J Intell ManufDOI 10.1007/s10845-014-0957-9

Analysis of enablers for the implementation of leagile supply chainmanagement using an integrated fuzzy QFD approach

A. Noorul Haq · Varma Boddu

Received: 9 April 2014 / Accepted: 6 August 2014© Springer Science+Business Media New York 2014

Abstract Global competition and market uncertainty hasforced organizations to become more responsive and efficientwhich thereby drives interest in the concept of supply chainleanness and agility. The leagile supply chain managementparadigm includes lean and agile principles and has attainedgreater importance in the current scenario. The objective ofthis work is to identify the most appropriate leagile enablersfor implementation by companies based on the characteris-tics of the related market by linking competitive bases, leagileattributes and leagile enablers. In this paper, a quality func-tion deployment (QFD) approach integrated with analyticalhierarchy process and technique for order preference by sim-ilarity to ideal solution is proposed to enhance the leagilityof the supply chain. Fuzzy logic is used to deal with lin-guistic judgments expressing relationships and the correla-tions required by QFD. The presentation of a case study fromthe Indian food processing industry illustrates the proposedmethodology. This approach will help the management toexploit the most influential enablers in achieving the desireddegree of leagility.

Keywords Supply chain management · Leagile supplychain · Quality function deployment · Fuzzy logic · TOPSIS ·AHP · Decision support

Introduction

Supply Chain is a network involving activities associatedwith the flow and transformation of goods from the raw

A. N. Haq · V. Boddu (B)Department of Production Engineering, National Instituteof Technology, Tiruchirappalli 620015, Indiae-mail: varma0609@gmail.com

A. N. Haqe-mail: anhaq@nitt.edu

material stage to the end customer as well as associatedinformation flows. Supply Chain Management is an inte-grating philosophy to manage the total flow of materials,products and information from suppliers to the end cus-tomers. Supply chain management increases the competi-tiveness of a firm and its supply chain, when the appro-priate supply chain strategy is chosen. Due to customerschanging needs, increasing global competition and demanduncertainty, organizations strive to become more responsiveand more efficient to sustain in stiff competition therebydriving the interest in concepts of supply chain leannessand agility. Leanness and agility are different paradigms,but when combined within one model, enable the supplychain’s success (Mason-Jones et al. 2000). The lean sup-ply chain is a strategy based on cost reduction and flexibil-ity, focused on processes improvement, through reduction orelimination of all non-value adding operations. Agile Sup-ply Chain focuses on promoting adaptability and flexibil-ity and has the ability to respond quickly and effectively tochanging markets. In general, Lean supply chain is recom-mended where demand is relatively stable and predictableand cost is the priority. Whereas, agile supply chain is sug-gested where demand is volatile and speed is the priority.(Mason-Jones et al. 2000; Agarwal et al. 2006). Neither par-adigm is better nor worse than the other, indeed they are com-plementary within the correct supply chain strategy (Nayloret al. 1999). According to Naylor et al. (1999), these twostrategies can be combined together to ensure the advan-tages of both. Combining agility and leanness in one sup-ply chain through the strategic use of a de-coupling pointhas been termed leagility (Naylor et al. 1999). Leagilityis defined as the combination of lean and agile paradigmsthat when applied to the strategy of supply chain, respondssatisfactorily to the volatile market demands. The lean andagile paradigms, though distinctly different, can be combined

123

J Intell Manuf

within successfully designed and operated total supply chains(Mason-Jones et al. 2000).

The food industry is a highly segmented market in whichmanufacturers and retails need to be able to respond quicklyto the changing nature of consumer preferences and tastes.The food industry has to implement lean practices to reducethe supply chain cost by eliminating all the waste. But, at thesame time, the food supply chain should be agile enough torespond to consumer demand. Agility in production seems tobe a very important factor in the food supply chain becausemarket sensitivity is absolutely essential in the food indus-try (Bourlakis and Weightman 2004). To sustain in globalcompletion, it is essential to be cost effective as well as mar-ket sensitive. It is necessary for the food industry to imple-ment leagile strategies in their supply chain incorporating theadvantages of the both lean and agile principles.

Based on conceptual models of lean and agile organiza-tions from literature, it is observed that organizations exploitsuitable enablers to achieve leanness or agility in their organi-zations. Similarly, organizations can exploit Leagile Enablers(LAEs) to enhance leagility. LAEs are enabling technologiesand methodologies which achieve leagility. In this work, anintegrated multi criteria decision making framework, basedon Fuzzy quality function deployment (QFD) is proposed toenhance supply chain leagility. An attempt is made to identifythe most appropriate enablers to be implemented by compa-nies, based on characteristics of the related market by link-ing Competitive Bases (CBs), Leagile Attributes (LAAs) andLAEs. An integrated approach, based on Analytical Hierar-chy Process (AHP), Technique for Order Preference by Sim-ilarity to Ideal Solution (TOPSIS) and QFD is developed toprioritize LAEs by considering their relationship with LAAs.Fuzzy logic is used to deal with linguistic judgments express-ing relationships and correlations required in QFD. The pro-posed methodology is illustrated through a case study fromthe food processing industry. The remainder of the paperis organized as follows. Literature review on leagile supplychain management and fuzzy QFD is presented in the nextsection. The proposed methodology is explained in Sect. 3.In Sect. 4, a case study from the food processing industryillustrates the application of the proposed approach. Resultsand discussion are presented in Sect. 5. Concluding remarksand the future scope of research are provided in Sect. 6.

