1-s2.0-S0305048315001334-main

A multiple objective optimization based QFD approach for efficientresilient strategies to mitigate supply chainvulnerabilities: The case of garment industry of Bangladesh☆,☆$

Md. Maruf Hossan Chowdhury, Mohammed A. Quaddus n

School of Marketing, Curtin University, Perth, WA, Australia

a r t i c l e i n f o

Article history:Received 1 January 2014Accepted 22 May 2015Available online 25 June 2015

Keywords:Supply chainResilience-efficiencyVulnerabilityMulti-objective modelAHPQFD

a b s t r a c t

With the upsurge of frequent disruptive events, organizations have become more vulnerable to theconsequences of these disruptive events. As a result, the need for more resilient supply chain (SC) tomitigate the vulnerabilities has become paramount. Supply chain resilience (SCR) has been discussed inthe literature and resilience index has been developed, but developing and selecting a portfolio of supplychain resilience capabilities in order to mitigate the vulnerabilities have not been studied. In thisresearch we develop a 0–1 multi-objective optimization model based on QFD methodology. Our multi-objective method is interactive and interacts with the decision makers to choose the most satisfactoryefficient portfolio of supply chain resilience strategies. We apply our methodology to three large ready-made garment (RMG) companies of Bangladesh. Results show that lack of materials (high dependenceon imported materials), disruptions in utility supply, increased competition (and hence competitivepressure), impact of economic recession, and reputation loss are the top most vulnerabilities ofBangladesh RMG industry. The most preferred resilience strategies to mitigate the vulnerabilities are:back-up capacity, building relation with buyers and suppliers, quality control, skill and efficiencydevelopment, ICT adoption, demand forecasting, responsiveness to customers, and security systemimprovement. Theoretical and managerial implications of our study are included.

& 2015 Elsevier Ltd. All rights reserved.

Introduction

Ready Made Garment (RMG) industry contributes hugely toBangladesh’s economy. It creates more than four million directemployment and several millions of indirect employment and acco-unts for 78.6 percent of countries export earnings [5]. RMG sector alsoimmensely contributes in reducing the high rate of women unem-ployment in the country as 80 percent of the garments workers arewomen [5]. Thanks to the RMG sector, Bangladesh is also the secondlargest apparel exporter in the world.

Despite its huge potentials the industry is struggling with numer-ous Supply Chain (SC) disruptions [49,42]. The consequences of thedisruptions are huge, for example, RMG industry of Bangladesh loses$26.15 million per day due to problems in SC functions caused bypolitical instability [1]. Moreover, the preferential access in U.S. marketis cancelled because of the poor safety standard in production plantsas building collapse in garment factory caused the death of more than

eleven hundred workers [37]. These disruptions have chain effect toall the members in SC network including the international buyers(retail chains) and suppliers. In the wake of such a critical state inRMG supply chain, developing resilience capabilities is vital, which is theprimary objective of this study.

Resilience has been defined by a number of authors in a relatedmanner. Vugrin et al. [99] define system resilience and resilience ingeneral. The authors highlight that resilience is the ability of a systemsto respond to a ‘disruption’ due to an event or set of events. Along thesame vein Christopher and Peck [24], Ponomarov and Holcomb [81]and Jüttner and Maklan [53] define supply chain resilience as the‘capability of the supply chain to responds to disruptions and recoverfrom them’. On the other hand Pettit et al. [79,80] developed a supplychain resilience framework by identifying seven categories of vulner-abilities and creating supply chain capabilities along 14 areas (sour-cing, order fulfilment, capacity development; among others). Theauthors surmise that current level of vulnerabilities and capabilitiesmust be assessed in order to ascertain the current level of resilience.Literature emphasizes that developing resilience capability is vital fororganizations. It enables organizations to improve system perfor-mance [80,99], achieve sustainable competitive advantage [81], gainmarket share in competitive environments [90], and decreasesvulnerabilities [53,79,80]. However current literature lacks in

Contents lists available at ScienceDirect

journal homepage: www.elsevier.com/locate/omega

Omega

http://dx.doi.org/10.1016/j.omega.2015.05.0160305-0483/& 2015 Elsevier Ltd. All rights reserved.

☆It is an equally authored paper. Author’s names are listed in alphabetical order.☆☆ This manuscript was processed by Associate Editor B. Lev.

n Corresponding author.E-mail addresses: [email protected] (Md.M.H. Chowdhury),

[email protected] (M.A. Quaddus).

Omega 57 (2015) 5–21

www.sciencedirect.com/science/journal/03050483

www.elsevier.com/locate/omega

http://dx.doi.org/10.1016/j.omega.2015.05.016



http://crossmark.crossref.org/dialog/?doi=10.1016/j.omega.2015.05.016&domain=pdf



mailto:[email protected]

mailto:[email protected]


proposing the ways and means to achieve supply chain resiliencecapabilities. In this research we stress that resilience capabilities of RMGsupply chain of Bangladesh must be developed to mitigate organizationsvulnerabilities. In pursuing this research objective we introduce theconcept of ‘supply chain resilience efficiency (SCREF)’ which hassignificant pedagogical importance.

It has been established that supply chain resilience capabilities hasmultiple dimensions (objectives). For example Pettit et al. [79,80] intheir framework highlight 14 areas of supply chain resilience capabil-ities to be developed from order fulfilment, capacity developmentto financial strengths. From multiple objectives decision making(MODM) perspectives the supply chain resilience capabilities mustbe ‘efficient’ to mitigate vulnerabilities. While literature on efficiencyapproach in MODM and its applications in production/operationsmanagement area are plentiful (for example see, [63]; among manyothers), the notion of ‘supply chain resilience efficiency (SCREF)’ frommultiple objectives perspective is novel.

In this research we define SCREF as follows:

(i) resilience capability must be resource efficient (e.g. minimumcost of implementation), and

(ii) portfolio of chosen resilience capabilities must be efficient (ornon-dominated) from multiple objectives perspective [63].

We shall elaborate on (i) and (ii) later. It is observed that a numberof logistics and SC related capabilities are discussed in the literature(for example; [80,38,81,90,24]; among many others) to develop SCresilience but most of those are conceptual studies and fall short ofintroducing the notion of resilience efficiency. Furthermore, in a stateof uncertainty, dynamic changes and resource limitation, selection ofoptimal and efficient portfolio of resilience capabilities has not yetbeen addressed adequately in the existing SC literature.

While a number of approaches could be undertaken to achieve theresearch objective, this study has adopted Quality Function Deploy-ment (QFD) [77,102] as a methodology to develop the resiliencecapabilities of the RMG supply chain of Bangladesh and find theoptimal efficient portfolio of the resilience capabilities using a non-linear 0–1 programming approach. Literature on QFD approach isplentiful, which will be reviewed briefly in a later section. It is sufficeto say that QFD enables organizations to be proactive to vulnerabilitiesmitigation rather than reactive and it is a proven technique fordesigning supply chain mitigation capabilities in such situations[34]. However we shall use Analytical Hierarchy Process (AHP) [86]within QFD for the analysis of data. It is important to note thatmethodologically our contribution lies as follows: we define supplychain resilience efficiency (SCREF) and find portfolio of efficientresilience capabilities for implementation using multiple objectivesbased non-linear 0–1 mathematical program.

It is noted that overall domain of our present study is EnterpriseRisk management (ERM). Enterprise Risk management (ERM) hasbeen defined in many different ways. However, one common themeof ERM is that it takes a ‘holistic and strategic’ approach to manage allrisks that an organization faces [32,74]. A recent literature review [20]has found that Desheng Wu and David Olson are two of the mostdominant contributors on ERM and various aspects of enterprise risk.One of their highly cited works is the application of ERM to assesscredit worthiness in bank [106]. Wu and Olson [104], Wu et al.[107,108] have edited special issues of various journals on variousaspects of ERM, ranging from risk methods and tools in operations,enterprise risk management in operations and business intelligence inrisk management. Various other applications of ERM are availableelsewhere [20] and hence will not be repeated here.

Our present research focusses on resilience capabilities of RMGsupply chain of Bangladesh to mitigate supply chain vulnerabilities.Literature suggests that vulnerability is an ‘exposure to seriousdisturbance arising from risks within supply chain’ [78]. Hence our

present study is highly relevant to the supply chain aspect ofenterprise risk management. There are applications of enterprise riskmanagement (ERM) in supply chain. For example, Wu and Olson [105]developed a DEA based value at risk (VaR) model to manage supplyrisks, specifically vendor selection problem. Olson and Wu [75]presented a review of ERM in supply chain. Jiang et al. [50] developeda LOGIT model of job satisfaction to reduce supply chain risks.However, it has been mentioned before that we developed a newapproach to find an optimal portfolio of efficient resilience capabilitiesto mitigate supply chain vulnerabilities.

In the next several sections we present the state of affairs ofRMG industry of Bangladesh, relevant literature, methodology(QFD based mixed qualitative–quantitative approach), applicationin RMG industry, followed by the results. The paper concludeswith the discussions & implications and conclusions.

Background

Bangladesh is one of the leading exporters of Ready MadeGarments (RMG) in the world. RMG industry is an economic propellerof Bangladesh and apparel exports stood-up at 19.90 billion US dollarin 2011 andmarked Bangladesh as the second largest apparel exporterin the world [5]. Because of enormous economic importance of RMGin the economy of Bangladesh, smooth and efficient functioning ofsupply chain activities is crucial. But, the RMG supply chain is facing aclimax situation owing to numerous challenges, such as, labour unrestfor violation of human rights, poor wages, poor and hazardousworking environment, political instability, interruption in utilitysupply especially power shortage, inefficiency in customs and portmanagement, exchange rate fluctuation, disruption in timely supply offabrics and other accessories, increased competition, inefficiency inoperations, intensive competitive pressure, strict compliance coderegarding social and environmental issues; among many others[49,22,42]. Furthermore, increased lead time and cost due to disrup-tions in procurement and shipment of goods, lack of linkages and co-ordination among related industries in the value chain, dependenceon imported inputs, limited variety of finished products [42], fall oforder because of global economic downturn are also issues of highconcern for the RMG supply chain of Bangladesh. As a result of thesedisruptions the growth of RMG export from Bangladesh has fallenfrom 23% in 2005–2006 to 15% in 2008-2009 [22]. In such a situationit is crucial to find ways and means to make RMG supply chainresilient and sustainable. Previous researchers focused mainly on RMGcompetitiveness, the existing problems and challenges of the industry.Table 1 summarizes these briefly. However, the issue of making RMGsupply chain resilient and efficient has not been investigated yet. Thisstudy aims to fill this gap in the literature.

Literature review

Supply chain vulnerabilities

Maintaining an effective Supply Chain (SC) has become challengingand difficult as the supply chains are inherently complex and in recenttimes are overwhelmed with disruptive events. These disruptiveevents make a supply chain vulnerable, as supply chain vulnerabilityis the susceptibility of the supply chains to the consequences ofdisruptive events [8,53]. Wagner and Neshat [101] posit that supplychain vulnerability is determined by the vulnerability drivers arisingfrom demand side, supply side and supply chain design issues.Similarly, supply chain vulnerability may also arise from a numberof factors such as, delay during transportation, port stoppages,frequent occurrence of natural disasters, weak communication, supplyshortages, demand volatility, quality problem, operational issues and

Md.M.H. Chowdhury, M.A. Quaddus / Omega 57 (2015) 5–216

terrorism; among many others [25,61,7]. Schmitt et al. [88] study theimpact of supply disruptions on both centralized and the decentra-lized multi-location system. Sawik [87] presents a bi-objective (mini-mum cost and maximum service level) model to study the impact ofsupplier disruptions Mizgier et al. [71] show the far-reaching effect ofdisruptions in a SC network and the impact on the performance ofoverall system. The studies by Hendricks and Singhal [45] show thatannouncement of SC disruptions, such as, operational issue or delay inshipment cause decrease in shareholder value significantly. Kleindor-fer and Saad [61] identify three main sources of SC vulnerability:firstly, operational factors which include equipment malfunctions andsystemic failures, abrupt discontinuity of supply, labour strikes, amongothers; secondly, natural hazards which include earthquakes, hurri-canes, storms; and thirdly, terrorism or political instability, amongothers. Blos et al. [8] suggest four major sources of SC vulnerabilitiessuch as, financial vulnerability, strategic vulnerability, hazard vulner-ability, and operations vulnerability. Similarly, a number of researchers(such as [79,24,90,7,61]; among others) discussed SC vulnerabilityfactors which are summarized in Table 2 in terms of various vulner-ability factors. However, these studies did not deal with strategies andcapabilities to mitigate SC vulnerabilities. Furthermore, most of thestudies are conceptual rather than empirical in nature. The researchreported in this paper prioritizes the existing vulnerabilities and findsthe efficient mitigation strategies and capabilities in the context ofRMG supply chain of Bangladesh by using AHP integrated QFDapproach. It thus addresses a specific gap in the existing literature.

