A SUPPLY CHAIN RISK EVALUATION METHOD BASED ON FUZZY TOPSIS

Similar documents
Logistics Management. Where We Are Now CHAPTER ELEVEN. Measurement. Organizational. Sustainability. Management. Globalization. Culture/Ethics Change

Evaluation Method for Enterprises EPR Project Risks

Supplier Quality Performance Measurement System*

The Credit Risk Assessment Model of Internet Supply Chain Finance: Multi-Criteria Decision-Making Model with the Principle of Variable Weight

The Credit Risk Assessment Model of Internet Supply Chain Finance: Multi-Criteria Decision-Making Model with the Principle of Variable Weight

A Two-Echelon Inventory Model for Single-Vender and Multi-Buyer System Through Common Replenishment Epochs

An Analysis on Stability of Competitive Contractual Strategic Alliance Based on the Modified Lotka-Voterra Model

Research on the Evaluation of Corporate Social Responsibility under the Background of Low Carbon Economy

The Study on Evaluation Module Architecture of ERP for Chemical Enterprises Yongbin Qin 1, 2, a, Jiayin Wei 1, b

A Group Decision Making Method for Determining the Importance of Customer Needs Based on Customer- Oriented Approach

Product Innovation Risk Management based on Bayesian Decision Theory

On Countermeasures of Promoting Agricultural Products E Commerce in China

Experiments with Protocols for Service Negotiation

Consumption capability analysis for Micro-blog users based on data mining

A SIMULATION STUDY OF QUALITY INDEX IN MACHINE-COMPONF~T GROUPING

Management of innovation processes at the enterprises of the construction materials industry

Supplier selection and evaluation using multicriteria decision analysis

LIFE CYCLE ENVIRONMENTAL IMPACTS ASSESSMENT FOR RESIDENTIAL BUILDINGS IN CHINA

Research on Evaluation Index System for Automobile Enterprise Brand Value

The research on modeling of coal supply chain based on objectoriented Petri net and optimization

Analysis of the Critical Success Factors of SOA Implementation in China Tobacco Company Based on DEMATEL Approach Yong Cen

A Distance-Based Multi-Criteria Decision Making Approach to Problem of Supplier Involvement in New Product Development

1 Basic concepts for quantitative policy analysis

EVALUATION METHODOLOGY OF BUS RAPID TRANSIT (BRT) OPERATION

Evaluation and Selection Model of Strategic Emerging Industries in Guangdong Province of China Based on AHP-TOPSIS

Calculation and Prediction of Energy Consumption for Highway Transportation

Sources of information

Internal and External Environment Analysis on Financial Strategy in Chinese PV Enterprise Yongchen Li1, a, Fang Li1, b

Why do we have inventory? Inventory Decisions. Managing Economies of Scale in the Supply Chain: Cycle Inventory. 1. Understanding Inventory.

Guidelines on Disclosure of CO 2 Emissions from Transportation & Distribution

Plan. IV. How to enhance energy security by partnerships with private companies: Brazil and Russia examples. China and India examples

Process Approach and Modelling in Organisation Competitiveness Management System

A Multi-Product Reverse Logistics Model for Third Party Logistics

Objectives Definition

A QUANTITATIVE APPROACH FOR CUSTOMER SATISFACTION MEASUREMENT OF REAL ESTATE DEVELOPMENT ENTERPRISE

Prediction algorithm for users Retweet Times

Analysis Online Shopping Behavior of Consumer Using Decision Tree Leiyue Yao 1, a, Jianying Xiong 2,b

The ranks of Indonesian and Japanese industrial sectors: A further study

Development and production of an Aggregated SPPI. Final Technical Implementation Report

Customer segmentation, return and risk management: An emprical analysis based on BP neural network

Pricing for Resource Allocation in Cloud Computing

Evaluation on external economies of renewable energy resource utilization: Taken wind power engineering project as example

Influencing Factors and Evaluation Index of Farmers Financial Needs based on Analytic Hierarchy Process

AHP and Value Engineering Application in Electrical Equipment Procurement Hong-qing ZHANG

MULTIPLE FACILITY LOCATION ANALYSIS PROBLEM WITH WEIGHTED EUCLIDEAN DISTANCE. Dileep R. Sule and Anuj A. Davalbhakta Louisiana Tech University

Study on Productive Process Model Basic Oxygen Furnace Steelmaking Based on RBF Neural Network

Co-opetition and the Stability of Competitive Contractual Strategic Alliance: Thinking Based on the Modified Lotka-Voterra Model

DESIGNING TWO-ECHELON SUPPLY CHAIN USING SIMULATION AND PRICING STRATEGY

EH SmartView. A SmartView of risks and opportunities. Monitoring credit insurance. ehsmartview.co.uk. Euler Hermes Online Services

EH SmartView. A SmartView of risks and opportunities. Monitoring credit insurance. Euler Hermes Online Services

RANKING OF VENDORS BASED ON CRITERIA BY MCDM-MATRIX METHOD-A CASE STUDY FOR COMMERCIAL VEHICLES IN AN AUTOMOBILE INDUSTRY

Optimal Issuing Policies for Substitutable Fresh Agricultural Products under Equal Ordering Policy

Identifying Factors that Affect the Downtime of a Production Process

The Critical Success Factors of Sourcing Production for Small and Medium-sized Clothing Firms in Hong Kong

Using Balance Score Card to Evaluate Performance of High-Tech Companies

Construction of Control Chart Based on Six Sigma Initiatives for Regression

Spatial difference of regional carbon emissions in China

A NOVEL DECISION APPROACH: INTUITIONISTIC FUZZY IMPORTANCE PERFORMANCE ANALYSIS

Supplier Selection in the International Environment: A Comparative Case of a Turkish and an Australian Company 1

