Analysis of the Sustainable Supply Chain Structure with Incomplete Preferences

Transcription

1 Proceedings of the Word Congress on Engineering 00 Vo III WCE 00, June 0 - Juy, 00, London, U.K. Anaysis of the Sustainabe Suppy Chain Structure with Incompete Preferences. Büyüözan and. Çifçi Abstract Integration of sustainabe deveopment in the business and suppy chain is potentiay a source of competitive differentiation for firms. Sustainabe suppy chain (SSC) management is the management of materia, information and capita fows as we as cooperation among companies aong the suppy chain whie taing goas from a three dimensions of sustainabe deveopment, i.e., economic, environmenta and socia, into account which are derived from customer and staehoder requirements. By using the quaity function depoyment (QFD) as a product/system deveopment too, an effective SSC structure can be obtained. The objective of this study is to appy an extended QFD methodoogy in SSC by introducing a new group decision maing (DM) approach that taes into account incompete information of decision maers by means of fuzzy set theory. This methodoogy is compatibe with the requirements of the various staehoders, suppiers, manufacturers and cients, invoved in the suppy chain. Finay, to assess the vaidity of the proposed approach, a specific suppy chain exampe is given. Index Terms Fuzzy sets, incompete preference reations, quaity function depoyment, sustainabe suppy chain management. I. INTRODUCTION Organizations wordwide are continuousy trying to deveop new and innovative ways to gain and maintain a competitive advantage in the goba maret. In response to heightened governmenta reguations and rising pubic awareness of the effect of industria production on the environment, many organizations are now undertaing major initiatives to transform their suppy chain processes []. Consideration is given to the convergence of suppy chains and sustainabiity []. The emergence of sustainabe suppy chain (SSC) is one of the most significant deveopments in the past decade, offering the opportunity for companies to aign their suppy chains in accordance with environmenta and sustainabiity goas. reat efforts have recenty resuted in increasing the environmenta performance of suppy chains. However, to obtain more sustainabe soutions, organization properties must meet SSC and customer requirements. Especiay, quaity function depoyment (QFD) is one of the techniques for designing needs of customer and turning them into Manuscript received March, 00. This wor was financiay supported by The Scientific and Technoogica Research Counci of Turey (TUBITAK).. Büyüözan is with the Industria Engineering Department, aatasaray University, İstanbu, 457 Turey (corresponding author to provide phone: ; fax: ; e-mai: gucin.buyuozan@gmai.com ). ISBN: ISSN: (Print); ISSN: (Onine) practica measures. This approach enabes the firms to become proactive to quaity probems rather than taing a reactive position by acting on customer compaints. The approach bases on tota quaity management, which offers a vast technique to ensure the improvement of quaity and productivity. QFD is comprised of major group decision maing (DM) processes. In practice, determining the weights of customer requirements (CRs) is a DM process. This mainy because of the danger of reying on a singe decision maer (DM) with his/her imitations of experiences, preferences or biases about the issues invoved, and the fact that individuas are often unabe to ceary identify their own states. Mutipe DMs, thus DM, are often preferred rather than a singe DM to avoid the bias and minimize the partiaity in the decision process [] [6]. eneray, different and/or even subjective opinions are quite often in a DM process due to the imitations of experience and impreciseness. Obviousy, the importance of each CR in QFD is determined by a group of peope with ambiguity. In addition, due to constraints as time pressure, ac of expertise in reated issue, etc.; decision maers (DMs) may deveop incompete preferences in which some of the eements cannot be provided. Under such circumstances, fuzzy set theory [7] and incompete preference reations [8] [4] can be appied to