Literature review

Literature review based on leagile supply chainmanagement

Naylor et al. (1999) compared the lean and agile manufactur-ing paradigms, highlighting similarities and differences. Theauthors introduced the leagility concept within the total sup-

ply chain by combining both lean and agile concepts witha decoupling point. They illustrated this with case studiesfrom the Hewlett Packard Company and a personal computermanufacturer. Soni and Kodali (2012) addressed the issue oflack of standard constructs within the frameworks of lean,agile and leagile supply chain. Their objective is achievedby evaluating reliability and the validity of lean, agile andleagile supply chain constructs in the Indian manufacturingindustry. Principle component analysis is performed on theseconstructs to identify the pillars of each type of supply chainfollowed by evaluating the reliability and validity of these pil-lars. Agarwal et al. (2006) presented an Analytical NetworkProcess based framework for selection of suitable strategyamong lean, agile and leagile supply chains. The frameworkencapsulates market sensitiveness, process integration, infor-mation driver and flexibility measures of supply chain per-formance. This paper explored the relationship among lead-time, cost, quality, service level and the enablers of leannessand agility of a case supply chain in a fast moving consumergoods business.

Rahimnia et al. (2009) investigated the applicability ofleagility in Iranian Fast food restaurant chains. This paperhas proposed the possibility of applying the leagility conceptin a case study organization to show that mass services canbenefit from the advantages of both lean and agile paradigms.Kisperska-Moron and De Haan (2011) presented the casestudy of a Fast moving consumer goods distributor in Polandwhich improved the supply chain performance through theapplication of leagile concepts. The firm managed its volatileperiod using agile principles and ensured reliable supplies byadopting lean principles. Vinodh and Aravindraj (2013) pre-sented the conceptual model of leagility embedded with leanand agile principles. A fuzzy logic approach was used toevaluate leagility in supply chains. The authors presented acase study from an Indian Transformer manufacturing indus-try. Azevedo et al. (2012) proposed an index to assess theagility and leanness of individual companies and the corre-sponding supply chain. The index is named Agilean indexand is obtained from a set of Agile and Lean supply chainpractices integrated in an assessment model. They illus-trated the approach with a case study from the Automobileindustry.

Literature review based on fuzzy QFD

Carnevalli and Paulo Miguel (2008) presented a review andanalysis of the literature on QFD. Xu et al. (2010) presenteda more balanced review of QFD that exhibits enough depthto be useful to researchers as well as enough breadth to caterto amateur readers based on publications between 2005 and2009. Costa et al. (2000) presented a detailed literature reviewon the application of QFD in the food industry. Their reviewwas extended with a thorough description of methodologies

123

J Intell Manuf

involved in the practice of QFD in food companies and furtherexemplified with a case study on ketchup quality improve-ment.

Bottani and Rizzi (2006) proposed a fuzzy-QFD approachand addressed the issue of deploying House of Qual-ity (HOQ) to efficiently and effectively improve logisticprocesses. Liang et al. (2012) applied a fuzzy QFD approachto prioritize knowledge management solutions for an inter-national port in Taiwan. Vinodh and Kumar (2011) appliedfuzzy QFD to enable leanness and validated the approachwith a case study from an Indian electronics switches man-ufacturing organization. The approach was very effective inidentifying lean competitive bases, lean decision domains,lean attributes and lean enablers for the organization. Bot-tani (2009) proposed an original approach based on QFDmethodology and fuzzy logic to identify the most appro-priate enablers for implementation by companies startingfrom competitive characteristics of related markets by link-ing competitive bases, agile attributes and agile enablers. Anillustrative example based on data available in literature, ispresented to show application of the tool developed. Ayag etal. (2013) analyzed the dairy industry and identified impor-tant dairy logistics requirements and Supply chain manage-ment strategies using Fuzzy QFD for maximizing customersatisfaction.

Some of the researchers integrated the QFD with fuzzylogic and other multi-criteria decision making (MCDM) toolswhich they claimed as an efficient and useful decision mak-ing tool. Kwong and Bai (2002) proposed a fuzzy AHPapproach to the determination of importance weights of cus-tomer requirements in QFD as it is a crucial and importantstep. The design of a bicycle splashguard is used to illustratethe proposed methodology. Ho et al. (2012) proposed an inte-grated approach, combining QFD and fuzzy AHP approachto evaluate and select optimal third-party logistics serviceproviders. The integrated QFD and fuzzy AHP approachcomprises of three HOQs, including HOQ1—linking com-pany stakeholders with their requirements, HOQ2—relatingstakeholder requirements to evaluating criteria and HOQ3—benchmarking alternative 3PLs with respect to various cri-teria. Fuzzy AHP is used to find importance ratings of stakeholders’ requirements in first HOQ and relationship weight-ings in all three HOQs. But, this methodology involves lot ofpair wise comparisons and calculations. Zarei et al. (2011)proposed an integrated approach based on fuzzy QFD andAHP to enhance leanness by analyzing lean enablers linkedto lean attributes. Priority weights of lean attributes basedon selected criteria are calculated using AHP. The authorsillustrated the approach with a case study from the canningindustry. From their work, it can be inferred that QFD inte-grated with fuzzy logic and other MCDM methods will bea successful tool in decision making. In our work, TOPSISis integrated with fuzzy QFD as it is a simple and effective

MCDM tool. Moreover, AHP is proposed to allocate priorityweights in TOPSIS.

From the literature, it is observed that very few workswere reported on leagile supply chain management and nowork has been reported on the application of fuzzy QFDfor leagility enhancement in supply chain management. Thiswork aims at enhancing leagility of supply chains usinga fuzzy QFD approach integrated with AHP and TOPSIS.Also, the food industry received limited attention in the con-text of lean and agile strategies in literature. This paper illus-trates the proposed methodology with a case study from afood processing industry.

