220 Int. J. Mass Customisation, Vol. 4, Nos. 3/4, 2012

Copyright © 2012 Inderscience Enterprises Ltd.

A multi-criteria approach for sustainable mass customisation in the fashion supply chain

Fabio De Felice Department of Mechanism, Structures and Environment, University of Cassino, via Marconi 10, 03043Cassino (FR), Italy E-mail: defelice@unicas.it

Maria Grazia Gnoni* Department of Innovation Engineering, University of Salento, Campus Ecotekne, 73100, Lecce, Italy E-mail: mariagrazia.gnoni@unisalento.it *Corresponding author

Antonella Petrillo Department of Mechanism, Structures and Environment, University of Cassino, via Marconi 10, 03043 Cassino (FR), Italy E-mail: a.petrillo@unicas.it

Abstract: Mass customisation (MC) relates to the ability to provide customised products or services through flexible processes in high volumes and at reasonably low costs which are currently key variables for the fashion supply chain. Sustainability concepts – which usually refer to three pillars i.e., economic, environmental and social ones – are new paradigms in this industrial sector, i.e., sustainable mass customisation. The paper proposes a methodological approach for evaluating sustainability performances of a fashion supply chain. Firstly, key performance indicators (KPIs) have been evaluated in order to assess sustainability level of a typical fashion supply chain according to the three single dimensions. KPIs have been derived from social and environmental life cycle analysis (LCA) and the supply chain operations reference (SCOR) model. Then, a multi-criteria model has been developed based on analytical network process (ANP) aiming both to model interrelationships among KPIs and to prioritise them.

Keywords: fashion supply chain; sustainable mass customisation; SMC; key performance indicators; KPIs; multi-criteria analysis.

Reference to this paper should be made as follows: De Felice, F., Gnoni, M.G. and Petrillo, A. (2012) ‘A multi-criteria approach for sustainable mass customisation in the fashion supply chain’, Int. J. Mass Customisation, Vol. 4, Nos. 3/4, pp.220–238.

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Biographical notes: Fabio De Felice is a Professor at the Faculty of Engineering of the University of Cassino, board member of several international organisations and responsible for scientific research and training in industrial plants. He is a holder of the course planning and management of industrial plants. His scientific activity focuses on studies and researches concerning industrial plant engineering. Such activity ranges over all fields from improvement of quality in productive processes to the simulation of industrial plants, from support multicriteria techniques to decisions (analytic hierarchy process, analytic network process), to RAMS analysis and human reliability analysis. He is the General Secretary of the Analytic Hierarchy Process – AHP Academy – international association for the promotion of multi-criteria decision making methods.

Maria Grazia Gnoni is an Assistant Professor at the Engineering Faculty of the University of Salento. Her research interests are focused on sustainability analysis of supply chains and reverse logistics models. She is an author of more than 40 papers and a reviewer for the International Journal of RF Technologies, International Journal of Environmental Management, and International Journal of Production Economics and Safety Science.

Antonella Petrillo has a degree in Mechanical Engineering and is now a Researcher at the Faculty of Engineering of University of Cassino where she conducts research activities on multi-criteria decision analysis (MCDA) at the Department of Mechanism, Structures and Environment.

