Research Article An Integrated Method of Supply Chains

Research ArticleAn Integrated Method of Supply ChainsVulnerability Assessment

Jiaguo Liu12 Fan Liu34 Huan Zhou1 and Yudan Kong1

1Transportation Management College Dalian Maritime University Dalian 116026 China2Collaborative Innovation Center for Transport Studies Dalian Maritime University Dalian 116026 China3School of Business Administration Zhongnan University of Economics and Law Wuhan 430073 China4Post-Doctoral Research Center Zhongnan University of Economics and Law Wuhan 430073 China

Correspondence should be addressed to Fan Liu 462399289qqcom

Received 2 May 2016 Accepted 28 August 2016

Academic Editor Xiaofeng Xu

Copyright copy 2016 Jiaguo Liu et al This is an open access article distributed under the Creative Commons Attribution Licensewhich permits unrestricted use distribution and reproduction in any medium provided the original work is properly cited

Supply chain vulnerability identification and evaluation are extremely important to mitigate the supply chain risk We present anintegrated method to assess the supply chain vulnerability The potential failure mode of the supply chain vulnerability is analyzedthrough the SCOR model Combining the fuzzy theory and the gray theory the correlation degree of each vulnerability indicatorcan be calculated and the target improvements can be carried out In order to verify the effectiveness of the proposed method weuse Kendallrsquos tau coefficient to measure the effect of different methods The result shows that the presented method has the highestconsistency in the assessment compared with the other two methods

1 Introduction

With the rapid development of economy and technologythe competition among enterprises has become competitionbetween supply chain Because of the frequency and intensityof terrorist attacks SARS hurricane and series of other dis-asters and crises [1ndash3] supply chainmanagement has arousedwidespread concern In 2011 Thailand suffered the worstflooding in the past fifty years which led to a fatal damage forsome giant industrial park around Bangkok and the floodingresulted in the supply chain disruption of motor industrieselectronic components and hard disk [4] As the supply chainstructure becomes more and more complicated the supplychain capacity that respond to the disruption is weaker thanbefore [5 6] and thus the supply chain vulnerability is moreserious [7] Researches on the vulnerability originated inthe 1970s when White (1974) first proposed the ldquovulnerablerdquoconcept and it is a new emerging research field in supply chainmanagement Christopher and Peck [8] defined supply chainvulnerability as ldquoa kind of exposure to serious disturbancerdquoIn the researches of natural disasters and crisis managementBlaikie et al [9] defined vulnerability as the ability of an indi-vidual or organization to predict process resist and recover

In the researches of ship supply chain Barnes andOloruntoba[10] described the vulnerability as ldquoa vulnerable constitutionthat leads to loss which caused by existing organizations orfunctional activity or external conditionrdquo Based on variousdefinitions we consider that the vulnerability is an instabilityand destructiveness that caused by supply chain external andinternal risks Supply chain vulnerability is an inherent traitof the supply chain which is determined by the structure andcharacteristics of the supply chain itself

Currently researches on supply chain vulnerabilitymainly concentrated on its definition connotation influencefactors and other aspects while little concern is paid tosupply chain vulnerability assessment [11] Based on interfer-ence performance losses the relationship between them andother factors Albino and Garavelli analyzed the sensitivity ofsupply chain systems under the condition that time is knownand interference occurs randomly then the supply chainvulnerability was evaluated [12] Prater et al [13] used fivecases in the paper to achieve optimal coordination betweenagility and complexity through controlling supply chain riskfactors and changing supply chain complexity Zhong andXie[14] proposed ldquo3Prdquo management principles and supply chain

Hindawi Publishing CorporationScientific ProgrammingVolume 2016 Article ID 2819238 10 pageshttpdxdoiorg10115520162819238

2 Scientific Programming

vulnerability management principles to prevent and respondto damage at the tactical level In the view of specializationof supply chain Barnes and Oloruntoba [10] analyzed thecharacteristics of the entire supply chain using the specialcase and they concluded that it is the complexity of theinteraction between marine operations and supply chain thatcaused the vulnerability In 2000 Svensson representativescholar in the supply chain vulnerability built a supply chainvulnerability theoretical framework and made a qualitativedescription and assessment of the vulnerability through thedisturbance source category and logistics mode [15] Aftertwo years he calculated the vulnerability again based ontime and relationship [16] In order to enrich the studyhe assessed the supply chain vulnerability from time func-tion and relationshiprsquos three dimensions in 2004 [17] Peckintroduced the network theory and complex systems intothe interactive production system supply chain conceptualmodel and analyzed the four levels from multiangles [18]D Bogataj and M Bogataj [19] evaluated the vulnerabilityfrom the perspective of vulnerable point and analyzed theinput-output tables and the relationship between storage anddelivery using the dynamic NPV method which can find therelationship of before and after items then he concluded theweak link of the supply chain and predicted the vulnerabilitypoint Based on the vulnerability index Stephan andWagner[20] proposed a method to confirm the validity of the policyby comparing different industry or enterprise vulnerabilityindex With the development of extensive Fang et al [21]proposed vulnerability assessment and early warning modelfor small andmedium-sized enterprises intellectual property

To sum up the existing researches have establishedtheoretical framework to evaluate the supply chain vulnera-bility However these basically are limited to the qualitativeframework Therefore we utilize the FMEA (failure modeand effect analysis) method which is a way for prospectivereliability analysis and safety assessment It analyzes everyfailure mode that existed in the system and it calculatedrisk priority number (RPN) for the consequences of allfailure modes [22] According to the value of RPN we makea quantitative assessment of the failure modes and takecorresponding measures to prevent and avoid the potentialfailure mode if necessary However the traditional FMEAmethoddoes not recognize the importance of the relationshipbetween all the failure modes and every decisive factor inthe vulnerability and the traditional FMEA method makesits evaluation effect limited when the expertsrsquo evaluationlanguage is qualitative and subjective Considering the aboveconditions we present an integrated method using the fuzzytheory and the gray relational theory to solve the aboveproblems The present method can help business managersimplement the improvements of supply chain vulnerability

2 Methodology and Assessment Process

21 Methodology FMEA namely failure mode and effectanalysis in fact originated in the 1950s Because of thesignificant value in safety and reliability assessment FMEAhas developed rapidly and has been widely used in manyareas such as the nuclear industry automotive machinery

aerospace electronics and ships It use three aspects forassessment They are severity (S) occurrence (O) and detec-tion (D) [23] It is a powerful tool for defining identifyingand eliminating potential failures from the system designprocess or service before they reach the customer [24]Severity is the degree of the effect of the potential failuremode on customers occurrence refers to the possibility of theoccurrence of the specific failure mode detection describesthe possibility that the current system cannot recognize thefailure modes or reasons The traditional FMEA method hastwo following disadvantages in practical application