dea with group decisions when the information is imprecise and missing. This paper deveops a new fuzzy ogic based DM approach in QFD with incompete preference reations. Moreover, a specific suppy chain exampe is provided to show the proposed DM approach can be effectivey used in QFD. As incompete preferences in fuzzy environments are not widespread yet, there exists no study in iterature that neither combines it with QFD or any other methods, nor appies it in SSC management (SSCM) fied. The paper is organized as foows. In section, SSCM concept and mode description are given. Section describes the proposed approach and computationa procedure step by step. After the appication of the SSC mode in Section 4, Section 5 contains some concuding remars. II. SUSTAINABLE SUPPLY CHAIN MANAEMENT A. Requirements for a SSCM Firsty to provide a sense of its concept, SSC contains the integration of environment, economic and socia performances. The interaction between sustainabiity and suppy chains is the critica next step from recent. Çifçi is with the Industria Engineering Department, aatasaray University, İstanbu, 457 Turey (e-mai: gizem.cifci@gmai.com ). WCE 00

2 Proceedings of the Word Congress on Engineering 00 Vo III WCE 00, June 0 - Juy, 00, London, U.K. examinations of operations and the environment [5] and operations and sustainabiity [6]. Interest in green and now sustainabe suppy chains has been growing for over a decade and the topic is becoming mainstream [6] [9]. Seuring and Müer [0] define SSC as the management of materia, information and capita fows as we as cooperation among companies aong the suppy chain whie taing goas from a three dimensions of sustainabe deveopment, i.e., economic, environmenta and socia, into account which are derived from customer and staehoder requirements. Another definition comes from Page and Wu [8]: SSC is one that performs we on both traditiona measures of profit and oss as we as on an expanded conceptuaization of performance that incudes socia and natura dimensions. Discussions of sustainabiity are driven by the basic notion that a suppy chain s performance shoud be measured not just by profits, but aso by the impact of the chain on ecoogica and socia systems [8], [] []. Different authors and researchers have defined SSCM from simiar and different perspectives, driving forces and purposes. However, a SSCM structures have commony tree main aspects, namey economica, environmenta and socia. Fig. depicts the requirements defined according to iterature survey and expert views. Fig.. Requirements from a SSC Economica requirements (CR ): There is no doubt that cost reduction and continuing financia benefit are fundamenta goas of a suppy chain. A number of studies have found that an increased emphasis on sustainabiity in the suppy chain is reated to ower costs and a neutra or positive effect on vaue [4] [6]. Asset utiization is another important concern in SSC [7] [0]. Reducing the materias used is needed for efficiency of the suppy chain. Quaity eve shoud be maximized because quaity is sine qua non for environmenta protection and sustainabe deveopment. Quaity is a widey accepted performance indicator for SSCs and it represents a common driving force for sustainabe suppy activities [4], [], []. Finay, to enhance customer service is one the main focuses of SSCs. Severa studies identified a trend that organizations are integrating environmenta processes to their suppy chains to reduce operating costs and improve their customer service [4] [6]. Environmenta Requirements (CR ): The four major economica requirements dimensions are waste reduction, emission reduction, energy efficiency and conservation. The environmenta based expectations of companies from a SSC are reduction of waste produced, materia substitution through environmenta sourcing of raw materias, waste minimization of hazardous materias, efficient use of energy, fue and ISBN: ISSN: (Print); ISSN: (Onine) environment conservation [], [8], [0], []. The environmenta practices are dependent on wider aspects to be integrated in order to achieve firm s goa of waste eimination and ower environmenta impact. Hence, firms must integrate environmenta