International Journal of Industrial Engineering Computations

Job Description. Department/School: Faculty of Humanities & Social Sciences Grade: 6 Department/Placements Office

DEVELOPMENT OF A MODEL FOR EVALUATING THE EFFECTIVENESS OF ACCOUNTING INFORMATION SYSTEMS

Evaluating Green and Resilient Supplier Performance: AHP-Fuzzy Topsis Decision-Making Approach

DEVELOPMENT OF DECISION SUPPORT SYSTEM FOR SELECTING QUALITY MANAGEMENT SYSTEMS AND MANAGEMENT TOOLS

Impacts of supply and demand shifts

Extended Abstract for WISE 2005: Workshop on Information Systems and Economics

Study on trade-off of time-cost-quality in construction project based on BIM XU Yongge 1, a, Wei Ya 1, b

CONSUMER PRICE INDEX METHODOLOGY (Updated February 2018)

Rantai Pasok Global The Challenges of Tomorrow

Power Distribution System Planning Evaluation by a Fuzzy Multi-Criteria Group Decision Support System

Innovation in Portugal:

BEAM: A framework for business ecosystem analysis and modeling

The design and management of logistics services markets

emissions in the Indonesian manufacturing sector Rislima F. Sitompul and Anthony D. Owen

RELATIONSHIP BETWEEN BUSINESS STRATEGIES FOLLOWED BY SERVICE ORGANIZATIONS AND THEIR PERFORMANCE MEASUREMENT APPROACH

Journals Evaluation and the Application Based on Entropy-TOPSIS

Application of Ant colony Algorithm in Cloud Resource Scheduling Based on Three Constraint Conditions

Numerical Analysis about Urban Climate Change by Urbanization in Shanghai

International Trade and California Employment: Some Statistical Tests

GETTING STARTED CASH & EXPENSE PLANNING

Experimental Validation of a Suspension Rig for Analyzing Road-induced Noise

INTEGRATED OPERATIONAL LOGISTICS NETWORK (IOLN) DESIGN (CASE STUDY: IRAN KHODRO AUTOMOTIVE CO)

Modified-LOPA; a Pre-Processing Approach for Nuclear Power Plants Safety Assessment

MODERN PRICING STRATEGIES IN THE INTERNET MARKET

Implementation of Supplier Evaluation and. Ranking by Improved TOPSIS

Competing Value Networks, Incomplete Contracts and IT

Research on the Economic Impact of New Energy Fiscal and Tax Policies Based on CGE Model -- A Case from Inner Mongolia

Research on Clustering Method for Government Micro-blogging User Segments Based on User Interaction Behavior Suozhu Wang1, a, Jun Wang2, b

Application of SCOR Model in an Oil- producing Company

Performance Evaluation of coal enterprises energy conservation and reduction of pollutant emissions base on GRD-TOPSIS

Research Article A Hybrid Support Vector Machines and Two-dimensional Risk Matrix Model for Supply Chain Risk Assessment

Qualification Evaluation in Virtual Organizations Based on Fuzzy Analytic Hierarchy Process

An Empirical Study on the Impact of the Low-Carbon Economy- Based Enterprise Financial Environment on Financial Accounting

R. Duane Ireland. ompetitiveness and Globalization. Concepts only. SOUTH-WESTERN CENGAGE Learning-

Sporlan Valve Company

Cost and Benefit Analysis for E-Service Applications

An Analysis of the Impact of Reputation on Supply Webs

SENSITIVITY ANALYSIS AND OPTIMIZATION OF DIFFERENT INVENTORY CONTROL POLICIES ALONG THE SUPPLY CHAIN

Study on dynamic multi-objective approach considering coal and water conflict in large scale coal group

Honorable Kim Dunning Presiding Judge of the Superior Court 700 Civic Center Drive West Santa Ana, CA 92701

Transcription:

C. Sun et al., Int. J. of Safety and Securty Eng., Vol. 5, No. 2 (2015) 150 161 A SUPPLY CHAIN RISK EVALUATION METHOD BASED ON FUZZY TOPSIS C. SUN 1, Y. XIANG 1, S. JIANG 2 & Q. CHE 1 1 School of Management, Harbn Insttute of Technology, Chna. 2 Faculty of Infrastructure Engneerng, Dalan Unversty of Technology, Chna. ABSTRACT A supply chan s a value-added chan and supply chan management (SCM) benefts enterprses through optmzng ther nternal busness processes, lowerng logstcs costs, mprovng customer satsfacton, and more. Whle enhancng compettve ablty and evadng the rsks of tradtonal management methods, SCM also carres ts own rsks. Intally, ths paper studes major rsk factors of the supply chan by analyzng ts operatng mechansm, essental characterstcs, and results of prevous research. Later, based on these rsk factors, a supply chan rsk evaluaton ndex s presented based on the prncple of comprehensve, ratonal, and systematc thnkng. Fnally, a method for evaluatng supply chan rsk s proposed based on Fuzzy TOPSIS (Fuzzy Technque for Order Preference by Smlarty to an Ideal Soluton F-TOPSIS), and ts valdty s demonstrated n a case study. The research contrbuton of ths paper can boost the practcal applcaton and theoretcal development of supply chan rsk management. Keywords: Fuzzy TOPSIS, rsk evaluaton, supply chan. 1 INTRODUCTION The development of the knowledge economy, the advances of scence and technology, and the accelerated process of global economc ntegraton create a hghly complex envronment for enterprses today; at the same tme, competton between enterprses s ncreasng. In ths new envronment, supply chan management (SCM) has receved much attenton, because t uses external resources to enable enterprses to rapdly react to market demand. The Global Supply Chan Forum defnes SCM as the ntegraton of key busness processes from end-user through orgnal supplers that provdes products, servces, and nformaton that add value for customers and other stakeholders [1]. The emergence of SCM drastcally changes tradtonal management theores, methods, and thoughts, and leads a new trend n corporate management. Due to the characterstcs of the supply chan and ts mportant role, there s wde and deep research on theoretcal models and ther applcaton. For example, Mn and Zhou [2] studed past supply chan modelng efforts, and have descrbed the key challenges, opportuntes, and future trends of the supply chan. They have also provded some gudance on how to develop and mplement a supply chan successfully. Cao and Ln [3] have presented a closed-loop supply chan recovery model wth the applcaton of game theory, analyzng optmal prcng decsons and profts for all enterprse members n a decentralzed structure. They too provded some useful suggestons for manufacturers, retalers, and thrd-party recyclers for mprovng ther profts and performance. In promotng the better applcaton of SCM, Askarany et al. [4] concluded, from a large questonnare study, that the technques of actvty-based costng (ABC) could mprove SCM and ts performance n organzatons. In fact, many nternatonally well-known enterprses such as IBM, Csco, Dell, Coca Cola, Wal-Mart, and Toyota have acheved great success by usng the supply chan approach. A benchmarkng study conducted by the Pttglo, Rabn, Todd & McGrath (PRTM) consultng company reported that SCM affords leadng companes a 40 50% advantage n the cash-to-cash cycle, a 44% hgher value added per employee, a 3 7% reducton n total logstcs costs as a percentage of revenue, a 50% lower cost of ownershp of materals, and a 30 50% mprovement n meetng commtment dates [5]. 2015 WIT Press, www.wtpress.com ISSN: 2041-9031 (paper format), ISSN: 2041-904X (onlne), http://www.wtpress.com/journals DOI: 10.2495/SDP-V5-N2-150-161