First the traditional FMEAmethod to assess products orsystem risks merely depends on RPN which can lead to largeproblems in the actual situation In the traditional FMEAwe get RPN simply through the product of the three factorsseverity (S) occurrence (O) and detection (D) In tradi-tional FMEA the relative importance of each factor is notconsidered and the three factors are given the same weightMeanwhile the different factors may be multiplied to get thesame RPN but corresponding degree of risk is inconsistentwhich makes the RPN inefficient in practical application invarious failure modes of the level of risk assessment

Second the application effect of FMEA was limitedbecause of the limited experience and knowledge that theexperts have To apply FMEA method you should set upa committee of experts first and experts grade every factorby their experience and knowledge In this process expertsneed to have a profound understanding investigation andresearch but it is difficult to describe their experience andknowledge because the subjective of the language so thetraditional FMEA can not make accurate judgments thusthere is a limit to the effect of traditional FMEA

From the preceding analysis we can find that there area lot of factors that influenced the supply chain vulnerabilityin the assessment index system and many factors mutuallyaffect each other so we need a comprehensive approach tointegrating the FMEA method to assess the vulnerabilityMeanwhile the comment that the FMEA expert team mem-bers assess often expressed ambiguous In order to modifythe defects of the traditional FMEA method and considerthe characteristics of supply chain vulnerability influencefactors FMEA method is combined with fuzzy theory andgray Correlative Method to evaluate and rank the RPN

22 Assessment Process When using the FMEA method weshould first give the operational model a clear definitionthen analyze the potential failure modes that existed in everyprocess and confirm the cause of potential failure modesNext we use the models to assess all failure modes rankthe vulnerability take preventive measures according to thevulnerability value and estimate the effect of the measuresIn this paper we give a special study on supply chainvulnerability which is based on traditional FMEA operationsteps so the supply chain based on FMEA vulnerabilityassessment process is shown as Figure 1

3 Supply Chain Vulnerability Analysis31 Supply Chain Vulnerability Analysis We adopted thesupply chain operations reference model (SCOR) that is

Scientific Programming 3

Supply chainvulnerability

analysis

Result analysisimprovements

making

Investigationand data

collection

Effective management ofvulnerability

Yes

Calculationand ranking

No Criterion for judgingwhether risks are

accepted

Figure 1 Assessment process

Buildassessment

experts groupDefine supplychain process

Identifypotential

failure modes

Establishassessment

index system

Figure 2 SC vulnerability analysis process

developed and authorized by the Supply Chain Council sothatwe can better identify failuremodes existing in the supplychain process and assess the supply chain vulnerability Basedon the SCOR model the supply chain vulnerability analysisprocess is given in Figure 2

Ten experts from academia and industry fully aware ofthe supply chain operation in the manufacture industry aredivided into two groups Group 1 adapt the SCOR modelto conceptualize the supply chain vulnerability analysis andgroup 2 validate the results of group 1 They define thesupply chain operation management processes as followsPlan Source Make Deliver and Return denoted as P SM D and R And they establish competitive performancegoals The second configuration layer is composed of 26kinds of core processes through a full discussion In thethird decomposition layer the second layer is detailed andspecific which makes the process more specific and givesthe companyrsquos competence ability in selected markets At thethird layer the experts group analyze the process identifyfailure modes and conclude consequences

32 Supply Chain Vulnerability Assessment Index System andFMEA Analysis In reviewing the relevant research resultsthe experts group built a supply chain vulnerability assess-ment index system We define the second layer as 119875119894 119878119894119872119894119863119894 119877119894 and its corresponding potential failure modes as 119875119894119865119895119878119894119865119895119872119894119865119895119863119894119865119895 119877119894119865119895 where 119894 = 1 2 119895 = 1 2 (1) Planning Process Failure Analysis In the SCOR modelldquoPlanrdquo is the processes that balance aggregate demand andsupply to develop a course of action which best meets sourc-ing production and delivery requirements The planningprocesses include confirming ranking and consolidatingneeds confirming evaluating and integrating resources andcapabilities balancing resources capabilities and needs andmaking plans

(2) Sourcing Process Failure Analysis ldquoSourcerdquo is the pro-cesses that procure goods and services to meet planned or

actual demand Sourcing processes include confirming sup-ply source selecting vendors and negotiating procurement ofraw materials product distribution arrangement acceptingproducts checking products transferring products authoriz-ing payments and other steps

(3) Making Process Failure Analysis ldquoMakerdquo is the processesthat transform product to a finished state to meet planned oractual demandThemaking processes include arranging pro-duction activities distribution of rawmaterials and productsproduction and testing packaging storage

(4) Delivering Process Failure Analysis ldquoDeliverrdquo is the pro-cesses that provide finished goods and services to meetplanned or actual demand typically including order man-agement transportationmanagement and distributionman-agementThedelivering processes include processing queriesoffering and receiving registration and verification of ordersreserving inventory and determining distribution date com-bining orders distribution packaging loading and generatingand distributing records transporting products customerreception and checking products

(5) Returning Process Failure Analysis ldquoReturnrdquo is the pro-cesses associated with returning or receiving returned prod-ucts for any reason These processes extend into postdeliverycustomer support The return processes include acknowl-edging of customer returning products disposal of returnedproducts requesting a return privilege arranging distribu-tion recycling returned products and other processes

4 Model

The proposed integrated FMEA model can be described asthe following steps in detail based on the basic FMEAmodel

41 Establishment of Assessment Index System Accordingto the previous introduction to supply chain vulnerabilityidentification we could put forward the factors set as follows119880 = 119875 119878119872119863 119877 and further divide them and the nextlayer factor is set as 119875 = 1198751 1198752 1198753 119878 = 1198781 1198782 119872 =119872111987221198723 119863 = 1198631 1198632 1198633 119877 = 1198771 1198772 1198773 1198774 Ifthere is a next layer we continue to define it

42 Establishment of Assessment Set We establish theassessment set by selecting ldquovery highrdquo ldquohighrdquo ldquohigherrdquoldquomediumrdquo ldquolowerrdquo ldquolowrdquo and ldquovery lowrdquo seven semanticitems [25]119881 = very high (VH) high (H) little high (VH)

medium (M) little low (LL) low (L) very low (VL) (1)

