WORKING PAPER ITLS-WP INSTITUTE of TRANSPORT and LOGISTICS STUDIES. The Australian Key Centre in Transport and Logistics Management

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1 WORKING PAPER ITLS-WP A reilient and utainable upply chain: I it affordable? By Behnam Fahimnia and Armin Jabbarzadeh June 2015 ISSN X INSTITUTE of TRANSPORT and LOGISTICS STUDIES The Autralian Key Centre in Tranport and Logitic Management The Univerity of Sydney Etablihed under the Autralian Reearch Council Key Centre Program.

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3 NUMBER: TITLE: ABSTRACT: Working Paper ITLS-WP A reilient and utainable upply chain: I it affordable? Developing environmentally and ocially utainable upply chain ha become an integral part of corporate trategy for virtually every indutry. However, little i undertood about the broader impact of utainability practice on the capacity of the upply chain to tolerate diruption. Thi article aim to invetigate the utainability-reilience relationhip at the trategic upply chain deign level uing a multi-objective optimization model and an empirical cae tudy. The propoed model utilize a utainability performance coring method and a novel programming approach to perform a dynamic utainability tradeoff analyi and deign a reiliently green upply chain. KEY WORDS: AUTHORS: Sutainability; Reilience; Supply Chain Deign; Multi- Objective Mathematical Model; Sourcing; Sutainability Performance Scoring; Stochatic Fuzzy Goal Programming CONTACT: INSTITUTE OF TRANSPORT AND LOGISTICS STUDIES (C13) The Autralian Key Centre in Tranport and Logitic Management The Univerity of Sydney NSW 2006 Autralia Telephone: buine.itlinfo@ydney.edu.au Internet: DATE: June 2015

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5 1. Introduction Sutainability ha become a major buzzword in buine vocabulary in recent year. Supply chain (SC) profeional are in an excellent poition to broadly impact utainability practice through the integration of economic, environmental and ocial goal when deigning and planning the SC. More organization are realizing the trategic importance of utainability invetment. In thi environment, the development and availability of analytical model and deciion-upport tool can help organization make more effective and informed deciion. To repond to thi call, academic reearch on utainable SC deign and management ha een ubtantial development over the pat two decade (Brandenburg et al., 2014; Fahimnia et al., 2015; Seuring, 2013). Mot of the effort to achieve SC utainability have been predominantly directed at reducing environmental burden of the SC, commonly meaured in term of greenhoue ga (GHG) emiion and reource conumption (Fahimnia et al., 2014c). The ocial utainability apect ha focued more on the potential damage to human health and the community/ociety at large (Boukherroub et al., 2015). Depite the growing effort on utainable SC deign and management, the broader impact of utainability intervention on the overall reilience of the SC ha remained unexplored. Sutainable SC management in an environment characterized by frequent unavoidable diruption neceitate utainability modeling and analyi that can accommodate thi complexity and dynamim. Static utainability analyi 1 i implitic becaue the economic and non-economic utainability performance of a SC can be affected by diruptive event uch a upply diruption. Thi call for management approache and optimization technique to develop reilient and utainable SC, or what we term a reiliently utainable SC, wherein utainability performance remain unaffected or lightly affected when diruption arie. 1 Static utainability analyi refer to the tudy of SC utainability performance in buine-a-uual, ituation, diregarding the likelihood of external diruption occurring. Dynamic utainability analyi tudie the SC performance in both buine-a-uual and diruption ituation. 1

6 SC reilience can be defined a the capacity of a SC to aborb diturbance and retain it baic function and tructure in the face of diruption (Pettit et al., 2010; Walker and Salt, 2006). Given the increaing frequency and intenity of natural diater a well a the continuou tream of anthropogenic catatrophe (Jabbarzadeh et al., 2014 ), the rikiet thing a company can do i to have no contingency plan. A general conenu i to improve the SC reilience given the demontrated quantifiable benefit that can be obtained from invetment in reilience (Cutter, 2013). We aim in thi paper to invetigate how SC utainability analyi and reilience improvement can be coupled in a complementary approach for developing reiliently utainable SC. Dicuion of marrying utainability cience with reilience theory are at a relatively early tage of development (Derien et al., 2011; Fikel, 2006; Perring, 2006; Walker and Salt, 2006). At the organizational level, the incorporation of utainability and reilience meaure into SC practice poe ignificant management and modeling challenge ome of which are tackled in thi paper. We aim to anwer a critical quetion: under what circumtance i it poible for SC to concurrently utain economic growth, minimize ocial and environmental impact, and yet be reilient to diruption? We limit the boundary of our tudy and invetigation to the upplier utainability performance and it impact on the general SC reilience. An explicit focu on uptream SC operation i of paramount importance due to the global price-baed ourcing trend forcing organization to purchae from cheaper but le reliable and le utainable upplier. Thi i exemplified in our empirical cae tudy of a portwear manufacturing company where the primary concern are the utainability performance and reliability of it ynthetic fiber upplier. The remainder of thi paper i continued in Section 2 by a review of the related SC modeling literature and the introduction of an important reearch gap which thi paper will addre. Problem decription, the mathematical model and olution approach are then preented in Section 3. An execution of the model uing real data from a multinational portwear clothing company i preented in Section 4. Numerical reult from tatic and dynamic utainability tradeoff analye and related dicuion are 2