1 Introduction

The clothing industry (or the fashion supply chain) is a high value sector supporting a significant number of economies and individual incomes around the world. Customer focus and operational competence represent the two key concepts that are critical to the success of mass customisation (MC) implementation (Jiao et al., 2003; Kristal et al., 2010). The fashion supply chain is currently an important sector where often firm strategies are oriented towards product personalisation – more than product customisation – with a reduced price (Piller, 2007; Larsson et al., 2009; Song and Fiore, 2011). Thus, firms have to face with short life cycles of products, low predictability of demand and an high lumpy purchasing activity (Christopher et al., 2004). New approaches are forcing firms to move towards networked organisations where upstream and downstream integration are realised (Wang and Chan, 2010). Reduced efforts could be outlined in order to integrate environmental and social issues as sustainable mass customisation (SMC) strategy. Traditional MC strategies focus on tools to deliver a wide range of products and services that meet specific needs of individual customers with a reduced cost (Da Silveira et al., 2001; MacCarthy, 2003, Salvador et al., 2009). On the other hand, sustainable development strategies are focusing on optimising the use of resources in several operational ways. Thus, the SMC concept forces to an integration of environmental and social consequences into the assessment of the value chain (Sarkis, 2003; Esty and Winston, 2009). Therefore, new models have to be developed for managing in more effective way issues concerning sustainability in MC supply chains. The increasing number of variables which could affect the decision process (e.g., the influence of reverse logistics, new methods for supplier selection, etc.) together with the

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dynamic market context are forcing a lower attitude towards the full scale application of these concepts. Furthermore, organisations need innovative frameworks to implement and evaluate operational strategies for increasing industrial process sustainability (Gnoni et al., 2011).

The aim of this study is to propose an integrated approach for SMC in the fashion supply chain: the approach will integrate economic, environmental and social impacts in order to assess the actual level of sustainability of the whole supply chain. A reference model based on key performance indicators (KPIs) allowing to assess sustainability of a fashion supply chain is proposed. Next, an analytical network process (ANP) model has been developed in order to point out an effective set of KPIs for assessing the sustainability level of the whole supply chain. Thus, the application of the ANP model allows to evaluate in a more effective way interrelationships between the three dimension of sustainability. The paper is organised as follows: the rationale of the study is described in Section 2; the proposed approach is detailed in Section 3; finally, an illustrative example is proposed in Section 4.

2 The rationale

The fashion industry is a high value sector, globally manage a total worth over $500 billion, employing approximately 26 million people and supporting a significant number of economies all over the world. USA and European Union (EU) together are the major consumers of textiles (IBEF, 2011). Furthermore, consumers and stakeholders have rising concerns over product quality and environmental issues regarding fashion products manufacturers (Lo, 2011; Lo et al., 2012). Sustainable development concepts are not fully applied in this sector even if customer awareness is increasing. The increasing phenomenon of production delocalisation to the Far East has determined both economic and social effects all over the world. According to the environmental dimension, the intensive use of chemical for garment and the high land and water use requested for raw materials production (Schoeberl et al., 2004; Ranganathan et al., 2007) could cause high consequences. According to the social dimension, one well known problem regards the application of fair labour standards, workers’ rights and wages.

Few recent studies are facing with the application of more sustainable strategies according to an environmental point of view. Woolridge et al. (2006) proposed a life cycle analysis aiming to compare benefits introduced by recycled materials in clothing and shoes production. The Business for Social Responsibility (BSR, 2009) – a non-profit organisation – developed a research study focused on assessing GHG emissions from activities along the full life cycle (i.e., from raw material acquisition through disposal) of individual garments.

Several industrial initiatives have been started in last years about these issues. Since 2007, a first structured approach has been proposed by the DEFRA – the English Department for Environment, Food and Rural Affairs – by the ‘Sustainable Clothing Roadmap’ project (DEFRA, 2011). The project aims to improve the sustainability of the fashion supply chain across its all lifecycle, from design phase to raw material production to retail, use and end-of-life. In 2011, a group of global apparel and footwear companies together with non-profit organisations, universities and government-run agencies has funded in 2011 the Sustainable Apparel Coalition aiming to define one common sustainability index for the apparel and footwear industry. This initiative has been

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integrated with one carried out by the Eco Working group of the Outdoor Industry Association, which has firstly launched a tool called Eco Index for supporting firms in developing environmental assessment.