The corresponding meaning of all semantic items isshown in Table 6 By using the seven semantic items expertsassess the potential failuremodes in the supply chain and givefuzzy scores to the severity S occurrence O and detectionD According to the seven semantic items we obtain thecorresponding clear number by using fuzzy mathematical


theory andmethod and establish the assessment set as shownin Table 6

We used experts scoring method to determine the weightof each failure mode Assuming that there are 119899 experts and119894 failure modes the weight of failure mode 119894 given by expert119896 is recorded as 120592 The corresponding fuzzy semantic valueof triangular fuzzy number is through the use of the Delphimethod experts make decisions depending on their ownexperience and knowledge The ability of expert 119896 is definedas 120597119896 and the fuzzy assessment of a certain critical factor inthe failure mode is defined as 119909119896 which can be expressed bythe triangular fuzzy number as 119909119896 = (119886119896 119887119896 119888119896) According tothe expertsrsquo experience we can get the value of the key factorfuzzy triangles corresponding to the digital fuzzy semanticitems by the following equation (note that sum119899119896=1 120597119896 = 1 120597119896 isin(0 1)) 119886 = 119899sum

119896=1

120597119896119886119896119887 = 119899sum119896=1

120597119896119887119896119888 = 119899sum119896=1

120597119896119888119896(2)

We use fuzzy theory to calculate the formula which isproposed by Xiao and Li [26] Formula is shown as follows119862 (119909) = 12 (1 + 119873) lowast 119886 + 119873 + 2119873119872 +1198722 (1 + 119873) lowast 119887+ 12 (1 +119872) lowast 119888 (3)

For the basic factor set having three layers we addedvariable ℎ Then the weight of the failure mode 119894 wascalculated by120587119894 = 1119899 119899sum

119896=1

120587119896119894ℎ(119896 = 1 2 119899 119894 = 1 2 119898 ℎ = 1 2 3) (4)

43 Establishment of FMEA Table In the previous sectionwe applied fuzzy number and fuzzy theory and obtained thecorresponding number of fuzzy semantic items assessed byan expert team Next FMEA assessment team will assessthe various failure modes by using the fuzzy semantic itemsIn this process we need to establish an expert survey andstatistics table in order to obtain the data

After experts scored the failure mode we can get everylayer factorrsquos weight which ups to the average scores In thetraditional FMEA experts score in the condition that everylayer factors unrelated In fact the factors of every layer existhierarchical relationships By using probability tree theorywe calculate the value of weight considering hierarchicalrelationships of factors If we used 120579 to represent the RPNcoefficient of every failure mode then 120579 = 120587119894ℎ120587119894ℎminus1120587119894ℎminus2

(120587119894ℎ indicates the weight of the failure mode regardless ofthe hierarchical relationships 120587119894ℎminus1 indicates the weight ofthe failure mode corresponding to the upper layer and 120587119894ℎminus2indicates weight of the failure mode corresponding to thenext higher layer of the upper layer)

44 Determination of Vulnerability Level According to theformula of improved risk priority number RPN = 120579 lowast 119878 lowast119874 lowast 119863 we can get RPN which indicates the level of risk ofvarious failure modes But the crux of the matter is that RPNcan help managers make decisions Then the degree of graycorrelation between the evaluation vectors of each scheme iscalculated by the gray relational grade and the decision andevaluation vector in the best scheme are obtained

Step 1 (establishing comparison matrix) We assume thatthere are 119901 types of failure modes Each failure mode isexpressed by 119894 and 119909119894 illustrates failure mode 119894 Whencomputing there are 119878 119874 119863 three variables of each failuremode so the data shows failure mode 119894 represented as 119909119894 =119909119894(119905) 119905 = 1 2 3 whose values can be got by (3) On thebasis of the method mentioned above to reflect various offailure mode we establish the comparison matrix as follows

119883119894 (119905) = 119909119894 (119905) =((11990911199092119909119901))

=((

1199091 (1) 1199091 (2) 1199091 (3)1199092 (1) 1199092 (2) 1199092 (3) 119909119901 (1) 119909119901 (2) 119909119901 (3)))

(5)

Step 2 (establishing reference matrix) Failure mode riskranking should be based on certain reference standards Ingeneral we choose the best or worst value as a reference Theworst value of each variable is chosen to build a referencematrix as follows

1199090 (119905) =(VH VH VHVH VH VH VH VH VH

) (6)

Step 3 (calculating gray correlation coefficients) When itcomes to the gray correlation theory the following formulacan be obtained120582 (1199090 (119905) 119883119894 (119905))= min119894min119905

10038161003816100381610038161199090 (119905) minus 119909119894 (119905)1003816100381610038161003816 + 120592max119894max11990510038161003816100381610038161199090 (119905) minus 119909119894 (119905)100381610038161003816100381610038161003816100381610038161199090 (119905) minus 119909119894 (119905)1003816100381610038161003816 + 120592max119894max119905

10038161003816100381610038161199090 (119905) minus 119909119894 (119905)1003816100381610038161003816 (7)

where 120592 is distinguishing coefficient with the value in interval(01) normally 120592 = 05


Table 1 Planning process assessment index and failure mode analysis

First-layer factors Second-layer factors Potential failure modes

Plan P

Strategy P1

P1F1 unclear strategic positioningP1F2 high positioningP1F3 low positioningP1F4 inaccurate plan dataP1F5 supply chain strategy adjustmentP1F6 inconsistent strategic objectives among supply chain membersP1F7 failed strategic investment

Culture P2

P2F1 insufficient understanding and emphasis of supply chain managers on the multiculturalconflict riskP2F2 low quality of the staff cultural skillsP2F3 weak risk awareness and corporate risk cultureP2F4 inconsistent corporate cultural values

Demand P3

P3F1 customer lossP3F2 low satisfaction of customersrsquo needsP3F3 demand fluctuationsP3F4 poor sales reputationP3F5 many competitive alternatives

Step 4 (calculating gray correlations) Because of the differentinfluence on risk the variable of failure mode has differentweights Assuming the weight of variables is 120585119905 so thecorrelation degree is computed by the following equation[27] 120583 (1199090 119909119894) = 3sum

119905=1

120585119905 120582 (1199090 (119905) 119883119894 (119905)) (8)