C. Sun et al., Int. J. of Safety and Securty Eng., Vol. 5, No. 2 (2015) 151 However, despte many successful mplementatons of supply chan technology, some enterprses have faced huge economc losses after mproper mplementaton. For example, a fre broke out at a semconductor factory n New Mexco, USA; because of Ercsson s Europe s largest electroncs company at the tme supply chan partnershp wth the factory, Ercsson s European dvson reported a loss of $1.68 bllon, a 3% loss of market share, and an enterprse operatng loss of $167 mllon [6]. On March 11, 2011, an earthquake of magntude 9.0 rocked the man sland of Honshu, Japan, and many electroncs and automotve companes both nsde and outsde Japan suffered huge losses from ths earthquake due to the dsrupton of global manufacturng supply chans; for example, Honda lost $558 mllon, HP lost $700 mllon, and Toshba lost $2,900 mllon [7]. These nstances are too numerous to lst. It s therefore necessary for managers to strengthen ther understandng of supply chan rsk and to mplement correspondng rsk management. Many researchers have studed the problems of rsk dentfcaton n the supply chan, strateges to avod these rsks, and the bullwhp effect. They have put forward many valuable methods. For example, Ghadge et al. [8] and Colccha and Strozz [9] summarzed supply chan rsk management (SCRM) by employng a systematc lterature revew and presentng some future research drectons for SCRM, such as behavoral perceptons n rsk management, rsk mtgaton through collaboraton contracts, rsk propagaton, and recovery plannng, modelng supply chans by consderng robustness and reslence. Based on a descrpton of supply chan rsk analyss, Jukka et al. [10] put forward two knds of supply chan rsk analyss tools: nternal audtng and computer-aded causal analyss. Despte these advances, based on an exploratory quanttatve survey, Jüttner [11] concluded that most companes beleve that the understandng of vulnerablty of ther supply chans and the concept of SCRM are stll n ther nfancy. Therefore, from a deep analyss of operatng mechansms, essental characterstcs, and prevous research results, ths paper hghlghts the man rsk factors present when a company apples the SCM method. Later, a supply chan rsk evaluaton ndex s establshed, and an evaluaton method based on Fuzzy TOPSIS (F-TOPSIS) s proposed. Fnally, wth the help of a case study, ts valdty s demonstrated. 2 RESEARCH METHODOLOGY The adopton of a research methodology should be based on the research content. There are two man aspects to the study n ths paper: the establshment of a supply chan rsk evaluaton ndex and the selecton of a supply chan rsk evaluaton method. For the frst, consderng the ratonalty and systematc nature of the evaluaton ndex, the lterature revew method s adopted. For the second, takng nto account ts ease of use and sutablty, the F-TOPSIS s adopted. TOPSIS, whch s smlar to the Smple Addtve Weghtng (SAW) method [12], s a sortng method that s most effectve at solvng mult-objectve decson-makng problems. TOPSIS has two knds of solutons: the deal soluton (recorded as v*) and the negatve deal soluton (recorded as v 0 ). The deal soluton s the best scheme for resolvng a decson-makng problem, and the negatve deal soluton s the worst. When comparng schemes wth v* and v 0, the scheme whch s nearest to v* and furthest from v 0 s the best scheme [13]. In practce, most of the evaluaton ndex comprses qualtatve ndcators, so the fuzzy concept s ntroduced nto TOPSIS, whch then becomes F-TOPSIS. The basc steps of F-TOPSIS are as follows [13,14]: 1. Organze the experts and evaluate each scheme. For one evaluaton problem wth m schemes and n ndexes, a fuzzy decson-makng matrx D = (d j ) M N s obtaned, where d j denotes the fuzzy evaluaton value of ndex j n scheme. 2. Calculate the weght of each ndex, where W = (w 1, w 2,, w n ), usng the defuzzfcaton method for the calculaton of matrx D = (d j ) M N, where d j s the value after defuzzfcaton of d j ;