7 preented in thi ection. Section 5 include a ummary of the reearch contribution and implication, model and tudy limitation, and future reearch direction. 2. Review of the Related Literature Given the explicit focu of thi tudy on integrating SC utainability and reilience, in the following ection we firt provide a review of the modeling effort in thee two area and will then draw upon thoe to poition our work in the nexu of thee two topic. 2.1 Meauring and Modeling SC Sutainability Reearch in the area of SC utainability ha tended to focu on empirical and conceptual tudie with only a cant, but rapidly growing, number of paper publihed on analytical modeling and quantitative analyi of the related problem (Brandenburg et al., 2014; Fahimnia et al., 2015). Mot of thee modeling effort locate within the context of green or environmentally utainable SC which involve the incorporation of economic and environmental utainability meaure when deigning and managing SC (Fahimnia et al., 2014b). Minimization of GHG emiion ha been the mot popular environmental objective (Benjaafar et al., 2013; Tang and Zhou, 2012) which i not urpriing given the global emiion reduction force and environmental regulatory mandate to tackle climate change. Green SC modeling effort have been expanding in the following ix direction: (1) optimization model for trategic SC deign eeking to balance SC cot and carbon emiion (Elhedhli and Merrick, 2012; Wang et al., 2011); (2) tactical and operational planning tool for SC cot-emiion tradeoff (Fahimnia et al., 2013a; Fahimnia et al., 2014b); (3) deign and planning of cloed-loop SC focuing on cot/emiion performance of the forward and revere network (Chaabane et al., 2011, 2012; Fahimnia et al., 2013b); (4) integration of life cycle aement principle for environmental impact aement of SC (Bojarki et al., 2009; Hugo and Pitikopoulo, 2005); 3

8 (5) development and application of multiple performance meaure (more than jut emiion) for green SC deign and management (Fahimnia et al., 2014c; Nagurney and Nagurney, 2010; Pinto- Varela et al., 2011; Pihvaee and Razmi, 2012); and (6) introducing and invetigating environmental policy intrument in SC planning and optimization (Diabat et al., 2013; Fahimnia et al., 2014a; Zakeri et al., 2015). Apart from tudie on green SC deign and management, there i only a handful of modeling effort incorporating performance meaure in three utainability dimenion. The fact that a conenu on meauring and reporting SC ocial utainability doe not exit (Varei et al., 2014) i the primary reaon for reearch carcity in thi pace. Pihvaee et al. (2012) ue the number of job created, the ue of hazardou material, and the labor working condition a ocial metric in a utainable SC deign model. You et al. (2012) preent a multi-objective model for deign of a celluloic ethanol SC uing SC cot, life cycle GHG emiion and the number of local job created per unit expenditure a economic, environmental and ocial performance meaure. A multi-objective poibilitic programming model i preented by Pihvaee et al. (2014) to deign a utainable SC network uing ReCiPe 2008 (Goedkoop et al., 2009) to etimate the environmental impact of the SC and GSLCAP (Beno ıt and Mazijn, 2009) to ae the SC ocial impact in three area: created job opportunitie, damage to worker and cutomer health, and local development. More recently, Boukherroub et al. (2015) tudy a tactical SC planning problem in which proximity of employee to production ite and employment tability (tranfer of employee between ite rather than laying them off) are ued a ocial performance meaure. A can be een in thee tudie, the election of environmental and ocial meaure to incorporate into SC model i indutry and problem pecific. Comprehenive lit of thee meaure can be obtained from the performance metric adopted by the exiting environmental impact aement method uch a IMPACT (Jolliet et al., 2003), Eco-indicator 99 (Goedkoop et al., 2009), and CML2001 (Guinèe et al., 2001) a well a the ocial performance tandard and guideline of SA8000 (SAI, 2008), GRI (GRI, 2011) and GSLCAP (Beno ıt and Mazijn, 2009). Given the broad cope and extenive 4

9 coverage of thee metric, an effort will then need to be made to refine the lit to only thoe that (1) are more relevant to SC deign and management deciion, (2) are quantifiable in ome form, and (3) account for the major characteritic of the concerned indutry and problem. An illutration of uch effort will be given in our empirical cae tudy invetigation in Section Meauring and Modeling SC Reilience The recent global financial crie and the increaing frequency of natural and anthropogenic catatrophe indicate the need for organization to hedge their SC againt major diruption. A common approach i to deign SC with inherent reilience to help remain unaffected or le affected in the face of unforeeen diruption (Chritopher and Peck, 2004; Emaeilikia et al., 2014b; Snyder et al., 2012). Once a reilient SC i developed, the frequent low-impact uncertaintie uch a regular variation in upply, demand and lead-time can be managed at the tactical planning level (intermediate timing term) through planning for more flexible SC (Emaeilikia et al., 2014a). We here provide a review of the related modeling approache that have been ued to meaure and manage diruption rik at the SC network deign level which i the explicit cope of thi tudy. Arguably, an expected value approach ha been one of the mot popular methodologie to meaure and account for SC reilience. The approach help in making mathematically ound deciion on invetment and prioritizing reilience building option by aigning weight to future event and calculating the expected value of different diruption cenario. Snyder and Dakin (2005) were early proponent to ue an expected value approach for the incorporation of diruption rik into a facility location problem. Aryanezhad et al. (2010) and Chen et al. (2011) extend thi model for joint location-inventory deciion making auming equal and independent likelihood for a diruption to occur. Unequal diruption probabilitie have alo been tudied by a number of other reearcher (Berman et al., 2007; Cui et al., 2010; Li et al., 2013; Li and Ouyang, 2010; Lim et al., 2010; O Hanley et al., 2013). SC deign model for ituation with dependent diruption probabilitie have been invetigated by Shen et al. (2011) and Jabbarzadeh et al. (2012). 5