Proposed methodology

It is understood from literature that leagile enterprises arecharacterised by proper attributes allowing companies to sus-tain in a business environment. Generally, companies com-pete along with many CBs, like costs, quality, responsivenessetc; whose relative importance in achieving a competitiveadvantage depends on specific market characteristics. Con-sequently, LAAs to be enhanced vary depending on the CBsthe companies are interested to excel in. Also, companies canexploit many LAEs to achieve LAAs. According to marketfield characteristics, companies should first define CBs theyare interested to excel in, to achieve a competitive advan-tage. Then, LAAs enhancing selected CBs should be iden-tified. Finally, LAEs to be exploited to achieve the requiredLAAs should be identified and implemented by companies(Bottani 2009). An integrated approach based on AHP, TOP-SIS and fuzzy QFD is proposed to enhance supply chainleagility in an organization by prioritizing LAEs, by link-ing CBs, LAAS and LAEs. The structure of the proposedmethodology is shown in the Fig. 1.

The framework to improve leagility in supply chain byFuzzy-QFD includes the following steps.

1. Identification of CBs, LAAs and LAEs based on literaturereview

2. Selection of CBs, LAAs and LAEs to suit needs of thecompany being studied

3. Finding priority weights for CBs using AHP (for usingin TOPSIS)

4. Finding priority weights for LAAs based on CBs usingTOPSIS (for using in QFD)

5. Prioritization of LAEs by considering relation with LAAsand correlation amongst themselves using Fuzzy QFD

Identification of CBs, LAAs and LAEs based on literaturereview

A leagile supply chain has many distinguishing attributes andenablers. The characteristics of a leagile supply chain are

123

J Intell Manuf

Identification of Competitive bases, Attributes and

Enablers for leagile supply chain based on

Literature Review

Finalization of Competitive bases, Attributes and

Enablers for leagile supply chain with case

company experts’ opinion

Data collection

Validation and Conclusions

Assigning priority weights to Attributes using

TOPSIS

Prioritizing Enablers using

Fuzzy QFD

Assigning priority weights to Competitive bases

using AHP

Fig. 1 Flow chart for the proposed methodology

considered LAAs which are elements constituting the under-lying structure of a leagile organization. They were origi-nally conceived as core concepts of the leagile manufactur-ing and supply chain. Accordingly, LAEs are enabling tools,technologies and methods critical to successfully accomplishleagile supply chain management. Based on related literature,CBs, LAAs and LAEs were identified.

Selection of CBs, LAAs and LAEs to suit needs of thecompany being studied

Based on literature review and with the help of experts fromthe case company, CBs, LAAs and LAEs are chosen accord-ing to the case company’s special characteristics. Based ondata collected through questionnaires and interviews, mostappropriate CBs, LAAs and LAEs relevant to the case com-pany are to be identified.

Finding priority weights for CBs using AHP

The priority weights for CBs are to be allocated with theobjective of enhancing the leagility of the supply chain. Thesepriority weights are used in TOPSIS. The reason for selectingAHP for this purpose is as it can be possible only with AHPusing pair wise comparison for prioritizing CBs. The AHPis a multi-criteria decision-making approach introduced bySaaty (2008), having three main operations; hierarchy con-struction, priority analysis and consistency verification. Thepair-wise comparison is established using a nine-point scaleshown in Table 1. Vaidya and Kumar (2006) presented aliterature review on the applications of AHP. Also, Subra-manian and Ramanathan (2012) reviewed literature on theapplications of AHP in operations management. Borade etal. (2013) adopted AHP based framework to prioritize Ven-dor Managed Inventory practices. Govindan et al. (2014)analysed the barriers to green supply chain managementusing AHP. Mathiyazhagan et al. (2014) proposed an AHPbased approach to prioritize pressures to adopt green supplychain management in Indian industries.

Finding the priority weights of LAAs based on CBs usingTOPSIS

TOPSIS, developed by Hwang and Yoon (1981), is a simpleranking method using for various decision making applica-tions. Among various MCDM methods available to solvereal-world decision problems, TOPSIS continues to worksatisfactorily in diverse application areas. On the other hand,TOPSIS is a well known MCDM technique because it hasa simple and successful computation procedure. TOPSISrequires less number judgments compared to AHP as it alle-viates the requirement of pair wise comparisons (Behzadianet al. 2012). The TOPSIS method chooses alternatives thatsimultaneously have shortest distance from positive idealsolution and farthest distance from a negative-ideal solu-tion. The positive ideal solution maximizes benefit criteriaand minimizes cost criteria, whereas the negative ideal solu-tion maximizes cost criteria and minimizes benefit criteria.(Behzadian et al. 2012; Yoon and Hwang 1995).

The first step in TOPSIS is the formation of an initial deci-sion matrix. Then the procedure starts by normalizing it, fol-lowed by building a weighted normalized decision matrix.Then positive and negative ideal solutions are determined.Calculating a separation measure for each alternative is thenext step. The procedure ends by calculating the relativecloseness coefficient. The alternatives are ranked accordingto the descending order of the closeness coefficient (Behza-dian et al. 2012).

