Interpretive Structural Modeling of Supply Chain Risks

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Interpretive structural modelingof supply chain risks

Hans-Christian Pfohl, Philipp Gallus and David ThomasTechnische Universitat Darmstadt, Darmstadt, Germany

Abstract

Purpose The aim of this paper is the structural analysis of potential supply chain risks. It willdemonstrate how interpretive structural modeling (ISM) supports risk managers in identifying andunderstanding interdependencies among supply chain risks on different levels (e.g. 3PL, first-tiersupplier, focal company, etc.). Interdependencies among risks will be derived and structured into ahierarchy in order to derive subsystems of interdependent elements with corresponding driving powerand dependency.

Design/methodology/approach ISM was used to identify inter-relationships among supply

chain risks and to classify the risks according to their driving and dependence power. The theoreticalfindings of the modeling and the applicability for practical use has been tested in two case studies withtwo German industry and trade companies.

Findings ISM was proven as a useful methodology to structure supply chain risks in an easy anddistributed approach that can also be carried out in a step-by-step process on several manufacturingstages. The input to the algorithm has to be well-defined to give the user an exact understanding of allrisks that have to be assessed, i.e. the better the input to ISM is prepared the better the outcome andrepresentation will be. Finally, when applying the method, a moderated process proved to be morereliable than an assessment based on paper questionnaires only.

Originality/value This models insight would assist supply chain (risk) managers in the effectiveallocation of risk management resources in the subsequent risk management phases.

KeywordsGermany, Supply chain management, Risk management, Decision making,Cause-effect relations, Interpretive structural modeling, MICMAC

Paper typeResearch paper

1. Research objectiveGrowing competition demands industry to widen the concentration on corecompetencies and the reduction of the vertical range of manufacture, leading to a newdimension of cooperation of numerous companies in form of value-added chains (supplychains). Todays supply chains are very complex inter-dependable structures, due to themultitude of (partly globally) participating suppliers, service providers and customers.Understanding and managing risk shifting around supply chains is an important issuein business and a complex problem. Identification of supply chain risks is the first step inthe risk management process. But transparency across the risk potential along thesupply chain is not the only prerequisite for a successful (in the sense of effective) riskmanagement. The selection of appropriate (mitigation or prevention) measures builds onthe structuralassessment andthe impact area of the various typesof risks (Chopra andShodhi, 2004). Even though there is a rich stream of (empirical) literature that dealswith supply chain risks and their management (Svensson, 2000; Juttner et al., 2003;Zsidisinet al., 2004; Pfohlet al., 2008b), and conceptual literature dealing with the newconcept of supply chain risk management (SCRM) (Hauser, 2003; Norrman and Lindroth,2004; Juttner, 2005; Faisalet al., 2007; Franck, 2007; Pfohl et al., 2008a), there has beenlittle research about the interconnectedness of supply chain risks.

The current issue and full text archive of this journal is available at

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ISM of supplchain risk

839

International Journal of Physi

Distribution& Logistics Managem

Vol. 41 No. 9, 20

pp. 839-8

q Emerald Group Publishing Limit

0960-00

DOI 10.1108/096000311111758


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This papers objective is the structural analysis of potential supply chain risks byapplying interpretive structural modeling (ISM). It will demonstrate how ISM supportsrisk managers in identifying and understanding interdependencies among supply chainrisks on different levels (e.g. 3PL, first-tier supplier, focal company, etc.).

Interdependencies among risks will be derived and structured into a hierarchy in orderto derive subsystems of interdependent elements with corresponding driving power anddependency. The point for departure for ISM methodology is the identification of relevantelements, in our case supply chain risks. The remainder of this paper is organised asfollows. The paper sets out to discuss definitions and classifications of supply chain risksin Section 2. Next, the concept of SCRM is emphasized and the elements to be modelled areidentified. Section 3 contains the methodology and provides the results of the ISM,followed by a concluding Section 4 of the major findings.Section5 contains the descriptionand results from two case studies conducted for testing the theoretical findings and thepractical applicability of the methodology. Section 6 highlights the practical use of themethodology. Finally, further research questions are provided in Section 7.