aspects to ensure corporate surviva and toward sustainabe deveopment. Rothenberg [] noted that poution prevention-environmenta activities are often vaue-added for the firm since they reduce costs through materia use reduction or through the avoidance of waste management costs. Socia Requirements (CR ): Socia requirements comprise four main dimensions such as reduced impact on community, heath and safety, strengthened reationships, and aws and reguations. The aims to compy with ega requirements and to create a systematic management system have been reported as important driving forces for companies to impement sustainabe/environmenta activities [6], [], [4]. Commitment to heath and safety which meets minimum ega requirements is aso needed as a socia responsibiity in a SSC. As fina target, strengthened customer and business partner reationships is an important requirement for a SSC. By this means, firms can gain competitive advantage and improve firm performance. B. Design Requirements for a SSCM structure As a resut of the iterature survey in the previous section and expert views, determined design requirements (DRs) for a SSCM are: DR : Price strategy DR : SC optimization DR : Suppier management DR 4 : Fexibe technoogy DR 5 : Deivery performance DR 6 :Environmenta management system (ISO quaity standards) DR 7 : Environmenta product design DR 8 :Environmenta activity capabiity (reuse, recyce, bac pacaging,...) DR 9 : Eco-friendy transportation DR 0 : Coaboration with partners DR : Empoyee Practices (abor education, ) DR : Protecting rights (suppiers, customers, property, etc.) A. HOQ of QFD III. INTERATED QFD METHODOLOY QFD was originay deveoped by Yoji Aao in 966 when the author combined his wor in quaity assurance and quaity contro points with function depoyment used in Vaue Engineering. Mr. Aao described QFD as method to transform user demands into design quaity, to depoy the functions forming quaity, and to depoy methods for achieving the design quaity into subsystems and component parts, and utimatey to specific eements of the manufacturing process [5]. Athough there have been many QFD studies and appications in the iterature, different authors use different terms and methods and they aso focus on different parts of the QFD system. There have been no consistent or WCE 00

3 Proceedings of the Word Congress on Engineering 00 Vo III WCE 00, June 0 - Juy, 00, London, U.K. unified accounts of the QFD concepts and procedures, which is uncommon for such a popuar methodoogy and may be quite confusing for non-speciaists [6]. The technique is characterized by a matrix caed the House of Quaity (HOQ) which is represented in Fig.. This matrix contains information about what to do (e.g., what customers want), how to do it (e.g., how technicay CRs can be achieved), and the reationships between each of these aspects; prioritization of CRs and DRs; and what are the company's target eves. Quaity functions are depoyed by carrying ''how to do'' into the successive HOQ as ''what to do'' [7]. The detaied steps of HOQ appied in this paper can be seen in Sec. III-C. Whats - SSC Customer Requirements Hows SSC Design Requirements Reation Matrix DR Anaysis Figure. HOQ for SSC deveopment B. Incompete Preference Reations CR Anaysis Recenty, inguistic preference reations used by decision maers to express their inguistic preferences when comparing decision aternatives have been investigated in many documents [8] [4]. Each of these preference reations necessitates the competion of a n(n-)/ judgments in its entire top trianguar portion. Sometimes, however, it is difficut to obtain such a preference reation. This may be due to an expert not possessing a precise or sufficient eve of nowedge of part of the probem, or because that expert is unabe to discriminate the degree to which some options are better than others [0]. Since QFD approach contains mutipe DMs and group decision process, with the use of incompete preference reations, the evauation woud be more strong and heathier. As incompete fuzzy preferences are not widespread as of today, incompete preference studies are imited in the iterature [0] [4], [4], [4]. In addition there exists