Supply chain resilience capability

Resilience is a multidisciplinary concept. Holling [46] was oneof the pioneers to conceptualize resilience ‘as the ability of systemto absorb changes’. Since then many authors echoed the concept ofresilience as system’s ability to recover and get back to the originalstate [70,81,24]. Heckmann et al. [44] however mention thatsupply chain resilience must have the ability to ‘overcome supplychain vulnerability and to reduce supply chain risk’. In line withextant literature [81,44], in this paper, we define supply chainresilience (SCR) as the capability of a supply chain to reduce theimpact of vulnerabilities (due to disruptions) through developingrequired level of readiness, quick response and recovery ability.Vulnerabilities in the SC are sometimes beyond the direct controlof SC managers. However, SC managers need to be proactive topredict the vulnerability factors in advance and develop resiliencecapacity for mitigating the vulnerabilities [53]. Otherwise, theconsequence will be the discontinuity of SC operations which willadversely affect both revenue and cost of the whole chain [81].

Researchers in supply chain management (SCM) emphasized oncapabilities, such as, adaptability, pro-activeness, diversity, flexibility,efficiency, reserve capacity, integration, market development,

cohesion, control, connectedness to measure resilience [80,81,98].Table 3 summarizes these capabilities. It is noted that two funda-mental organizational resilience capabilities are buffering and bridging[9,35]. All others are related to these two fundamental strategies.Buffering is external to a current relationship with a supply chainpartner and acts as ‘shock absorbers’ to mitigate the detrimentalconsequences [9]. For example the capability factors of ‘flexibility’,‘reserve/backup capacity’ of Table 3 fall in this category. On the otherhand bridging is internal to a current relationship and refers tostrengthening the current relationship via formal structure [9]. Thecapability factors of ‘integration’, ‘efficiency’ of Table 3 fall in thiscategory. The resilience capability needed by a system depends oncontext, extent and type of vulnerabilities [16]. Therefore, in order todeal with resilience it is important to identify the vulnerability factorsof the specific SC and the corresponding mitigation capabilities.

Along with developing resilience it is also important to measureresilience to ensure a better resilience outcome. In the literature,resilience is measured in a number of ways: (i) based on the extent ofsystems departure from desired state [46,68], (ii) based on recoverytime after disaster or disruptions [92,90], (iii) based on reduction ofimpact and consequences [85,67], (iv) based on time to respond [90]and (v) based on cost of recovery [69,99].

Once the resilience capabilities are designed and measured, it isalso important to determine the efficiency of the resilience capabilityfor reducing the impact of vulnerabilities [99]. Literature lacks sign-ificantly in conceptualizing resilience efficiency. In this paper, there-fore, we offer a unique and elegant operationalization of resilienceefficiency in the methodology section.

Quality function deployment (QFD)

QFD is a systematic process used by cross-functional teams toidentify and resolve the issues involved in providing products,processes, services, and strategies that enhance customer satisfaction[41]. The benefits of QFD model have been highlighted by variousresearchers. For example Chan and Wu [17] in their review of QFDtheory and applications have noted wide range successful applicationsof QFD from product development, customer needs analyses todecision making. Carnevalli and Miguel [15] in another review ofQFD highlight QFD’s ability in adapting into various research methodsfrommodelling, theoretical-conceptual to action-research, experimen-tal. Because of its wide applicability QFD has been used in variousfields, such as, determining customer needs [94], developing priorities[43], manufacturing strategies [29,52], logistics and SCM [10,4]. QFDhas also been applied successfully for supply chain risk identificationand mitigation [82,34]. In line with previous literature we haveadapted QFD methodology to identify supply chain vulnerabilitiesand mitigate the vulnerabilities with optimal and efficient resiliencecapabilities.

Table 1Vulnerability factors and the mitigation capabilities in RMG industry of Bangladesh.

Khondker et al. [60] Deals with important issues and challenges facing the RMG industry during the post-MFA regime. It also discusses the competitiveness issue inrelation to productivity, working environment and stakeholders.

Choudhury and Hossain[21]

The authors have taken step to define the challenges and opportunities in the post-MFA period in the RMG sector and indicated thegovernment response to combat post-MFA challenges.

Karim [55] Discusses how the Bangladeshi RMG sector coped startegically with the post-MFA challenges.Tewari [97] Emphasized on timely supply, short lead times, low inventories, innovation and the ability to contribute to design and full package supply.Ferdousi and Ahmed[36]

Investigated the improvement of manufacturing performance through lean practice that helps to reduce lead time and cost and to improveproductivity.

Hossan et al. [47] Discuss the recent unrest in the RMG industry which indicates political action in this industry.Islam et al. [48] Operational disturbances, manufacturers are facing competition with respect to quality, cost and time to market.Nuruzzaman et al. [73] Described how to create competitive advantage through SCM.Chowdhury et al. [23] Addressed supply chain disruptions and mitigation processesChowdhury et al. [23] Dealt with upstream supply chain barriers and mitigation processesHaider [42] Presents the challenges and the surface-level and deep-level competitiveness of the Bangladeshi RMG industry.

Md.M.H. Chowdhury, M.A. Quaddus / Omega 57 (2015) 5–21 7

In QFD modelling, ‘customer requirements’ or existing problems ofthe organizations (for example vulnerabilities) are referred to asWHATs and ‘how to fulfil the customer’s requirements’ or organiza-tional problems are referred to as HOWs (resilience capabilities). Thebasic QFD framework is shown in Fig. 1 where CRi and DRj are theHOWs and WHATS respectively. The process of using appropriateHOWs to meet the given WHATs is represented in the relationshipmatrix (Rij in Fig. 1). Different researchers build different QFD modelsinvolving various elements but the most widely used QFD modelcontains at least the requirements/problems (WHATs) and theirrelative importance (Wi in Fig. 1), technical measures or designrequirements (HOWs) and their relationships with the WHATs, andthe correlation between the HOWs (see Fig. 1).

In our case row elements CRi (WHATs) of Fig. 1 represent thevulnerabilities that RMG supply chains are facing currently. Thecolumn elements DRj (HOWs) are the resilience strategies or capabil-ities to mitigate the vulnerabilities. We define the elements of therelationship matrix Rij (see Fig. 1) as the ‘extent of mitigating thespecific vulnerabilities by specific resiliency strategies’. In line withQFD literature [17,82,34] we measure Rij using the scale of 9 (strongmitigation), 3 (moderate mitigation), 1 (little mitigation) and 0 (nomitigation). The AI and RI in Fig. 1 are the absolute and relativeimportances of the HOWs (resilience strategies) which are found asfollows [77]:

AIj ¼Xmi ¼ 1

wiRij 8 j; j¼ 1;…;n ð1Þ

where AIj is the absolute importance of jth design requirement (DR)(or, resilience strategy), wi is the weight of the ith supply chainvulnerability. Rij is the relationship value; extent of mitigating ithvulnerability by jth resilience strategy (9, 3, 1, or 0), n is the number ofdesign requirements (resilience strategies); m is the number of supplychain vulnerabilities.

It is noted that in our case AIj is interpreted as ‘total resilience’of the jth resilience strategy to mitigate the vulnerabilities.

The relative importance (resilience) of the resilience strategy jis

RIj ¼AIjPn

j ¼ 1 AIjð2Þ

The correlation between the DRjs (HOWs) (see Fig. 1) plays asignificant part in many QFD applications including ours. It representsthe extent of correlation (similarities) when two HOWs are imple-mented. Literature suggests that there is some degree of dependenciesamong the HOWs in real applications [103,77]. If HOWi and HOWj arecorrelated then there is cost savings sij in their implementation [77].These sij’s need to be estimated from the decision makers. We arguethat in most business, management and social science applications(including our present case) some HOWs will be highly correlated.

Integration with Analytical Hierarchy Process (AHP)In basic QFD three fundamental data are needed: (i) relative

importance of WHATs, (ii) the relationships between the WHATs

Table 2Supply chain vulnerability factors.

Vulnerability factors Specifc vulnerabilities References

Hazard vulnerability Natural disaster (flood, cyclone) Christopher and Peck [24]; Sheffi and Rice [90]; Kleindorfer and Saad [61]; Wu et al.[109]; Blackhurst et al. [7]

Political instability Kleindorfer and Saad [61]; Wu et al. [109]; Blackhurst et al. [7]; Blos et al. [8]Fire and other accidental damage Blos et al. [8]Labour unrest Sheffi and Rice [90]; Kleindorfer and Saad [62]; Wu et al. [109]; Blackhurst et al. [7]

Strategic vulnerability Increased competition Haider [42]; Blos et al. [8]Non-compliance of social and environmentalfactors

Islam and Deegan [49]

Problem of relation with buyer (switching of buyer) Blos et al. [8]Problem of integration and real-time information Gaudenzi and Borghesi [39]Problem of relation with supplier Blos et al. [8]Plant location problem Field study

Financial vulnerability Currency fluctuation Blos et al. [8]; Blackhurst et al. [7]Economic recession Blos et al. [8]Raw material price fluctuation Blos et al. [8]High bank interest & fund shortage Blos et al. [8]Bankruptcy or credit default of any supply chainmember

Blos et al. [8]; Blackhurst et al. [7]

Operationalvulnerability

Shortage of skilled worker Haider [42]Switching and absenteeism of workers Chowdhury et al. [22].Fault in production planning and inventorymanagement

Chowdhury et al. [22]; Wu et al. [109]

Failure of IT system and machineries Blos et al. [8]Disruption in utility supply Blos et al. [8]Product quality defection (poor quality) Blos et al. [8]Illiteracy of workers and supervisors Chowdhury et al. [22]

Infrastructurevulnerability

Delay in custom clearance Colicchia [25]Delay for Congestion and inefficiency in port Colicchia [25]; Blackhurst et al. [7]Strike by port workers Colicchia [25]; Blos et al. [8]Delay in transportation for Poor infrastructure Blackhurst et al. [7]

Demand & supplyvulnerability

Suppliers’ delay Blackhurst et al. [7]Dependence on imported material and lack ofbackward linkage

Haider [42]; Nuruzzaman and Rafiq [73]; Craighead et al. [26]

Lack of alternative for some critical items Craighead et al. [26]Defection or nonconformity of material Blackhurst et al. [7]Opportunism of buyers (expect discount) Ponomarov and Holcomb [81]Demand fluctuation/uncertainaty Wu et al. [109]Suppliers opportunism Ponomarov and Holcomb [81]


and HOWs, and (iii) the correlation between the HOWs. In finding therelative importance of WHATs (i.e.wi’s; see Fig. 1) we plan to use AHP.AHP was originally developed by Saaty [86] which is an well-established multi-criteria decision making approach that employs aunique method of hierarchical structuring of a problem and subse-quent ranking of alternative solutions by a paired comparisontechnique. For brevity full description of AHP process will not bepresented in this paper, which is available elsewhere in the literature[86]. AHP is frequently used in QFD process, for instance see, Kamvysiet al. [54], Park and Kim [77], Bhattacharya et al. [6], Chan and Wu[18]; among others. Methodologically QFD has been frequentlycombined with other tools to increase its robustness and applicability.For example Ramanathan and Yunfeng [84] combined QFD with DataEnvelopment Analysis (DEA) and applied it to design security fasten-ers in a Chinese company. Lin et al. [66] combined QFD with DEA andAHP and applied it to evaluate the economic performance of localgovernments in China.

QFD optimizationIt is noted that in QFD an optimization method is always needed to

determine the most desirable HOWs to satsify the WHATs [31,57]under certain constraints. For example, Park and Kim [77] optimizetotal absolute importance of the HOWs by formulating a 0–1 linearand quadratic program to find the most desirable HOWs under budgetconstraint. Wasserman [103] formulates a linear program to find themost desirable design requirements, HOWs, under budget constraints.Zhou [110] proposes a mixed integer linear program that maximizesan utility function under budget, technological feasibiity and competi-tion constraints to determine the HOWs.

Multiple objective optimization approaches [62,30] have alsobeen used in determining the HOWs. For example, Karsak et al.[59] formulated a 0–1 goal program combining analytical networkprocess in QFD application in product planning. Delice and Güngör[31] proposes a mixed integer goal program where the objectives

functions (customer satisfaction, cost and technicial difficulties)are converted into goals. Other multiple objectives optimizationbased QFD applications also include various variations of goalprogram, for example see Karsak and Özogul [58], Chen and Weng[19], Lee et al. [65], Büyüközkan and Berkol [13]; among others.Karsak [56,57] on the other hand has applied multiple objectiveoptimization in QFD and has determined the non-dominated(efficient) solutions of QFD design requirements (HOWs). Theauthor however has not gone far enough and developed anyprocedure of obtaining efficient design requirements in QFDapplications. In our research we contribute in this aspect anddevelop a procedure to obtain efficient design requirements(efficient resilience capabilities in our case).

Proposed methodology to determine efficient resiliencecapabilities in QFD

The concept of efficiency and generating efficient solutions isprevalent in multiple objective decision making domain [62,30]. Ageneral multiple objective decision problem is represented asfollows:

Max Minð Þ f iðXÞ ¼ CiðXÞ; i¼ 1…p

Subject to : gj Xð Þrbj; j¼ 1…q

)ð3Þ

where X¼(x1, x2, …, xn) are n-dimensional decision variables; fi (.)represents p linear conflicting objective functions and gj(.) are qdifferent constraints inequalities. A feasible solution Xn to problem(3) is said to be efficient (for a maximizing problem) if there doesnot exist any other feasible solution X such that for all i¼1, …, p,fi(X)Z fi(Xn), and fi(X)4 fi(Xn) for at least one i. In other words Xn isnot dominated by any other solution in terms of achievement inthe objective function. As will be shown in a later section in ourapplication we have three objective functions in QFD optimization

Table 3Supply chain resilience capabilities.