TOPSIS is used to prioritize LAAs by finding priorityweights. The priority weights for LAAs are determined basedon their importance regarding CBs. Priority weights obtained

123

J Intell Manuf

Table 1 Nine -point scale (Saaty 2008)

Scale Definition Explanation

1 Equal importance Two activities contribute equally

3 Moderate importance Experience and judgment slightly favour one activity over another

5 Strong importance Experience and judgement strongly favour one activity over another

7 Very strong importance An activity is favoured very strongly over another

9 Extreme importance Evidence favouring one activity over another is of the highest possible order of affirmation

2, 4, 6, 8 For compromise values For compromise values, sometimes one needs to interpolate a compromise between the abovejudgment numerically because there is no good word to describe it

Reciprocals of above If activity A has one of the above numbers assigned to it when compared with activity B, then Bhas the reciprocal value when compared with A; for example, if the pair wise comparison of A toB is 3.0, then the pair wise comparison of B to A is 1/3

Table 2 Degree of relationships and corresponding fuzzy numbers

Degree of relationships Fuzzy numbers

Strong (0.7; 1; 1)

Medium (0.3; 0.5; 0.7)

Weak (0; 0; 0.3)

by TOPSIS for LAAs are used in the proposed model’sFuzzy-QFD component.

Prioritization of LAEs by considering the relation withLAAs using fuzzy QFD

Fuzzy QFD is used to prioritize LAEs by determining therelationships between LAAs and LAEs and the correlationamongst the latter. QFD is a comprehensive quality systemtargeting customer satisfaction based on HOQ. Fuzzy logicapproach is used, as it eliminates drawbacks like vague-ness, ambiguity, uncertainties and impreciseness in decisionmaking. The evaluator can express performance ratings andimportant weights using linguistic variables. Then linguisticvariables are converted into fuzzy numbers. In this study, tobuild HOQ, LAAs are treated as the voice of the customer(WHATs), as these are the requirements of an improvedLeagile organization. LAEs are considered as the ‘HOWs’in a HOQ.

The relationship degree between LAAs and LAEs isexpressed by the corresponding Triangular Fuzzy Number(TFN) and placed in the HOQ matrix. Also, the degree ofcorrelation between LAEs is expressed by TFNs in fuzzyHOQ. Linguistic Variables and corresponding fuzzy num-bers for relationship degrees and correlation are shown inTables 2 and 3 (Bottani and Rizzi 2006).

TFN can be represented as a triplet (a, b, c), as shown inFig. 2, where, a ≤ b ≤ c. When a = b = c, it is a non-fuzzy

Table 3 Degree of correlations and corresponding fuzzy numbers

Degree of correlation Fuzzy numbers

Strong positive (SP) (0.7; 1; 1)

Positive (P) (0.5; 0.7; 1)

Negative (N) (0; 0.3; 0.5)

Strong negative (SN) (0; 0; 0.3)

Fig. 2 Triangular fuzzy number (TFN)

number by convention. The membership function is definedas (Zimmermann 1991):

µA(x) =

⎧⎪⎪⎪⎨

⎪⎪⎪⎩

0 i f x ≤ ax−ab−a i f a ≤ x ≤ bc−xc−b i f b ≤ x ≤ c0 i f x ≥ c

⎫⎪⎪⎪⎬

⎪⎪⎪⎭

If M = (a1, b1, c1) and N = (a2, b2, c2) represent twoTFNs, then,Fuzzy addition : M + N = (a1 + a2; b1 + b2; c1 + c2)

Fuzzy multiplication: M × N = (a1 × a2; b1 × b2; c1 × c2)

Fuzzy division: M/N = (a1/a2; b1/b2; c1/c2)

Fuzzy reciprocal: 1/M = (1/c1; 1/b1; 1/a1)

Fuzzy and natural number multiplication: r×M = (r.a; r.b;r.c)

123

J Intell Manuf

Table 4 CBs and LAAs for thecase company

Competitive bases (CBs) Leagile attributes (LAAs)

CB1 Cost LAA1 Conformance quality

CB2 Quality LAA2 Delivery reliability

CB3 Availability LAA3 Cost efficiency

CB4 Responsiveness LAA4 Market sensitivity

CB5 Speed LAA5 Customer focus

CB6 Flexibility LAA6 Volume flexibility

CB7 Innovation LAA7 New product flexibility

CB8 Competency LAA8 Delivery responsiveness

LAA9 Enterprise integration

LAA10 Improved supply chain materialand information flow

Illustration with case study

Food and food products are the largest consumption cate-gory in India, with a market size of USD 181 billion. It issignificant to note that challenges for the food processingsectors are diverse and need to be addressed on many frontsto derive maximum market benefits. Also, despite the Foodsector’s importance, food industry has received little atten-tion in literature in the context of lean and agile strategies.Thus, there is a need to analyze leagile enablers for Indianfood processing industries. The proposed approach is illus-trated with the help of a case study from a food processingindustry.

About the case company

The case company is in the business of manufacturing, sell-ing and marketing of packaged snack foods with its base inNorth India. The company has a turnover of Rupees 224.95crores with employee strength of 390. The company plans tomeet the demand for their products across the nation. Therewas a need for the organization to improve its performanceto sustain global competition. The management decided toimplement leagile supply chain strategy as it was cost effec-tive and responsive to market demands.

Identification and selection of CBs, LAAs and LAEs

An expert committee of senior managers of the case com-pany and academic experts was formed to facilitate appli-cation of this approach in the company. Initially, with thehelp of literature review, all the available CBs, LAAs andLAEs are collected (Agarwal et al. 2006; Bottani 2009; Naimand Gosling 2011; Narasimhan et al. 2006; Soni and Kodali2012; Shah and Ward 2003; Vinodh and Aravindraj 2013;Yusuf and Adeleye 2002; Zarei et al. 2011). Then, discus-

sions were held with the experts to identify the CBs, LAAsand LAEs which are relevant to the case company. Apartfrom choosing from the literature, the experts tried to identifythe decisive factors which are specific to their company. Forexample, ‘availability’ is identified as competitive base fromcompany’s perspective as consumer can opt for an alterna-tive product if the product is unavailable in the market. Sim-ilarly, ‘advanced technology in food processing’ is added asan enabler by the experts. Finally, with the help of literaturereview, questionnaires and discussions, 8 CBs, 10 LAAs and20 LAEs relevant to the case company were identified. Theseare shown in Tables 4 and 5.