Few famous brands are developing tools that could certify environmental sustainability level of their own fashion products. Nike has launched in 2010 a web-based software called Environmental Apparel Design Tool in order to support companies and customer to assess more sustainable design of apparel products across all sport categories. Puma has published in 2010 its Environmental Profit & Loss Account report: the report has focused on the reduction of GHG emissions and water consumption obtained by the firm in its whole supply chain. Six major athletic firms (including Adidas Group, H&M, Nike, and Puma) have started in November 2011 a project defined as Roadmap towards zero discharge of hazardous chemicals: the purpose is to eliminate the application of hazardous substances in all their supply chain by 2020.

Recent studies are now developing focusing on the social dimension of sustainability. van Yperen (2006) proposed a study focused on the textile industry.The interest is growing also in developing countries such as China (Linfei and Qingliang, 2009). All these proposal are focusing on one single dimension of sustainability; a first attempt to integrate different dimensions of sustainability has been started by the Nordic Initiative Clean and Ethical Fashion (NICE Fashion) to support designers and textile companies to more environmentally safe and ethical design and sourcing. The present paper focuses on integrating the three dimensions (i.e., economic, environmental and social factors) in a fast but effective way: a reference model for evaluating the actual level of sustainability is proposed for the fashion supply chain. Starting from defining a framework for analysing the fashion supply chain, a KPIs priorisation analysis has been carried out aiming to highlight critical KPIs for controlling, by an operational point of view, performances of each stage of a typical fashion supply chain.

The ANP – a multi-criteria decision making (MCDM) technique developed by Saaty (Saaty, 2005) – has been applied for KPIs priorisation activity.

Traditionally, the multi-criteria analysis has just been applied for outlining critical activities in several logistics contexts (Chan et al., 2006; Korpela et al., 2007; Ramanathan, 2007; Sharma and Agrawal, 2009; Chan and Chan, 2010; Sarminento and Thomas, 2010). Recently, Wang et al. (2012) proposed the application of the analytic hierarchy process combined with fuzzy logic aiming to assess effectively risks due to the application of green initiatives in the fashion industry.

Differently from traditional MCDM tools, the ANP allows to develop both hierarchical and interconnected relationships between criteria as the network is made on different cluster of interrelated criteria (Khan et al., 2002). The application of an ANP model will allow to manage relationships among indicators and uncertainties in multi-criteria analysis when qualitative assessment is needed (Wang and Chan, 2010). The aim is to effectively manage multiple, conflicting objectives such as social, environmental and economic dimensions of sustainable development (Çiğdem and Tayfun, 2007). In detail, the ANP provides a general framework to deal with decisions without making assumptions about the independence of higher-level elements from lower level elements and about the independence of the elements within a level as in a hierarchy. Results are a priority set of KPIs evaluated according to the main goal of the decision problem (i.e., assessing the actual level of sustainability of the whole supply chain).

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Moreover, the ANP provides consistency feedback to decision makers by quantitative assessment human judgments have been regulated throw a structured approach. Thus, the dynamic features and the complexity of the decision problem make the ANP technique a suitable tool to reduce the number of KPIs for assessing quickly and effectively the whole sustainability performance of a fashion supply chain.

3 The proposed approach for sustainable mass customisation in the fashion supply chain

3.1 Phase 1: the fashion supply chain analysis

The fashion industry supply chain can be categorised into five major components; a brief description is proposed as follows:

1 Raw materials production: it usually involves all phases from raw materials sourcing, ginning and fibre extrusion.

2 Textile plants: traditionally, spinning, weaving and knitting processes are carried out.

3 Apparel plants: at this stage, product design and manufacturing represents core activities.

4 Export chains: it involves all logistics activities to delivery semi-finished or finished product to the final producers. It will became critical for delocalisation productions.

5 Apparel manufacturers, retail stores and customers: it represents the end point of the direct supply chain and the starting point of the reverse chain.

Next, the fashion supply chain has been analysed according to main factors affecting a typical MC process as defined by MacCarthy et al. (2003). Thus, six main processes are critically analysed for the fashion supply chain as follows:

• Product development and design: this process involves all steps required to translate customer and legislative requirements in product specifications. According to sustainability point of view, it represents a critical phase as eco-design strategies about raw materials (e.g., natural fibres) could be evaluated.