2. SCRM and identification of elementsGenerally, each business activity is connected with risks. The focus on just-in-timeproduction, lean manufacturing, single sourcing and offshoring implies a certainvulnerability of supply chains (Peck, 2006). Similarly, outsourcing and offshoring resultin geographically more diverse supply chains, which are therefore exposed to all sorts ofrisks. To minimize potential negative effects or even avoid them, a systematic andcomprehensive risk management process is essential. An SCRM approach for theidentification, assessment, treatment/control and monitoring of supply chain risks hasgained in importance over the past few years (Hauser, 2003; Norrman and Lindroth,2004; Juttner, 2005; Faisalet al., 2007; Franck, 2007; Straube and Pfohl, 2008). The focusof SCRM is broader than normal risk management. The unit of analysis represents a

dyadic relationship (buyer-seller) or a supply chain of three or more companies. Owing tothe high interconnectedness of todays supply chains, supply chain risks can bemanifold, may be difficult to identify and could consist of difficult to analyze cause-effectrelations. A structured and detailed collection of all risk potentials including theirinterdependencies is of crucial importance for the subsequent phases. Identifyingcause-effect correlations between individual risks is important, because hiddeninfluences of a certain risk in connection with other risk(s) may cause substantialdamages (Chopra and Shodhi, 2004).

The literature offers most different definitions as to what is to be understood bysupply chain risks (Ziegenbein, 2007; Li and Hong, 2007; Kajuter, 2007). This paper isbased on the following definition:

Supply chain risks cover risks that are related to disturbances and interruptions of the flowswithin the goods-, information- and financial network as well as the social and institutionalnetworks and may negatively effect the objective accomplishment of the individual company,respectively, the entire supply chain, in regards of end-user advantage, costs, time or quality.

Not only the definition of supply chain risks turns out to be difficult, but theircategorization proves difficult as well. Potential risks in the supply chain are categorizedin different ways in the literature (Juttneret al., 2003; Spekman and Davis, 2004; Gotzeand Mikus, 2007). Based on the cause-oriented risk categorization, risks can be divided

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into three areas related to their point of origin (Gotze and Mikus, 2007; Juttner et al., 2003):risks within a focal company, risks outside of this company and within the supply chainas well as risks outside the supply chain (Figure 1).

Risks originating within a focal company can be differentiated between process and

control risks. Process risks describe disruptions within the value-increasing activities ofa company, like production delay or loss of operating resources. Control risks includedisturbances of the management systems as well as imprecise and wrong decisionguidelines, with which the company co-ordinates its own processes and those of thesuppliers and customers. These include, e.g. wrongly planned lot sizes or even missingand/or not practicable instructions for the co-workers. Additionally, risks rootingoutside of a company but within the supply chain and with an impact on the focalcompany are differentiated according to their effective direction, namely into supply anddemand risks (Juttner, 2005). Supply risks are based on disturbances, respectively, lossof key suppliers, whereas demand risks aremostly related to the customer, and exhibitedin fashionable or seasonal demand fluctuation. Risks outside the supply chain areso-called external or environmental risks and are exemplified by natural disasters,

terrorist attacks and/or changes in federal guidelines (Kerstenet al., 2006).This paper uses the results of a study of German logistics service providers and

industrial and commercial companies supply chain risks (Straube and Pfohl, 2008;Pfohl et al., 2008b), to show the utility of ISM to structure risks and highlightinterdependencies between them. To remain within the scope of this paper, the relevantpart of the survey, which pertains to the ranking of the most important supply chainrisks, has been used in this paper. The showcase underlying this research uses avirtual supply chain consisting of a focal industrial company, its first-tier supplier andthe first-tier suppliers 3PL which is responsible for the transportation on the first-tierssupply side. Only downstream risks will be considered.

Risks in the focal companys sphereAccording to the risk categories and the virtual supply chain, therelevant risks are processand control risks as well as supply risks. Within the company, long-term productiondowntimes (1), IT breakdowns (2) and short-term production downtimes (3) are criticalrisks. Long-term production downtimes last longer than three days whereas short-termdowntimes are everything between a couple of hours to three days. On the supply side, thecompany is facing primarily capacity variances/bottlenecks on the supply market (4),dependency on suppliers (5) and delayed deliveries (6). Dependencies on suppliers can becaused by single sourcing as well as by a lack of alternative suppliers (e.g. monopolies).