no study in the iterature that neither combines those with QFD, nor any appications in the SSCM fied. C. The Computationa Procedure of Proposed Approach Step by step description of the proposed approach is as foows. Step : Whats : This first step can aso be caed as identifying the CRs. In this step customer requirements must be identified and paced on the eft side of the house. These requirements can be identified by maing questionnaires to customers, by expert views, by iterature survey, etc. Step : Priority Anaysis : In this step, a comparison of the CRs is wanted to determine the importance degrees. However, the information gained from DMs may not be adequate to accuratey assign the importance degrees. We wi overcome this obstace through fuzzy DM. Step.: CR Evauation. Firsty, for the purpose of measuring the importance degrees among CRs, it is required to design a comparison scae. The scae shown in Tabe I is used to indicate the reative strength of each pair of eements ~ p = (p, p m, p u ) which indicates the importance among the compared criteria (importance of i over j) where i = j =,,,n. Tabe I. Corresponding inguistic terms Linguistic variabes Fuzzy Scaes No infuence (No) ( ) Very ow infuence (VL) (0. 0., 0.) Low infuence (L) (0., 0., 0.5) Medium infuence (M) (0., 0.5, 0.7) High infuence (H) (0.5, 0.7, 0.9) Very high infuence (VH) (0.7, 0.9, ) Extreme infuence (E) (0.9,, ) Step.: Competion of the missing vaues. Once the DMs construct and evauate the fuzzy pairwise comparison matrices of components, defuzzify those using Eq. (). F p / p ~ inf ~ sup p ~ x x d () 0 Then, missing vaues in a DM s incompete preference reation can be computed. Based on Tanino s [44] additive transitivity property, by using an intermediate aternative a y, the preference vaue of p (i j) can be cacuated in three ways [0]:. From p p p 0. 5, we obtain the estimate iy yj y cp piy p yj 0.5 (). From p p p 0. 5, we obtain the estimate yj yi y cp p yj p yi 0.5 (). From p p p 0. 5, we obtain the estimate iy jy y cp piy p jy 0.5 (4) The preference vaue of one aternative over itsef is aways assumed to be equa to 0.5. Step.: Checing the consistency eve. When woring with the incompete preference reation, the foowing sets can be used to estimate its consistency eve: H = {y i, j (i, y), (y, j) EV} (5) H = {y i, j (y, i), (y, j) EV} (6) H = {y i, j (i, y), (j, y) EV}. (7) where EV is the set of pairs of aternatives for which the expert provides preference vaues, and H, H, H the sets of intermediate aternative a y (y i,j) that can be used to estimate the preference vaue p (i j) using (5) (7), respectivey. The consistency eve CL, associated with a preference vaue p (i j) EV, p CP i CP j CL, α [0,] (8) is defined as a inear combination of the average of the competeness vaues associated to the two aternatives invoved in that preference degree CP i and CP j, # EV CP i (9) ( n ) are ISBN: ISSN: (Print); ISSN: (Onine) WCE 00

4 Proceedings of the Word Congress on Engineering 00 Vo III WCE 00, June 0 - Juy, 00, London, U.K. where # EV is the number of preference vaues nown. And its associated error p, p p p p (0) where cp # p H p j H, if # H 0;,, () 0, otherwise and, if # H 0 # H 0 # H 0 a b c, if # H 0 # H 0 # H 0; a, b, c,, (), otherwise. with α = f # EVi # EVj # EVi EVj, being f a decreasing function with f 0 and 4( n) 0 f, # EV i # EV j # EV i EV j. () 4( n ) Detaied information about incompete fuzzy preference reations and their mathematica formuation are given in [0]. Step.4: Aggregation of the evauations. This process wi refect the opinions of the majority of the DMs. On this ine, et p,..., p be the set of vaues to be aggregated for any i, jr where the number DMs is denoted as =,..., L. Then the ordered weighted geometric (OW) operator which is defined as: L L p p,..., p p w,. (4) where, W( w,..., w L ) is an exponentia weighting vector, such that w 0, and w, and each p is the th argest vaued eement in the set L p, p,..., p [4], [45]. The OW operator refects the fuzzy majority if we cacuate its weighting vector W by means of a fuzzy inguistic quantifier [46]. In this study, we mae use of the fuzzy majority which is a soft majority concept expressed by a