• Product validation and manufacturing engineering: it affects the process design mainly in terms of production resources (e.g., cycle time definition, routing processes, etc.). As the previous step, it also affects the eco-design of a fashion product as different technologies (e.g., for treating fibres) could be evaluated.

• Order fulfilment management: this process manages all activities involved in the whole supply chain of a fashion product. Information management is the core activities.

• Order fulfilment realisation: it represents the core of the value chain of a fashion product as it includes all processes carried out from fibre production to manufacturing and delivery activities. Critical processes affects mainly supply performance in terms of time and product quality.

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• Coordination: this process manages all information shared in the whole supply chain starting from customers to suppliers. Moreover, it also manages data for the reverse chain: an example is information about product recall or defects.

• Post-order process: it involves all additional activities supplied after purchasing e.g., reverse logistics, re-use, disposal, etc.

This preliminary analysis according to two different points of view has supplied information in order to assess KPIs for the fashion supply chain.

3.2 Phase 2: KPIs identification

Several approaches have been developed in the literature facing with economic and environmental assessment of such a supply chain; the proposed model is based on the interaction of two well known reference models: the supply chain operation reference (SCOR) defined by the Supply Chain Council and the life cycle assessment (LCA) approach defined by ISO standards.

Figure 1 The proposed approach for sustainable MC in the fashion supply chain (see online version for colours)

The SCOR model is a cross-industry diagnostic tool for evaluating performances of a supply chain according to a hierarchical index method (SCC, 2010); it integrates all dimensions of supply chain process in a standardised way aiming to supply an effective tool to compare different contexts (Poluha, 2007). On the other hand, the LCA has been demonstrated as an effective standardised model to assess environmental impacts due to all stages characterising a product or a service (ISO, 2006). Moreover, the social dimension will be integrated according to a recent standard, the s-LCA, proposed from the UNEP (UNEP, 2009). LCA tools are mainly based on a cradle-to-grave strategy by focusing on environmental point of view. Benoit and Vickery-Niederman (2010) proposed a review analysis of standards and models applied for assessing the social dimension of sustainability. All these tools – i.e., SCOR, LCA and s-LCA – have a

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common feature: they are focusing only on one dimension of sustainability; thus, interdependent impacts have to be evaluated several times. Thus, KPIs have been evaluated according to a ‘sustainability macro cube model’ as depicted in Figure 1: each single cube represents a single tier of the supply chain; each cube is defined by a specific sustainability level defined by economic, environmental and social KPIs. The general framework applied in order to point out KPIs is reported in Table 1. Table 1 The proposed general framework for assessing KPIs for the fashion supply chain

SCOR category LCA phase s-LCA stakeholder Estimated impact category

Local community Access to natural resources, delocalisation and migration, local

employment Employers Health and safety, social and

environmental benefits Value chain actors Promoting social responsibility

Plan Product design

Consumers Reverse logistics, end of life responsibility

Local community Access to natural resources, delocalisation and migration, local

employment, health and safety, secure living conditions

Employers Child labour, working hours, equal opportunities, social benefits, health

and safety Value chain actors Supplier relationship

Consumers Product safety, transparency

Source Raw material production

Society Technology development, public commitments to sustainability issues

Local community Delocalisation and migration, local employment, health and safety

Employers Child labour, working hours, equal opportunities, social benefits, health

and safety

Value chain actors Fair competition, promoting social responsibility

Consumers Product safety, transparency, end of life responsibility

Make Product manufacturing

Society Technology innovation

Deliver Packaging, distribution and sales

Local community Secure living conditions

Value chain actors Promoting social responsibility, supplier relationship

Society Technology development, contribution to economic

development, public actions for sustainability public commitments to

sustainability issues

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Table 1 The proposed general framework for assessing KPIs for the fashion supply chain (continued)