Capability Factors Specific capabilities References

Flexibility Flexibility in production (different volume of order, flexible production schedule) Duclos et al. [33]; Braunscheidel andSuresh [11]

Ability to modify a wide varity of product as per buyer requirement (mix flexibility) Braunscheidel and Suresh [11]Flexibility in contract with SC partners (Partial order and payment, partial shipment) Duclos et al. [33]Efficient and effective logistics and supply chain functions (e.g. sourcing, producing,distribution)

Duclos et al. [33]

Ability to respond to additional order or sudden demand Jüttner and Maklan [53]Ability to supply new and different products to different customer groups (mix flexibility) Braunscheidel and Suresh [11]

Reserve/backup capacity Alternative and reserve capacity (logistical options) Pettit et al. [79]Buffer stock Pettit et al. [79]Backup energy source Pettit et al. [79]

Integration Sharing information with supply chain partners Braunscheidel and Suresh [11]; Blackhurstet al. [7]

Communication and information flow with different departments (e.g. supply chain andother departments)

Braunscheidel and Suresh [11]

Joint or collaborative planning (e.g product development) Braunscheidel and Suresh [11]Communication with supply chain partners Braunscheidel and Suresh [11]ICT supported planning and integration Narasimhan and Kim [72]

Efficiency Waste elimination (efficient use of resource) Pettit et al. [79]; Fiksel [38]; Sheffi andRice [90]

Efficient and hardworking employeesQuality control and less defection Pettit et al. [79]; Kleindorfer and Saad [61]

Customer satisfaction & marketposition

Buyer satisfaction (product quality and service) Pettit et al. [79]Customer loyalty and Preference Pettit et al. [79]Good relation with buyers and suppliers Zsidisin et al. [111]; Pettit et al. [79]

Financial strength Fund availability Pettit et al. [79]; Tang [95]Profitability Pettit et al. [79]Insurance Pettit et al. [79]


problem as: maximize procurement strategies, maximize proces-sing strategies, and maximize distribution strategies, to mitigatethe vulnerabilities.

As presented earlier we define ‘supply chain resilience effi-ciency (SCREF)’ as follows:

(i) resilience capability must be resource efficient (e.g. minimumcost of implementation), and

(ii) portfolio of chosen resilience capabilities must be efficient (ornon-dominated) from multiple objectives perspective [63].

Vugrin et al. [99] touched on the efficiency of system resilience.According to the authors system resilience must use ‘lowestpossible amount of resources’ to be efficient. In line with Vugrinet al. [99] we define ‘Resilience Efficiency’ REj as AIj=Cj

� �, where Cj

is the cost of implementing jth resilience strategy. It is noted thatparameters in the objective functions in our QFD optimizationproblem are the REj. This satisfies our condition (i) of SCREF.

To satisfy condition (ii) of SCREF we ascertain that the solutionof the QFD multiple objective optimization problem is efficient(non-dominated). We develop a multiple objective 0–1 optimiza-tion problem that will find the efficient portfolio of resiliencestrategies to mitigate the vulnerabilities. Pettit et al. [79] recentlyhave mentioned portfolio approach to resilience capabilities.However, the authors did not offer any methodology of achievingthat. Our 0–1 QFD multiple objective optimization problem is

formulated as follows:

Max f 1 Xð Þ ¼PjAnREjxj

Max f 2 Xð Þ ¼PkAn;ka jREkxk

…Max f p Xð Þ ¼P

lAn;laka jRElxl

Subject to :Xnj ¼ 1

cjxj�Xni ¼ 1

Xn

j4isijxixjrB

xAX

9>>>>>>>>>>>>=>>>>>>>>>>>>;

ð4Þ

where n is the number of resilience startegies; REj is the resilienceefficiency (described earlier) of resilience strategy j, xj is one orzero depending on if the corresponding resilience strategy j isselected or not; cj is the expected cost of implementing resiliencestrategy j; sij is the savings if resilience strategies i and j areimplemented together; B is the available budget.

It is noted that our constraints are non-linear and similar toPark and Kim [77]. But depending on the application the con-straint set could be formulated in an extensive way including thebudget, technological feasibility and competition constraints [110].We argue that there are p different conflciting objectives amongthe REj’s which need to be optimized simultaneously, hencesatisfactory effcient solution of problem (4) need to be found outby interacting with the decision maker. It is noted that anysolution to problem (4) will offer a portfolio of resilience strategiesto mitigate the vulnerabilities. To find the efficient portfolios ofstrategies we need to reformulate problem (4) as follows:

MaxXpi ¼ 1

λif i ðXÞ

Subject to :Xnj ¼ 1

cjxj �Xni ¼ 1

Xnj4 i

sijxixjrB

x AX

9>>>>>>>=>>>>>>>;

ð5Þ

where λi (i¼1,…,p) are positive numbers representing the weights(importance) attached to the objective function f i ðXÞ by the decisionmaker. Theorems from multiobjective optimization domain suggestthat any solution of problem (5) above is an efficient (non-dominated)solution to problem (4) [63]. It is noted that the important weights λiare only needed to find the first efficient solution to problem (4). It isnoted that Karsak [56,57] has also determined efficient solution formultiple objective optimization in QFD. However our multi-objectivemodel formulation and solution approach are different from Karsak[56,57] in the following ways: (i) Karasak deals with fuzzy multipleobjectives, while we deal with non-fuzzy multiple objective formula-tion, (ii) Karasak finds fuzzy priorities of the objectives and use thosepriorities to find a single efficient solution, while we provide aninteractive method which finds an initial efficient solution thenexplores other efficient solutions by changing the weights as par thelikings of the decision makers. It is noted that in the domain ofmultiple objective decision making interactive approaches are pre-ferred than the non-interactive approaches [91, p. 98].

We now offer an interactive procedure to find satisfactory portfolioof efficient resilience strategies to mitigate the vulnerabilities.

Step 1: Optimize each objective function of problem (4). Therewill be p such solutions. Offer them to the decision maker.These will act as maximum goal of each individual objective.Any efficient solution will be a compromise solution fromthese goals.Step 2: Formulate problem (5) where each λi ¼ 1 (i¼1,…,p).Solve problem (5). The solution will be efficient (non-domi-nated) for problem (4). Offer it to the decision maker.Step 3: If the decision maker is satisfied with this solution (aftercomparing it with the solutions found in step 1), Stop. This will

Fig. 1. QFD Framework. Note: CRi¼customer requirements; Wi¼degree of impor-tance of CRi’s; DRj¼design requirements; Rij¼relationship matrix (i.e. degree towhich CRi is met by DRj). A.I.¼absolute importance of DRj’s; R.I.¼relative impor-tance of DRj’s.


be the satisfactory portfolio of resilience strategies to mitigatethe vulnerabilities.If the decision maker is not satisfied, go to step 4.Step 4: Interact with the decision maker to find new values ofλi’s which represent his/her preferences for the objectivefunctions.Step 5: Formulate and solve problem (5) with the new values ofλi’s. Offer it to the decision maker. Go to Step 3.

Application in RMG industry of Bangladesh

Methodology

There are fundamentally two research paradigms: positivist andinterpretivist [76]. Positivist paradigm is associated with the quanti-tative research method based on specific research questions andhypotheses testing [51,27]. Whereas the interpretivist paradigm relieson the qualitative method and there is subjective interpretation of theresearcher involved [27]. However, in recent times research based onmixed methods, a combination of qualitative and quantitative meth-ods, has gained popularity [12], because it assists in increasing thequality, accuracy, validity and reliability of data [27,2].

It is noted that the primary objective of this study is to ‘developefficient resilience capabilities of RMG supply chain of Bangladesh tomitigate organizations vulnerabilities’. To effectively conduct this studywe have adopted mixed methods of qualitative and quantitativeapproaches under positivist paradigm. This uniquely fits with theQuality Function Deployment (QFD) as a research methodology whichhas aspects of both qualitative and quantitative methods [77,102] andwhich we have embraced in our current study.

Our applications in RMG industry of Bangladesh are conductedin three studies and in three stages as follows.

Study 1

Our study 1 is conducted on one of the largest apparel manufac-turer in Bangladesh. Since its establishment in 1984, the company hasbeen attaining specific experience in designing and manufacturingdifferent types of apparels. It specializes in high quality apparelproduction, and is one of the leading apparel exporters in Bangladesh.It has 30,000 employees in 28 apparel production units in differentparts of Bangladesh as well in Cambodia and Vietnam. It producesBottom, Shirt, Sportswear, Polo knit and Sweater compliant withvarious quality requirements. It exports its products to North America,South America and Western Europe. The total export volume of the

company is 120,000 dozen/month which accounts for an aggregateturnover of US$100million. Sears, C & A, PVH, GAP, Wal-mart, JCPannyand H&M are the major buyers of its products. Paired with the apparelproduction, it has developed the backward linkage facilities as it hasits own textile (waiving, cotton yarn spinning) with dying facilities ofcotton & synthetic, poly, label, button, zipper, thread and cartonfactories. The apparel export of the company is increasing every yearhowever, it is still bogged with multiple disruptive events like labourunrest, political instability, interruption in utility supply especiallypower shortage; among others. These disruptive events expose thecompany to various types of vulnerabilities. Although the conglomer-ate has 30 years of experience in combatting various vulnerabilities, aformal resilient approach to mitigate the vulnerabilities is needed.Consequently, the management of this group of companies has agreedto take part in QFD approach proposed in this paper to explicate thevulnerabilities and resilience strategies to overcome the vulne-rabilities.

Stage 1In stage 1 the supply chain vulnerabilities (WHATs) and corre-

sponding resilience strategies (HOWs) to mitigate these vulnerabilitiesare found. To accomplish this, data have been collected from threeRMG manufacturing companies and two accessory production com-panies of the parent Group. Although under the same conglomerate,these five companies have unique features, and face and try to resolveproblems independently. It also allows us to collect data frommultiplesources (five decision makers) and thus, enhances the reliability of ourdata [3]. The data have been collected via semi-structured detailedinterviews from five respondents. Table 4 provides the profiles of thefive companies and the respondents. Each interview lasted between60 and 80min.

Table 5 presents the explicated supply chain vulnerabilities andresilience strategies. There are 26 vulnerabilities. Out of these, 17vulnerabilities have been supported by majority of the respondents.These 17 vulnerabilities are considered for further analysis. Corre-sponding to the 17 vulnerabilities the respondents identified 13resilience strategies to mitigate the vulnerabilities. It is observed thatmost of these vulnerabilities and resilience strategies are consistentwith the literature (as per Tables 2 and 3). This adds further validity toour collected data [3].

Stage 2In stage 2 we collect the quantitative data (wi, Rij) to find the AIj

and RIj (see Table 2 and Eqs. (1) and (2)). We also collect the data oncosts (cj) of implementing the resilience strategies in order to find theresilience efficiency REj (see earlier discussion) and the savings sij’swhen two resilience strategies i and j are implemented together. As

Table 4Profile of five companies and Participants.

Participant Position Company type Product(s) Company size (number ofemployees)

Age ofcompany

Production andsales

D1 ManagerMerchandising

RMGmanufacturingunit

Bottom item (shorts, trousers) More than 5000 10 years 60–80,000 dozens/year

D2 Deputy generalmanager

Accessorysupplying unit

Cartoon and poly bags which are used for packingproducts during shipment.

Less than 1000 5 years 14–1,500,000cartoons each year

D3 ManagerMerchandising


Main product is shirt. 2000–3000 10 years 25–30,000 dozens/year

D4 Supply chainmanager


Main product is shirt. More than 5000 25 years 40–50,000 dozen/year

D5 Deputy Generalmanager

Accessorysupplying unit

Process raw cotton to yarn and then spin the yarn forknitting.

Less than 1000 15 years 40–5,000,000 yard/year


these data are extremely demanding to collect, we select one RMGmanufacturing company from the parent group of companies (D1 inTable 4). Table 4 shows that it is a large company both in terms ofnumber of employees and sales volume. It also produces more thanone product type and hence susceptible to more vulnerabilities.Barratt et al. [3] mention that single case company allows to collectmuch deeper data, which is the situation in our study. However, wecollect data from multiple sources (three decision makers) of the casecompany which enhances reliability of the collected data [3].

To find the wi we use Saaty’s [86] AHP method in a hierarchicalsetting. The wi’s are averaged for three respondents and are shownin the last column of Table 5. To find Rij’s we ask the respondents toindicate (in their opinions) the ‘extent of mitigating the vulner-ability i by resiliency strategy j’ using the widely used scale of 9, 3,1 and 0 [17,82,34]. The Rij’s are also averaged for three respondents.Fig. 2 shows the wiRij values ( in the main body of the matrix) andthe AIj’s and the RIj’s for different resilence strategies. It is observedthat resilience strategies 13 (building relations with with buyersand suppliers) and 4 (back-up capacity) have the highest AIs of 4.11and 3.74 respectively.