Finding priority weights for CBs using AHP

Based on the requirements of the company, 8 CBs were cho-sen by experts as shown in Table 4. AHP allocates priorityweights to these CBs. The objective in prioritizing CBs is toenhance the leagility of the case company.

In AHP, a complex decision problem is structured as a hier-archy of objectives, criteria and alternatives. But, in this case,AHP is used to allocate priority weights to alternatives basedon the objective. Pair wise comparison of CBs regarding theobjective is determined based on Saaty’s 9 point scale. Thenpriority weights are computed and consistency ratio (CR)determined to check the consistency of the judgements. Ifthe final CR exceeds 0.1, evaluation procedure should berepeated to improve consistency. The pair wise matrix andcorresponding weights are shown in Table 6.

Finding the priority weights of LAAs based on CBs usingTOPSIS

TOPSIS is used to allocate priority weights of LAAs. 10LAAs selected by experts are prioritized with respect to CBs.

123

J Intell Manuf

Table 5 LAEs for the case company

Sl.No Leagile enablers Description

1 LAE1 Virtual enterprise (VE) Developing a common working environment between organizations tomanage collection of resources toward attainment of common goals

2 LAE2 Collaborative relationship with suppliersand customers

Promises improved supply chain performance in several core areas

3 LAE3 Vendor managed inventory (VMI) Improves supply chain performance by decreasing inventory levelsand improving customer service levels

4 LAE4 Just in time (JIT) Simplifies various processes by eliminating waste

5 LAE5 Collaborative planning forecastingreplenishment (CPFR)

Improves supply chain performance results in lower inventories,logistic costs and creates efficiency in the supply chain for allparticipants

6 LAE6 Positioning of decoupling point A strategic inventory point of supply chain and integration point oflean and agile strategy

7 LAE7 Electronic data interchange (EDI) andinformation technology (IT) tools

Enables partners in supply chain to act on the same real data ratherthan relying on distorted and noisy picture that emerges in anextended supply chain

8 LAE8 E-business Improves service levels by serving the customers and collaboratingwith partners through internet

9 LAE9 Cycle time reduction measures Provides benefits like quicker customer responsiveness, improvedprofitability, improved competitiveness etc

10 LAE10 Transhipments in inventory management Common practice in multi-location inventory systems involvingmonitored movement of stock between locations at the same level ofthe supply chain

11 LAE11 Human resource management (HRM) Training and managing employees to sustain the practices followed toachieve leagility

12 LAE12 Total quality management (TQM) Implementing a corporate culture emphasizing customer focus,continuous improvement, employee empowerment, and data drivendecision-making

13 LAE13 Customer focus and satisfaction When customer’s expectations are unmet, dissatisfaction results andlowers satisfaction level, the customer is to stop purchasing theproduct

14 LAE14 Flexible automation Provides customized products through flexible processes in highvolumes and at reasonably low costs

15 LAE15 Process integration Shared information between supply chain partner is possible throughprocess integration

16 LAE16 Advanced technology in food processing Helps to improve food quality and productivity

17 LAE17 Integrated logistics management Integration of various logistics functions to make better decisions aslogistics plays important role in food industry

18 LAE18 Information technology (IT) in productionplanning and control (PPC)

Helps effective planning in less time

19 LAE19 Strategic alliance with other organizations Policy to maintain competitive advantage to achieve various benefitslike technology sharing and cost savings

20 LAE20 New product development Pressures due to increased competition and shortening of life cycles

Various steps involved in TOPSIS are presented below.Step 1: The alternatives (LAAs) regarding CBs are evalu-ated and initial decision matrix formed as shown in Table 7.For this assessment, 1–9 scale is used (Saaty 2008). Also,weightage for CBs obtained using AHP are used in TOPSIS.Step 2: Then, the decision matrix is converted into a normal-ized decision matrix (ri j ).

ri j = Xi j/

√(∑X2

i j

)for i = 1 to m; j = 1 to n

where, Xi j is the decision matrix obtained in the previousstep.Step 3: The weighted normalized matrix (Vi j ) is formed bymultiplying each column with their relative weights.

Vi j = W j × ri j

where, W j is weight for J criterionStep 4: Then, positive ideal solution and negative ideal solu-tion are determined.

123

J Intell Manuf

Table 6 AHP decision matrixand priority weights

Consistency ratio = 0.0461

CB1 CB2 CB3 CB4 CB5 CB6 CB7 CB8 Priority weights

CB1 1 2.1 2.3 2.4 2.4 2.6 2.5 2.6 0.23458

CB2 0.48 1 1.4 2.1 1.8 2.1 1.3 2.8 0.1632

CB3 0.43 0.71 1 1.8 2.1 1.7 1.2 2.9 0.1437

CB4 0.42 0.48 0.55 1 1.8 2.8 2.9 2.5 0.1342

CB5 0.38 0.55 0.47 0.55 1 1.2 1.8 1.8 0.0933

CB6 0.4 0.47 0.59 0.36 0.83 1 2.8 2.1 0.092

CB7 0.38 0.77 0.83 0.34 0.55 0.36 1 2.4 0.0755

CB8 0.38 0.36 0.34 0.4 0.55 0.48 0.42 1 0.0522

Table 7 Decision matrix forTOPSIS WCB 0.236 0.163 0.144 0.134 0.093 0.092 0.076 0.052