SCOR category LCA phase s-LCA stakeholder Estimated impact category

Local community Secure living conditions Value chain actors Promoting social responsibility,

supplier relationship

Deliver Packaging, distribution and sales

Society Technology development, contribution to economic

development, public actions for sustainability public commitments to

sustainability issues Local community Contribution to economic

development, promoting social responsibility

Consumers Reverse logistics, promoting social responsibility, transparency

Return End-of-life management

Society Contribution to economic development, promoting social

responsibility

In brief, process categories defined by the SCOR model have been interconnected at first with LCA phases and, after, with s-LCA stakeholder. Results obtained are global impact categories essentially according to environmental and social perspective. Finally, impact categories have been estimated; KPIs belong to these estimated impact categories. Thus, each stage of the product life cycle starting from raw materials design to customer delivery will be analysed according to the macro-cube prospective. The estimated total number of KPIs evaluated for the fashion supply is 72 grouped as following: a total number of seven critical KPIs have been outlined according to an environmental perspective; five and 60 are the total number of critical KPIs that have been outlined for the social perspective and economic (or process) point of view. All estimated KPIs are reported in Table 2. Table 2 Estimated KPIs

SC tier SCOR indicators ANP model code

Forecast accuracy EC1

Planning costs as a % of total supply chain management costs EC2 Product data (MIS) management costs EC3 Quality improvement EC4 Number of data sources for data collection EC5 Availability and accuracy of supplier/source data EC6 Cost of maintaining source data EC7 ECO costs EC8* Frequency of parameter updates EC9

Design

Labour cost EC10* Note: *This indicator has been applied in multiple levels of the SC.

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Table 2 Estimated KPIs (continued)

SC tier SCOR indicators ANP model code

Days of supply (DOS) inventory value EC11

Total replenishment costs EC12

Supplier on-time delivery performance EC13

Order fulfilment compatibility with production plan EC14 Order fulfilment cycle time EC15

Source adaptability EC16

Raw material costs EC17

Supplier cycle time EC18

% orders/lines received on-time to demand requirement EC19

Storage costs EC20 Source fixed asset costs EC21

Storage utilisation EC22

Out of stock occurrences EC23

Supplier selection costs EC24

Storage maintenance costs EC25

Equipment maintenance costs EC26* Production planning costs EC27

Economic values of stocks EC28

Raw materials production

Labour costs EC10* Production cycle time EC29

Plan make cost EC30*

Number of non-compliant products EC31*

ECO costs EC8*

Number of ECOs EC32*

Failure rates EC33* The degree and frequency of conformance to business rules that is achieved

EC34*

Compliance costs EC35*

Mean time between failure EC36*

Asset maintenance costs EC37* Work in progress costs EC38* Material handling costs EC39*

Labour costs EC10*

Raw materials transformation

Safety device costs EC40*

Note: *This indicator has been applied in multiple levels of the SC.

A multi-criteria approach for sustainable mass customisation 229

Table 2 Estimated KPIs (continued)

SC tier SCOR indicators ANP model code

Production cycle time EC29*

Plan make cost EC30*

Test cycle time EC41*

Number of non-compliant products EC31*

ECO costs EC8*

Number of ECOs EC32*

Failure rates EC33*

The degree and frequency of conformance to business rules that is achieved

EC34*

Compliance costs EC35*

Mean time between failure EC36*

Asset maintenance costs EC37*

Work in progress costs EC38

Material handling costs EC39

Labour costs EC10*

Safety device costs EC40

Production and test costs EC42 Downside supply chain adaptability EC43 Production efficiency ratio EC44

Fashion product manufacturing

ECO/production costs EC45

Total cost of non-conformance EC46

Packaging costs EC47

Equipment maintenance costs EC26*

Packaging

Labour costs EC10*

Deliver cycle time EC48

Upside deliver flexibility EC49

Transportation costs EC50

Perfect order fulfilment EC51

Distribution

Labour costs EC10*

Sale costs EC52

Labour costs EC10* Fixed assets costs EC53

Sales

Inventory carrying costs EC54

Note: *This indicator has been applied in multiple levels of the SC.