Note thatsum3119905=1 120585119905 = 1 120585119905 is computed from the front expertquestionnaire statistics

Step 5 (ranking) Finally according to formula 120579 lowast 120583(1199090 119909119894)we could calculate the correlation degree in the case ofconsidering single weight of failure mode and three criticalfactors of weight of each failure mode Then we sort thecorrelation degree in descending order Determine the RPNand make target improvements

5 Case Study

51 Case Introduction Shipbuilding Yard RS was founded in2005 which is mainly engaged in shipbuilding marine engi-neering marine engine manufacturing and constructionmachinery and it concentrates on the relevant customers andmarkets of oil and gas industry According to the 2010 annualreport it had the largest proportion of business in BrazilChina Germany and Turkey and it is the fastest growingprivate shipbuilding company in China According to theBritishmaritime agencyClarkson report RS is also the largestshipbuilding enterprises at present in China by calculatinghand-held orders In October 2012 a RSrsquos subsidiary namedRSMaritime which aims to achieve the development strategyof its upgrading and transformation and develop the oceanengineering operations which are high value-added andrapidly increasing was set up in Singapore

The company attaches great significance to the vulnera-bility of the supply chain As a result the FMEA assessmentteam is set up to assess the enterprisesrsquo vulnerability Becauseof the difficulty in obtaining the accurate risk factors thesemantic variables are used in Table 8 to score the potentialfailure modes The FMEA assessment team includes 5 cross-department assessment members and it is mainly used as thescoring expert group which is named Expert Group 1 Owingto the different expertise and background of the experts theimportance and ability of them are also different On thesegrounds different relative weights are given as follows 0302 02 02 and 01 In order to verify the effectiveness ofExpert Group 1 construction index and scoring results we askthe company to set up another expert group named ExpertGroup 2 which is mainly used as validation expert group

52 Model Application521 Establishment of Assessment Index System We have athorough initial assessment with a total of 68 third-layerfactors and 15 second-layer factors (Tables 1ndash5) In this partExpert Group 1 is providedwith questionnaires to identify themost important variables in their opinion from Tables 1ndash5After a discussion of the Expert Group 1 they summarized aset of vulnerability assessment index system for the RSThenthe index system is presented to an additional independentgroup (Expert Group 2) for the purpose of model and resultvalidation so the index system is corrected and improvedand eventually we obtained themost suitable index system forRS (see Appendix in SupplementaryMaterial available onlineat httpdxdoiorg10115520162819238)

522 Establishment of Fuzzy Assessment Set In order toget clear numbers of corresponding fuzzy semantic itemsExpert Group 1 scores the fuzzy semantic items According


Table 2 Sourcing process assessment index and failure mode analysis


Source S

Purchasing S1

S1F1 high purchasing priceS1F2 frequent fluctuations in exchange ratesS1F3 inappropriate supplier selectionS1F4 unable to deliver goods on timeS1F5 low quality of procurement productS1F6 irresponsible purchase personS1F7 purchasing accidentS1F8 acceptance lax of procurement materials

Supply S2

S2F1 key suppliers failing to bankruptcy or lossS2F2 supplier business outsourcingS2F3 suppliersrsquo insufficient production capacityS2F4 unable to deliver goods on timeS2F5 insufficient supplier technical innovation capabilityS2F6 shortages of raw materials marketS2F7 inadequate supply elasticity of supplier

Table 3 Making process assessment index and failure mode analysis


MakeM

EnvironmentM1

M1F1 macroeconomic fluctuationM1F2 political instability and government interventionM1F3 industrial policy constraintsM1F4 natural disasters and so forthM1F5 insufficient in public utilities supply

ProductionM2

M2F1 improper control of the production processM2F2 inelastic production capacityM2F3 low level of information sharingM2F4 technical level restrictionsM2F5 labor cost RisingM2F6 talent loss and labor disputeM2F7 less rigorous of production inventory control

ControlM3

M3F1 imperfect financial systemM3F2 imperfect job security systemM3F3 failed risk response mechanismM3F4 inappropriate product cost controlM3F5 substandard productM3F6 intellectual property rights and other legal issues

Table 4 Delivering process assessment index and failure mode analysis


Deliver D

Relation D1

D1F1 self-interested behavior of partnersD1F2 distrust between partnersD1F3 inequitable benefits distribution among partnersD1F4 unfair competition between partners

Transportation D2

D2F1 inappropriate choice of transportation routes and meansD2F2 immature delivery technology or distribution equipment failureD2F3 product damage or lossD2F4 unscientific delivery personnel managementD2F5 inappropriate distribution modelD2F6 errors or delays in delivery

Origination D3D3F1 irrational supply chain structureD3F2 improper delegation of supply chain authority


Table 5 Returning process assessment index and failure analysis


Return R

Return way R1R1F1 no consensus with customerR1F2 no consensus with supplier

Return route R2 R2F1 improper return routeReturn procedure R3 R3F1 imperfect return process and rules

Return management R4

R4F1 imperfect return asset managementR4F2 imperfect return distribution managementR4F3 improper return inventory managementR4F4 incomplete return collection data

Table 6 Corresponding meaning of semantic items

Semanticitems Severity Occurrence Detection

Very low Products are not affected Failure hardly occurs Almost all detected

Low Products are slightly affected Failure rarely occurs The probability that failure is notdetected is low

Little low Some characteristics are slightly affected but productfunction properly Failure occurs less The probability that failure is not

detected is relatively low

MediumProduct can be used but some important

characteristics are affected while there is nodissatisfied customer

Failure occurs occasionally Failure is not occasionally detected

Little highProduct can be used but some important

characteristics are affected strongly some customersare not satisfied

Failure occurs frequently Failure cannot be detected occursfrequently

High Product has problems and has lost basic function Failure occurs repeatedly Failure most cannot be detected

Very high Products are complete lost basic function andthreaten personal safety or violate laws Failure is almost inevitable Failure is almost undetectable

Table 7 Rating levels for decision-making factor 119894 of failure mode FM119894

Expert 120597119896 Very low (VL) Low (L) Little low (LL) Medium (M) Little high (LH) High (H) Very high (VH)1 03 (01328) (082338) (274563) (385878) (537188) (789199) (881010)2 02 (01326) (062136) (224166) (335376) (456587) (718999) (831010)3 02 (01323) (072437) (244567) (355582) (51719) (759399) (851010)4 02 (0133) (07234) (264565) (36518) (536589) (819299) (921010)5 01 (01327) (062137) (244564) (34568) (56889) (768999) (861010)Total 1 (01327) (072738) (252464) (365679) (516889) (769199) (871010)

to the seven items proposed in the previous section ExpertGroup 1 assesses fuzzy decision-making factor of each failuremodeThe following table shows the expert scores which aretriangular fuzzy numbers 120597 is the relative weight mentionedabove