152 C. Sun et al., Int. J. of Safety and Securty Eng., Vol. 5, No. 2 (2015) then, through weghtng and standardzed processng a standardzed decson matrx V = (v j ) M N = (w j d j ) M N s obtaned. 3. Confrm the deal soluton v * and the negatve deal soluton v 0 of the evaluaton problem, as n eqn (1): v * = {v * 1, v* 2,, v* n }; vo = {v 0 1, v0 2,, v0 n } (1) where maxv j,jîj1 mnv,jîj * v j = V,jÎJ and v 0 j = j 1 mn j 2 maxv j,jîj2 J 1 and J 2 are the sets of beneft crtera and cost crtera, respectvely. 4. Calculate the dstance of each scheme from the deal soluton and the negatve deal soluton: S * and S 0, as n eqns (2) and (3): S * S 0 = = n (vj -v) * 2 j ( = 1, 2,, m) (2) j= 1 n (vj -v) 0 2 j ( = 1, 2,, m) (3) j= 1 5. Calculate the degree of closeness S between each scheme and the deal scheme, as n eqn (4): 0 S S = * S + S 6. The value of S vares from 0 to 1. Closer to 1 means a better scheme and closer to 0 means a worse scheme. 3 ANALYSIS OF RISK FACTORS OF THE SUPPLY CHAIN 3.1 Operatng mechansm and essental characterstcs of the supply chan Accordng to the common defnton of supply chan, t s not only related to the work processes of one enterprse, but also to ts supplers, manufacturers, retalers, and other agents related to the 0 (4) Fgure 1: The structural model of the supply chan.

C. Sun et al., Int. J. of Safety and Securty Eng., Vol. 5, No. 2 (2015) 153 enterprse. Therefore, some scholars have splt the supply chan nto an nternal and an external supply chan [15]. The nternal supply chan (ISC) s the supply and demand network consstng of procurement, manufacturng, warehousng, sales, and other departments n the enterprse. The external supply chan (ESC) contans all enterprses nvolved n the process of transformng raw materals nto products delvered to fnal consumers. Ths operatng mechansm s presented n Fg. 1. SCM s very complcated and nvolves eght major busness processes: customer relatonshp management (CRM), customer servce management, demand management, order performance, processng flow management, suppler relatonshp management, product development and commercalzaton, and returns management [16]. Therefore, the supply chan can be regarded as a cross- organzatonal management and coordnaton model wth these essental characterstcs: 1. Requrement for unfed management: Although SCM requres the coordnaton of all partcpatng companes, they are all ndependent economc enttes and have ther own ndependent organzatons, makng t dffcult to establsh a unfed regulaton mechansm. 2. Dynamcs of symboss between enterprses: The product market s changng, and customer demand s dverse. Ths requres that enterprses are flexble, rapdly react to market varatons, and set up a supply chan quckly. For most supply chans, the cooperaton among enterprses s not stable, t s fundamental to consder the fludty of cooperaton n the supply chan. 3. Integraton of a varety of enablng technologes: The supply chan s a cross-enterprse management mode, whch needs a lot of related techncal support ncludng CRM, Computer Integrated Manufacturng Systems (CIMS), Enterprse Resource Plannng (ERP), the nternet, e-commerce, and vrtual and smulaton technology. The effectve ntegraton of these technologes enables the goal of the supply chan to be realzed. 4. Bullwhp effect n the process of nformaton transfer: The bullwhp effect s also called the demand amplfcaton effect. Because of t, upstream supplers often have hgher nventory levels than downstream supplers [17]. 5. Maxmzaton of both overall and ndvdual companes nterests n the supply chan: The maxmzaton of profts s not only each company s goal, but t s the foundaton of an enterprse s development. The supply chan pursues a wn wn stuaton between enterprses, and realzes the best nterests of each enterprse by usng performance evaluaton and an ncentve mechansm, provdng that t does not negatvely affect the nterests of the whole supply chan. 3.2 Man rsk factors affectng the supply chan Takng nto account the operatng mechansm of the supply chan, ts essental characterstcs, and the results of prevous research bu scholars n supply chan rsks (see Table 1), ths paper presents a fshbone dagram [27] of supply chan rsk factors (shown n Fg. 2). The man rsk factors of the supply chan are: 1. Organzaton management: The supply chan s a network composed of many upstream and downstream enterprses. Management effcency and qualty of the supply chan have a drect nfluence on the everyday operatons of each enterprse. 2. IT technology: Ths can be dvded nto nternal and external IT rsks. Dependng on the stuaton, each of these can be further dvded nto software and hardware rsks. In addton, the degree of nformaton sharng wll also brng about rsks, manly caused by the bullwhp effect of the supply chan, and by artfcal exaggeraton and manpulaton n the nformaton transfer process.

154 C. Sun et al., Int. J. of Safety and Securty Eng., Vol. 5, No. 2 (2015) Table 1: Sources of supply chan rsks: a lterature revew. Reference no. Year Rsk factors [18] 2004 Fnancal condton, complexty and uncertanty wthn supply chan, unexpected and unpredctable dsruptons, wrong or neffectve decsons, market [19] 2004 Process, controls, demand, supply, envronment [20] 2005 Problems of coordnatng supply and demand, dsruptons to normal actvtes (operatonal contngences, natural hazards, terrorsm, and poltcal nstablty) [21] 2006 Transport, manufacturng, warehousng, procurement, upstream and downstream relatons [22] 2006 Operatonal rsks (uncertan customers, demand, uncertan supply, uncertan cost), dsrupton rsks (natural and man-made dsasters or economc crses) [23] 2008 Currency fluctuaton, transt tme varablty, changng forecasts, qualty, safety, busness dsrupton, survval, nventory (and tools) ownershp, culture, dependency and opportunsm, ol prce fluctuaton, dsruptve events affectng supplers and customers [24] 2009 Envronment, ndustry, organzatonal rsk, problem-specfc rsk, decson-maker [25] 2010 Global sourcng, dverse suppler base, volatle market, product complexty [26] 2011 Complexty of supply chans, demand and supply of resources [8] 2012 Organzatonal rsks (nventory rsk, process/operatonal rsk, qualty rsk, management rsk), network rsks (supply rsk, suppler default, demand rsk), envronmental rsks (arsng from weather, earthquakes, poltcal, regulatory, market forces, etc.). Fgure 2: The fshbone dagram of man rsk factors n the supply chan.