CB1 CB2 CB3 CB4 CB5 CB6 CB7 CB8

LAA1 2.1 5.5 2.1 1.9 1.6 1.2 1.4 3.2

LAA2 2.1 1.9 1.6 4.1 3.9 1.9 3.8 2.1

LAA3 2.3 3.6 2.1 2.6 2.8 1.5 1.6 1.9

LAA4 2.2 1.5 3.9 4.1 4.3 2.1 3.9 2.5

LAA5 2.3 3.4 3.9 2.9 2.8 4.1 4.2 3.3

LAA6 2.2 1.9 4.1 3.8 2.9 2.8 2.1 2.3

LAA7 2.9 2.1 4.3 3.1 2.8 4.1 3.2 1.9

LAA8 2.4 3.1 3.2 4.6 4.9 3.1 4.2 2.5

LAA9 2.3 2.9 3.1 3.2 3.3 2.9 2.7 2.1

LAA10 2.4 3.1 3.2 3.1 3.9 3.8 4.1 3.1

(a) Positive ideal solution (A∗) in each row is determinedas follows

A∗ = {V ∗

1 , . . . . . . . . . ..V ∗n

}

where, V ∗j = {max

(Vi j

)i f j ∈ J ; min

(Vi j

)i f j ∈

J ′}(b) Negative ideal solution (A′) in each row is determined

as follows.

A′ = {V ′

1, . . . . . . . . . ..V′n

}

where, V ′j = {min

(Vi j

)i f j ∈ J ; max

(Vi j

)i f j ∈

J ′}

Step 5:

(a) Separation from ideal solution A* is calculated using,

S∗j =

√[∑ (V ∗

j − Vi j

)2]

for i = 1, . . .. . .m

(b) Separation from negative ideal solution A’ is calculatedusing,

S′j =

√[∑(V ′

j − Vi j

)2]

for i = 1, . . .. . .m

Step 6: Relative closeness to ideal solution is calculated using

C∗i = S′

i/(S∗

i + S′i)

Calculations are shown in Table 8. These are priority weightsfor LAAs. Then, priority weights for LAAs are normalizedand used in HOQ.

Prioritization of LAEs using fuzzy QFD

The degree of relationship between LAAs and LAEs (Rij)

was judged by the experts committee using linguistic terms,defined in Table 2. The correlation among LEs (Tkj) wasassessed in terms of linguistic terms, defined in Table 3. Tkj

is shown on the roof of HOQ. These were entered in HOQat corresponding places as shown in Fig. 3. Priority weightsfor LAAs from TOPSIS were also entered.

The two parameters RIj and RIj* are calculated to deter-mine LAEs which impact the leagile supply chain.

RIj =∑

Wi × Rij j = 1, 2, 3 . . . . . . m (1)

RI∗j = RIj +∑

Tkj × RIk j = 1, 2, 3 . . . . . . m (2)

123

J Intell Manuf

Table 8 Priority weights byTOPSIS

S∗i = ∑ (

vj∗ − vij)

2 S′i = ∑[(v′

j − vij)2]2 Priority weights

C∗i = S′

i/S∗i +S′

i

Normalizedweights

LAA1 0.39451 0.486598 0.55226 0.09803

LAA2 0.27781 0.255973 0.47954 0.085122

LAA3 0.35288 0.216374 0.38010 0.06747

LAA4 0.15369 0.317687 0.67395 0.119631

LAA5 0.21467 0.331655 0.60706 0.107758

LAA6 0.20943 0.280969 0.57294 0.1017

LAA7 0.21138 0.291447 0.57962 0.102886

LAA8 0.13854 0.367419 0.72618 0.128903

LAA9 0.24606 0.221555 0.47379 0.084102

LAA10 0.19978 0.285283 0.58813 0.104398

Fig. 3 Fuzzy HOQ developed for the case company between LAAS and LAEs

Then, to rank the LAEs, scores of RI∗J were de-fuzzified tofind crisp values. Suppose M (a, b, c) is a TFN; then, thede-fuzzified value is calculated using

(a + 4b + c)/6 (3)

Based on corresponding fuzzy numbers, values of RIj andRI∗j were calculated using Eqs. (1) and (2). Then, RI∗j scoreswere de-fuzzified using Eq. (3) to obtain crisp values. LAEswere ranked according to crisp values. These computational

values are shown in Fig. 3. LAEs are ranked according tocrisp values in a highest to lowest value order.

Results and discussion

LAEs with high crisp values must be accorded priority toenhance leagility of the organisation. Hence, such LAEsmust be selected for implementation. From results, it is

123

J Intell Manuf

understood that LAE2 (Collaborative relationship with sup-pliers and customers) has highest crisp value followed byLAE7 (EDI and IT tools) which was the next highest in crispvalue. Collaborative relationship promises improved supplychain performance in several core areas including increasedsales, improved forecasts, more accurate and timely infor-mation, reduced costs, reduced inventory and improved cus-tomer service (Soni and Kodali 2012). The presence of trustat all stages reduces transaction time and makes flow of fundsand material faster, thereby increasing responsiveness of thesupply chain. The integration of information systems likeelectronic data interchange facilitates improved managementof food product inventories, greater efficiency in distributionand improved customer service levels. That is why EDI andIT tools are the second priority among enablers. Strategicalliance with other organizations (LAE19) is the third prior-ity due to strategic alliances that helps companies access newmarkets, expands geographic reach and obtains state of arttechnology and core competencies at relatively faster rates.Strategic alliances enable management with increasing orga-nizational and technological complexities which emerged inthe global market. Flexible automation (LAE14) is the nextpriority to LAE19. Flexible automation offers benefits likereduced work-in-process, lead-time reduction, increased pro-ductivity and improved quality. Logistics plays a vital rolein the food industry to transport products in time at vari-ous stages. Logistics comprises several inter-related activitiesincluding freight transport, warehousing, inventory manage-ment, materials handling and related information processing.The supply and deliveries at various stages can be moni-tored with integrated logistics management enhancing deliv-ery speed and reliability. Integrated logistics management(LAE17) obtained the fifth priority.