The cost (cj) of implementing the resilience strategies is foundin a more elaborate way. Each respondent is asked to give theirmost likely, optimistic and pessimistic estimates of cj. Then theexpected cost is found by the formula Ce¼(4CmþCoþCp)/6, whereCe, Cm, Co and Cp are the expected, most likely, optimistic andpessimistic cost estimates. These costs are then averaged for threerespondents. Fig. 2 shows the costs cj’s and the resilience efficien-cies REj’s. It is noted that resilience strategy 13 (building relationswith buyers and suppliers) has the highest RE of .19 followed byresilience strategy 8 (forecasting and prediction) of .15. The costfigures are in millions of Taka.1 To find the savings sij therespondents were asked to indicate which resilience strategiescould be implemented simultaneously and what could be theestimated savings. The roof of Fig. 2 shows these savings data. Forexample, resilience strategies 1 and 10 can be implemented sim-ultaneously and the estimated savings would be Taka 3.6 million.

Table 5Vulnerabilities and corresponding resilience capabilities.

Vulnerability factors(WHATs)

Specific vulnerabilities Enterprises AHP Weights Adjusted weights

1 2 3 4 5

Hazard (.132) Natural disaster (flood, cyclone) (HV1) y y y .169 .022Political instability and labour unrest (HV2) y y y y y .534 .071Fire and other accidental damage (HV3) y y y .297 .039Sabotage y yPiracy and theft y

Strategic (.223) Increased competition (SV1) y y y y y .445 .099Failure to comply with social-environmental factors (SV2) y y y y .383 .085Problem of relation with buyer & suppliers (SV3) y y .172 .04Problem of integration y y

Financial (.154) Currency & raw material price fluctuation (FV1) y y y y .253 .039Economic recession (FV2) y y y y y .602 .093Bankruptcy of supply chain members (FV3) y y .145 .022Higher rate of bank intest y

Operational (.195) Worker (skill, absenteeism, illeteracy) (OV1) y y y y .282 .055Disruption in utility supply (OV2) y y y y y .543 .106Product quality defection (poor quality) (OV3) y y y .175 .034Machinery breakdown and failure y yIT system failureProduction planning problem y y

Infrastructural (.115) Delay in port and customs (IV1) y y y y .578 .067Delay in transportation for poor infrastructure and port facilities (IV2) y y .422 .049Strike by port workers y

Demand & supply (.181) Suppliers’ disruptions (DSV1) y y y .242 .043Lack of material (dependence on imported material and lack of backward linkage)(DSV2)

y y y y y .597 .108

Buyers’ disruptions (expect discount) (DSV3) y y y .161 .029Unpredictability of demand y

Resience strategies(HOWs) Product differentiation and customization (St1) y y y

Multiple sources of supply (St2) y y y y yChannel rerouting, reconfiguration (St3) y yBack up capacity (St4) y y y yQuality control and reducing defection (St5) y y y ySkill and efficiency development through traing and counselling (St6) y y y y yProduct and process improvement for efficiency and waste reduction (St7) y y y yForecasting and predictive analysis (St8) y y y yForward and backward linkage (St9) y y yResponsiveness to customer (St10) y y y y yCompliance of social and environmental issues (St11) y y y y yICT adoption and information intergartion (St12) y y y yCooperation, communication and building relation with buyers and suppliers(St13)

y y y y y

Note: Factors with low responses have not been considered for importance rating.

1 Taka is Bangladeshi currency. At the time of this study the exchange rate was1 US$¼77 Taka.


Stage 3In stage 3 we develop the 0–1 multiple objective problem (as in

(4)) and apply the stepwise procedure to find the satisfactoryportfolio of efficient resilience strategies. In order to find themultiple objectives among the resilience strategies we interactedwith the three decision makers of the case company and came upwith three objectives to be maximized as follows:

f1(X)¼ ‘Procurement’ related strategies which includes strate-gies ST2, ST4, ST9 and ST13.f2(X)¼ ‘Processing’ related strategies which includes ST5, ST6,ST7, ST11 and ST12.f3(X)¼ ‘Distribution’ related strategies which includes ST3, ST8,ST1, and ST10.

Problem (4) now becomes:

Max f1(X)¼RE2x2þRE4x4þRE9x9þRE13x13Max f2(X)¼RE5x5þRE6x6þRE7x7þRE11x11þRE12x12Max f3(X)¼RE3x3þRE8x8þRE1x1þRE10x10

Subject to:

c1x1þc2x2þc3x3þc4x4þc5x5þc6x6þc7x7

þc8x8þc9x9þc10x10þc11x11þc12x12

þc13x13�S1;6x1x6�S1;10x1x10 � S2;4x2x4

�S3;8x3x8�S3;9x3x9� S3;10x3x10�S4;8x4x8

�S4;10x4x10�S4;12x4x12�S4;13x4x13�S5;6x5x6

�S5;7x5x7�S6;7x6x7�S7;10x7x10

�S7;11x7x11�S7;13x7x13�S8;13x8x13�S9;13x9x13�S10;11x10x11

�S10;12x10x12�S11;13x11x13rB where xj ¼ 0 or 1

The values of REj and cj are obtained from Fig. 2. According tothe decision makers the budget can be set aside as 110 millionTaka. Hence the budget B is set at 110 million Taka.

We now follow the stepwise procedure to find the satisfactoryportfolio of efficient resilience strategies. As per step 1 of the stepwiseprocedure, each objective is optimized separately. We use EXCELSolver as the optimization software. The optimal solutions andcorresponding portfolio of resilience strategies are shown in Table 6.It is observed that optimal values of the resilience efficiencies (REs) ofthe procurement (f1), processing (f2) and distribution (f3) strategies are.416, .411 and .316 respectively. If f1 is optimized alone then ‘multiplesources of supply’ (St2), ‘back up capacity’ (St4) and ‘cooperation andcommunication with buyers and suppliers’ (St13) should be imple-mented (see Table 5) for a total cost of 85.3 million Taka leaving 24.7million Taka unspent. However, since (St2, St4) and (St4, St13) areimplemented together there is potential savings of 13.6 million Taka(see roof of Fig. 2). Thus, there is still 38.3 million Taka available forimplementing other strategies. Since ‘forward and backward linkages’(St9; another component of f1) costs 71.8 million Taka to implementthe management can look into other strategies to be implemented forthe remaining budget of 38.3 million Taka. Other optimized solutionfor f2 and f3 can be analysed similarly.

In our case the three decision makers wanted to explore moreefficient solutions in order to obtain a compromise among thethree objectives. We then applied step 3 of the stepwise procedureusing equal weighting for the three objectives. This produces anefficient solution as shown in Table 7 (first row). It is noted thatthe objective function values (resilience efficiencies) are .327 (forf1; a deviation of 21.65% from the optimal value of f1), .329 (for f2; adeviation of 19.95% from the optimal value of f2) and .231 (for f3; adeviation of 26.89% from the optimal value of f3). The decisionmakers now wanted to weight the objectives according to theirpreferences and explore further compromise and efficient solu-tions. The steps 3 and 4 come into action now. After some

88

3.6

7.2

3.7

3.35.6

8.3

4

7

6.4

4

5.57.9

4.39.7

5.8

4.65.2

4.8

2.9 5.65.6

SCVsSCVs WeightsWeights St1St1 St2St2 St3St3 St4St4 St5St5 St6St6 St7St7 St8St8 St9St9 St10St10 St11St11 St12St12 St13St13 A.IA.IHV1HV1 0.0220.022 0 0.0660.066 0.0660.066 0.980.98 0 0 0 0.0370.037 0.0220.022 0 0 0.0510.051 0 1.2221.222HV2HV2 0.0710.071 0 0.1650.165 0.4970.497 0 0 0.0710.071 0.0710.071 0 0 0.4970.497 0.1650.165 0 1.4661.466HV3HV3 0.0390.039 0 0.0390.039 0.1170.117 0.3510.351 0 0 0 0 0 0 0.3510.351 0.1170.117 0 0.9750.975SV1SV1 0.0990.099 0.8910.891 0.1650.165 0 0.0990.099 0.6930.693 0.6930.693 0.8910.891 0.0990.099 0.2310.231 0.6930.693 0.4950.495 0.2310.231 0.8910.891 6.0726.072SV2SV2 0.0850.085 0 0 0 0.2550.255 0 0.1980.198 0 0 0.0850.085 0.5950.595 0 0 1.1331.133SV3SV3 0.040.04 0.0670.067 0.120.12 0 0.120.12 0.040.04 0 0.20.2 0 0.120.12 0.280.28 0.040.04 0.040.04 0.360.36 1.3871.387FV1FV1 0.0390.039 0 0.1170.117 0 0.1950.195 0 0 0 0.3510.351 0.0390.039 0 0 0 0.1950.195 0.8970.897FV2FV2 0.0930.093 0.2790.279 0.2790.279 0 0 0.220.22 0.6510.651 0.8370.837 0.8370.837 0.1550.155 0.2790.279 0.0930.093 0.220.22 0.8370.837 4.6874.687FV3FV3 0.0230.023 0 0.1610.161 0 0.0230.023 0 0 0 0.0690.069 0.0380.038 0.0230.023 0 0.380.38 0.540.54 1.2341.234OV1OV1 0.0550.055 0 0 0 0 0 0.4950.495 0.0550.055 0 0 0 0.1650.165 0 0 0.7150.715OV2OV2 0.1060.106 0 0 0 0.2470.247 0.1060.106 0.1060.106 0.1060.106 0.1060.106 0 0.1060.106 0 0.1060.106 0.2470.247 1.131.13OV3OV3 0.0340.034 0.0340.034 0.080.08 0 0 0.3060.306 0.2380.238 0.3060.306 0 0.0670.067 0.1020.102 0.0570.057 0.0340.034 0.1020.102 1.3261.326IV1IV1 0.0670.067 0 0.560.56 0.180.18 0.1120.112 0 0 0 0.180.18 0.180.18 0.0670.067 0 0.0670.067 0.1120.112 1.4581.458IV2IV2 0.0490.049 0.1140.114 0 0.1470.147 0 0 0 0 0 0 0 0 0 0.2610.261

DSV1DSV1 0.0430.043 0 0.3870.387 0.720.72 0.1290.129 0.0430.043 0 0 0.10.1 0.2150.215 0.1290.129 0 0.1290.129 0.3870.387 2.2392.239DSV2DSV2 0.1080.108 0.3240.324 0.9720.972 0.2520.252 0.7560.756 0 0.1080.108 0.180.18 0.2520.252 0.7560.756 0 0 0.1080.108 0.180.18 3.8883.888DSV3DSV3 0.0290.029 0.0290.029 0 0.0290.029 0.0870.087 0.2030.203 0.2610.261 0.2030.203 0.0290.029 0.0870.087 0.0680.068 0.1450.145 0.0870.087 0.2610.261 1.4891.489A.IA.I 1.6241.624 3.2253.225 1.3641.364 3.7433.743 1.8661.866 2.5522.552 3.0473.047 2.1312.131 1.911.91 1.8321.832 2.4382.438 1.7351.735 4.1124.112

CostCost 51.651.6 36.436.4 25.225.2 27.327.3 22.722.7 18.818.8 37.237.2 14.314.3 71.871.8 22.422.4 56.756.7 15.615.6 21.621.6RERE 0.0314730.031473 0.0885990.088599 0.0541270.054127 0.1371060.137106 0.0822030.082203 0.1357450.135745 0.0819090.081909 0.1490210.149021 0.0266020.026602 0.0817860.081786 0.0429980.042998 0.1112180.111218 0.190370.19037

Fig. 2. Supply Chain resilience model: Study 1. Note: A.I.¼absolute importance; Stj¼resilience strategy j; HV, SV, FV, OV, IV, DSV¼various vulnerabilities, RE¼resilienceefficiency.


deliberations the weights of (.2, .5, and .3) were settled with forobjectives f1, f2 and f3 respectively. It shows that the decisionmakers prefer ‘processing’ objective (f2) more compared to othertwo objectives. Applying step 5 we now obtain a second efficientsolution as shown in Table 7 (second row). The objective functionvalues of this solution are .327 (for f1; a deviation of 21.65% fromthe optimal value of f1), .411 (for f2; a deviation of 0% from theoptimal value of f2), and .149 (for f3; a deviation of 52.8% from theoptimal value of f3). It is interesting to note that this solutionproduces optimal value for objective f2 (processing), as moreweight (.5) was given to this objective. However, the deviationfrom the optimal solution for f3 was on the high side. The decisionmaker therefore chose the earlier (equal weighting) solution withobjective function values of .327, .329, and .231. It is observed fromTable 7 that this solution selects strategies St4 (back-up capacity),St13 (building relation with buyers and suppliers), St5 (qualitycontrol), St6 (skill and efficiency development), St12 (ICT adop-tion), St8 (forecasting), and St10 (responsiveness to customers) tobe implemented which require a total budget of 142.7 millionTaka. However there is a savings of 41.1 million Taka as (St4, St8),(St4, St10), (St4, St12), (St4, St13), (St5, St6), (St8, St12), and (St10,St12) are implemented together. Thus net required budget is 101.6million Taka which is well within the budget constraint of 110million Taka.

Study 2

The study 2 is conducted on one of the leading manufacturer ofjeans in Bangladesh to ensure the transferability of our findings andapplicability of our method in another setting. From its modestbeginning in 1984 the company has come a long way. It has modernresearch and development facility. Within its six production units thecompany now employs over 22,000 employees, produces over 30million jeans per year and exports to more than 25 countries. Weconducted the case study on one of the units of the company whichproduces casual wears for both men and women. Two executives ofthe unit took part in our study.