10 Apart from the popular expected value approach and it branche, there are alo cenario-baed SC deign model that incorporate the rik preference of a deciion maker (Baghalian et al., 2013) or thoe that aim to minimize the relative regret of the SC under a et of diruption cenario (Peng et al., 2011). Mot of thee model and robutne approache focu on a ingle cot-baed objective to meaure and account for SC reilience. A multi-objective optimization approach ha been recently preented by Hernandez et al. (2014) eeking to tradeoff the total weighted travelled ditance before and after diruption. 2.3 Marrying SC Sutainability and Reilience: A Reearch Gap Literature how that utainability cience and reilience theory have been tudied independently (Derien et al., 2011; Redman, 2014). In the ame fahion, the quantitative modeling effort in thee two area have been conducted in complete iolation. In reality, there are ituation in which utainability initiative and practice can influence SC capacity in tackling unanticipated diruption. For example, efficiency maximization and wate minimization practice neceitate the ue of fewer tock point and torage area along the SC. Whilt uch trategie may be environmentally ound and economically prudent, they may inadvertently impact the SC reilience given the limited availability of afety tock inventory to cope with upply and demand variation. Likewie, utainable ourcing practice imply the need to purchae from and outource to more utainable upplier only. Yet, working with a handful of better performing upplier come with an unintended inability to witch between upplier when facing a upply crii. It i therefore unrealitic to perform a SC utainability analyi without touching upon the quetion of how utainability initiative can affect the ytem reilience. Conidering utainability tradeoff a a teady-tate equilibrium i an unrealitic aumption given the increaing frequency of diruption facing today organization and their inevitable conequence on the utainability performance of the SC. We ee thi a major reearch gap and call for management approache and deciion upport tool and technique for integrating SC utainability and reilience practice. We alo realize that uch 6

11 intricate exercie require dynamic and multifactorial utainability analyi for developing reiliently utainable SC whoe utainability remain le affected when diruption arie. Recognizing thi gap in the exiting literature, our aim in thi paper i to tudy the relationhip between SC utainability and reilience at the trategic SC deign level. A multi-objective optimization model i preented that utilize a utainability performance coring approach to quantify the environmental and ocial impact of the SC. A tochatic fuzzy goal programming approach i developed to find tradeoff olution to the propoed multi-objective problem. The application of the propoed model and methodology i invetigated in an empirical cae tudy of a portwear manufacturing company. Our analyi and dicuion focu on comparing the numerical reult obtained from tatic and dynamic utainability tradeoff analye. 3. Mathematical Modeling 3.1 Problem Statement We tudy a SC compried of geographically dipered factorie, each erved by a number of raw material upplier with limited upply capacitie. Item produced in factorie are ditributed to market zone through intermediate ditribution center (DC). Factorie and DC can be etablihed in different capacitie (e.g. mall, medium and large ize) which would make a difference in fixed and variable cot of production and torage. Multiple tranport mode, with different per unit hipping cot, may be available for the tranportation of item between SC node. The cot of raw material and the aociated utainability performance core may vary from one upplier to another. The utainability performance of a upplier i repreented by an environmental performance core (EPS) and a ocial performance core (SPS). Determining EPS and SPS require a et of aement criteria upon which a upplier can be aeed. The aement criteria for EPS can be obtained from the comprehenive performance metric adopted by the etablihed environmental impact aement method uch a IMPACT (Jolliet et al., 2003), Eco-indicator 99 (Goedkoop et al., 2009) and CML2001 (Guinèe et al., 2001). Similarly, the metric defined by ocial performance tandard and guideline of SA8000 (SAI, 2008), GRI (GRI, 2011) and GSLCAP (Beno ıt and Mazijn, 7

12 2009) can be ued to et the criteria for determining SPS. Such aement criteria may however need to be further refined to focu on thoe quantifiable item that (1) are directly related to trategic SC deign deciion and (2) comply with the characteritic of the pecific cae ituation (ee the example preented in Section 4). Once the environmental and ocial performance criteria are etablihed, the upplier performance will be aeed againt each criterion. A core, on a cale of 1-10, i aigned to the performance againt each criterion (with 10 being the bet practice). Thee core are then averaged to generate aggregate averaged core for EPS and SPS. A more precie approach to determine the aggregate core would be to aign a weight to each criterion baed upon it degree of importance to the focal company, and ue a weighted averaging method to develop aggregate weighted EPS and SPS core. The raw material upply i ubject to diruption. A et of cenario are developed to repreent ituation where one or more upplier are affected by diruption. The model and methodology preented in thi ection aim to determine the ourcing trategie (i.e. the quantitie to purchae from each upplier) and network deign deciion (i.e. the location and capacity of factorie and DC) that minimize the overall SC cot and maximize it utainability performance in both buine-a-uual and upply diruption ituation. The primary goal of our cae tudy invetigation in Section 4 i to utilize thi model to perform a dynamic utainability analyi for developing a reiliently utainable SC. 3.2 A Multi-Objective Mathematical Model A et of indice, parameter and deciion variable are ued for mathematical modeling of thi problem. Set and indice: R I N M W Set of raw material type, indexed by r Set of product type/familie, indexed by i Set of upplier, indexed by n Set of candidate location for factorie, indexed by m Set of candidate location for DC, indexed by w 8