Virtual Enterprise (LAE1), Vendor Managed Inventory(LAE3), E- Business (LAE8), Process Integration (LAE15)and new product development (LAE20) are the next prior-ities among the top 10 enablers. Based on market charac-teristics and customer demand, Virtual Enterprise can beformed to take advantage of core competitiveness of eachpartner. The promising benefits from Vendor managed Inven-tory like reduced inventory costs for supplier and buyer andimproved customer service levels motivated the organizationto implement Vendor Managed Inventory practices (Achabalet al. 2000). Organizations have understood that conductingbusiness electronically reduces transaction costs, improvesrevenues and enables them to manage corporate resourcesefficiently and effectively. Process integration is the collab-orative working between buyers and suppliers, joint prod-uct development, common systems and shared information.Introducing a new product into the market can definitely bringpromising benefits like greater market share and higher prof-itability (Jayaram et al. 1999). The positioning of Decou-pling Point, Customer focus and satisfaction, just –in-time,

advanced technology in food processing, Cycle time reduc-tion Measures, Total Quality Management, CollaborativePlanning Forecasting Replenishment, Transhipments in inven-tory management, human resource management and Infor-mation Technology in Production Planning and Control arethe next priorities. The management can improve the leagilityof the organisation by exploiting these enablers, priority-wise. The management focussed on measures to improvethe leagility of its supply chain based on the prioritization ofenablers.

Validation of the approach

To validate the feasibility of this approach, the results areplaced before three experts individually, for validation. Theexperts are from the top management of the case companywith rich experience in the company and are not part ofthe experts committee which participated in this study. Theexperts express their satisfaction on results obtained fromthis approach and agreed that the study was useful to enhancethe competitiveness of their firm by improving its leagility.Based on the analysis of results and validation by experts, thisapproach is practically feasible for organizations to improvetheir leagility based on their preferences which suit their mar-ket characteristics.

Managerial implications

The framework presented in this paper helps in the empir-ical analysis of LAEs which management could considerin the implementation of leagile supply chain. It is evidentfrom the results that the prioritization of LAEs is helpful toenhance the performance of the organization by improvingits leagility. This approach will helpful to management toimplement leagile supply chain management, thus achievingthe both cost effectiveness and responsiveness in their supplychain. Depending upon the competitive priorities of the mar-ket, this approach will guide the management in selecting thebest practices which should be implemented by organizationsto enhance leagility.

Conclusions

The current competitive and unstable market trends, incre-ased product variety, and demand fluctuations compel orga-nizations to adopt new strategies, to enable them to sustain inglobal competition. Meeting customer demands at a quickerrate and keeping costs low are key factors for organizationsto sustain in a global competition. With the developmentof economy and technology, more intense competition hasemerged. The leagile supply chain management paradigmwhich includes both lean and agile principles has attained

123

J Intell Manuf

greater importance in this scenario. In this work, an integratedfuzzy QFD approach was proposed to enhance leagility of thesupply chain by identifying the most influential enablers. Tocope well with vagueness of judgments required in buildingHOQs, linguistic variables defined with TFNs find relation-ships and correlations. Finally, the proposed methodology isillustrated with the help of a case study from a food indus-try. This approach is suitable for organizations to enhancethe leagility of the supply chain. This methodology willhelp management to exploit the most influential enablers toachieve the desired degree of leagility.

In future, researchers can integrate other multi-criteriadecision making methods with QFD to prioritize LAEs. MoreCBs, LAAs and LAEs can be considered for future study.Also, advanced membership functions of fuzzy logic can beused to enhance the approach’s effectiveness. This paper pre-sented a case study from the food industry. Case studies fromother sectors can also be considered.

References

Achabal, D., McIntyre, S. H., Smith, S. A., & Kalyanam, K. (2000). Adecision support system for vendor managed inventory. Journal ofretailing, 76(4), 430–454.

Agarwal, A., Shankar, R., & Tiwari, M. K. (2006). Modeling the metricsof lean, agile and leagile supply chain: An ANP-based approach.European Journal of Operational Research, 173(1), 211–225.

Ayag, Z., Samanlioglu, F., & Büyüközkan, G. (2013). A fuzzy QFDapproach to determine supply chain management strategies in thedairy industry. Journal of Intelligent Manufacturing, 24(6), 1111–1122.

Azevedo, S. G., Govindan, K., Carvalho, H., & Cruz-Machado, V.(2012). An integrated model to assess the leanness and agility ofthe automotive industry. Resources, Conservation and Recycling,66, 85–94.

Behzadian, M., Khanmohammadi Otaghsara, S., Yazdani, M., &Ignatius, J. (2012). A state-of the-art survey of TOPSIS applications.Expert Systems with Applications, 39(17), 13051–13069.

Borade, B., Kannan, G., & Bansod, S. V. (2013). Analytical hierarchyprocess-based framework for VMI adoption. International Journalof Production Research, 51(4), 963–978.