Based on (3) and the scores in Table 7 clear numericalvalues of fuzzy semantics can be calculated The results ofthese calculations are summarized in Table 8

523 Establishment of FEMA Table andWeight Scoring TableOwing to space limitations detailed FMEA tables are notdisplayed in the body part but in Appendix A In additionFEMA scoring is shown in Table A1 and the weights at alllayers scoring are shown in Table A2 When establishing

Table 8 Corresponding clear number of fuzzy semantic items

Items VL L LL M LH H VHClear number 13 23 45 57 69 9 98

FMEA table we omit corresponding causes and results offailure modes on the basis of the practical needs but we haveimproved the traditional FMEA table to be applicable for themacro supply chain system

524 Processing of Data By summarizing statistics of expertscoring sheets we got Tables B1 andB2 inAppendix BOn thebasis of the clear number in Table 8 we get decision-making


Table 9 Risk ranking of third-layer factors based on FEMA andgray correlation

Third-layer factors Weight Correlation RankingP1F1 004 00260 11P1F2 003 00198 14P2F1 002 00128 20P2F2 002 00116 22P3F1 002 00163 16P3F2 002 00136 19P4F1 002 00119 21P4F2 001 00062 25P4F3 001 00069 24P4F5 001 00064 26S1F1 007 00433 5S1F2 005 00315 9S2F1 008 00501 3S2F2 004 00255 12M1F1 006 00432 8M1F2 006 00385 6M1F3 006 00482 4M2F1 005 00373 7M2F2 004 00267 10M2F3 003 00197 15D1F1 002 00145 17D1F2 003 00208 13D2F1 002 00137 18D2F2 001 00055 27D2F3 002 00106 23R1F1 008 00506 2R1F2 008 00630 1Sum 10 mdash mdash

factors set named B2 Owing to space limitations we do notlist the source data but show the average index weight set B3

525 Determination the Level of Vulnerability(1) Establishing Related Matrix According to decision-making factor set obtained in the previous step we establisha comparison matrix and choose the worst value including119909119894(119905) and 1199090(119905) to establish reference matrix

(2) Calculating Gray Correlation Coefficient Taking 120592 =05 we compute the gray correlation coefficient betweendecision-making factor variables and reference values by (7)and get 120582(1199090(119905) 119883119894(119905))(3) Calculating Gray Correlation Degree On the basis of (8)(120585119905 available in Appendix) we can get the following data(064964065921063872058098081740068236059689061506069091057574061907063095062653063831071990064093080330074667066641065730072529069363068583054514 053203056261073313)

(4) Ranking Vulnerability Level Again according to 120579 lowast120583(1199090 119909119894) we obtain the risk ranking summarized in Tables9 10 and 11

Table 10 Risk ranking of second-layer factors based on FEMA andgray correlation

Second-layer factors Weight Correlation RankingP1 0068 00458 6P2 004 00244 11P3 0044 00299 9P4 0048 00314 8S1 01248 00748 5S2 01152 00756 4M1 0168 00867 2M2 0112 00837 3D1 0048 00353 7D2 0052 00298 10R1 018 01136 1Sum 1 mdash mdash

Table 11 Risk ranking of first-layer factors based on FEMAand graycorrelation

First-layer factors Weight Correlation Ranking119875 02 01315 3119878 024 01504 2119872 028 02136 1119863 01 00651 5119877 018 01136 4Sum 1 mdash mdash

53 Result Analysis From Table 11 we conclude that fivefirst-grade indicators ranking is make gt source gt plan gtreturn gt deliver The vulnerability of RS is chiefly reflectedin three aspects make source and plan The make risk isthe largest so its weight is the heaviest Meanwhile factorof source is the second place in the secondary indicatorsof manufacturing which indicates that the manufacturingprocess attaches great importance to the shipbuilding supplychain and it is the most vulnerable part and the mosterror-prone part which have relations with the charactersof the shipbuilding enterprises industry Source is nearly assignificant as make Especially the overcapacity of RS shouldbe taken seriously Furthermore source is also important inthe second part of the shipbuilding supply chain Owing tothe expanding scope of business and cost savings resultingfrom zero inventory RS has higher and higher requirementsfor the raw materials RS becomes increasingly dependentupon suppliers but suppliers which lead to risk are amongall failure modes that are ranking in the top place whichshould concern more about the supply chain flexibility whenpursuing efficiency and low cost at the same time Large-scaleshipbuilding overseas orders make the process of planningmore crucial and higher request for the accurate strategicplan and the effective buildingWhat requires more attentionis that the weightof the last link of supply chain in return


Table 12 Comparison of 120591 value of various methods

Improved FMEA Average Only fuzzy theory FEMA1205911 069 016 0451205912 071 017 0501205913 070 036 0461205914 068 032 0451205915 065 033 0371205916 065 011 0321205917 071 minus001 0241205918 069 000 0431205919 073 028 05312059110 075 013 064120591 070 018 044

process is not big It is calculated ranking top in the layer 3 riskfactors and they are company cancel the order and contractrenegotiation which is the first and second respectivelyThe mismanagement between the firm and the demand sidemanagement causes that there is a large-scale overseas ofinvalid orders

54 Comparison with Other Methods In order to verify thevalidity of improved FMEA in evaluating the supply chainExpert Group 2 is also requested to score the FMEA table andweight table In this paper we useKendallrsquos tau coefficient [2829] to verify the quality of order-preserving

Given two rating score vectors 119886119894119899119894minus1 and 119887119894119899119894minus1 119862119894119895defined as

119862119894119895 = 1 If (119886119894 lt 119886119895 and 119887119894 lt 119887119895) or (119886119894 gt 119886119895 and 119887119894 gt 119887119895) or (119886119894 = 119886119895 and 119887119894 = 119887119895)05 If (119886119894 = 119886119895 and 119887119894 = 119887119895) or (119886119894 = 119886119895 and 119887119894 = 119887119895)0 Other

(9)