C. Sun et al., Int. J. of Safety and Securty Eng., Vol. 5, No. 2 (2015) 155 3. Cooperatve enterprses: Effectve supply chans must have good cooperaton of enterprses at ther foundaton. The credt degree, producton capacty, supply ablty, development ablty, and the management level of the cooperatve enterprse have a drect mpact on the operaton and development of the supply chan. 4. Market: Product demand analyss s one of the most mportant prerequstes for settng up a supply chan. Its results drectly decde both how the supply chan s set up and ts practcal outcome. Not performng a product demand analyss or relyng on a faulty one are some of the most mportant factors n supply chan falure. In addton, the supply chan, smlar to any other aspect of enterprse organzaton, s faced wth threats from compettors and alternatve products. These two aspects are the man market rsks for the supply chan. 5. Nature and socety: Natural factors such as earthquake, fre, storm, and heavy snow are rsks for the supply chan. These rsk factors manly nfluence producton, supply, and sales n one or several enterprses n a gven geographcal regon. Socal rsks for the supply chan stem manly from changes n the socal envronment, changes n poltcal stablty, changes to relevant laws, the development and fluctuaton of the economy, and transportaton condtons. 6. Other: In addton to the above, other rsks nclude the enterprse culture, ts fnancal status, the technology content of producton, and proft dstrbuton. An enterprse culture encompasses the external nteractons of the enterprse. A good enterprse culture affects the supply chan postvely, but a bad enterprse culture has a negatve nfluence, not only on the enterprse s sprt, but also on socety and the cooperaton enterprse. The technology content of a product s key to an enterprse s survval and development. A hgh techncal content makes the company domnant and secures ts leadershp poston among the competton, and nversely. For the supply chan, products wth a hgh techncal content wll consoldate the status of a core enterprse and determne routne operaton of the supply chan. Fnancal condtons of the overall supply chan, on the one hand, nfluence the fnancal condton of each enterprse n the supply chan; on the other hand, they refer to the cost of the establshment and mantenance of the normal operaton of the supply chan. In large part, the operaton costs of the supply chan determne whether or not t should be adopted. In the operaton process, some enterprses may take up a lot of captal from upstream and downstream frms. If the fnancal condton s not stable, the whole supply chan mght face a fatal blow at any tme. As for proft dstrbuton, the supply chan wll eventually collapse, f there s no reasonable proft dstrbuton system between the enterprses n the supply chan. 4 RISK EVALUATION INDEX SYSTEM Based on the man rsk factors and the essental characterstcs for the supply chan, ths paper puts forward the rsk evaluaton ndex for the supply chan as shown n Table 2. 5 CASE STUDY A Chnese manufacturer wants to produce one of the two products that have good market potental. After conductng market research and assessng processng capacty, producton technology, competton, and other factors, the manufacturer decdes to adopt the supply chan operaton mode. After a prelmnary round of selecton, there are two cooperatve enterprses left for each product for further evaluaton. That means, four schemes exst: scheme I to scheme IV. The manufacturer wll want to choose the scheme wth the least amount of rsk.

156 C. Sun et al., Int. J. of Safety and Securty Eng., Vol. 5, No. 2 (2015) Table 2: Rsk evaluaton ndex for the supply chan. No. Index Index descrpton 1 Techncal content of product 2 Credt of cooperatve enterprse 3 Supply ablty of cooperatve enterprse 4 Informaton sharng level The techncal competence of the supply chan for the product The cooperatve enterprse s reputaton and ntegrty, whch can be measured by delvery delay rate, hstorcal credt records, etc. The producton capacty of the cooperatve enterprse The nformaton communcaton capablty between enterprses n the supply chan that, from another perspectve, also embodes the degree of bullwhp effect between enterprses The nformaton technology applcaton level n the supply chan 5 Support from hardware/ software system 6 Threats from The level of competton between enterprses compettve enterprses 7 Competton from The qualty, cost, popularty, etc. of the product tself; t can smlar and be measured by the market share degree of the product, the alternatve products alternatve products condton, and the substtuton degree 8 Laws and regulatons The support for, or the lmts placed on, the products by laws and regulatons 9 Organzatonal The level of effcency n the organzaton and management of effcency the entre supply chan 10 Fnancal stuaton The cost of enterprse management, the turnover tme of funds, and the fnancal poston of the company n the supply chan 11 Satsfacton of the The satsfacton degree of all the enterprses wth the current dstrbuton of profts dstrbuton scheme for profts In a frst step, three expert SCM professonals are nvted to assgn rsk evaluaton qualty ratngs to each scheme. The weght of each rsk ndex s found usng the Analytc Herarchy Process (AHP) [14,28]. The two products are denoted as 1 and 2, and the cooperatve enterprses are denoted by A, B, C, and D. The schemes and detals of each ndex s ratngs are shown n Table 3. To use the F-TOPSIS method to sort the schemes, t s necessary to quantfy the qualtatve ratngs n Table 3 by usng trapezodal fuzzy numbers, wth bad (0.1,0.2,0.2,0.3), ordnary (0.3,0.4,0.4,0.5), good or hgh (0.5,0.6,0.7,0.9), and better or hgher (0.9,0.95,0.95,1.0). The trapezodal fuzzy numbers are just the reverse when quantzng the ndex for threats from compettve enterprses and competton of smlar products and alternatve products. The quantfed contents of Table 2 become decson matrx D:

C. Sun et al., Int. J. of Safety and Securty Eng., Vol. 5, No. 2 (2015) 157 Table 3: Evaluaton ndex ratngs of the manufacturer s four schemes. Scheme I Scheme II Scheme III Scheme IV Product 1 Product 1 Product 2 Product 2 Rsk ndex Weght Enterprse A Enterprse B Enterprse C Enterprse D Techncal content of 0.0524 Hgher Hgher Hgh Hgh producton Credt of cooperatve 0.3250 Better Good Better Ordnary enterprse Supply ablty of 0.0385 Good Better Good Better cooperatve enterprse Informaton sharng level 0.1675 Ordnary Ordnary Good Ordnary Support from hardware/ 0.0318 Good Ordnary Ordnary Bad software system Threats from compettve 0.0575 Hgher Hgher Hgh Hgh enterprses Competton from smlar 0.1092 Ordnary Ordnary Hgh Hgh and alternatve products Laws and regulatons 0.0332 Better Better Good Good Organzatonal effcency 0.0838 Good Good Better Ordnary Fnancal stuaton 0.0360 Better Good Ordnary Better Satsfacton wth the dstrbuton of profts 0.0652 Good Good Better Better (0.9,0.95,0.95,1.0) (0.9,0.95,0.95,1.0) (0.5,0.6,0.7,0.9) (0.5,0.6,0.7,0.9) (0.9,0.95,0.95,1.0) (0.5,0.6,0.7,0.9) (0.9,0.95,0.95,1.0) (0.3,0.4,0.4,0.5) (0.5,0.6,0.7,0.9) (0.9,0.95,0.95,1.0) (0.5,0.6,0.7,0.9) ( 0.9,0.95,0.95,1.0) (0.3,0.4,0.4,0.5) (0.3,0.4,0.4,0.5) (0.5,0.6,0.7,0.9) (0.3,0.4,0.4,0.5) (0.5,0.6,0.7,0.9) (0.3,0.4,0.4,0.5) (0.3,0.4,0.4,0.5) (0.1,0.2,0.2,0.3) D = (0.1,0.2,0.2,0.3) (0.1,0.2,0.2,0.3) (0.3,0.4,0.4,0.5) (0.3,0.4,0.4,0.5) (0.5,0.6,0.7,0.9) (0.5,0.6,0.7,0.9) (0.3,0.4,0.4,0.5) (0.3,0.4,0.4,0.5) (0.9,0.95,0.95,1.0) (0.9,0.95,0.95,1.0) (0.5,0.6,0.7,0.9) (0.5,0.6,0.7,0.9) (0.5,0.6,0.7,0.9) (0.5,0.6,0.7,0.9) (0.9,0.95,0.95,1.0) (0.3,0.4,0.4,0.5) (0.9,0.95,0.95,1.0) (0.5,0.6,0.7,0.9) (0.3,0.4,0.4,0.5) (0.9,0.95,0.95,1.0) (0.5,0.6,0.7,0.9) (0.5,0.6,0.7,0.9) (0.9,0.95,0.95,1.0) (0.9,0.95,0.95,1.0) For trapezodal fuzzy numbers, eqn (5) s used for defuzzfcaton [14]: 2 2 2 2 ( a b c d ab cd M = + + + ) 3 ( a b+ c+ d) In eqn (5), a, b, c, and d are four numercal trapezodal fuzzy numbers. (5)

158 C. Sun et al., Int. J. of Safety and Securty Eng., Vol. 5, No. 2 (2015) After defuzzfcaton of matrx D, the matrx D s obtaned, and after weghtng matrx D wth the weghts shown n Table 3, matrx V s obtaned: 095. 0. 95 068. 0. 68 0. 0498 0. 0498 0. 0356 0. 0356 095. 0. 68 095. 0. 40 0. 3088 0. 2210 0. 3088 0. 1300 068. 0. 95 068. 0. 95 0. 0262 0.0366 0. 0262 0. 0366 040. 0. 40 068. 040. 0. 0670 0. 0670 0. 1139 0. 0670 068. 0. 40 040. 0. 20 0. 0216 0. 0127 0. 0127 0. 0064 D = 020. 0. 20 020. 0. 20 V = 0. 0115 0. 0115 0. 0230 0. 0230 068. 0. 68 040. 0. 40 0. 0743 0. 0743 0. 0437 0. 0437 095. 0. 95 068. 0. 68 0.0315 0. 0315 0. 0226 0. 0226 068. 0. 68 095. 0. 40 0. 0570 0. 0570 0. 0796 0. 0335 095. 0. 68 040. 095. 0. 0342 0. 0245 00144. 0. 0342 068. 0. 68 0. 95 095. 0. 0443 0. 0443 0. 0619 0. 0619 The deal soluton v * and the negatve deal soluton v 0 are calculated wth the TOPSIS method: v * = [0.0498,0.3088,0.0366,0.1139,0.0216,0.0230,0.0743,0.0315,0.0796,0.0342,0.0619] v 0 = [0.0356,0.1300,0.0262,0.0670,0.0064,0.0115,0.0437,0.0226,0.0335,0.0144,0.0443] Table 4: Results of scheme selecton wth F-AHP. Rsk ndex Weght Scheme I Scheme II Scheme III Scheme IV Techncal content of 0.0524 0.95 0.95 0.68 0.68 producton Credt of cooperatve 0.3250 0.95 0.68 0.95 0.40 enterprse Supply ablty of 0.0385 0.68 0.95 0.68 0.95 cooperatve enterprse Informaton sharng level 0.1675 0.40 0.40 0.68 0.40 Support from hardware/ 0.0318 0.68 0.40 0.40 0.20 software system Threats from compettve 0.0575 0.20 0.20 0.20 0.20 enterprses Competton from smlar 0.1092 0.68 0.68 0.40 0.40 and alternatve products Laws and regulatons 0.0332 0.95 0.95 0.68 0.68 Organzatonal effcency 0.0838 0.68 0.68 0.95 0.40 Fnancal stuaton 0.0360 0.95 0.68 0.40 0.95 Satsfacton wth the 0.0652 0.68 0.68 0.95 0.95 dstrbuton of profts Score for each scheme 0.72615 0.630171 0.730888 0.482983 Rank of schemes 2 3 1 4