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Table 2 Estimated KPIs (continued)

SC tier SCOR indicators ANP model code

Percentage of non-compliant products/total sales EC55

Reverse logistics cycle time EC56

Reverse logistics total costs EC57

Verify product cycle time EC58

Verify product total costs EC59

Reverse logistics

% capital assets acquired without processing defects EC60

Note: *This indicator has been applied in multiple levels of the SC. Social indicators ANP model code

Rate of productreuse S1

Level of safety work climate S2

Level of dailyworkload S3

Level of emissions at workplace S4

Diffusion of biological raw materials S5

Environmental indicators ANP model code

Natural resource consumption (water and fossil fuels) E1

Diffusion of organic raw production E2

Reduction of using hazardous substances E3

Reduction of green house emissions E4

Level of soil degradation E5

Diffusion of toxic substances in the air/water or in the production processes E6

Quantity and typology of wastes E7

3.3 Phase 3: KPIs priorisation

As the total estimated number of KPIs is high, a priorisation analysis based on ANP has been carried out. The aim of this analysis is to prioritise most effective indicators for a strategic sustainability analysis. The ANP is a multi-criteria technique that breaks down a decision-making problem into several levels by developing a network of criteria: thus, the proposed ANP model consists of elements grouped into clusters. Elements of a cluster could be connected to elements of both another cluster and/or of the same cluster (i.e., in a feedback scheme). Final alternatives form an additional cluster in the network.

By a theoretical point of view, the ANP incorporates component dependence and feedback by using a supermatrix (defined as W) approach.

A multi-criteria approach for sustainable mass customisation 231

1 2

1 11 12 1

2 21 22 2

1 2

n

n

n

n n n nn

C C CC w w w

W C w w w

C w w w w

=

………

… … … … …

Each Ci cluster of the network is linked by the supermatrix through the system weights (wij): if there is no linkage between components Cx and Cy, then wxy would be 0; otherwise, the wxy assumes non-zero values.

The final priority weights – which determine the alternative ranking - are derived by multiplying the supermatrix by itself until the columns stabilise, which occurs when the supermatrix entries become identical across each row or cycle in blocks.

The proposed ANP model is based on four clusters of criteria and one cluster of final alternatives: the structure is depicted in Figure 2.

Figure 2 The proposed ANP structure

A brief description of elements and clusters is proposed as follows:

• Cluster 1 – the SCOR Level: criteria in this cluster refers to core economic processes which characterise the whole supply chain. Performance metrics related to this cluster aim to define process measurement into a cross-functional framework. It involves the design, the manufacturing and the direct and reverse logistics flows of a fashion product.

• Cluster 2 – the LCA level: criteria for managing the environmental dimension of the fashion supply chain are grouped in this cluster. Several strategies could be outlined for forcing companies to move towards sustainable manufacturing in

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the fashion supply chain. KPIs connected to this dimension regards mainly levels of emissions, wastes and energy consumption, the diffusion of low-impact raw materials (e.g., organic materials than chemical ones).

• Cluster 3 – s-LCA level: criteria in this cluster refer to traditional category of internal and external stakeholders in social corporate responsibility such as ‘customers’, ‘employers’, ‘value chain actors’, ‘society’ and ‘local community’.

• Cluster 4 – estimated impact category level: this cluster has been developed according to potential impact category evaluated for pressures caused economic, environmental and social dimensions of a fashion supply chain.

• Cluster 5 – KPI level: the total number of KPIs evaluated in the previous stage has been grouped in a specific cluster: according to final ranking of the proposed alternatives, a set of critical KPIs is defined.

Alternatives have been introduced in one specific cluster: as the expected result of the ANP application is a priority set of interconnected KPIs, there main groups of criteria have been introduced such as environmental perspective, social perspective and economic perspective defined as A1, A2, A3 respectively.