Then the number of concordant pairs is 119862 =sum119899119894minus1sum119899119895=119894+1 119862119894119895 Based on the above definition Kendallrsquostau rank-correlation coefficient can be calculated namely 120591Besides 120591 value has three properties (1) if the two ratingsimply the same ranking 120591 = 1 Otherwise 120591 = minus1 (2)For all the cases 120591 value lies between minus1 and 1 (3) Oneproperty of 120591 index is as follows the larger value 120591 that showsorder-preserving is better and the method applied duringsorting is more in line with the actual situation Table 12shows that the improved FMEA result is more in line withthe actual situation compared with the other two methods

6 ConclusionsIn this study an improved FMEA is provided to assess thesupply chain vulnerability The main results are as follows

(1) With lots of expert interviews and investigationsof related literature we establish the supply chainvulnerability assessment index system including 5first-layer factors 10 second-layer factors and 27potential failure modesThe assessment index systemalmost contains thewhole process of supply chain andprovides the corresponding potential failure modesand it can enhance the operability of the assessmentwork

(2) To solve the two shortcomings of traditional FMEAwe provide an improved FMEA that combined withgray correlation and fuzzy theory In the improvedFMEA the weights consider every layer factorrsquos cor-relation Also we introduce the gray relational degreeto rank which makes the scoring more scientific andreliable

(3) In order to test applicability of the improved FMEAmodel we choose a shipbuilding Yard that is calledRS to study The results showed that vulnerability is

mainly concentrated on the manufacturing procure-ment excess capacity and other linksWemake someproposals to make vulnerability identification andassessment which might be helpful for the similarshipbuilding enterprise

(4) In order to verify the effect of the improved FMEAthe paper used Kendallrsquos tau coefficient to assess theten scoring methods only fuzzy theory FMEA andimproved FMEA The study found that the improvedFMEAmethod is the best in isotonicity which meansthat the improved FMEAmethod is more in line withreality

DisclosureThe draft of this paper has been presented in 2014 Inter-national Joint Conference on Computational Sciences andOptimization [30]

Competing InterestsThe authors declare that they have no competing interests

AcknowledgmentsThe authors are grateful for all the revision suggestions thatwere provided in the conference And this paper is partlysupported by Natural Science Foundation (71402038) andHigher Education Development Fund of Liaoning Province(20110116202) And thanks are due to all the referencesrsquoauthors

References

[1] L Coleman ldquoFrequency of man-made disasters in the 20thcenturyrdquo Journal of Contingencies and Crisis Management vol14 no 1 pp 3ndash11 2006


[2] O K Helferich and R L Cook Securing the Supply Chain Council of Logistics Management Oak Brook Ill USA 2002

[3] Re Munich Annual Review Natural Catastrophes Munich RePublications Munich Germany 2005

[4] K B Hendricks and V R Singhal ldquoAn empirical analysisof the effects of supply chain disruptions on long-run stockprice performance and equity risk of the firmrdquo Production andOperations Management vol 1 no 14 pp 35ndash52 2005

[5] Z Lin X Zhao K M Ismail and KM Carley ldquoOrganizationaldesign and restructuring in response to crises lessons fromcomputational modeling and real-world casesrdquo OrganizationScience vol 17 no 5 pp 598ndash618 2006

[6] G A Zsidisin G L Ragatz and S A Melnyk ldquoThe dark side ofsupply chain managementrdquo Supply Chain Management Reviewvol 2 no 9 pp 46ndash52 2005

[7] M Christopher and H L Lee ldquoMitigating supply chain riskthrough improved confidencerdquo International Journal of PhysicalDistribution amp Logistics Management vol 34 no 5 pp 388ndash396 2004

[8] M Christopher and H Peck ldquoBuilding the resilient supplychainrdquo International Journal of Logistics Management vol 2 no15 pp 1ndash13 2004

[9] P Blaikie T Cannon I Davis and B Wisner At Risk NaturalHazards Peoplersquos Vulnerability and Disasters Routledge Lon-don UK 2014

[10] P Barnes andROloruntoba ldquoAssurance of security inmaritimesupply chains conceptual issues of vulnerability and crisismanagementrdquo Journal of International Management vol 11 no4 pp 519ndash540 2005

[11] L Wang and Z Y Chu ldquoLiterature review on vulnerability insupply chainrdquo Soft Science vol 9 no 25 pp 136ndash139 2011

[12] V Albino and A C Garavelli ldquoA methodology for the vulnera-bility analysis of just-in-time production systemsrdquo InternationalJournal of Production Economics vol 41 no 1ndash3 pp 71ndash80 1995

[13] E PraterM Biehl andMA Smith ldquoInternational supply chainagilitymdashtradeoffs between flexibility and uncertaintyrdquo Interna-tional Journal of Operations and Production Management vol21 no 5-6 pp 823ndash839 2001

[14] B Zhong and T Xie ldquoResearch on the model of supply chainSystemrsquos brittlenessrdquo Chinese Journal of Management Sciencevol 10 pp 443ndash445 2005

[15] G A Svensson ldquoA conceptual framework for the analysis ofvulnerability in supply chainsrdquo International Journal of PhysicalDistribution amp Logistics Management vol 30 no 9 pp 731ndash7492000

[16] G Svensson ldquoA conceptual framework of vulnerability in firmsrsquoinbound and outbound logistics flowsrdquo International Journal ofPhysical Distribution and Logistics Management vol 32 no 2pp 110ndash134 2002

[17] G Svensson ldquoSub-contractor and customer sourcing and theoccurrence of disturbances in firmsrsquo inbound and outboundlogistics flowsrdquo Supply Chain Management vol 8 no 1 pp 41ndash56 2003

[18] H Peck ldquoDrivers of supply chain vulnerability an integratedframeworkrdquo International Journal of Physical Distribution ampLogistics Management vol 35 no 4 pp 210ndash232 2005

[19] D Bogataj and M Bogataj ldquoMeasuring the supply chain riskand vulnerability in frequency spacerdquo International Journal ofProduction Economics vol 108 no 1-2 pp 291ndash301 2007

[20] M Stephan and N N Wagner ldquoAssessing the vulnerability ofsupply chains using graph theoryrdquo Production Economics vol126 no 1 pp 121ndash129 2010

[21] Y L Fang W Song and Z Y Wang ldquoResearch of assessmentand pre-warning of vulnerability of SMEsrsquo independent intel-lectual propertyrdquo Economic Management Journal vol 10 no 31pp 141ndash146 2009

