important No Yes Monthly Error - free Tracking

50 Not

important

Yes Yes Quarterly Timely Delivery Tracking

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53 Important Yes Yes Yearly Timely Delivery Reliability

56 Important Yes No Yearly Timely Delivery Tracking

63 Not

important

Yes Yes More than a

64 Not

important

Yes Yes Monthly Timely Delivery Reliability

65 Important No Yes Monthly Timely Delivery Reliability

66 Important No Yes Monthly Error - free Cost

73 Important Yes No Quarterly Timely Delivery Tracking

75 Not

important

Yes Yes Yearly Timely Delivery Tracking

77 Important Yes Yes Yearly Quality Cost

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81 Important Yes No Yearly Timely Delivery Tracking

84 Important Yes Yes More than a

87 Important No Yes Monthly Timely Delivery Tracking

88 Important No Yes Monthly Timely Delivery Reliability

89 Important Yes Yes Monthly Quality Tracking

92 Indifferent Yes Yes Quarterly Timely Delivery Tracking

94 Indifferent Yes Yes Quarterly Timely Delivery Tracking

95 Not

important

Yes Yes Yearly Quality Tracking

96 Not

important

Yes No Yearly Timely Delivery Tracking

98 Not

important

99 Important Yes Yes Yearly Timely Delivery Reliability

100 Not

important

101 Not

important

102 Not

important

103 Indifferent Yes No Yearly Timely Delivery Tracking

104 Not

important

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105 Important Yes No Quarterly Timely Delivery Reliability

107 Important Yes No Quarterly Timely Delivery Tracking

108 Not

important

Yes Yes Quarterly Timely Delivery Tracking

109 Important No No Quarterly Timely Delivery Tracking

110 Not

important

No Yes More than a

No. "Will

implementatio

n of technology

solve the

problems

related to

logistics? "

"What effects will

technology have

on logistics? "

"Do you

think use

technology

in logistics

increase

the cost

involved? "

"Which logistic activity

at Milma requires

improvement? "

1 May be Competitive edge Yes Transportation and

delivery

2 Yes Competitive edge Yes Transportation and

delivery

6 Yes Cost saving Yes Inventory management

7 Yes Competitive edge Yes Inventory management

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delivery

11 Yes Competitive edge May be Transportation and

delivery

12 No Competitive edge Yes Transportation and

delivery

14 Yes Competitive edge Yes Packaging

delivery

17 Yes Time saving Yes Transportation and

delivery

22 Yes Competitive edge No Transportation and

delivery

23 No Competitive edge No Transportation and

delivery

25 Yes Time saving No Overall process

26 Yes Time saving No Overall process

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27 Yes Competitive edge Yes Overall process

32 Yes Increase

traceability

May be Inventory management

33 Yes Increase

traceability

Yes Transportation and

delivery

38 Yes Competitive edge No Inventory management

39 Yes Competitive edge No Overall process

delivery

44 Yes Cost saving Yes Transportation and

delivery

45 Yes Increase

traceability

Yes Overall process

46 No Increase

traceability

Yes Overall process

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delivery

53 Yes Time saving May be Transportation and

delivery

60 Yes Time saving Yes Inventory management

61 Yes Time saving Yes Inventory management

delivery

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delivery

72 Yes Increase

traceability

Yes Overall process

73 Yes Increase

traceability

Yes Overall process

74 Yes Competitive edge May be Transportation and

delivery

79 May be Competitive edge Yes Inventory management

80 No Competitive edge No Inventory management

delivery

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delivery

95 Yes Competitive edge May be Overall process

96 Yes Increase

traceability

delivery

97 Yes Increase

traceability

delivery

101 Yes Competitive edge No Overall process

102 No Competitive edge No Overall process

103 No Competitive edge Yes Overall process

delivery

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delivery

108 Yes Time saving No Transportation and

delivery

109 No Time saving No Inventory management

110 No Time saving No Inventory management

The above data are the responses that were compiled from the survey that was carried out. It

consists of 10 questions and a total of 110 responses were collected. The respondents for this

survey are the employees at Milma to know what is their take on application of analytics to

supply chain and other information related to logistics process.