Stages 1 and 2Tashakkori and Teddlie [96] mention that external validity of

quantitative research and transferability of qualitative research aresimilar in nature, which refer to the degree to which results of onecontext can be applicable to other situation. Since our study 2 is also

on apparel industry, we offered the vulnerabilities of study 1 to thetwo executives of study 2 and asked them to delve into them andcome up with vulnerabilities specific to their situation. In the end theexecutives came up with 25 vulnerabilities of which 20 are similar tostudies 1 and 5 are new. Table 8 shows these vulnerabilities. Thebolded ones are the new vulnerabilities. It is noted that for the nextstage of the analysis (i.e. developing the supply chain resiliencemodel) the highest three weighted vulnerabilities from each groupwere considered. For example, for hazard vulnerability group HV1,HV2, and HV3 were selected which had the highest weights amongthe five hazard vulnerabilities (see Fig. 3).

Next, resilience strategies to mitigate these vulnerabilities weresought. Again the strategies of study 1 were given to the executives.They explored these and at the end came up with 14 strategies ofwhich 13 are similar to study 1 and one is new (see Table 8). Thisacceptability of the outcomes from study 1 to study 2 ensures thetransferability of our results.

In the next stage of the analysis, the quantitative data (wi, Rij)were collected from the two executives. The data on cost cj andsavings sij were also estimated. Finally the supply chain resiliencemodel was developed as shown in Fig. 3. Unlike study 1 theresilience strategy 9 (Demand forecasting) has the highest RE of.108 followed by resilience strategy 5 (Maintaining reserve capa-city). However it is noted that demand forecasting was ranked 2 instudy 1. This highlights the need for accurate demand forecastingin the apparel industry of Bangladesh to mitigate the demand–supply related vulnerabilities.

Stage 3Like study 1 in stage 3 we develop and solve the 0–1 multiple

objective model and find the satisfactory portfolio of efficientresilience strategies. After interacting with the two executives thefollowing three objectives were formulated:

f1(X)¼ ‘Procurement’ related strategies comprising ST3, ST5,ST10 and ST14.f2(X)¼ ‘Processing’ related strategies comprising ST1, ST6, ST7,ST8, ST12, ST13.f3(X)¼ ‘Distribution’ related strategies comprising ST4, ST9, ST2and ST11.

For brevity the multi-objective model is not presented here whichis similar to study 1. The stepwise procedure is now followed to find

Table 6Aspiration levels of the objectives for study 1.

Objective Function Aspiration level Procurement strategy (f1) Processing strategy (f2) Distribution strategy (f3) Budget

X2 X4 X9 X13 X5 X6 X7 X11 X12 X1 X3 X8 X10

f1 .416 1 1 0 1 0 0 0 0 0 0 0 0 0 110f2 .411 0 0 0 0 1 1 1 0 1 0 0 0 0f3 .316 0 0 0 0 0 0 0 0 0 1 1 1 1

Note: X2, X4, X9, X13¼procurement strategy (f1); X5, X6, X7, X11, X12¼processing strategy (f2); X1, X3, X8 and X10¼distribution strategy (f3).

Table 7Efficient resilient portfolios of study 1.

Objective function Procurement strategies Processing strategies Distribution strategies Budget

f1 f2 f3 X2 X4 X9 X13 X5 X6 X7 X11 X12 X1 X3 X8 X10

Weighted (1,1,1) .327 .329 .231 0 1 0 1 1 1 0 0 1 0 0 1 1 110Weighted (.2, .5, .3) .327 .411 .149 0 1 0 1 1 1 1 0 1 0 0 1 0

Note: .2 weight for f1; .5 weight for f2; .3 weight for f3.


the satisfactory efficient portfolio for study 2. First, each objectivefunction is optimized separately. The optimal solutions and corre-sponding portfolio of resilience strategies are shown in Table 9. It isobserved that optimal values of the resilience efficiencies (REs) of theprocurement (f1), processing (f2) and distribution (f3) strategies are.163, .173 and 0. 196 respectively. It is noted that the optimal values ofthe objectives are quite different from that of study 1. When f1 isoptimized alone ‘maintaining reserve capacity’ (St5), and ‘cooperation

and collaboration with suppliers’ (St14) should be implemented. It isnoted that these two strategies were also selected in study 1.

Having seen the aspiration levels of three optimized objectivesthe two executives wanted to explore further efficient portfolios ofresilience strategies. Thus step 3 of our stepwise procedure cameinto action at this stage. We first found an efficient solution byequal weighting of the objectives. This solution is shown inTable 10 (first row). Next the executives wanted to weight the

Table 8Vulnerabilities and strategies for study 2.

Vulenarabilities Strategies

HV1¼Sabotage St1¼Security system improvementHV2¼Political instability St2¼Product customizationHV3¼Factory fire St3¼Alternative sources of supplyHV4¼Natural disaster (flood, cyclone) St4¼Alternative transportation routingHV5¼Foreign government policy change St5¼Maintaining reserve capacitySV1¼Competitive pressure St6¼Quality controlSV2¼Reputation loss St7¼Skill development trainingSV3¼Problem of integration with supply chain members St8¼ Improving process technologySV4¼Failure to comply with socio-environmental issues St9¼Demand forecastingFV1¼ Increasing raw material price St10¼Backward linkage developmentFV2¼ Impact of Economic recession St11¼Quick response to customers’ requirementsFV3¼Cost of Finance St12¼ Improving social and environmental performanceFV4¼Exchnage rate fluctuation St13¼ Information integrationOV1¼Shortage of skilled worker St14¼Cooperation and collaboration with suppliers.OV2¼ Impact of Power crisisOV3¼Concentrated production locationOV4¼Machine breakdown & failureOV5¼ IT system failureOV6¼Production planning problemIV1¼Delay in portIV2¼Poor transportation infrastructureIV3¼Delay due to export import document processingDSV1¼Suppliers’ disruptionDSV2¼High dependence on imported materialsDSV3¼Buyers’ disruption

SCVsSCVs WeightsWeights St1St1 St2St2 St3St3 St4St4 St5St5 St6St6 St7St7 St8St8 St9St9 St10St10 St11St11 St12St12 St13St13 St14St14 AIAIHV1HV1 0.020.02 0.270.27 0 0 0.10.1 0 0 0 0 0 0 0 0 0 0.370.37HV2HV2 0.0630.063 0 0 0.1470.147 0 0.5670.567 0 0 0 0.1190.119 0 0 0 0.1190.119 0.0840.084 1.0361.036HV3HV3 0.0340.034 0.2380.238 0 0.0390.039 0 0.2730.273 0 0 0 0 0 0 0.3510.351 0.0650.065 0.1020.102 1.0681.068SV1SV1 0.0820.082 0 0.8910.891 0.1650.165 0 0 0.6930.693 0.8910.891 0.6930.693 0.2970.297 0.2310.231 0.8910.891 0.2310.231 0.2310.231 0.2970.297 5.5115.511SV2SV2 0.0890.089 0 0 0 0 0 0.6230.623 0 0 0 0 0.8010.801 0 0.410.41 1.8341.834SV3SV3 0.070.07 0 0.0670.067 1.21.2 0.0930.093 0.040.04 0 0.120.12 0 0.120.12 0.280.28 0 0.040.04 0.360.36 2.322.32FV1FV1 0.0410.041 0 0 0.0910.091 0 0.1170.117 0 0 0 0.3510.351 0.0390.039 0 0 0 0.1170.117 0.7150.715FV2FV2 0.0810.081 0 0.4650.465 0.2790.279 0 0 0.0930.093 0.8370.837 0.6510.651 0.8370.837 0.0930.093 0.2170.217 0.0930.093 0.0930.093 0.8370.837 4.4954.495FV3FV3 0.0230.023 0 0 0.2070.207 0 0.0540.054 0 0 0 0.0690.069 0.0230.023 0.0540.054 0 0.0540.054 0.0690.069 0.530.53OV1OV1 0.0480.048 0 0 0 0 0 0 0.4950.495 0.550.55 0 0 0 0.090.09 0 0 1.1351.135OV2OV2 0.1040.104 0 0 0 0 0.9540.954 0 0 0 0.1770.177 0 0 0 0 0.1060.106 1.2371.237OV3OV3 0.0330.033 0 0 0 0 0.2310.231 0 0 0 0 0 0 0 0 0 0.2310.231IV1IV1 0.0610.061 0 0 0.3350.335 0.4690.469 0.180.18 0 0 0 0.180.18 0.1560.156 0 0 0 0.180.18 1.51.5IV2IV2 0.0450.045 0 0.080.08 0 0.2450.245 0 0 0 0 0 0 0 0 0 0.3250.325IV3IV3 0.0360.036 0 0 0 0 0.180.18 0 0 0 0.080.08 0.1080.108 0 0 0 0 0.3680.368

DSV1DSV1 0.0460.046 0 0 0.3870.387 0 0.2150.215 0 0 0 0.2150.215 0.2450.245 0.1290.129 0 0.0720.072 0.1290.129 1.3921.392DSV2DSV2 0.1060.106 0 0 0.9720.972 0 0.540.54 0 0.2520.252 0 0.3240.324 0.9720.972 0 0 0 0.540.54 3.63.6DSV3DSV3 0.0250.025 0 0.0750.075 0 0.1750.175 0.0580.058 0.1750.175 0.0580.058 0.1250.125 0.0870.087 0.0680.068 0.2250.225 0.1250.125 0.0580.058 0 1.2291.229

AIAI 1.0071.007 0.5080.508 1.4981.498 2.7022.702 1.8441.844 3.8073.807 1.6241.624 2.5332.533 2.1392.139 2.7362.736 2.0552.055 1.7961.796 1.6911.691 0.7320.732 3.2313.231CostCost 11.511.5 82.282.2 45.745.7 37.437.4 41.641.6 43.643.6 36.336.3 75.775.7 25.225.2 120.4120.4 46.846.8 102.3102.3 33.633.6 45.445.4RERE 0.0440.044 0.0182240.018224 0.0591250.059125 0.0493050.049305 0.0915140.091514 0.0372480.037248 0.069780.06978 0.0282560.028256 0.1085710.108571 0.0170680.017068 0.0383760.038376 0.016530.01653 0.0217860.021786 0.0711670.071167

9.5

8

4.57

9

4.5

5

7.5

8.5

3.5

3.7

Fig. 3. Supply Chain resilience model: Study 2.


objectives now and a weighting scheme of (.25, .45, and .3) wassettled with for objectives f1, f2 and f3 respectively. With this, thesecond efficient solution is obtained which is shown in second rowof Table 10. Compared to the equal weighting solution this solutionstill offers the optimal value for objective f1, with some improve-ment in objective f2 at the sacrifice of objective f3. The twoexecutives preferred this solution compared to the equal weight-ing solution. It is observed from Table 10 that this compromisesolution selected five out of fourteen resilience strategies asfollows: St1 (security system improvement), St5 (maintainingreserve capacity), St6 (quality control), St7 (skill developmenttraining) and St9 (demand forecasting). The study 2 also selectsone of the resilience strategies ‘security system improvement’(St1) which was not even one of the resilience strategies of study 1.It is noted that while in study 1 the preferred optimal efficientsolution was spread out among the three objectives (procurement,processing, and distribution), in study 2 the preferred solutionconcentrated more on the processing strategies. This highlightsthe difference in management attitude of the two giant garmentcompanies in Bangladesh.

Study 3

Study 2 was primarily conducted to ensure the transferability[96] of results and findings from one context to another context (i.e. transferring vulnerabilities and resilience strategies from study1 to study 2). To ensure further external validity [14] of ourmethod we have conducted study 3 on another garment manu-facturer from Bangladesh. It is noted that data from this companywere independently collected without any reference to studies1 and 2. Two executives from the company took part in the study.

Study 3 company is a family owned business, situated inChittagong, Bangladesh and was established in 1983. It has fourmanufacturing factories with work force of more than 3000. Theproducts of the company are casual and dress pants, shirts andmen’s shorts. The company is certified by the buyers for complyingwith the social and environmental sustainability factors. It marketsits products mainly in the USA, the UK and Australia. It has its ownwashing plant, screen printing unit, embroidery unit and in-houseclearing unit. This group also has their own transportation andlogistical services. The company is growing steadily but theexternal uncertainties, specifically the political and economicfactors, pose threat to the smooth operation and growth of its

business. A resilience approach is therefore essential for thesurvival and long term growth of the company.

Stages 1 and 2The vulnerabilities and corresponding resilience strategies

were collected first from the two executives of the company.Table 11 shows these vulnerabilities and resilience strategies alongwith their commonality with studies 1 and 2. There are threehazard, five strategic, three financial, five operational, two infra-structural, and five demand–supply vulnerabilities. Among the 23vulnerabilities of study 3, nine vulnerabilities are common to bothstudies 1 and 2. These are: HV1—Political unrest, HV2—Fire andother accident, HV3—Natural disaster, SV2—Increased competition,SV4—Problem of relationwith buyers & suppliers, OV1—Disruptionof utility, IV1—Inefficient port facility, IV2—Inefficient customsprocess, and DSV1—Dependence of imported material. It can bethus reasonably assumed that these are the most commonvulnerabilities of Bangladesh garment industry. It is noted thatfor the next stage of the analysis (i.e. developing the supply chainmodel) the highest three weighted vulnerabilities from each groupwere considered.