4 An illustrative example

4.1 The model application

The application of the proposed model is illustrated through a case scenario concerned a clothing company which designs and sells apparel and accessories for children (i.e., ages six months to 16 years). This sector of the fashion industry is characterised by an increasing market demand. As an example, according to a recent market research report, this sector represents in the USA determines $10 billions of revenues with an estimated annual growth of 0.4% in 2006–2011. According to a study proposed by the European Apparel and Textile Confederation, the overall size of the textile and clothing industry in the EU-27 represents a turnover of 172 billion € and investments of around 5 billion € in 2010 in all the European area. On the other hand, few initiatives are developing focused on applying sustainable development concepts in this supply chain. Most widespread pilot projects are facing with raw materials certification (e.g., organic cotton), by focusing on a single level of the supply chain.

The firm in analysis carried out the last tier of the supply chain, i.e., the apparel manufacturing and sales. The aim of the model application is to evaluate most effective control strategies for applying over the whole product life cycle sustainable development concepts.

The model application starts from assessing quantitatively the ANP model proposed in the previous section. Once the ANP structure has been developed, the decision maker systematically evaluate each element (i.e., criterion) of a cluster to another with a pair wise comparison: pairwise judgments are developing with respect to their impact on elements interconnected in the specific network. From a mathematical point of view, pairwise comparisons are made in order to determine the so-called dominance coefficient, Aij, which represents an importance estimation of the first element (i) compared to the second (j). An example regarding the Cluster 2 – the LCA level is proposed in Table 3.

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Table 3 Example of a pairwise comparison matrix for the Cluster 2

j

End of life

management Product design

Production manufacturing

Raw material

production

Packaging, distribution and sales

End of life management

1 3 3 4 2

Product design

1/3 1 2 1/3 2

Production manufacturing

1/3 1/3 1 1/4 3

Raw material production

1/4 1/2 4 1 3 i

Packaging, distribution and sales

1/2 1/2 1/3 1/3 1

The ANP converts these evaluations to numerical values that can be processed and compared over the entire range of the problem. Comparisons have been carried out by the semantics scale of Saaty, which varies from 1 to 9: the value 1 represents ‘equal importance’, 9 represents ‘an extreme importance’ (Saaty, 1980).

Next, the supermatrix W has been estimated in order to assess scores regarding the three decision alternatives (A1, A2, A3): they represent the ‘alternatives’ relative ability to achieve the decision goal, i.e., assessing the sustainability of the whole fashion supply chain in analysis.

Final results supplied by the proposed ANP model are discussed following:

• Environmental dimension: the highest score is the ‘reduction of using hazardous substances’ indicator (E3) which is characterised by a final score of 0.426. Next estimated indicators are the ‘natural resource consumption’ (E1) with a final score of 0.230 the ‘reduction of greenhouse emissions’ (E4) with a final score of 0.222 and finally the ‘level of soil degradation’ (E5) with a final score of 0.122.

• Social dimension: the ‘level of emissions at workplace’ indicators (S4) has the highest score (i.e., 0.300); next, the ‘rate of product reuse’ indicator (S1) with a score of 0.250, the ‘level of daily workload’ (S3) with a score of 0.180; the ‘level of safety work climate’ (S2) with a final score of 0.127 and finally the ‘diffusion of biological raw materials’ (S5) with a score of 0.143.

• Economical prospective (alternative A3): the first two indicators are ‘the production cycle time’ (EC29) and the ‘number of non-compliant products’ (EC31) with a score of 0.450 and 0.234 respectively; next, the ‘inventory carrying costs’ (EC54) and the ‘failure rate’ (EC33) with a score of 0.156 and 0.160 respectively.