Data Interpretation

From the above graph it can be comprehended that logistics is an essential process at Milma.

The products at Milma are perishable and thus needs an efficient supply chain to make sure the

products are delivered to the customers according to proper quality standards.

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Milma in its future prospects has a lot of this lined up to improve the logistics which includes

the implementation of blockchain technology, IoT.

It is seen that the logistics at Milma is reviewed mostly on a yearly basis, this can be worked

upon by having implementing half yearly reviews and gradually making it a monthly reviewing

system which will help to scale up the logistics to much better level with changes made

according to the needs.

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There is a need for logistics to be part of the company strategies as we know these strategies

are considered a very essential for helping mold the company’s future. It involves a lot of

brainstorming to decide how to increase customer satisfaction.

One of the most important metrics in logistics is the timely delivery of the products to satisfy

the needs and requirements of the customers. Timely delivery also helps to maintain the

freshness of the products.

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It is noticed that the problem that is reported the most in logistics in the lack to be able to track

the items. This affects the overall process as the delivery time cannot be estimated leading to

many problems in turn leading to let-down customers. The use of blockchain can help to resolve

this issue as it provides easy traceability of the goods with timely updates.

Technology can help solve many issues as well as to predict the future in a better way,

understand the risks that are likely to occur and also provide an accuracy when in comes to the

planning of resources.

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The effects of technology are many, with proper insights about the applications of technology

we understand it leads to cost cutting, time saving and traceability. As the employees are not

much aware of the latest technologies and their benefits, they failed to point these as the major

effects.

The main activity that requires improvement is transportation and delivery with the use of

technologies like “blockchain and internet of things” helps to increase traceability of goods and

have an agile supply chain.

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Most of the respondents think that incorporation of technology to logistics would increase the

cost involved, but there are many other benefits which can be achieved by the use of technology

which in turn would actually reduce the cost involved.

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Chapter 5

Findings, Suggestions and Conclusion

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Findings

Power BI one of the latest business analytical tools is used in this project to show procurement

and sales at Milma. Power BI represents the data as visual reports and dynamic dashboards that

can be used by Milma improve their supply chain.

Data models of procurement and sales data

Data models are used to find the connections among the different data present in different

tables. The properties of the connections vary from one to one and one to many.

The above is the data model for procurement data. There exists a one to many (1: *) relationship

between the tables Dim_Category, Dim_Date to Fact-Data. Single cross filter from

Dim_Category to Fact-Data and Dim_Date to Fact-Data means that only one table in the

relationship can be used to filter the data.

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The above is the data model for sales data. There exists a one to many (1: *) relationship

between the tables Dim_Category, Dim_Date to Fact-Regionwise, Dim_Category, Dim_Date

to Fact-Productwise and Dim_SubCategory to Fact-Productwise. Single cross filter from

Dim_Category, Dim_Date to Fact-Regionwise and Dim_Date to Fact_Productwise means that

only one table in the relationship can be used to filter the data. Bi-directional cross filter from

Dim_Category, Dim_SubCategory to Fact-Productwise means that both tables in the

relationship can be used to filter the data.

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Dashboard of Procurement data

The dashboard consists of the products procured each year and the procurement from each of

the regions like Malabar, Ernakulam and Thiruvananthapuram.

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Quantity procured each year

Quantity procured from each region

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Dashboard of Sales data

The sales data consists of three dashboards – region wise sales, product wise sales and product

sales for each region.

Dashboard for Region wise sales at Milma

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Quantity sales each year

Quantity sales for each region

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Dashboard for Product sales at Milma

Productwise sales at Milma

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Product sales for each year

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Dashboard for Product sales for each region

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Productwise sales

Product wise sales across each region

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From the analysis of data, we can see that there is a 0.44% increase in sales between 2018 and

2019. CC03(MRCMPU) accounted for the majority of the increase among the other regions,

offsetting the decrease of CC01(TRCMPU). There were no significant changes to the relative

contributions.