Table 11 also shows the 14 resilience strategies. It is noted thatsix of them are common to both studies 1 and 2. These are: St1—Back up capacity, St3—Focusing on sustainability practise, St5—Customer relationship development, St10—Multiple suppliers,St12—Developing relationship with suppliers, and St13—Productdifferentiation.

In the next stage of the analysis the supply chain resiliencemodel was developed as shown in Fig. 4. It is noted that theweight (wi) of the vulnerabilities, relationship value (Rij), cost (cj)and savings (sij) were also collected from the two executives of thecompany which are shown in Fig. 4. It is observed that resiliencestrategy 5 (customer relationship development) has the highest REvalue of .332 followed by resilience strategy 6 (developing newbuyers and markets) and resilience strategy 8 (strict qualitycontrol at different stage). Interestingly these rankings of strate-gies are quite different from studies 1 and 2.

Stage 3Like studies 1 and 2 we then develop the 0–1 multiple

objective model and find the satisfactory efficient resilient


Objective function Aspiration level Procurement strategy (f1) Processing strategy (f2) Distribution strategy (f3) Budget

X3 X5 X10 X14 X1 X6 X7 X8 X12 X13 X2 X4 X9 X11

f1 .163 0 1 0 1 0 0 0 0 0 0 0 0 0 130f2 .173 0 0 0 0 1 1 1 0 0 1 0 0 0 0f3 .196 0 0 0 0 0 0 0 0 0 0 1 1 1

Note: X3, X5, X10, X14¼procurement strategy (f1); X1, X6, X7, X8, X12, X13¼processing strategy (f2); X2, X4, X9, and X11¼distribution strategy (f3).

Table 10Efficient resilient portfolios of study 2.

Objective Function Procurement strategies Processing strategies Distribution strategies Budget

f1 f2 f3 X3 X5 X10 X14 X1 X6 X7 X8 X12 X13 X2 X4 X9 X11

Weighted (1,1,1) .092 .114 .158 0 1 0 0 1 0 1 0 0 0 0 1 1 0 130Weighted (.25, .45, .3) .092 .151 .109 0 1 0 0 1 1 1 0 0 0 0 0 1 0



strategies. As per the two executives of the company there arethree objectives to be optimized as follows:

f1(X)¼ ‘Procurement’ related strategies comprising ST1, ST10,ST11 and ST12.f2(X)¼ ‘Processing’ related strategies comprising ST2, ST3, ST4,ST7, ST8, ST9.f3(X)¼ ‘Distribution’ related strategies comprising ST5, ST6,ST13 and ST14.

The stepwise procedure is now followed to find the satisfactoryefficient portfolio of strategies for study 3. First, each objective isoptimized separately. Table 12 shows the aspiration level ofobjectives for the budgetary restriction of 80 million Taka. Steps2 and 3 now comes into play to interact with the decision makersfor satisfactory efficient solution. The executives settled with aweighting scheme of (.2, .4, .35) for objectives f1, f2 and f3respectively. The corresponding satisfactory solution is shown inTable 13 (2nd row). It is noted that the satisfactory solution is acompromise solution among the three objectives. Table 13 showsthat the selected portfolio of strategies is: St10 (Multiple suppli-ers), St11 (Supplier selection and evaluation), St12 (Developingrelationship with suppliers), St2 (Risk management team), St7(Flexibility in production), St8 (Strict quality control at differentstage), St9 (Training and development), St5 (Customer relationshipdevelopment), and St6 (Developing new buyers and markets).

It is noted from the above findings of study 3 that our methodpossesses good external validity. In spite of the fact that data onstudy 3 has been collected independently following the structuredprocedure of our method, the results have similarities with studies1 and 2. This was expected as all three companies belong to thereadymade garment industry in Bangladesh and they operate insimilar competitive environment. The differences in results can beattributed to the differences in management attitudes of threedifferent companies and their tangible and intangible resources.

Discussions and implications

This research aimed to develop resilience capabilities of Ban-gladesh readymade garment (RMG) supply chain in order to

mitigate various vulnerabilities that RMG supply chain face. Indoing so, we adopted mixed-method research design with quali-tative and quantitative approaches [27]. We defined supply chainresilience efficiency (SCREF) and develop an interactive methodol-ogy to determine efficient resilience capabilities based on QFDapproach. The proposed methodology is then applied on threelarge RMG companies in Bangladesh. The three studies wereconducted to ensure external validity (and transferability) [14,96]of our proposed methodology. Our experience indicates that theproposed methodology can be successfully applied to determ-ine efficient portfolio of resilience strategies to mitigate vulne-rabilities.

While three companies face somewhat similar vulnerabilitiesand their resilience strategies are also similar, there are somedifferences in quantitative results that deserve attention. Table 11shows the similarities and differences of vulnerabilities andresilience strategies among the three companies. It is noted thatcompany 3 has come up with 11 new vulnerabilities which aredifferent from both companies 1 and 2. In terms of importance ofthese vulnerabilities study 1 has identified four top vulnerabilitiesas DSV2 (lack of materials), OV2 (disruptions in utility supply), SV1(increased competition) and FV2 (impact of economic recession)(see Fig. 3 and Table 6). While top four important vulnerabilities ofstudy 2 are DSV2 (high dependence on imported materials), OV2(impact of power crisis), SV2 (reputation loss), SV1 (competitivepressure), and FV2 (impact of economic recession) with equalweighting with SV1 (see Fig. 3 and Table 8). It is noted that‘reputation loss’ is a significant vulnerability of study 2, while thiswas not even considered as a possible vulnerability by study 1.Both companies are export oriented. However study 2 companyprides in its R & D and takes quality issue very seriously, whichmight explain why this company takes ‘reputation loss’ veryseriously. The top four vulnerabilities of study 3 are FV2 (lack oforder), OV1 (disruption of utility), FV1 (increased production cost),and HV1 (political unrest) (see Fig. 4 and Table 11). It is noted that‘lack of order’ and ‘increased production cost’ are significantvulnerabilities of study 3, while these two were not even con-sidered as possible vulnerabilities of studies 1 and 2 (see Table 11).Being family owned, company 3 works in an extremely competi-tive environment. Hence these two vulnerabilities are very sig-nificant for the company.

Table 11Vulnerabilities and strategies for Study 3.

Vulnerabilities Study 1 Study 2 Strategies Study 1 Study 2

HV1—Political unrest ✓ ✓ St1—Back up capacity ✓ ✓

HV2—Fire and other accident ✓ ✓ St2—Risk management teamHV3—Natural disaster ✓ ✓ St3—Focusing on sustainability practise ✓ ✓

SV1—Reputation risk ✓ St4—Using updated technology ✓

SV2—Increased competition ✓ ✓ St5—Customer relationship development ✓ ✓

SV3—Selecting wrong production site St6—Developing new buyers & marketsSV4—Problem of relation with buyers & suppliers ✓ ✓ St7—Flexibility in productionSV5—Lack of sustainability planning & standard St8—Strict quality control at different stage ✓

FV1—Increased production cost St9—Training & development ✓

FV2—Lack of order St10—Multiple suppliers ✓ ✓

FV3—Loss due to rejection of shipment St11—Supplier selection & evaluationOV1—Disruption of utility ✓ ✓ St12—Developing relationship with suppliers ✓ ✓

OV2—Switching of workers St13—Product differentiation ✓ ✓

OV3—lead time failure St14—Alternative transportation (e.g. air shipment) ✓

OV4—Lack of efficiency of workers ✓

OV5—Fault in Quality control ✓

IV1—Inefficient port facility ✓ ✓

IV2—Inefficient customs process ✓ ✓

DSV1—Dependence of imported material ✓ ✓

DSV2—Switching of buyersDSV3—Lack of commitment of suppliers (late delivery, quality )DSV4—Supply shortageDSV5—Buyers' opportunism


As pointed out earlier the three companies have differentmanagement attitudes while mitigating vulnerabilities via resili-ence strategies. This is reflected while finding the satisfactoryefficient resilience strategies via our interactive method. In study1 the decision makers settled with efficient resilience strategieswhich assigned equal weights to the ‘procurement’ (f1), ‘proces-sing’ (f2) and ‘distribution’ (f3) strategies. Their preferred portfolioof resilience strategies included seven out of possible 13 strategieswith two from procurement objective, three from processingobjective and two from distribution objective. This is a balancedmanagement approach practised by this company in order to

mitigate the vulnerabilities. On the other hand, in study 2 thedecision makers settled with efficient resilience strategies thatassigned more weight to the ‘processing’ strategy. Their preferredsolution included five out of possible 14 strategies with one from‘procurement’ objective, three from ‘processing’ objective and onefrom ‘distribution’ objective. The management of this company ismore concerned with satisfying the ‘processing’ objective in orderto mitigate the vulnerabilities. It is observed that in terms ofchoosing the portfolio of resilience strategies study 3 is similar tostudy 1, where both companies chose a compromise solutionspread out among the three objectives. Study 3 thus chose nine

SCVsSCVs WeightsWeights St1St1 St2St2 St3St3 St4St4 St5St5 St6St6 St7St7 St8St8 St9St9 St10St10 St11St11 St12St12 St13St13 St14St14HV1HV1 0.1130.113 0.3390.339 1.0171.017 0 0 0 0 0.3390.339 0 0 0 0 0 0 0.3390.339HV2HV2 0.0280.028 0.2520.252 0.2520.252 0.2520.252 0 0 0 0 0 0.0840.084 0 0 0 0 0HV3HV3 0.0110.011 0.0990.099 0.0990.099 0 0 0 0 0 0 0 0 0 0 0 0SV1SV1 0.0690.069 0 0 0.2070.207 0.6210.621 0.2070.207 0.6210.621 0.2070.207 0.2070.207 0.2070.207 0 0 0 0.6210.621 0SV2SV2 0.0210.021 0 0 0 0 0 0 0 0 0.0630.063 0 0 0 0 0SV3SV3 0.0380.038 0 0.1140.114 0.3420.342 0 0 0 0 0.3420.342 0 0 0.3420.342 0.1140.114 0 0FV1FV1 0.1130.113 0 0 0.3390.339 1.1071.107 0 0 0.3390.339 0.3390.339 1.1071.107 0.3390.339 0 0.3390.339 0.3390.339 0FV2FV2 0.2250.225 0 0 0.6750.675 0 2.0252.025 2.0252.025 0.6750.675 0.6750.675 0 0 0 0 0.6750.675 0FV3FV3 0.0560.056 0 0 0 0 0.1680.168 0 0 0.5040.504 0 0 0 0 0 0OV1OV1 0.130.13 1.171.17 0 0 0 0 0 0.390.39 0 0 0 0 0 0 0OV2OV2 0.0410.041 0 0 0.1230.123 0 0 0 0 0 0.1230.123 0 0 0 0 0OV3OV3 0.0170.017 0 0 0 0.0510.051 0 0 0.1530.153 0 0.0510.051 0 0 0.0510.051 0 0.1530.153IV1IV1 0.0240.024 0 0 0 0 0 0 0 0 0 0 0 0 0 0.0720.072IV2IV2 0.0120.012 0 0 0 0 0 0 0 0 0 0 0 0 0 0DSV1DSV1 0.0580.058 0.1740.174 0 0 0 0 0 0 0 0 0.5220.522 0 0 0 0.1740.174DSV2DSV2 0.0290.029 0 0 0.0870.087 0.0870.087 0.2610.261 0 0 0 0 0 0 0 0.0870.087 0DSV3DSV3 0.0150.015 0.0450.045 0 0 0 0 0 0 0 0 0.1350.135 0.1350.135 0.1350.135 0 0A.IA.I 2.0792.079 1.4821.482 2.0252.025 1.8661.866 2.6612.661 2.6462.646 2.1032.103 2.0672.067 1.6351.635 0.9960.996 0.4770.477 0.6390.639 1.7221.722 0.7380.738CostCost 3030 8 4040 3535 8 1010 1515 1010 1010 8 5 8 2525 2222RERE 0.0690.069 0.1850.185 0.0510.051 0.0530.053 0.3320.332 0.2650.265 0.140.14 0.2070.207 0.1640.164 0.1250.125 0.0950.095 0.080.08 0.0670.067 0.0340.034

2

5

2

2 2

1.5

Fig. 4. Supply Chain resilience model: Study 3.


Objective function Aspiration level Procurement strategy (f1) Processing strategy (f2) Distribution strategy (f3) Budget

X1 X10 X11 X12 X2 X3 X4 X7 X8 X9 X5 X6 X13 X14

f1 .369 1 1 1 1 0 0 0 0 0 0 0 0 0 0 80f2 .749 0 0 0 0 1 0 1 1 1 1 0 0 0 0f3 .698 0 0 0 0 0 0 0 0 0 0 1 1 1 1

Note: x1,x10,x11,x12¼ procurement strategy (f1); x2,x3,x4,x7,x8, x9¼processing strategy (f2); x5,x6,x13 and x14¼distribution strategy (f3).

Table 13Efficient Resilient Portfolios of study 3.