fuzzy inguistic quantifier. Proportiona quantifiers, such as most, at east haf, may be represented by fuzzy subsets of the unit interva, [0,]. Then, for any r [0,], Q(r) indicates the degree to which the proportion r is compatibe with the meaning of the quantifier it represents. For a non decreasing reative quantifier, Q, the weights are obtained as w Q/ L Q / L, =,,L where Q(y) is defined as: 0, if y < a; (y - a)/(b - a), if a y b; and, if y b. Note that a, b, y [0,] and Q(y) indicates the degree to which the proportion y is compatibe with the meaning of the quantifier it represents. Some exampes for the reative quantifiers are most (0., 0.8), at east haf (0, 0.5) and as many as possibe (0.5, ). When the fuzzy quantifier Q is used for cacuating the weights of the OW operator represented by Q W, it is. Therefore, the coective mutipicative reative importance reation is obtained as foows; Q L p p, p,..., p, i j n. (5) ISBN: ISSN: (Print); ISSN: (Onine) Step.5: Obtaining priorities from the judgment matrix. After the group opinion coected in the matrix P, it must be expoited to determine the importance weights of the criteria. Note that in P, the eement refects the reative importance of criterion i compared to criterion j. Next, cacuate the quantifier guided importance degree of each criterion, which quantifies the importance of one criterion compared to others in a fuzzy majority sense. By using the OW operator, we have QID i og 9 Q p : j,..., n. (6) for a i =,...,n. Finay, the obtained QID i vaues shoud be normaized, i.e., QIDi QIDi i QIDi /, to have the importance degrees in percentage. These steps need to be pursued in a nodes of the evauation mode. Step : Hows : This step can aso be caed as deveoping/defining DRs. The first step of the DR part is transforming CRs to technica attributes. DRs are specified on the basis of the company s operationa or manageria resource aocation pans in order to satisfy the customers. In defining the DRs, the most important point is, finding direct soutions to defined CRs. Step 4: Reation Matrix : Here reationship matrix is constructed between CRs and DRs. Each of the DRs is correated individuay to each of the CRs by considering to what extent a requirement contributes to meeting customer needs for the attribute. Depending upon the impact of the DRs on meeting CRs for the attribute, Empty=no reationship, =possibe reationship, =moderate reationship, and 9=strong reationship is assigned. Step 5: Prioritizing DRs : The importance of each DR is computed using the reationship matrix and the reative importance of each CR. The accuracy of the resuts in this step reies heaviy on the quaity of the reationship matrix. This computation process intertwines CRs with DRs. That is, the resuting vaue determines the reative weight of each DRs as compared to CRs. The importance of each DR is cacuated as the sum of each CR importance vaue mutipied by the quantified reationship between the same CR and the current DR. IV. APPLICATION OF THE SSC MODEL Here, the SSC mode determined in Sec. II is iustrated with the proposed approach. In our exampe there are three DMs, namey project manager, R&D engineer and top manager. Step : Whats : The SSC mode determined in Sec. II comprises the whats-crs. Step : Priority Anaysis : Step.: Tabe II gives the evauation of the DMs for the purpose of measuring the importance degrees among primary eve factors namey economic (CR), environmenta (CR) and socia (CR) requirements. Tabe II. Incompete inguistic evauation of DMs DM DM DM CR CR CR CR CR CR CR CR CR CR - M M - x x - x x CR x - x M - VH x - x WCE 00