C. Sun et al., Int. J. of Safety and Securty Eng., Vol. 5, No. 2 (2015) 159 Usng eqns (2) and (3), the Eucldean dstance of the deal soluton v* and the negatve deal soluton of each scheme s calculated: S * and S 0 S 1 * = 0.0571; S 2 * = 0.1050; S 3 * = 0.0424; S 4 * = 0.1943 S 1 0 = 0.1862; S2 0 = 0.1024; S 3 0 = 0.1919; S 4 0 = 0.0358 Fnally, each relatve ndex S of the dstance between each scheme and the deal soluton s calculated on the bass of the equaton: S = S /( S + S 0 0 * ) S 1 = 0.7653; S 2 = 0.4937; S 3 = 0.8190; S 4 = 0.1555. So, t can be seen that S 3 > S 1 > S 2 > S 4. As the thrd scheme s score s hghest, t means that ts rsk s lowest, and that t s the best scheme. 6 COMPARING RESULTS WITH F-AHP To check the results wth other technques, F-AHP was adopted, referred to n Tesfamaram and Sadq [28], to solve the above case. Table 4 shows the results of scheme selecton accordng to the data n Table 3 usng the F-AHP method. Table 4 shows scheme III as the best scheme and scheme IV as the worst. The selecton result usng F-AHP s the same as that usng F-TOPSIS, whch llustrates the valdty of F-TOPSIS. Compared wth F-AHP, F-TOPSIS has the followng advantages and dsadvantages: 1. Both F-TOPSIS and F-AHP can perform quanttatve analyss of qualtatve problems. 2. By applyng F-TOPSIS, the Eucldean dstance between the schemes and the deal soluton and negatve deal soluton can be obtaned, and the dfference of the results between schemes s greater by usng F-TOPSIS than F-AHP, so F-TOPSIS can dstngush smaller dfferences between two very smlar schemes. 3. The dsadvantage of F-TOPSIS s that t s more complcated than F-AHP. However, a lot of software has been developed to easly solve F-TOPSIS, such as Topss Solver 2013 by Informer Technologes Inc. [29]. 4. Other dfferences between F-AHP and F-TOPSIS are referred to n Ertuğrul and Karakaşoğlu [14]. 7 CONCLUSION Supply chan rsks have great nfluence on the operaton of the supply chan. Therefore, the correct evaluaton of supply chan rsks s the key to successful mplementaton of a supply chan mode of operaton. By analyzng the operaton mechansm and essental characterstcs of the supply chan, and by revewng the latest lterature, ths paper presents a fshbone dagram of supply chan rsk factors. In the next step, a supply chan rsk evaluaton ndex system s set up, takng nto account the techncal content of the product, credt of the cooperaton enterprses, supply ablty of the cooperatve enterprses, nformaton sharng level, support from the software/hardware system, threats from compettve enterprses, competton from smlar and alternatve products, laws and regulatons, organzatonal effcency, fnancal stuaton, and satsfacton wth the dstrbuton of profts. Fnally, ths paper proposes the F-TOPSIS-based rsk evaluaton method and demonstrates the applcaton of ths method wth a case study. It was found that ths method has a good applcablty for supply chan rsk evaluaton.