Thus, the estimated set of critical KPIs consists of the 13 KPIs previously detailed. In order to assess the whole consistency of judgments in the ANP model, a

Consistency Index (CI) has been defined (Saaty, 2005) as reported in equation (1):

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max( ) / ( 1)CI n nλ= − − (1)

where λmax is the maximum eigenvalue of the positive reciprocal matrix characterising each cluster and n is the number of objectives in the cluster. As an example, the λmax for Cluster 1 is equal to 5.10 and n is equal to 5. If the CI value is less than 0.10, judgments could be defined as consistent; therefore, final results are reliable by an analytic point of view.

Thus, the CI estimation has been carried out to verify the coherence of the judgments in the illustrative example. Results show that all estimated values are under the target threshold: in detail, the estimated CI values are of 0.055, 0.048, 0.037 for the A1, A2, and A3 groups respectively.

4.2 Discussion

Applying concept of sustainable development – which involves economic, environmental and social issues – in industrial systems is usually a complex problem as several factors are involved and a number of competitive objectives has to be evaluated (Vachon and Mao, 2008). On the other hand, potential advantages have been demonstrated in several studies both for large (Azevedo et al., 2011) and for small and medium sized enterprises (Moore and Manring, 2009). The triple bottom line (3BL) approach is being applied quite in different way in industrial supply chains. Results obtained by the model application are following discussed in a more general way.

• One main burden due to an effective application of 3BL approach in complex supply chain is the difficulty to quantify such a factor (Young and Kielkiewicz-Young, 2001). As an example, social and ethical issues are often perceived as intangible issues. The integration between a multi-criteria technique – the ANP – with KPIs development supplies effective solutions to the problem of assessing the whole sustainability level of a supply chain. By comparing with traditional cost-benefit analysis, the proposed approach allows to quantify in a structured way critical factors for controlling sustainability level of a fashion supply chain. Thus, it could support firm managers to measure their whole sustainability and to address new strategies for continuous improvement.

• Another key point in 3BL application is to manage conflicting objectives. The multi-criteria proposed approach allows to manage efficiently conflicting objectives usually characterising the application of 3BL concepts in operations management. Moreover, the ‘macro-cube’ approach based on the integration of SCOR, LCA and s-LCA reference models reduces redundancy in KPI assessment as interconnected indicators have been evaluated. Thus, results supplied – i.e., the estimated set of KPIs – will allow firms involved in a fashion supply chain to share a common and integrated reference model for sustainability performance analysis. Furthermore, results could be applied to communicate firm sustainability performance both to external stakeholders (e.g., customers) and to shareholders.

• One key feature of 3BL application in supply chain is to evaluate the entire lives of products, including the reverse logistics flow and end-of-life management (Kleindorfer et al., 2005; Kuei and Madu, 2009, Winkler, 2011). The proposed

A multi-criteria approach for sustainable mass customisation 235

approach allows to design and control a closed-loop fashion supply chain as reverse logistics activities are fully evaluated in the KPI assessment (Wu et al., 2012).

5 Conclusions

Sustainability and mass customisation are two concepts that are gaining more and more importance to customers, institutions and firms across a broad range of sectors. MC has been widespread for several years aiming to align process of a single organisation with its customers’ needs. Traditionally, MC paradigm has focused on optimising operational costs always guarantying customised products. On the other hand, sustainable development strategies – which usually involves three dimensions i.e., economic, environmental and social dimensions – are widespread in several industrial fields.

Sustainable development require a wider point of view as a life cycle approach is essential: the focus is amplified from a single organisation to its whole supply chain. Thus, new organisational and performance analysis approaches are required for applying SMC strategies in the fashion supply chain. The paper proposes a methodological model for analysing sustainability level of a fashion supply chain which represents one core example of MC processes. The model is based on KPIs evaluated starting from well know approach for economic (or process), environmental and social analysis tools. KPIs have been constructed by integrating environmental and social LCA and the well known SCOR reference models. A set of critical and interrelated KPIs is the final result obtained by applying a multi-criteria approach based on the application of ANP.

The application of ANP model has allowed to identify and incorporate major interdependencies among many factors and clusters which usually characterise a traditional fashion supply chain.

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