5 Proceedings of the Word Congress on Engineering 00 Vo III WCE 00, June 0 - Juy, 00, London, U.K. CR x x - x x - VH H - Step.: To compete the missing vaues, firsty by using Eq. (), Tabe III which shows the defuzzified incompete preferences of the group is obtained. Eqs. (-4) are used to estimate the missing vaues shown in Tabe IV. Tabe III. Defuzzified incompete evauation of DMs DM DM DM CR CR CR CR CR CR CR CR CR CR x x - x x CR x - x x - x CR x x - x x Tabe IV. Estimated compete evauation of DMs DM DM DM CR CR CR CR CR CR CR CR CR CR CR CR As an exampe, to estimate p in the evauation of DM, the procedure is as foows: H = {} as cp = p p0. 5= unnown H H = Ø as cp = p p 0. 5 = 0.50 = Ø as cp = p p 0. 5 = unnown thereby, cp = Step.: After missing vaues are competed, finay consistency is checed. The corresponding consistency eve matrix is shown in Tabe V. Tabe V. Consistency eves of DMs evauations DM DM DM CR CR CR CR CR CR CR CR CR CR CR CR Continuing on the same exampe, the consistency eve is cacuated using Eqs. (8-) as foows: EV = {(,),(,)}; EV = {(,)}; EV = {(,)}. CP = /4, CP = /4, CP = /4. / As there is no intermediate aternative to cacuate an estimated vaue except a, consequenty p 0 and, /4/4 CL 0,67 0 0, Step.4: Taing into account a matrices obtained from project managers group, using of Eqs. (4-5), the OW operator with fuzzy inguistic quantifier at east haf is used to compute the group importance reation matrix as shown in Tabe VI with weighting vector (0.666, 0.4, 0.000). Tabe VI. Importance reation matrix of DMs CR CR CR CR CR ISBN: ISSN: (Print); ISSN: (Onine) CR Step.5: Eq. (6) is used to compute group aggregated importance vaues with weighting vector (0.066, 0.667, 0.67) corresponding to the fuzzy inguistic quantifier most. The coaborative importance vaues are cacuated as (0.994, 0.766, 0.6). Then by normaizing these vaues, priority of the primary eve requirements is determined as (0.5, 0., 0.). Same computationa procedure is carried out for a secondary eve comparisons to obtain the priorities. Finay, goba importance vaues are cacuated by mutipying primary eve priorities with reated secondary eve priorities. Step : Hows : The SSC mode determined in Sec. II again comprises the hows-drs. Step 4: Reation Matrix : Reation degrees between CRs and DRs can be seen from the fina HOQ matrix in Fig.. For instance, first economica factor cost reduction is in direct reationship with amost a the DRs because firms essentiay aim to provide reduction in costs. Reationship Strength: S: Strong M: Moderate P: Possibe CR CR CR CR DR DR DR DR 4 DR 5 DR 6 DR 7 DR 8 DR 9 DR 0 DR DR Importance CR S M M M S M M S P P CR S P M M M CR S S M M M M M M M M P CR S S M M S M M S M 0.08 CR P S S M S 0.06 CR S S S CR 4 S M M S M S 0.0 CR S M S M S M S S 0.6 CR P M M P M CR P P S M P M S CR 4 M M P M P S S 0.09 CR M S S M S 0.06 Importance of DRs Importance % Raning Figure. The fina HOQ Step 5: Prioritizing DRs : Priorities of the DRs can be seen from the fina HOQ matrix in Fig.. For exampe, importance weight of DR is cacuated such that (9*<0.0805) + (*0.0608) = After it is normaized, its priority is equa to and its ran. Looing at the DRs for a SSC, the most important factor is found as environmenta management system. The reason is that factor has positive reations with amost a CRs. Foowing ey factors are determined as SC optimization and empoyee practices. Whie SC optimization provides benefit to firms economicay, it has direct positive reations with conditions such as reduction of emissions, energy efficiency, etc. Empoyee practices factor is aso important for a SSCM design and it is in reationship with vita factors such as aws and reguation, heath and safety, and improved quaity WCE 00

6 Proceedings of the Word Congress on Engineering 00 Vo III WCE 00, June 0 - Juy, 00, London, U.K. V. CONCLUSION In this study, requirements of a SSCM structure are investigated by the aid of HOQ which underies the QFD technique. The most important issue to be considered is that firms avoid of SSC activities due to the cost of investments. Firms shoud be aware of the great advantages of these investments in terms of sustainabiity. REFERENCES [] L. Y. Y. Lu, C. H. Wu, and T.-C. Kuo. (007). Environmenta principes appicabe to green suppier evauation by using mutiobjective decision anaysis. Internationa Journa of Production Research. 45. pp [] J. D. Linton, R. Kassen, and V. Jayaraman. (007). Sustainabe suppy chains: An introduction. Journa of Operations Management. 5. pp [] F. Chicana, F. Herrera, and E. Herrera Viedma. (998). Integrating three representation modes in fuzzy mutipurpose decision maing based on fuzzy preference reations. Fuzzy Sets and Systems. 97. pp. 48. [4] F. 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