Risks in the first-tier suppliers sphereAlong the lines of the virtual supply chain, the first-tier supplier is exposed to mainly the

same process and control risks as the focal company, namely long-term production

Figure Supply chain ris

categorizatio

Supply Chain Risks

Supplier Focal Company

Control risks

Process risks

3 PL 3 PL Customer

Supply risks Demand risks

External risks


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downtimes (7), IT breakdown (8) and short-term production downtimes (9), respectively.On the supply side, the relevant risks are different which we call logistics risks. Asregards the relationship to its 3PL, the first-tier supplier is facing primarily theft (10),poor delivery quality (11) and a lack of sufficient equipment, staff or

transport/warehouse capacity (12). In contrast to delayed deliveries, poor deliveryquality means deviance in quantity, sort and condition.

Risks in the 3PLs sphereProcess and control risks within the 3PLs company are hauling claim (13), unqualifiedstaff (14) and IT breakdown (15). Unqualified staff is a potential risk to the 3PLsoperations. The supply risks could in this case also be called resource risks and areprimarily poor performance of subcontractors (16), lack of transport capacities (17) anda general shortage of staff (18). Poor performance of subcontractors is a relevant riskbecause 3PL regularly source certain services, e.g. from carriers.

External risks from outside of the supply chainBesides the above-described risks, a potential danger arises from external risks such asterrorist attacks (19), natural disasters (20) or employee strikes (21). Theses risks havea direct impact on all stakeholders in the virtual supply chain and can cause severedisruptions.

3. ISM methodology and model developmentISM is a qualitative and interpretive method which generates solutions for complexproblems through discourses based on the structural mapping of complexinterconnections of elements (Malone, 1975; Watson, 1978). A structure of theelements results within the context of the ISM dependingon a certain relation type whichdescribes the connections of the elements to each other (Warfield, 1994). The method

supports the identification and order of the complex relations between the elements of asystem so that the influence can be analysed between the elements. ISM has been appliedto various problems (Saxena and Sushil, 1990; Mandal and Deshmukh, 1994; Singh et al.,2003; Jharkharia and Shankar, 2005; Thakkaret al., 2005).

Figure 2 shows the basic logic of the ISM. A complex problem or the dependenciesbetween elements to be examined are interpreted as a (badly or not at all structured)complex system (object system) (Szyperski and Eul-Bischoff, 1983). The modelingconverts the object system into a well-defined and representative system consistingof directed graphs (digraph). An interpretation of the object system as regards contentis also carried out besides the structural one, i.e. the digraphs are completed withcontext (information). The object system mapped as digraphs becomes the basicstructural model. The expansion with content finally leads to an interpretive

structural model.The ISM is described as interpretive since a group discussion is deciding, whether

and how the elements are related. Thus, the methodology is appropriate for use byexperts who are knowledgeable of the problems context. The method is structuringsince it produces (on the basis of the relations) a comprehensive structure of allthe complex elements by considering all possible pairwise interactions of theelements. The method is modeling since the complete structure and the individualrelations of the elements are illustrated in digraphs (Agarwal et al., 2007).

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Primarily, in complex systems, indirect relations are of great importance, which at times,greatly influence the system under consideration.

3.1 Steps involved in ISM methodologyThe various steps involved in ISM technique are as follows:

(1) Selection of elements relevant to the problem. Starting point is the identification ofelements relevant to the problem. This can be done by secondary research (deskresearch) or primary research techniques (survey, group problem solving).

(2) Establishing contextual relation type. Next, the contextual relation must becogently stated as a possible statement of relationship among the elements. Relationsmay be of several types like comparative, influence, neutral or temporal relations(Austin and Burns, 1985; Warfield, 1994).

(3) Construction of structural self-interaction matrix (SSIM) by pairwise comparison.Phase (3) of ISM is the most tedious and demanding. During this phase, the participantsmust decide upon the pairwise relationship between the elements. Keeping in mind thecontextual relationship for each element, the existence of a relation between any twosub-elements (iand j) and the associated direction of the relation is questioned. Foursymbols are used to denote the direction of the relationship between the elements iandj:

V for the relation from ito j but not in both directions;

A for the relation from j to ibut not in both directions;X for both direction relations from ito j and j to i; and

O if the relation between the elements does not appear to be valid.

(4) Developing a reachability matrix from the SSIM and checking for transitivity. Phase(4) is concerned with the construction of the reachability matrix M. It is a binary matrixsince the entry V, A, X and O of the SSIM are converted into 1 and 0 as per thefollowing rules:

Figure ISM-log

Set of Elements

Basic Structural

Model

Context Relations

Structure

Interpretive

Structural Model

Structure and

Content

Representative Model

Note:Possible iterations are shown as dashed linesSource:With kind permission from Springer Science + Business Media: ISM-Logik

(Szyperski and Eul-Bischoff (1983))

Object System

Complex

problem

Structure and

Content


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. If the (i, j) entry in the SSIM is V, then the (i,j) entry in the reachability matrixbecomes 1 and the (j, i) entry becomes 0.