13 J U V K L S Set of market zone, indexed by j Set of capacity level in factorie, indexed by u Set of capacity level in DC, indexed by v Set of tranport mode for the hipment of product from factorie to DC, indexed by k Set of tranport mode for the hipment of product from DC to market zone, indexed by l Set of diruption cenario, indexed by Input parameter: an Equal to 1 if upplier n i dirupted in cenario ; 0, otherwie. a rnm Equal to 1 if upplier n i available to upply raw material r for factory m; 0, otherwie. hri crn dij Amount of raw material r required for production of a unit of product i (kg) Supply capacity of raw material r by upplier n (kg) Forecated demand for product i in market zone j in cenario (unit) fn Fixed cot of evaluating and electing upplier n ($) f um Fixed cot of etablihing a factory with capacity level u at location m ($) f vw Fixed cot of etablihing a DC with capacity level v at location w ($) trnm gim Variable cot of purchaing raw material r from upplier n to factory m ($/unit) Variable cot of manufacturing a unit of product i in factory m ($/unit) h im Proceing time to produce a unit of product i in factory m (hour) c um Production capacity of a factory with capacity level u at location m (hour) t imwk t iwjl Unit cot of tranportation for the hipment of product i from factory m to DC w uing tranport mode k ($/unit) Unit cot of tranportation for the hipment of product i from DC w to market zone j uing tranport mode l ($/unit) h Volume of a unit of product i (m 3 ) i c vw ermn Storage capacity of a DC with capacity level v at location w (m 3 ) EPS of upplier n for the upply of raw material r to factory m (core) 9

14 e rmn SPS of upplier n for the upply of raw material r to factory m (core) q Probability of occurrence of cenario Deciion variable: X n X um X vw Qrnm Pim Y imwk Y iwjl A binary variable, equal to 1 if upplier n i elected; 0, otherwie. A binary variable, equal to 1 if a factory with capacity level u i etablihed at location i; 0, otherwie. A binary variable, equal to 1 if a DC with capacity level v i etablihed at location w; 0, otherwie. Quantity of raw material r hipped from upplier n to factory m under cenario Quantity of product i produced in factory m under cenario Quantity of product i hipped from factory m to DC w uing tranport mode k under cenario Quantity of product i hipped from DC w to market zone j uing tranport mode l under cenario We ue a two-tage programming approach (ee Birge and Louveaux (2011)) to formulate the problem under invetigation. For thi, deciion variable are plit into two categorie: cenario-independent variable, including X n, X um and X vw, and cenario-dependent variable, including all deciion variable except for X, X and X vw. Determining the value of cenario-independent variable i not n um reliant on the cenario realization. Thee are determined at tage 1. Deciion on cenario-dependent variable are then made in tage 2 once a diruption cenario i realized. The propoed model ha three primary objective function correponding to the economic, environmental and ocial performance of the SC. Objective function 1, formulated in Equation (1), repreent the cot performance of the SC under cenario. The component of Equation (1) include the cot of upplier evaluation and election, cot of etablihing factorie, cot of etablihing DC, cot of raw material, production cot, tranportation cot from factorie to DC, and tranportation cot from DC to market zone. The economic goal i to minimize the value of objective function (1). 10

15 Objective Function 1 fn Xn fum Xum fvw Xvw trnmqrnm n N u U m M v V w W r Rn N m M = gimpim timwk Yimwk tiwj vyiwjl i Im M i Im Mw W k K i I w W j J l L (1) Objective function 2, preented in Equation (2), calculate the aggregate weighted environmental core of all upplier under cenario. The environmental goal of the model i to maximize the value of objective function (2). Objective Function 2 r Rn Nm M rmn rnm = e Q (2) Objective function 3 i formulated in Equation (3) and compute the aggregate weighted ocial core of all upplier under cenario. The ocial goal of the model i to maximize the value of objective function (3). Objective Function 3 r Rn Nm M e rmn Q rnm = (3) The propoed model i ubject to the following contraint. Xum 1 m M u U X vw 1 w W (5) vv rnm Q a M r R, n N, m M, S (6) n N rnm Q r R, m M, S (7) rnm = hripim i I (4) im P wwk K imwk = Y i I, m M, S (8) Yimwk = Yiwjl,, m M k K j J l L Yiwjl dij,, w Wl L i I w W S (9) i I j J S (10) 11

16 ( 1 ) Qrnm an crn Xn,, m M i I im im um um u U A reilient and utainable upply chain: I it affordable? r R n N S (11) h P c X m M, S (12), hy it imwk cvw Xvw i Im Mk K v V X {0,1} n N n w W S (13) (14) X {0,1} u U, m M (15) um X {0,1} v V, w W (16) vw rnm Q 0 r R, n N, m M, S (17) im P 0 i I, m M, S (18) imwk Y 0 i I, m M, w W, k K, S (19) iwjl Y 0 i I, w W, j J, l L, S (20) Contraint (4) enure that no more than one factory can be etablihed in a candidate location. Contraint (5) applie the ame for etablihing DC. Contraint (6) enure that raw material are upplied to a factory only by upplier available to that factory. Contraint (7) guarantee the fulfillment of raw material requirement in factorie. Contraint (8), (9) and (10) repreent the flow balance contraint in factorie, DC and market location, repectively. Contraint (11), (12) and (13) enforce the capacity limitation of the upplier, factorie and DC, repectively. Contraint (14)-(20) define the domain of the deciion variable. 3.3 A Stochatic Fuzzy Goal Programming Approach In problem with more than one objective function, there i no one unique optimal olution that can atify multiple objective. In mot cae, an objective function i improved at the cot of compromiing at-leat one other objective. Multi-objective olution approache eek a tradeoff olution or a et of tradeoff olution (the co-called Pareto optimal olution) that imultaneouly atify multiple, uually conflicting, objective. 12