[22] F Lolli A Ishizaka R Gamberini B Rimini and M MessorildquoFlowSort-GDSSmdasha novel group multi-criteria decision sup-port system for sorting problems with application to FMEArdquoExpert Systems with Applications vol 42 no 17-18 pp 6342ndash6349 2015

[23] F Men and S Q Ji ldquoAn improved FMEA based on fuzzytheory and gray Relational Theoryrdquo Industrial Engineering andManagement vol 2 pp 55ndash59 2008

[24] H-C Liu P Li J-X You and Y-Z Chen ldquoA novel approachfor FMEA combination of interval 2-tuple linguistic variablesand gray relational analysisrdquoQuality and Reliability EngineeringInternational vol 31 no 5 pp 761ndash772 2015

[25] C X Zhang ldquoImproved QFD and integrated framework ofFMEArdquo Chinese Journal of Management vol 2 no 6 pp 207ndash212 2009

[26] Y Xiao and H Li ldquoImprovement on judgment matrix based ontriangle fuzzy numberdquo Fuzzy Systems and Mathematics vol 2no 17 pp 59ndash64 2003

[27] L LiuH C Liu andQ L Lin ldquoAn improved FMEAusing fuzzyevidential reasoning approach and gray theoryrdquo Fuzzy Systemsand Mathematics vol 2 no 25 pp 72ndash80 2011

[28] MVanhoucke ldquoUsing activity sensitivity and network topologyinformation to monitor project time performancerdquoOmega vol38 no 5 pp 359ndash370 2010

[29] Y Li P Luo and C Wu ldquoInformation loss method to measurenode similarity in networksrdquo Physica A Statistical Mechanicsand its Applications vol 410 pp 439ndash449 2014

[30] J Liu and Y Zhou ldquoImproved FMEA application to evalua-tion of supply chain vulnerabilityrdquo in Proceedings of the 7thInternational Joint Conference on Computational Sciences andOptimization (CSO rsquo14) pp 302ndash306 Beijing China July 2014

Submit your manuscripts athttpwwwhindawicom

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vulnerability management principles to prevent and respondto damage at the tactical level In the view of specializationof supply chain Barnes and Oloruntoba [10] analyzed thecharacteristics of the entire supply chain using the specialcase and they concluded that it is the complexity of theinteraction between marine operations and supply chain thatcaused the vulnerability In 2000 Svensson representativescholar in the supply chain vulnerability built a supply chainvulnerability theoretical framework and made a qualitativedescription and assessment of the vulnerability through thedisturbance source category and logistics mode [15] Aftertwo years he calculated the vulnerability again based ontime and relationship [16] In order to enrich the studyhe assessed the supply chain vulnerability from time func-tion and relationshiprsquos three dimensions in 2004 [17] Peckintroduced the network theory and complex systems intothe interactive production system supply chain conceptualmodel and analyzed the four levels from multiangles [18]D Bogataj and M Bogataj [19] evaluated the vulnerabilityfrom the perspective of vulnerable point and analyzed theinput-output tables and the relationship between storage anddelivery using the dynamic NPV method which can find therelationship of before and after items then he concluded theweak link of the supply chain and predicted the vulnerabilitypoint Based on the vulnerability index Stephan andWagner[20] proposed a method to confirm the validity of the policyby comparing different industry or enterprise vulnerabilityindex With the development of extensive Fang et al [21]proposed vulnerability assessment and early warning modelfor small andmedium-sized enterprises intellectual property

To sum up the existing researches have establishedtheoretical framework to evaluate the supply chain vulnera-bility However these basically are limited to the qualitativeframework Therefore we utilize the FMEA (failure modeand effect analysis) method which is a way for prospectivereliability analysis and safety assessment It analyzes everyfailure mode that existed in the system and it calculatedrisk priority number (RPN) for the consequences of allfailure modes [22] According to the value of RPN we makea quantitative assessment of the failure modes and takecorresponding measures to prevent and avoid the potentialfailure mode if necessary However the traditional FMEAmethoddoes not recognize the importance of the relationshipbetween all the failure modes and every decisive factor inthe vulnerability and the traditional FMEA method makesits evaluation effect limited when the expertsrsquo evaluationlanguage is qualitative and subjective Considering the aboveconditions we present an integrated method using the fuzzytheory and the gray relational theory to solve the aboveproblems The present method can help business managersimplement the improvements of supply chain vulnerability

2 Methodology and Assessment Process

21 Methodology FMEA namely failure mode and effectanalysis in fact originated in the 1950s Because of thesignificant value in safety and reliability assessment FMEAhas developed rapidly and has been widely used in manyareas such as the nuclear industry automotive machinery

aerospace electronics and ships It use three aspects forassessment They are severity (S) occurrence (O) and detec-tion (D) [23] It is a powerful tool for defining identifyingand eliminating potential failures from the system designprocess or service before they reach the customer [24]Severity is the degree of the effect of the potential failuremode on customers occurrence refers to the possibility of theoccurrence of the specific failure mode detection describesthe possibility that the current system cannot recognize thefailure modes or reasons The traditional FMEA method hastwo following disadvantages in practical application

First the traditional FMEAmethod to assess products orsystem risks merely depends on RPN which can lead to largeproblems in the actual situation In the traditional FMEAwe get RPN simply through the product of the three factorsseverity (S) occurrence (O) and detection (D) In tradi-tional FMEA the relative importance of each factor is notconsidered and the three factors are given the same weightMeanwhile the different factors may be multiplied to get thesame RPN but corresponding degree of risk is inconsistentwhich makes the RPN inefficient in practical application invarious failure modes of the level of risk assessment

Second the application effect of FMEA was limitedbecause of the limited experience and knowledge that theexperts have To apply FMEA method you should set upa committee of experts first and experts grade every factorby their experience and knowledge In this process expertsneed to have a profound understanding investigation andresearch but it is difficult to describe their experience andknowledge because the subjective of the language so thetraditional FMEA can not make accurate judgments thusthere is a limit to the effect of traditional FMEA

From the preceding analysis we can find that there area lot of factors that influenced the supply chain vulnerabilityin the assessment index system and many factors mutuallyaffect each other so we need a comprehensive approach tointegrating the FMEA method to assess the vulnerabilityMeanwhile the comment that the FMEA expert team mem-bers assess often expressed ambiguous In order to modifythe defects of the traditional FMEA method and considerthe characteristics of supply chain vulnerability influencefactors FMEA method is combined with fuzzy theory andgray Correlative Method to evaluate and rank the RPN