Bottani, E. (2009). A fuzzy QFD approach to achieve agility. Interna-tional Journal of Production Economics, 119(2), 380–391.

Bottani, E., & Rizzi, A. (2006). Strategic management of logistics ser-vice: A fuzzy QFD approach. International Journal of ProductionEconomics, 103(2), 585–599.

Bourlakis, M. A., & Weightman, P. W. (Eds.). (2004). Food supply chainmanagement. Oxford: Blackwell Pub.

Carnevalli, J. A., & Miguel, P. C. (2008). Review, analysis and classi-fication of the literature on QFD–types of research, difficulties andbenefits. International Journal of Production Economics, 114, 737–754.

Costa, A. I. A., Dekker, M., & Jongen, W. M. F. (2000). Quality functiondeployment in the food industry: A review. Trends in Food Science& Technology, 11(9), 306–314.

Govindan, K., Kaliyan, M., Kannan, D., & Haq, A. N. (2014). Bar-riers analysis for green supply chain management implementationin Indian industries using analytic hierarchy process. InternationalJournal of Production Economics, 147, 555–568.

Ho, W., He, T., Lee, C. K. M., & Emrouznejad, A. (2012). Strategiclogistics outsourcing: An integrated QFD and fuzzy AHP approach.Expert Systems with Applications, 39(12), 10841–10850.

Hwang, C. L., & Yoon, K. P. (1981). Multiple attribute decision making:Methods and applications. New York: Springer.

Jayaram, J., Vickery, S. K., & Droge, C. (1999). An empirical studyof time-based competition in the North American automotive sup-plier industry. International Journal of Operations and ProductionManagement, 19(10), 1010–1034.

Kisperska-Moron, D., & De Haan, J. (2011). Improving supply chainperformance to satisfy final customers: “Leagile” experiences of apolish distributor. International Journal of Production Economics,133(1), 127–134.

Kwong, C. K., & Bai, H. (2002). A fuzzy AHP approach to the deter-mination of importance weights of customer requirements in qualityfunction deployment. Journal of Intelligent Manufacturing, 13(5),367–377.

Liang, G. S., Ding, J. F., & Wang, C. K. (2012). Applying fuzzy qualityfunction deployment to prioritize solutions of knowledge manage-ment for an international port in Taiwan. Knowledge-Based Systems,33, 83–91.

Mason-Jones, R., Naylo, B., & Towill, D. R. (2000). Engineering theleagile supply chain. International Journal of Agile ManagementSystems, 2(1), 54–61.

Mathiyazhagan, K., Govindan, K., & Noorul Haq, A. (2014). Pres-sure analysis for green supply chain management implementationin Indian industries using analytic hierarchy process. InternationalJournal of Production Research, 52(1), 188–202.

Naim, M., & Gosling, J. (2011). On leanness, agility and leagile supplychains. International Journal of Production Economics, 131(1), 342–354.

Narasimhan, R., Swink, M., & Kim, S. W. (2006). Disentangling lean-ness and agility: An empirical investigation. Journal of OperationsManagement, 24(5), 440–457.

Naylor, J. B., Naim, M., & Berry, D. (1999). Leagility: Integrating thelean and agile manufacturing paradigms in the total supply chain.International Journal of Production Economics, 62(1), 107–118.

Rahimnia, F., Moghadasian, M., & Castka, P. (2009). Benchmarkingleagility in mass services: The case of a fast food restaurant chainsin Iran. Benchmarking: An International Journal, 16(6), 799–816.

Saaty, T. L. (2008). Decision making with the analytic hierarchy process.International Journal of Services Sciences, 1(1), 83–98.

Shah, R., & Ward, P. T. (2003). Lean manufacturing: Context, prac-tice bundles, and performance. Journal of Operations Management,21(2), 129–149.

Soni, G., & Kodali, R. (2012). Evaluating reliability and validity of lean,agile and leagile supply chain constructs in Indian manufacturingindustry. Production Planning and Control, 23(10–11), 864–884.

Subramanian, N., & Ramanathan, R. (2012). A review of applications ofanalytic hierarchy process in operations management. InternationalJournal of Production Economics, 138(2), 215–241.

Vaidya, O. S., & Kumar, S. (2006). Analytic hierarchy process: Anoverview of applications. European Journal of operational research,169(1), 1–29.

Vinodh, S., & Aravindraj, S. (2013). Evaluation of leagility in supplychains using fuzzy logic approach. International Journal of Produc-tion Research, 51(4), 1186–1195.

Vinodh, S., & Kumar Chintha, S. (2011). Application of fuzzy QFDfor enabling leanness in a manufacturing organisation. InternationalJournal of Production Research, 49(6), 1627–1644.

Xu, J., Xu, X., & Xie, S. Q. (2010). A comprehensive review on recentdevelopments in quality function deployment. International Journalof Productivity and Quality Management, 6(4), 457–494.

Yoon, K. P., & Hwang, C. L. (1995). Multiple attribute decision making.Thousand Oaks, CA: Sage.

123

J Intell Manuf

Yusuf, Y. Y., & Adeleye, E. O. (2002). A comparative study of leanand agile manufacturing with a related survey of current practicesin the UK. International Journal of Production Research, 40(17),4545–4562.

Zarei, M., Fakhrzad, M. B., & Jamali Paghaleh, M. (2011). Food sup-ply chain leanness using a developed QFD model. Journal of FoodEngineering, 102(1), 25–33.

Zimmermann, H. J. (1991). Fuzzy set theory and its applications (2nded.). Boston: Kluwer.

123