Objective Function Procurement strategies Processing strategies Distribution strategies Budget

f1 f2 f3 X1 X10 X11 X12 X2 X3 X4 X7 X8 X9 X5 X6 X13 X14

Weighted (1,1,1) .30 .696 .597 0 1 1 1 1 0 0 1 1 1 1 1 0 0 80Weighted (.25, .40,.35) .30 .696 .597 0 1 1 1 1 0 0 1 1 1 1 1 0 0



strategies (among 14 strategies) with three from procurementobjective, four from processing objective and two from distribu-tion objective.

Theoretical implications

The most important theoretical contribution of our research is thatwe propose an interactive methodology to obtain satisfactory portfolioof resilience efficient strategies. This extends traditional QFD basedoptimization method to determine the most desirable HOWs [31,57],which are non-interactive and do not guarantee to find efficientsolutions. Our method interacts with the decision makers in asystematic way and generates efficient solutions (portfolios) whichsatisfy the decision makers. We therefore take the ‘satisficing’approach to decision making in our proposed methodology [93,89].

It is worthwhile to compare our methodology with existingmethodologies which deal with vulnerabilities in supply chains.Wagner and Neshat [100] proposed a methodology to mitigate supplychain vulnerabilities. The authors first found various vulnerabilitydrivers and then used graph theory to quantify vulnerability index.The directed graph shows the relationship among the vulnerabilitydrivers, the knowledge of which is useful to mitigate vulnerabilities.The authors mention that supply chain managers can use ‘riskmanagement methods and implement mitigation strategies’ to easevulnerability drivers. However, the authors do not offer any guide toexplicate the mitigation strategies nor do they show any relationshipbetween the vulnerabilities and mitigation strategies. In contrast inour proposed method, although we do not develop any index forvulnerability, we prioritize the vulnerabilities which are grouped ashazard, strategic, financial, operational, infrastructure, and demand–supply vulnerabilities [8]. We also develop specific resilience strategiesto mitigate the vulnerabilities and help the decision makers in aninteractive way to find the satisfactory efficient portfolio of resiliencestrategies. Goh et al. [40] also developed a quantitative stochasticmodel and methodology to deal with supply chains and vulnerabil-ities. However their theoretical model development was for interna-tional facility location and distribution problem for a company withone product. Their primary objective was to find the optimal open–shut decision of plants and the corresponding shipment quantitiesfrom various plants to various markets in order to maximize profitand minimize risks. It is noted that although along the same veinGoh et al.’s problem definition and solution methodology is quitedifferent. It needs rich and elaborate data. We are not yet aware of itsapplication in any real world problem.

Managerial implications

Organizations need to develop resilience capabilities in order tomitigate supply chain vulnerabilities [101]. In this vein our researchcan help managers, primarily in the RMG sector in Bangladesh,answer three fundamental questions: (i) what are the supply chainvulnerabilities that RMG sector currently facing? (ii) What are theresilience strategies to mitigate these vulnerabilities? (iii) What is theefficient resilient portfolio of strategies to mitigate vulnerabilitiessubject to budget constraint? Our study has identified a number ofvulnerabilities across three studies on three large RMG companies inBangladesh. With respect to question (i) it has been shown earlier thatthere are lot of similarities among these vulnerabilities of threecompanies. Other RMG companies in Bangladesh (and for that matterelsewhere) can start with these vulnerabilities and contextualize themfor their specific situation. It has been shown in our study that thevulnerabilities have good external validity. With respect to question(ii) it has been observed that resilience strategies are also similar forthree companies. Once again, other RMG companies (in Bangladeshand elsewhere) can start with these strategies and contextualize themfor their own use. For both questions (i) and (ii) starting with the

findings of our study will help the managers of RMG companies to beone-step ahead instead of developing the vulnerabilities and resiliencestrategies from clean slate.

For question (iii) we used our methodology to find the portfolioof efficient resilient strategies subject to budget constraint. Wefound that the three companies had different approaches tomanaging the resilient strategies. Our findings can help the RMGmanagers as an eye opener to find satisfactory efficient resilientstrategies in their own situation. It is highlighted here that ourinteractive methodology is targeted for interaction with themanagers (decision makers) where they can see the results whenthey input various weight for various objectives.

Conclusions

In this research we address the problem of mitigating vulnerabil-ities of the ready-made garment industry of Bangladesh. Our basicmethodology is QFD [17,41], which we use to find the vulnerabilitiesand their resilience strategies. We then develop an interactive multi-objective methodology to find the satisfactory (as per the decisionmakers) efficient portfolio of resilience strategies to mitigate thevulnerabilities. We argue that this approach is novel and has a numberof advantages over existing approaches [31,57]. First, our methodensures efficient portfolio of strategies and second, it interacts withthe decision makers and thus finds the satisfactory efficient solutionsin an interactive way.

We applied our method to three large garment companies inBangladesh. Using the stepwise procedure of QFD we found thevulnerabilities, and corresponding resilience strategies in study 1. Weobserved that these vulnerabilities and resilience strategies aretransferrable to study 2, although study 2 has few specific vulner-abilities and resilience strategies of its own. Data for study 3 has beencollected independently of studies 1 and 2. Even then there are somesimilarities among the vulnerabilities and resilient strategies of studies1, 2 and 3. Combining the top prioritized vulnerabilities of the threestudies we conclude that most important vulnerabilities of the RMGindustry of Bangladesh are: lack of materials (high dependence onimported materials), disruptions in utility supply, increased competi-tion (and hence competitive pressure), impact of economic recession,and reputation loss. Similarly, combining the preferred resiliencestrategies of the two studies we conclude that the most preferredstrategies to mitigate the vulnerabilities are: back-up capacity, build-ing relation with buyers and suppliers, quality control, skill andefficiency development, ICT adoption, demand forecasting, respon-siveness to customers, and security system improvement.

Our research is not free from limitations. First, for practicalapplication any QFD based method requires rich and detailed data.Hence data collection is the major limitation. In our case one of theresearchers had good contacts with the RMG industry of Bangladesh.It helped us to make good connections with three of the largest RMGcompanies in Bangladesh. The management of these companiesunderstood the value of our analysis and were deeply motivated inparticipating in the study. Second, in our data collection process weused qualitative methods to collect various data. Hence reliability ofthe data could be an issue. However, we dealt with real decisionmakers and as such all the qualitative data (vulnerabilities, resiliencestrategies) reflect their perception as they see fit for the companies.

We surmise that despite the limitations we have offered a detailedmethodology and its applications in RMG industry of Bangladesh in asystematic manner. The methodology can be effectively applied inother industrial settings and in other applications elsewhere. How-ever, in any future applications the decision makers of the targetedcompany need to be convinced about the value of the study. We havefound that once the value of the research is disseminated well thedecision makers are happy to be involved in the study.


Future research can be directed along the following ways. First,instead of multi-objective optimization multi-attribute methods canbe used based on the resilience efficiency values (RE) to find theportfolio of resilience strategies [28]. Second, to cater for uncertaintiesin input data stochastic methodology can be used instead of fuzzymethods and thus develop the stochastic efficient resilience strategies[64]. Third, the concept of dynamics of vulnerability [83] can beintroduced in our methodology and thus develop dynamic portfolio ofresilience strategies. Lastly, a decision support system based on ourmethodology can be developed and make it more comprehensive toinclude a variety of constraints and objective functions.

Acknowledgements

We are indebted to Prof. Ben Lev, the special issue editor andthree anonymous reviewers for their extensive feedbacks whichgreatly improved the paper.

References

[1] Asia News Network, A. Garment sector in B'desh hit hard by instability,⟨http://www.asianewsnet.net/Garment-sector-in-Bdesh-hit-hard-by-instability-45844.html⟩; 2013 [accessed 18.06.13].

[2] Babbie E. The practice of social research. 11th ed. Belmont, CA, USA: ThomsonWadsworth; 2007.

[3] Barratt M, Choi TY, Li M. Qualitative case studies in operations management:trends, research outcomes, and future research implications. Journal ofOperations Management 2011;29(4):329–42.

[4] Bevilacqua M, Ciarapica FE, Giacchetta G. A fuzzy-QFD approach to supplierselection. Journal of Purchasing and Supply Management 2006;12(1):14–27.

[5] BGMEA. Bangladesh apparel and textiles exposition, ⟨http://www.bgmea.com.bd/batexpo/index.htm⟩; 2012 [accessed 04.04.13].

[6] Bhattacharya A, Sarkar B, Mukherjee SK. Integrating AHP with QFD for robotselection under requirement perspective. International Journal of ProductionResearch 2005;43(17):3671–85.

[7] Blackhurst JV, Scheibe KP, Johnson DJ. Supplier risk assessment and mon-itoring for the automotive industry. International Journal of Physical Dis-tribution & Logistics Management 2008;38(2):143–65.

[8] Blos MF, Quaddus M, Wee HM, Watanabe K. Supply chain risk management(SCRM): a case study on the automotive and electronic industries in Brazil.Supply Chain Management: An International Journal 2009;14(4):247–52.

[9] Bode C, Wagner SM, Petersen KJ, Ellram LM. Understanding responses tosupply chain disruptions: insights from information processing and resourcedependence perspectives. Academy of Management Journal 2011;54(4):833–56.

[10] Bottani E, Rizzi A. Strategic management of logistics service: a fuzzy QFDapproach. International Journal of Production Economics 2006;103(2):585–99.

[11] Braunscheidel MJ, Suresh NC. The organizational antecedents of a firm’ssupply chain agility for risk mitigation and response. Journal of OperationsManagement 2009;27(2):119–40.

[12] Bryman A. Integrating quantitative and qualitative research: how is it done?Qualitative Research 2006;6(1):97–113.

[13] Büyüközkan G, Berkol C. Designing a sustainable supply chain using anintegrated analytic network process and goal programming approach inquality function deployment. Expert Systems with Applications 2011;38(11):13731–48.

[14] Calder BJ, Phillips LW, Tybout AM. The concept of external validity. Journal ofConsumer Research 1982;9(3):240–4.

[15] Carnevalli JA, Miguel PC. Review, analysis and classification of the literatureon QFD – types of research, difficulties and benefits. International Journal ofProduction Economics 2008;114(2):737–54.

[16] Carpenter S, Walker B, Anderies JM, Abel N. Frommetaphor to measurement:resilience of what to what? Ecosystems 2001;4(8):765–81.

[17] Chan LK, Wu ML. Quality function deployment: a literature review. EuropeanJournal of Operational Research 2002;143(3):463–97.

[18] Chan LK, Wu ML. Prioritizing the technical measures in Quality FunctionDeployment. Quality Engineering 1998;10(3):467–79.

[19] Chen LH, Weng MC. An evaluation approach to engineering design in QFDprocesses using fuzzy goal programming models. European Journal ofOperational Research 2006;172(1):230–48.

[20] Choi Y, Ye X, Zhao L, Luo AC. Optimizing enterprise risk management: aliterature review and critical analysis of the work of Wu and Olson. Annals ofOperations Research 2015:1–20.

[21] Choudhury SR, Hossain MG. Post-MFA issues and challenges: social dimen-sion. Dhaka, Bangladesh: Ministry of Commerce; 2005.

[22] Chowdhury GH, Sarker MAR, Afroze R. Recent unrest in the RMG sector ofBangladesh: is this an outcome of poor labour practices? InternationalJournal of Business and Management 2012;7(3):206–18.

[23] Chowdhury MH, Dewan MNA, Quaddus MA. Supply chain resilience tomitigate disruptions: a QFD approach. In: Pacific Asia Conference onInformation Systems (PACIS). Ho Chi Minh city, Vietnam: AIS; 2012.

[24] Christopher M, Peck H. Building the resilient supply chain. InternationalJournal of Logistics Management 2004;15(2):1–13.

[25] Colicchia C, Dallaria F, Melacini M. Increasing supply chain resilience in aglobal sourcing context. Production Planning & Control 2010;21(7):680–94.

[26] Craighead CW, Blackhurst J, Rungtusanatham MJ, Handfield RB. The severityof supply chain disruptions: design characteristics and mitigation capabil-ities. Decision Sciences 2007;38(1):131–56.

[27] Creswell JW, Clark VLP. Designing and conducting mixed methods research.Thousand Oaks, CA: Sage Publications; 2007.

[28] Cristiano JJ, White III CC, Liker JK. Application of multiattribute decisionanalysis to quality function deployment for target setting. IEEE Transactionson Systems, Man, and Cybernetics, Part C: Applications and Reviews 2001;31(3):366–82.

[29] Crowe TJ, Cheng C-C. Using quality function deployment in manufacturingstrategic planning. International Journal of Operations & Production Man-agement 1996;16(4):35–48.

[30] Deb K. Multi-objective optimization. In: Burke EK, Kendall G, editors. Searchmethodologies: introductory tutorials in optimization and decision supporttechniques. New York: Springer; 2014. p. 403–49.

[31] Delice EK, Güngör Z. A mixed integer goal programming model for discretevalues of design requirements in QFD. International Journal of ProductionResearch 2011;49(10):2941–57.

[32] Dickinson G. Enterprise risk management: its origins and conceptualfoundation. Geneva Papers on Risk and Insurance – Issues and Practice2001;26(3):360–6.

[33] Duclos LK, Vokurka RJ, Lummus RR. Delphi study on supply chain flexibility.International Journal of Production Research 2005;43(13):2687–708.