22 Assessment Process When using the FMEA method weshould first give the operational model a clear definitionthen analyze the potential failure modes that existed in everyprocess and confirm the cause of potential failure modesNext we use the models to assess all failure modes rankthe vulnerability take preventive measures according to thevulnerability value and estimate the effect of the measuresIn this paper we give a special study on supply chainvulnerability which is based on traditional FMEA operationsteps so the supply chain based on FMEA vulnerabilityassessment process is shown as Figure 1

3 Supply Chain Vulnerability Analysis31 Supply Chain Vulnerability Analysis We adopted thesupply chain operations reference model (SCOR) that is



analysis


making


collection


Yes



accepted


Buildassessment


Identifypotential

failure modes

Establishassessment

index system










4 Model









119896=1

120597119896119886119896119887 = 119899sum119896=1

120597119896119887119896119888 = 119899sum119896=1

120597119896119888119896(2)



119896=1

120587119896119894ℎ(119896 = 1 2 119899 119894 = 1 2 119898 ℎ = 1 2 3) (4)






119883119894 (119905) = 119909119894 (119905) =((11990911199092119909119901))

=((

1199091 (1) 1199091 (2) 1199091 (3)1199092 (1) 1199092 (2) 1199092 (3) 119909119901 (1) 119909119901 (2) 119909119901 (3)))

(5)



) (6)



10038161003816100381610038161199090 (119905) minus 119909119894 (119905)1003816100381610038161003816 (7)





Plan P

Strategy P1


Culture P2


Demand P3



119905=1

120585119905 120582 (1199090 (119905) 119883119894 (119905)) (8)



5 Case Study








Source S

Purchasing S1


Supply S2




MakeM

EnvironmentM1


ProductionM2


ControlM3




Deliver D

Relation D1


Transportation D2






Return R















































(9)











References







































Advances in

FuzzySystems


Volume 2014












Journal of

Journal of





Advances in

Multimedia


Biomedical Imaging



Advances in


RoboticsJournal of










Advances in





analysis


making


collection


Yes



accepted


Buildassessment


Identifypotential

failure modes

Establishassessment

index system










4 Model









119896=1

120597119896119886119896119887 = 119899sum119896=1

120597119896119887119896119888 = 119899sum119896=1

120597119896119888119896(2)



119896=1

120587119896119894ℎ(119896 = 1 2 119899 119894 = 1 2 119898 ℎ = 1 2 3) (4)






119883119894 (119905) = 119909119894 (119905) =((11990911199092119909119901))

=((

1199091 (1) 1199091 (2) 1199091 (3)1199092 (1) 1199092 (2) 1199092 (3) 119909119901 (1) 119909119901 (2) 119909119901 (3)))

(5)



) (6)



10038161003816100381610038161199090 (119905) minus 119909119894 (119905)1003816100381610038161003816 (7)





Plan P

Strategy P1


Culture P2


Demand P3



119905=1

120585119905 120582 (1199090 (119905) 119883119894 (119905)) (8)



5 Case Study








Source S

Purchasing S1


Supply S2




MakeM

EnvironmentM1


ProductionM2


ControlM3




Deliver D

Relation D1


Transportation D2






Return R















































(9)











References







































Advances in

FuzzySystems


Volume 2014












Journal of

Journal of





Advances in

Multimedia


Biomedical Imaging



Advances in


RoboticsJournal of










Advances in






119896=1

120597119896119886119896119887 = 119899sum119896=1

120597119896119887119896119888 = 119899sum119896=1

120597119896119888119896(2)



119896=1

120587119896119894ℎ(119896 = 1 2 119899 119894 = 1 2 119898 ℎ = 1 2 3) (4)






119883119894 (119905) = 119909119894 (119905) =((11990911199092119909119901))

=((

1199091 (1) 1199091 (2) 1199091 (3)1199092 (1) 1199092 (2) 1199092 (3) 119909119901 (1) 119909119901 (2) 119909119901 (3)))

(5)



) (6)



10038161003816100381610038161199090 (119905) minus 119909119894 (119905)1003816100381610038161003816 (7)





Plan P

Strategy P1


Culture P2


Demand P3



119905=1

120585119905 120582 (1199090 (119905) 119883119894 (119905)) (8)



5 Case Study








Source S

Purchasing S1


Supply S2




MakeM

EnvironmentM1


ProductionM2


ControlM3




Deliver D

Relation D1


Transportation D2






Return R















































(9)











References







































Advances in

FuzzySystems


Volume 2014












Journal of

Journal of





Advances in

Multimedia


Biomedical Imaging



Advances in


RoboticsJournal of










Advances in






Plan P

Strategy P1


Culture P2


Demand P3



119905=1

120585119905 120582 (1199090 (119905) 119883119894 (119905)) (8)



5 Case Study








Source S

Purchasing S1


Supply S2




MakeM

EnvironmentM1


ProductionM2


ControlM3




Deliver D

Relation D1


Transportation D2






Return R















































(9)











References







































Advances in

FuzzySystems


Volume 2014












Journal of

Journal of





Advances in

Multimedia


Biomedical Imaging



Advances in


RoboticsJournal of










Advances in






Source S

Purchasing S1


Supply S2




MakeM

EnvironmentM1


ProductionM2


ControlM3




Deliver D

Relation D1


Transportation D2






Return R















































(9)











References







































Advances in

FuzzySystems


Volume 2014












Journal of

Journal of





Advances in

Multimedia


Biomedical Imaging



Advances in


RoboticsJournal of










Advances in






Return R















































(9)











References







































Advances in

FuzzySystems


Volume 2014












Journal of

Journal of





Advances in

Multimedia


Biomedical Imaging



Advances in


RoboticsJournal of










Advances in






















(9)











References







































Advances in

FuzzySystems


Volume 2014












Journal of

Journal of





Advances in

Multimedia


Biomedical Imaging



Advances in


RoboticsJournal of










Advances in










(9)











References







































Advances in

FuzzySystems


Volume 2014












Journal of

Journal of





Advances in

Multimedia


Biomedical Imaging



Advances in


RoboticsJournal of










Advances in








































Advances in

FuzzySystems


Volume 2014












Journal of

Journal of





Advances in

Multimedia


Biomedical Imaging



Advances in


RoboticsJournal of










Advances in










Advances in

FuzzySystems


Volume 2014












Journal of

Journal of





Advances in

Multimedia


Biomedical Imaging



Advances in


RoboticsJournal of










Advances in



Documents

Research Article An Integrated Method of Supply Chains