17 Numerou approache have been developed and applied to olve multi-objective mathematical problem. Argubaly, weighted um method and goal programming are amongt the implet and mot popular technique. Weighted um method aim to convert multiple objective into a ingle objective equivalent by aigning a weight to each objective function correponding to it importance (Arntzen et al., 1995). A weight will be a normalization contant if objective value have different unit/dimenion. In goal programing, intead of minimizing or maximizing the objective function, their deviation from goal, alo called apiration level, are minimized (Aouni and Kettani, 2001). A weighted goal programming approach aign weighting coefficient (or normalization contant if different dimenion) to the deviation value to generate a unified objective function. The primary difficulty with thee method i determining the weight of each objective function. A fuzzy programming approach (Zimmermann, 1978) aim to tackle thi by expreing the relative importance of each goal (Aköz and Petrovic, 2007; Chen and Tai, 2001; Naraimhan, 1980; Tiwari et al., 1987). Fuzzy goal programming ha been a popular approach to olving multi-objective operation, logitc and SC management problem and it application ha been tudied in a breadth of problem ranging from aggregate production planning (Jamalnia and Soukhakian, 2009; Wang and Liang, 2004) to upplier evaluation and election (Amid et al., 2006; Chen et al., 2006; Kumar et al., 2004), SC network deign (Özceylan and Pakoy, 2012; Selim and Ozkarahan, 2008) and SC planning (Liang, 2007; Selim et al., 2008; Torabi and Haini, 2008). For the multi-objective model encountered in thi paper, we propoe a tochatic fuzzy goal programming approach in which the expected value of the objective function are obtained for a et of diater cenario (the tochatic programming component) and then the weight of objective function are expreed uing a fuzzy linguitic approach (the fuzzy programming component). In other word, the tochatic and fuzzy apect are combined to tackle the co-occurrence of uncertainty in diruption likelihood and imprecie weight of objective function. The firt tep i to develop a et of diruption cenario to repreent ituation where one or more upplier are affected by diruption. We define cenario 1 a buine-a-uual where no diruption 13

18 occur. Next i to formulate the economic, environmental and ocial goal of the SC for both buinea-uual (=1) and upply diruption ituation. Uing Equation (1)-(3) a the three primary objective function, Equation (21)-(23) preent the economic, environmental and ocial utainability goal for the buine-a-uual and Equation (24)-(26) preent thee goal for upply diruption ituation (>1). Goal 1 (minimizing the SC cot in the buine-a-uual): 1 Minimize G1 = fn Xn + fum Xum + fvw Xvw + trnmqrnm n N u U m M v V w W r Rn N m M gimpim timwk Yimwk tiwjv Yiwjl i Im M i I m M w W k K i I w W j J l L (21) Goal 2 (maximizing the aggregate weighted EPS in the buine-a-uual): 1 Maximize G2 ermnqrnm r Rn Nm M = (22) Goal 3 (maximizing the aggregate weighted SPS in the buine-a-uual): 1 Maximize G3 ermn Qrnm r Rn Nm M = (23) Goal 4 (minimizing the expected SC cot in upply diruption): Minimize G4 n n um um vw vw n N u Um M v Vw W + S {1} q = f X + f X + f X trnmqrnm + gimpim r Rn N m M i I m M + imwk imwk + i I m M w W k K i I w W j J l L iwjv iwjl t Y t Y (24) 14

19 Goal 5 (maximizing the expected aggregate weighted EPS in upply diruption): Maximize G 5 r Rn Nm M S {1} Goal 6 (maximizing the expected aggregate weighted SPS in upply diruption): rmn rnm = qe Q (25) Maximize G 6 r Rn Nm M S {1} qe rmn Q rnm = (26) Fuzzy programming i ued to expre the relative importance of each goal. Equation (27)-(32) formulate the degree of atifaction of each goal (Aköz and Petrovic, 2007; Chen and Tai, 2001; Naraimhan, 1980; Tiwari et al., 1987). β Degree of atifaction of goal 1 = 1 G1 µ 1 = β α 1 1 G Degree of atifaction of goal 2 = 2 β2 µ 2 = α β 2 2 G Degree of atifaction of goal 3 = 3 β3 µ 3 = α β 3 3 β Degree of atifaction of goal 4 = 4 O4 µ 4 = β α 4 4 G Degree of atifaction of goal 5 = 5 β5 µ 5 = α γ 5 5 G Degree of atifaction of goal 6 = 6 β6 µ 6 = α β 6 6 (27) (28) (29) (30) (31) (32) Where α 1 α 6 denote the apiration level of the goal 1-6, repectively. β 1 and β 4 repreent the upper tolerance limit for the total SC cot in buine-a-uual (goal 1) and upply diruption (goal 4) ituation, repectively. β 2 and β 5 denote the lower tolerance limit for the aggregate EPS in buinea-uual (goal 2) and upply diruption (goal 5) ituation, repectively. Likewie, β 3 and β 6 indicate the lower tolerance limit for the aggregate SPS in buine-a-uual (goal 3) and upply diruption (goal 6) ituation, repectively. 15