160 C. Sun et al., Int. J. of Safety and Securty Eng., Vol. 5, No. 2 (2015) ACKNOWLEDGMENTS The research was supported by the Natonal Natural Scence Foundaton of Chna under grants No. 71071043 and No. 51378160, and the Scentfc Research Foundaton for Returned Overseas Chnese Scholars of the Harbn Scence and Technology Bureau of Chna under Grant No. JJ20120070. REFERENCES [1] Lambert, D.M., Supply Chan Management: Processes, Partnershps, Performance, The Hartley Press: Jacksonvlle, USA, 2008. [2] Mn, H. & Zhou, G., Supply chan modelng: past, present and future. Computer & Industral Engneerng, 43, pp. 231 249, 2002. do: http://dx.do.org/10.1016/s0360-8352(02)00066-9 [3] Cao, K. & Ln, J., Prce decson analyss of closed-loop supply chan under thrd-party recyclers competton. Internatonal Journal of Advancements n Computng Technology, 21(4), pp. 632 641, 2012. do: http://dx.do.org/10.4156/jact.vol4.ssue21.75 [4] Askarany, D., Yazdfar, H. & Askary, S., Supply chan management, actvty-based costng and organzatonal factors. Internatonal Journal of Producton Economcs, 127, pp. 238 248, 2010. do: http://dx.do.org/10.2139/ssrn.1370783 [5] Lockamy III, A. & Smth, W.I., Target costng for supply chan management: crtera and selecton. Industral Management & Data Systems, 100(5), pp. 210 218, 2000. do: http://dx.do. org/10.1108/02635570010304789 [6] Mukherjee, A., The Spder s Strategy: Creatng Networks to Avert Crss, Create Change, and Really Get Ahead, Fnancal Tmes Prentce Hall: USA, 2008. [7] Brennan, P., Lessons learned from the Japan earthquake. Dsaster Recovery Journal, 24, pp. 22 26, 2011. [8] Ghadge, A., Dan, S. & Kalawsky, R., Supply chan rsk management: present and future scope. Internatonal Journal of Logstcs Management, 23(3), pp. 313 339, 2012. do: http:// dx.do.org/10.1108/09574091211289200 [9] Colccha, C. & Strozz, F., Supply chan rsk management: a new methodology for a systematc lterature revew. Supply Chan Management: An Internatonal Journal, 17(4), pp. 403 418, 2012. do: http://dx.do.org/10.1108/13598541211246558 [10] Hallkas, J., Vrolanen, V.-M. & Tuomnen, M., Rsk analyss and assessment n network envronments: a dyadc case study. Internatonal Journal of Producton Economcs, 78, pp. 45 55, 2002. do: http://dx.do.org/10.1016/s0925-5273(01)00098-6 [11] Jüttner, U., Supply chan rsk management: understandng the busness requrements from a practtoner perspectve. Internatonal Journal of Logstcs Management, 16(1), pp. 120 141, 2005. [12] Modarres, M. & Sad-nezhad, S., Fuzzy smple addtve weghtng method by preference rato. Intellgent Automaton and Soft Computng, 11(4), pp. 235 244, 2005. do: http://dx.do.org/ 10.1080/10642907.2005.10642907 [13] Wang, Y.-M. & Elhag, T.M.S., Fuzzy TOPSIS method based on alpha level sets wth an applcaton to brdge rsk assessment. Expert Systems wth Applcatons, 31(2), pp. 309 319, 2006. do: http://dx.do.org/10.1016/j.eswa.2005.09.040 [14] Ertuğrul, İ. & Karakaşoğlu, N., Comparson of fuzzy AHP and fuzzy TOPSIS methods for faclty locaton selecton. Internatonal Journal of Advanced Manufacturng Technology, 39(7 8), pp. 783 795, 2008. do: http://dx.do.org/10.1007/s00170-007-1249-8

C. Sun et al., Int. J. of Safety and Securty Eng., Vol. 5, No. 2 (2015) 161 [15] Harland, C.M., Supply chan management: relatonshps, chans and networks. Brtsh Journal of Management, 7, pp. 63 80, 1996. do: http://dx.do.org/10.1111/j.1467-8551.1996. tb00148.x [16] García-Dastugue, S.J. & Lambert, D.M., Internet-enabled coordnaton n the supply chan. Industral Marketng Management, 32, pp. 251 263, 2003. do: http://dx.do.org/10.1016/s0019-8501(02)00269-9 [17] Bhattacharya, R. & Bandyopadhyay, S., A revew of the causes of bullwhp effect n a supply chan. Internatonal Journal Advanced Manufacturng Technology, 54, pp. 1245 1261, 2011. do: http://dx.do.org/10.1007/s00170-010-2987-6 [18] Chrstopher, M. & Lee, H., Mtgatng supply chan rsk through mproved confdence. Internatonal Journal of Physcal Dstrbuton & Logstcs Management, 34(5), pp. 388 396, 2004. do: http://dx.do.org/10.1108/09600030410545436 [19] Chrstopher, M. & Peck, H., Buldng the reslent supply chan. Internatonal Journal of Logstcs Management, 15(2), pp. 1 14, 2004. do: http://dx.do.org/10.1108/09574090410700275 [20] Klendorfer, P.R. & Saad, G.H., Managng dsrupton rsks n supply chans. Producton and Operatons Management, 14(1), pp. 53 68, 2005. do: http://dx.do.org/10.1111/j.1937-5956.2005. tb00009.x [21] Gaudenz, B. & Borghes, A., Managng rsks n the supply chan usng the AHP method. Internatonal Journal of Logstcs Management, 17(1), pp. 114 136, 2006. do: http://dx.do. org/10.1108/09574090610663464 [22] Tang, C.S., Perspectves n supply chan rsk management. Internatonal Journal of Producton Economcs, 103(2), pp. 451 88, 2006. [23] Manuj, I. & Mentzer, J., Global supply chan rsk management strateges. Internatonal Journal of Physcal Dstrbuton & Logstcs Management, 38(3), pp. 192 223, 2008. do: http:// dx.do.org/10.1108/09600030810866986 [24] Rao, S. & Goldsby, T., Supply chan rsks: a revew and typology. Internatonal Journal of Logstcs Management, 20(1), pp. 97 123, 2009. do: http://dx.do.org/10.1108/09574090910954864 [25] Ghadge, A., Dan, S. & Kalawsky, R.A., Framework for managng rsks n the aerospace supply chan usng systems thnkng. Proc. of the 5th Int. Conf on System of Systems Engneerng, IEEE, pp. 22 24, 2010. [26] Gannaks, M. & Lous, M., A mult-agent based framework for supply chan rsk management. Journal of Purchasng & Supply Management, 17, pp. 23 31, 2011. do: http://dx.do. org/10.1016/j.pursup.2010.05.001 [27] Wkpeda, avalable at http://en.wkpeda.org/wk/ishkawa_dagram [28] Tesfamaram, S. & Sadq, R., Rsk-based envronmental decson-makng usng fuzzy analytc herarchy process (F-AHP). Stochastc Envronmental Research and Rsk Assessment, 21(1), pp. 35 50, 2006. do: http://dx.do.org/10.1007/s00477-006-0042-9 [29] Informer Technologes, Inc., avalable at http://softadvce.nformer.com/download_fuzzy_ Topss_Software.html