[34] Faisal MN. Managing risk in small and medium enterprises (SMEs) supplychains' using quality function deployment approach. International Journal ofOperations Research and Information Systems 2013;4(1):64–83.

[35] Fennell ML, Alexander JA. Organizational boundary spanning in institutio-nalized environments. Academy of Management Journal 1987;30(3):456–76.

[36] Ferdousi F, Ahmed A. An investigation of manufacturing performanceimprovement through lean production: a study on Bangladeshi GarmentFirms. International Journal of Business and Management 2009;4(9):106–16.

[37] Fibre2fashion News Desk. US suspends GSP facility to Bangladesh, ⟨http://www.fibre2fashion.com/news/apparel-news/newsdetails.aspx?news_id=147897⟩; 2013 [accessed 04.06.13].

[38] Fiksel J. Designing resilient, sustainable systems. Environmental Science &Technology 2003;37(23):5330–9.

[39] Gaudenzi B, Borghesi A. Managing risks in the supply chain using the AHPmethod. The International Journal of Logistics Management 2006;17(1):114–36.

[40] Goh M, Lim JYS, Meng F. A stochastic model for risk management in globalsupply chain networks. European Journal of Operations Research2007;182:164–73.

[41] González ME, Quesada G, Picado F, Eckelman CA. Customer satisfaction usingQFD: an e-banking case. Managing Service Quality 2004;14(4):317–30.

[42] Haider MZ. Competitiveness of the Bangladesh Ready-made GarmentIndustry in major international markets. Asia-Pacific Trade and InvestmentReview 2007;3(1):3–27.

[43] Han SB, Chen SK, Ebrahimpour M, Sodhi MS. A conceptual QFD planningmodel. The International Journal of Quality & Reliability Management2001;18(8):796–812.

[44] Heckmann I, Comes T, Nickel S. A critical review on supply chain risk –

definition, measure and modeling. Omega 2015;52:119–32.[45] Hendricks KB, Singhal VR. The effect of supply chain glitches on shareholder

wealth. Journal of Operations Management 2003;21:501–22.[46] Holling CS. Resilience and stability of ecological systems. Annual Review of

Ecology and Systematics 1973;4:1–23.[47] Hossan CG, Sarker MAR, Afroze R. Recent unrest in the RMG Sector of

Bangladesh: is this an outcome of poor labour practices? InternationalJournal of Business and Management 2012;7(3):206.

[48] Islam MA, Begum MN, Rashed CAA. Operational disturbances and theirimpact on the manufacturing business—an empirical study in the RMGSector of Bangladesh. International Journal of Research in Management &Technology 2012;2(2):184–91.

[49] Islam MA, Deegan C. Motivations for an organisation within a developingcountry to report social responsibility information: evidence from Bangla-desh. Accounting, Auditing & Accountability Journal 2008;21(6):850–74.

[50] Jiang B, Baker RC, Frazier GV. An analysis of job dissatisfaction and turnoverto reduce global supply chain risk: evidence from China. Journal of Opera-tions Management 2009;27(2):169–84.

[51] Johnson RB, Onwuegbuzie AJ. Mixed methods research: a research paradigmwhose time has come. Educational Researcher 2004;33(7):14–26.

[52] Jugulum R, Sefik M. Building a robust manufacturing strategy. Computers &Industrial Engineering 1998;35(1):225–8.

[53] Jüttner U, Maklan S. Supply chain resilience in the global financial crisis: anempirical study. Supply Chain Management: An International Journal2011;16(4):246–59.


http://www.asianewsnet.net/Garment-sector-in-Bdesh-hit-hard-by-instability-45844.html

http://www.asianewsnet.net/Garment-sector-in-Bdesh-hit-hard-by-instability-45844.html

http://refhub.elsevier.com/S0305-0483(15)00133-4/sbref0005







http://www.bgmea.com.bd/batexpo/index.htm

http://www.bgmea.com.bd/batexpo/index.htm






















































































http://www.fibre2fashion.com/news/apparel-news/newsdetails.aspx?news_id=147897















































[54] Kamvysi K, Gotzamani K, Andronikidis A, Georgiou A. Capturing andprioritizing students’ requirements for course design by embedding Fuzzy-AHP and linear programming in QFD. European Journal of OperationalResearch 2014;237:1083–94.

[55] Karim S. Coping with post-MFA challenges: strategic response for Bangla-desh RMG sector. Dhaka: CPD; 2003.

[56] Karsak EE. Fuzzy multiple objective decision making approach to prioritizedesign requirements in quality function deployment. International Journal ofProduction Research 2004;42(18):3957–74.

[57] Karsak EE. Fuzzy multiple objective programming framework to prioritizedesign requirements in quality function deployment. Computers & IndustrialEngineering 2004;47(2):149–63.

[58] Karsak EE, Özogul CO. An integrated decision making approach for ERPsystem selection. Expert Systems with Applications 2009;36(1):660–7.

[59] Karsak EE, Sozer S, Alptekin SE. Product planning in quality functiondeployment using a combined analytic network process and goal program-ming approach. Computers & industrial engineering 2002;44(1):171–90.

[60] Khondker BH, Razzak A, Ahmed N. Exports, employment and workingconditions: emerging issues in the post-MFA RMG industry(revised draft).International Labour Office; 2005.

[61] Kleindorfer PR, Saad GH. Managing disruption risks in supply chains.Production & Operations Management 2005;14(1):53–68.

[62] Köksalan MM, Wallenius J, Zionts S. Multiple criteria decision making: fromearly history to the 21st century. World Scientific; 2011.

[63] Larbani M, Aouni B. A new approach for generating efficient solutions withinthe goal programming model. Journal of the Operational Research Society2011;62(1):175–82.

[64] Lee DE, Lim TK, Arditi D. Automated stochastic quality function deploymentsystem for measuring the quality performance of design/build contractors.Automation in Construction 2009;18(3):348–56.

[65] Lee AH, Kang HY, Yang CY, Lin CY. An evaluation framework for productplanning using FANP, QFD and multi-choice goal programming. InternationalJournal of Production Research 2010;48(13):3977–97.

[66] Lin CM, Lee Y, Ho T. Applying integrated DEA/AHP to evaluate the economicperformance of local governments in China. European Journal of OperationalResearch 2011;209:129–40.

[67] Lockwood JL, Pimm SL. Biological diversity: species: would any of them bemissed? Current Biology 1994;4(5):455–7.

[68] Ludwig D, Walker B, Holling CS. Sustainability, stability and resilience.Conservation Ecology 1996;1:1–27.

[69] Martin S. The cost of restoration as a way of defining resilience: a viabilityapproach applied to a model of lake eutrophication. Ecology and Society2004;9(2):8.

[70] Mitroff IL, Alpaslan MC. Preparing for evil. Harvard Business Review 2003;81(4):109–15.

[71] Mizgier KJ, Wagner SM, Holyst JA. Modeling defaults of companies in multi-stage supply chain networks. International Journal of Production Economics2012;135(1):14–23.

[72] Narasimhan R, Kim SW. Information system utilization strategy for supplychain integration. Journal of Business Logistics 2001;22(2):51–75.

[73] Nuruzzaman A Haque, Rafiq A. Is Bangladeshi RMG Sector fit in the GlobalApparel Business? Analyses the supply chain management The South EastAsian Journal of Management 2010;4(1):53–72.

[74] Olson DL, Wu D. Enterprise risk management models. Heidelberg: Springer;2010.

[75] Olson DL, Wu D. A review of enterprise risk management in supply chain.Kybernetes 2010;39(5):694–706.

[76] Onwuegbuzie AJ, Leech NL. On becoming a pragmatic researcher: theimportance of combining quantitative and qualitative research methodolo-gies. International Journal of Social Research Methodology 2005;8(5):375–387.

[77] Park T, Kim KJ. Determination of an optimal set of design requirements usinghouse of quality. Journal of Operations Management 1998;16(5):569–81.

[78] Peck H. Reconciling supply chain vulnerability, risk and supply chainmanagement. International Journal of Logistics Research and Applications2006;9(2):127–42.

[79] Pettit TJ, Croxton KL, Fiksel J. Ensuring supply chain resilience: developmentand implementation of an assessment tool. Journal of Business Logistics2013;34(1):46–76.

[80] Pettit TJ, Fiksel J, Croxton KL. Ensuring supply chain resilience: developmentof a conceptual framework. Journal of Business Logistics 2010;31(1):1–21.

[81] Ponomarov SY, Holcomb MC. Understanding the concept of supply chainresilience. International Journal of Logistics Management 2009;20(1):124–139.

[82] Pujawan IN, Geraldin LH. House of risk: a model for proactive supply chainrisk management. Business Process Management Journal 2009;15(6):953–67.

[83] Raharjo H, Xie M, Brombacher AC. A systematic methodology to deal withthe dynamics of customer needs in Quality Function Deployment. ExpertSystems with Applications 2011;38(4):3653–62.

[84] Ramanathan R, Yunfeng J. Incorporating cost and environmental factors inquality function deployment using data envelopment analysis. Omega2009;37:711–23.

[85] Rose A. Defining and measuring economic resilience to disaster. DisasterPrevention and Management 2004;13(4):307–14.

[86] Saaty TL. AHP: the analytic hierarchy process. New York: McGraw-Hill; 1980.[87] Sawik T. On the fair optimization of cost and customer service level in a

supply chain under disruption risks. Omega 2015;53:58–66.[88] Schmitt A, Sun S, Snyder L, Shen Z. Centralization versus decentralization:

risk pooling, risk diversification, and supply chain disruptions. Omega2015;52:201–12.

[89] Schwartz B, Ben-Haim Y, Dacso C. What makes a good decision? Robustsatisficing as a normative standard of rational decision making Journal forthe Theory of Social Behaviour 2011;41(2):209–27.

[90] Sheffi Y, Rice JB. A supply Chain View of the resilient Enterprise. MIT Sloanmanagement 2005;47(1):47–8.

[91] Shin WS, Ravindran A. Interactive multiple objective optimization: Survey I—continuous case. Computers & Operations Research 1991;18(1):97–114.

[92] Simchi-Levi D, Schmidt W, Wei Y. From superstorms to factory firesmanaging unpredictable supply-chain disruptions. Harvard Business Review2014;92(1–2):96–101.

[93] Simon H. Theories of bounded rationality. In: McGuire CB, Radner R, editors.Decisions and organizations. North Holland; 1972.

[94] Stratton B. The refined focus of automotive quality. Quality Progress 1989;22(10):47–50.

[95] Tang CS. Robust strategies for mitigating supply chain disruptions. Interna-tional Journal of Logistics Research and Applications 2006;9(1):33–45.

[96] Tashakkori A, C Teddlie C, editors. Sage handbook of mixed methods in social& behavioral research. Sage; 2010.

[97] Tewari M. Is price and cost competitiveness enough for apparel firms to gainmarket share in the world after quotas? A review Global Economy Journal2006;6(4):1–46.

[98] Tomlin B. On the value of mitigation and contingency strategies formanaging supply chain disruption risks. Management Science 2006;52(5):639–57.

[99] Vugrin ED, Warren DE, Ehlen MA. A resilience assessment framework forinfrastructure and economic systems: quantitative and qualitative resilienceanalysis of petrochemical supply chains to a hurricane. Process SafetyProgress 2011;30(3):280–90.

[100] Wagner SM, Neshat N. Assessing the vulnerability of supply chains usinggraph theory. International Journal of Production Economics 2010;126(1):121–9.

[101] Wagner SM, Neshat N. A comparison of supply chain vulnerability indices fordifferent categories of firms. International Journal of Production Research2012;50(11):2877–91.

[102] Wang HF, Hong WK. An integrated service strategy by QFD approach: a caseof a telecom company in Taiwan. International Journal of Management andDecision Making 2007;8(2–4):251–67.

[103] Wasserman GS. On how to prioritize design requirements during the QFDplanning process. IIE Transactions 1993;25(3):59–65.

[104] Wu D, Olson DL. On the special issue of risk issues in operations: methodsand tools. Production Planning and Control 2009;20(4):293–4.

[105] Wu DD, Olson D. Enterprise risk management: a DEAVaR approach in vendorselection. International Journal of Production research 2010;48(16):4919–32.

[106] Wu DD, Olson D. Enterprise risk management: coping with model risk in alarge bank. Journal of the Operational Research Society 2010;61(2):179–90.

[107] Wu D, Olson DL, Birge JR. Introduction to special issue on "Enterprise riskmanagement in operations”. International Journal of Production Economics2011;134:1–2.

[108] Wu D, Chen SH, Olson DL. Business intelligence in risk management: somerecent progress. Information Sciences 2014;256:1–7.

[109] Wu T, Blackhurst J, Chidambaram V. A model for inbound supply riskanalysis. Computers in Industry 2006;57(4):350–65.

[110] Zhou M. Fuzzy logic and optimization models for implementing QFD.Computers & Industrial Engineering 1998;35(1):237–40.

[111] Zsidisin GA, Melnyk SA, Ragatz GL. An institutional theory perspective ofbusiness continuity planning for purchasing and supply management. Inter-national Journal of Production Research 2005;43(16):3401–20.



























































































































































1-s2.0-S0305048315001334-main

Documents

supply chain resilience

garment rmg industry

rmg industry of bangladesh

utility supply

supply chainvulnerabilities

rmg sector

resilience index

chain effect toall