20 Linguitic term are ued to expre the comparative importance of each goal. The linguitic term include ignificantly more important, moderately more important, lightly more important, and equally important. For example goal 1 can be ignificantly more important than goal 2, and goal 2 can be equally important to goal 3. To implify the notation, let u et R 0, R 1, R 2, and R 3 denote the relation equally important, lightly more important, moderately more important, and ignificantly more important, repectively. Alo, let Rzz (, ) denote the imprtance relationhip between the two goal z and z (i.e. the imprtance relationhip between G z and G z ). For example, R (1, 3) = R implie that goal 1 ( 1 2 G ) i moderately more important than goal 3 ( G 3 ). Uing the approach introduced by Aköz and Petrovic (2007), the propoed tochatic fuzzy goal programming model can be formulated a: z + ( ) Rzz (, ) z= 1 z= 1 z = 1 maximize λ 1 µ λ µ (33) The propoed model i ubject to: Contraint (4)-(20) Contraint (27)-(32) µ z 1 z = 1, 2,...,6 (34) µ µ + 1 µ for all and Rzz (, ) = R 1 (35) z z R (, ) 1 zz µ z µ z z z R (, ) 3 zz for all Rzz (, ) = R (36) µ R (, ) 2 zz 2 µ µ µ for all Rzz (, ) = R 3 (37) µ for all Rzz (, ) 1 R ( zz, ) (38) µ z 0 z = 1, 2,...,6 (39) µ for all Rzz (, ) 0 R ( zz, ) (40) 16

21 In thi model, the priority tructure (i.e. the importance relationhip between the goal) may be only atified to a certain degree. µ R i defined a a deciion variable that repreent the degree of ( zz, ) atifaction of the importance relationhip Rzz (, ). Changing parameter λ within the interval [0,1] (i.e. 0 λ 1) generate different olution. A λ decreae, the relative priority relation receive greater weight and olution that better atify thee relation will be ought. A uitable value for λ need to be determined by a deciion maker through a parameter adjutment exercie. More detail about the fuzzy goal programming approach can be found in Aköz and Petrovic (2007). 4. Cae Study and Dicuion 4.1 The Cae Environment and Deciion Scenario ACO i a multinational corporation involved in the production and ditribution of portwear clothing. ACO i headquartered in Autralia and ha factorie in four Aian countrie China (Quanzhou), Vietnam (Ho Chi Minh), Cambodia (Phnom Penh) and Bangladeh (Dhaka). Synthetic fabric i the primary raw material ued in all product type. The required fabric at each factory are ourced from a number of local upplier. The factorie in China and Bangladeh are each erved by ix raw material upplier and factorie in Vietnam and Cambodia have five local upplier each. Synthetic fiber are produced at upplier ite by forcing liquid through tiny hole in a metal plate, called a pinneret, and allowing them to harden. The ue of different liquid and pinneret produce variou type of fiber uch a polyeter, nylon, acrylic and rayon. The fiber production proce i highly energy intenive and involve ubtantial water ue. ACO manufacture four familie of product including top, pant, hort, and jacket. Production procee are identical in all factorie and include deign, cutting, ewing, aembly, and packaging. In a SC reconfiguration problem, which i the cope of thi cae tudy analyi, a factory can be reized to match the network requirement. The capacity of a factory can be increaed at a fixed facility expanion cot. Three capacity level are conidered for a factory correponding to the required production output. 17

22 Product are hipped from factorie to wholealer (market zone) in the five Autralian tate of New South Wale (NSW), Victoria (VIC), Queenland (QLD), South Autralia (SA) and Wetern Autralia (WA) through three DC in WA (Perth), SA (Adelaide) and NSW (Sydney). A DC can be leaed in three ize: large, medium, and mall. The leae are igned for trategic period, typically longer than two year, to allow for the long-term intallation of helve and material handling ytem. Sea tranport i the only option for the hipment of product from Aian factorie to Autralian DC (although, ample for deign purpoe are uually hipped via air tranport). The inbound tranportation for the hipment of item from DC to wholealer can be via rail, road and ea tranport mode. The chematic view of the SC for ACO i hown in Figure 1. A ytematic mechanim wa employed in 2014 for aement and coring the environmental and ocial performance of each upplier (determining aggregate EPS and SPS value). A panel of indutry expert, compried of three individual from two Aian and one Autralian utainability conultancy firm with pecialized expertie in the apparel indutry, wa formed to ait with thi proce. Due to the energy and water intenive nature of ynthetic fabric production, alternative energy ource and water conumption were identified by the panel of expert a the primary performance metric for determining EPS. The upplier GHG emiion performance wa alo added a a third criterion in repone to the global emiion reduction trend and regulatory mandate. The three criteria were weighted baed on their importance a , correponding to available energy ource, water conumption and GHG emiion generation, repectively. For the ocial aement criteria, the performance metric defined in the reporting guideline of GRI (GRI, 2011) were ued to et the foundation. The criteria were further refined by the panel of expert to thoe concerning the trategic SC deciion for ynthetic product manufacturing in Aia-pacific region. A imilar approach ha been undertaken in the pat by other reearcher (Boukherroub et al., 2015; Pihvaee and Razmi, 2012; Pihvaee et al., 2012; You et al., 2012). The criteria were organized in four equally weighted categorie of labor practice and decent work (including fair wage, working condition, occupational health and afety, and training and education), human right (including child 18

23 labor, forced labor, and dicrimination incident), ociety (including local community invetment and public policy involvement), and product reponibility (including product labeling and cutomer privacy). Once the environmental and ocial performance criteria were etablihed, ite viit and direct invetigation were completed by the panel of expert to ae the upplier performance againt each criterion. All obervation related to the upplier auditing proce were documented. The performance of each upplier againt each criterion received an aement core on a cale of 1-10, with 10 being the bet practice. With thee aement core, the aggregate weighted EPS and SPS value for each upplier could then be generated uing a weighted averaging method (i.e weighted criteria for EPS calculation and equally weighted criteria for SPS calculation, a dicued above) for the upply of a certain raw material to factory. For the purpoe of our analye in thi paper, upplier of each factory are numbered on the bai of their EPS and SPS value. For example, for the factory in China, Ch6 (upplier #6) poe the highet EPS and SPS, while Ch1 (upplier #1) how the pooret utainability performance amongt the ix. 19