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There is a 2.54% increase in sales between 2019 and 2020. CC01 (TRCMPU) had the largest

increase among other regions. There were no significant changes to the relative contribution.

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The product wise sales across the three regions in the year 2020. CC01 refers to TRCMPU,

CC02 refers to ERCMPU and CC03 refers to MRCMPU.

TRCMPU

ERCMPU

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MRCMPU

CC01(TRCMPU) consist of 20% of the records, CC02(ERCMPU) consists of 17.14% of the

records and CC03(MRCMPU) consist of 62.85% of the records, most affects the distribution.

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Suggestions

Implementation of blockchain technology can help to lighten the disharmony in the

logistics at Milma. It also helps to reduce the cost and time of organizational processing.

A mix of a many technologies can be used to provide better of materials which in turn

leads to exceptional relationships with customers.

Right management of the dashboards that are created can be used to take rational

decisions, to redesign the chain to increase the value in logistics and to improve the

overall supply chain at Milma which in turn can increase the profitability.

Training the employees of Milma by conducting workshops to figure out handle the

new implemented technologies with ease, making it simpler for them to understand the

actual working and trouble shoot efficiently.

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Conclusion

This study is based on Supply Chain Analytics at Milma, the application of analytics to supply

chain and the data from the analytical technologies can be reported using Power BI, which is a

business analytics tool used to make powerful dashboards. The reports created using Power BI

provides a multidimensional view of the datasets of Milma. The visualizations are not fixed, it

is highly interactive and can be personalized according to the needs of the company. The model

created can discover insights and get solutions when data is added or deleted. Milma can make

use of this model to find data points where the sales are less, finding solutions for it using better

marketing strategies and ways to increase the sales in that particular region. By using this model

intensively Milma can make sure that the products are being tracked timely with live updates

being reflected in the dashboards. This will help to increase customer satisfaction with proper

clarity on the delivery of products which in turn will increase the profits flowing into the

company.

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Bibliography

milma.com

https://www.jigsawacademy.com

https://www.sas.com

https://searchbusinessanalytics.techtarget.com

https://scm.ncsu.edu

https://www.cips.org

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ANNEXURE

04/04/2021 Questionnaire on Logistics at Milma

https://docs.google.com/forms/d/1YWK070sfkblf-vcuSQWwLoDxK3y1S1f8KCqnPnfR2Q4/edit 1/4

Mark only one oval.

Not important

Indifferent

Important

Mark only one oval.

Questionnaire on Logistics at MilmaThis questionnaire is used to get more in sights about the supply chain at Milma

*Required

How important is logistics at Milma? *

Is Milma taking steps to improve the logistics? *

Should logistics be a part of the company's strategy? *

Mark only one oval.

Monthly

Quaterly

Yearly

More than a year

Mark only one oval.

Quality

Timely Delivery

Error - free

Mark only one oval.

Tracking

Lead time

Reliability

How often is logistics critically reviewed at Milma? *

What are the quality metrics used in logistics at Milma? *

Problems that influence logistics at Milma? *

Mark only one oval.

Time saving

Cost saving

Increase traceability

Competitive edge

Mark only one oval.

May be

Mark only one oval.

Transportation and delivery

Inventory management

Packaging

Overall process

Will implementation of technology solve the problems related to logistics? *

What effects will technology have on logistics? *

Do you think use of technology in logistics will increase the cost involved? *

Which logistic activity at Milma requires improvement? *

0%SIMILARITY INDEX

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0%PUBLICATIONS

%STUDENT PAPERS

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A Study on Supply Chain Analytics At MilmaORIGINALITY REPORT

PRIMARY SOURCES

I. H. Yigin, H. Taşkin, I. H. Cedİmoglu, B. Topal."Supplier selection: an expert systemapproach", Production Planning & Control,2007Publication

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