24 Figure 1. The SC configuration in ACO 20

25 Our experiment and dicuion in thi ection focu on the upplier utainability performance and it impact on the overall SC reilience. The reaon for thi tudy cope i the paramount importance of the utainability performance and reliability of ynthetic fiber upplier in garment manufacturing (which i alo the cae in many other indutrie). To help our analye and dicuion, a et of diruption cenario are defined o that the SC utainability tradeoff can be invetigated in both buine-a-uual and upply diruption ituation. The characteritic of the diruption cenario are hown in Table 1. Scenario 1 repreent the SC tatu in buine-a-uual when no upply diruption occur. Scenario 2-23 repreent ituation when one upplier i affected by an unforeeen diruption (i.e. one upplier i affected at a time). Scenario repreent ituation when all upplier of a factory in one region are affected imultaneouly (i.e. no production occur in that region). Obviouly, additional diater cenario can be developed compriing other poible combination of affected upplier. However, our aim and focu in thi ection i to illutrate the application of the propoed model and methodology for a reaonable number of cenario. Table 1. Characteritic of the upply diruption cenario Diruption cenario Buine-a-uual cenario Scenario 1 (1) Supply diruption cenario Scenario 2-7 (2-7) Scenario 8-12 (8-12) Scenario (13-17) Scenario (18-23) Scenario 24 (1) Scenario 25 (1) Scenario 26 (1) Scenario 27 (1) Affected upplier() Ch1-Ch6 affected, repectively V1-5 affected, repectively C1-C5 affected, repectively B1-B6 affected, repectively Ch1-Ch6 imultaneouly affected V1-5 imultaneouly affected C1-C5 imultaneouly affected B1-B6 imultaneouly affected The model preented in Section 3 wa coded in GAMS The following ection preent a tatic utainability tradeoff analyi (in buine-a-uual) and a dynamic utainability tradeoff analyi (under potential upply diruption) for the propoed cae company and it parametric data. All 21

26 experiment are completed on a laptop with Intel Core i7-4702hq CPU, 2.2GHz with 16GB of RAM. The runtime are not reported ince they were hown to be negligible (only a few econd in mot run). 4.2 Static Sutainability Tradeoff Analyi Thi ection preent a baic SC utainability analyi that aim to explore the tradeoff between the economic and non-economic goal in a buine-a-uual environment (i.e. diregarding the likelihood of a diruption occurrence). The non-economic utainability goal include both environmental and ocial goal. In aid of a more focued dicuion, we aume equal importance of the environmental and ocial goal and focu our analye on evaluating the tradeoff between the economic goal (minimizing the SC cot) and the non-economic goal (minimizing the equally-weighted aggregate EPS and SPS value). Figure 2 how the initial reult of the tatic utainability analyi (uing the goal 1-3 in Equation (21)-(23)), for variou degree of the relative importance of the economic goal to the non-economic goal. The figure illutrate how the economic, environmental and ocial performance of the SC varie with change in the relative importance of the economic goal. Not urpriingly, the greater i the relative importance of the economic goal, the lower i the SC cot and average aggregate EPS/SPS value. The SC cot in thi cae increae nonlinearly, by a much a 17%, while the economic and environmental performance of the SC (meaured by the average weighted aggregate EPS and SPS value) rie relatively linearly a the relative importance of the economic goal diminihe. Thi obervation can help a deciion maker identify opportunitie where greater enhancement in environmental and ocial performance can be achieved per dollar SC cot increae. 22

27 Figure 2. Static analyi: SC performance when varying the relative importance of the economic goal The relative importance of the economic goal to the non-economic goal ha impact on the ourcing deciion and ubequently on the overall configuration of the SC (i.e. location and capacity of factorie and DC). Table 2 and Table 3 how the reulting ourcing and facility location/capacity deciion for each relative importance degree of the economic goal. Table 2 how the level of involvement of each upplier. In all four ituation, approximately half of the upplier are utilized for raw material acquiition. There are upplier that are elected under all configuration (V3-V4 and C4-C5) and thoe that are not elected under any (Ch1-Ch3 and C1). The level of a upplier involvement i obviouly a function of it economic, environmental and ocial performance. Evidently, there i a tendency to elect the more utainable upplier a the economic goal become le emphaized. Table 3 how variation in the location and capacity of SC facilitie a change occur in the relative importance of the economic goal. We ee that deciion on a factory location and it production capacity are very much dependent on the related ourcing deciion. All configuration, regardle of the degree importance of economic goal, etablih one medium and two mall factorie. Under no circumtance i a large factory opened. Factory m1 in China, the mot utainable in term of it upplier performance, i the leat preferred option (alo confirmed by the ourcing deciion in Table 2) unle the economic 23

28 and non-economic goal are equally weighted. The factory location reult do not hold for locating DC. All DC are operational in all configuration to atify the product ditribution requirement, although w1 i alway the mallet in ize amongt the three. Table 2. Static analyi: percentage raw material purchaed from each upplier under different SC configuration Supplier Significantly more important Relative importance of the economic goal to the non-economic goal Moderately more important Slightly more important Equally important Ch1 Ch2 Ch3 Ch4 1.7 Ch Ch V V V V V C1 C2 0.6 C C C B1 9.4 B2 3.1 B3 1.4 B B B Table 3. Static analyi: change in facility location/capacity deciion when varying relative importance of the economic goal Factorie DC Relative importance of the economic goal m1 m2 m3 m4 w1 w2 w3 Significantly more important S* S M S L S Moderately more important S M S S M M Slightly more important M S S S M M Equally important S M S S M M * Facility ize L: Large, M: Medium, S: Small 24

29 Now, let u examine how a SC developed through the tatic utainability analyi can cope with unforeeen upply diruption. None of the four SC configuration reulting from the tatic utainability analyi are able to fully atify the demand of all market in diruption cenario a defined in Table 1. Figure 3 how the average and maximum percentage lot ale generated when upply diruption occur (i.e. average percentage lot ale obtained from model run in 26 upply diruption cenario outlined in Table 1). While the SC may experience a much a 40 percent demand under-fulfillment in a wort-cae cenario, on average between 7 and 8 percent of the entire ale will be unatified when diruption occur. Thee rate are almot independent of the relative importance of the economic goal. Therefore, we conclude that none of the four SC configuration can provide a feaible olution to the problem in diruption. With no feaible olution available, providing a complete and comparative tradeoff analyi for thee four SC configuration i not poible. One may ugget increaing the maximum production capacity of factorie a an eay-fix trategy to hift production between factorie when upply diruption occur in one region. To examine thi propoition, we performed a et of experiment in which we increaed the production capacity of factorie by 10 time. We found that not only did the trategy fail to find a feaible olution in diruption, but alo that the quantity of lot ale wa increaed by about two time. The reaon for thi i that fewer factorie are opened to atify the ame demand in the buine-a-uual ituation when higher-capacity factorie are ued. In thi cae, when a factory i affected by a upply diruption, there are fewer other factorie and upplier to compenate the upply hortage. Thu, increaed production capacity cannot help improve demand fulfillment in the face of upply diruption. The above dicuion explain why a tatic tradeoff analyi i implitic and hence impractical in a real world context. The next ection explain how a dynamic utainability tradeoff analyi can help ACO deign a SC that i able to provide efficient and effective olution in both buine-a-uual and diruption ituation. 25

30 Figure 3. Static analyi: abolute and percentage lot ale in the face of diruption 4.3 Dynamic Sutainability Tradeoff Analyi For the cae tudy and it parametric data, we now complete a dynamic tradeoff analyi where ourcing and facility location deciion are made conidering the SC performance in both buine-a-uual and upply diruption ituation (i.e. all ix goal formulated in Section 3.3 are ued for thi analyi). Figure 4 how the reult in both ituation (i.e. buine-a-uual and upply diruption) when varying the importance degree of the economic goal and the importance degree of the buine-a-uual performance. The figure illutrate the economic and non-economic performance of 16 SC configuration in buine-a-uual and diruption ituation (i.e. 32 performance et in total). For a given relative importance degree of the economic goal (column 1), four SC configuration are generated correponding to the relative degree importance of the SC performance in buine-a-uual (relative to the SC performance in diruption). Similar to what we preented for the tatic tradeoff analyi in Table 2 and Table 3, ourcing deciion and facility location/capacity deciion correponding to each of the 16 SC configuration are preented in Table 4 and Table 5 for the dynamic tradeoff analyi. It hould be noted that all reult hown in the SC performance in diruption column of Figure 4 are obtained from olving Equation (24)-(26) which relate to the average SC performance in 26 upply 26

31 diruption cenario defined in Table 1. For example, average expected SC cot i calculated by averaging 26 expected cot value obtained from 26 upply diruption cenario. Similarly, EPS and SPS value are obtained from averaging the weighted aggregate EPS and SPS value in 26 upply diruption cenario. Thi being aid, a total of 108 individual model run (4*1 + 4*26) were completed to obtain the required data for thi dynamic analyi. 27

32 SC performance in buine-a-uual SC performance in diruption The economic goal i ignificantly more important Figure 4a. Figure 4b. The economic goal i moderately more important Figure 4c. Figure 4d. The economic goal i lightly more important Figure 4e. Figure 4f. The economic goal i equally important Figure 4g. Figure 4h. Figure 4. Dynamic analyi: SC performance in buine-a-uual and diruption ituation 28

33 The buine-a-uual performance i ignificantly more The buine-a-uual performance i moderately more The buine-a-uual performance i lightly more important The buine-a-uual performance i equally important A reilient and utainable upply chain: I it affordable? Table 4. Dynamic analyi: percentage raw material purchaed from each upplier under different SC configuration i i 29 Relative importance of the economic goal Significantly more important Moderately more important Slightly more important Equally important Significantly more important Moderately more important Slightly more important Equally important Significantly more important Moderately more important Slightly more important Equally important Significantly more important Moderately more important Slightly more important Equally important Situation Ch1 Ch2 Ch3 Ch4 Ch5 Ch6 V1 V2 V3 V4 V5 C1 C2 C3 C4 C5 B1 B2 B3 B4 B5 B6 Buine-a-uual Diruption Buine-a-uual Diruption Buine-a-uual Diruption Buine-a-uual Diruption Buine-a-uual Diruption Buine-a-uual Diruption Buine-a-uual Diruption Buine-a-uual Diruption Buine-a-uual Diruption Buine-a-uual Diruption Buine-a-uual Diruption Buine-a-uual Diruption Buine-a-uual Diruption Buine-a-uual Diruption Buine-a-uual Diruption Buine-a-uual Diruption