. If the (i,j) entry in the SSIM isA, then the (i, j) entry in the reachability matrixbecomes 0 and the (j, i) entry becomes 1.

. If the (i, j) entry in the SSIM is X, then both the (i, j) and (j, i) entries of thereachability matrix become 1.

. If the (i, j) entry of the SSIM is O, then both the (i, j) and (j, i) entries of thereachability matrix become 0.

Transitivity is a basic assumption in ISM that leads to the final reachability matrix.It states that if element A is related to B and B is related to C, it may be inferred thatA is related to C. If element (i,j) of the final reachability matrix is zero, there will not beany direct as well as indirect relationships from element i to element j. The initialreachability matrix may not have this characteristic because when there is no directbut an indirect relationship from element i to j, entry (i, j) is also zero. Indirect

relationships can be found by raising the initial reachability matrix (with diagonalentries set to 1) to successive powers until no new entries are obtained (Malone, 1975).That is until the steady-state condition is reached such that Mn21 , Mn Mn1.

(5) Level partitioning of reachability matrix. The fifth phase involves extraction of ahierarchical ordering from the reachability matrix by level partitioning (Warfield,1977). The purpose of this phase is to facilitate the construction of the digraph from thereachability matrix. The level partition makes use of sets associated with each elementsjins. The reachability set R(si) consists of the element itself and other elements whichare reachable from si. Similarly, there may be some elements which can reach theelement si constituting the antecedent set A(si). Thereafter, an intersection of thereachability set and antecedent set (R(si) > A(si)), i.e. the common elements in bothsets, is derived for each element. The element for which R(s i) R(si) > A(si) is the

top-level element in the ISM hierarchy. The top-level element has no relation to anyother elements above their own level. Once top-level elements are identified, they areseparated out from the other elements. Then, the same process undergoes iterations tillthe level of all elements is achieved. These identified levels help in building the digraphand final ISM model.

(6) Drawing of digraph with removed transitivity links. An initial digraph includingtransitivity links is obtained from the conical form of the reachability matrix. Theconical matrix is achieved from the partitioned reachability matrix by rearranging theelements according to their level, which means all the elements having the same levelare pooled, i.e. with most zero (0) elements in the upper diagonal half of the matrix andmost unitary (1) elements in the lower half. For the sake of simplicity, transitivity linksare removed to obtain the final digraph. If there is a relationship between risk iand j,

this is shown by an arrow which points from ito j .(7) Conversion of digraph into an ISM and checking of conceptual inconsistency . The

resultant digraph from step (6) is converted into an ISM by replacing element nodeswith statements. Finally, the ISM model is reviewed to check for incompatibilities.

3.2 Formation of the SSIMThe relevant elements have already been discussed in Section 2. A contextual relationof affects type is chosen, meaning that one risk affects another risk. For example,

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capacity variances/bottlenecks on the supply market will have a (negative) effect onthe focal companies production resulting in short-term production downtimes.

Keeping in mind the contextual relationship for each risk, the existence of a relationbetween any two sub-risks (i and j) and the associated direction of the relation is

questioned. The inter-relationships are analysed based on group discussions betweenthe authors and fellow researchers. Table I describes the pairwise relationship existingbetween two sub-elements.

3.3 Reachability matrix and level partitioningThe SSIM is transformed into a reachability matrix as described in step (4) of the ISMmethodology. After incorporating the transitivities, the final reachability matrix isachieved which is presented in Table II (1 * denotes transitivity).

The final reachability matrix depicts the driving and dependence power of each risk.Driving power of each risk is the total number of risks (including itself) which it affects,i.e. the sum of interactions in the rows. Conversely, dependence power of each risk is thetotal number of risks (including itself) by which it is affected, i.e. the sum of interactionsin the columns. Depending on their driving and dependence power, the risks will later beclassified into autonomous, dependent, linkage and independent risks.

The final reachability matrix leads to the reachability and antecedent set for eachrisk. The reachability set R(si) of the element si is the set of elements defined in thecolumns that contain 1 in row si. Similarly, the antecedent set A(si) of the element siisthe set of elements defined in the rows which contain 1 in the column si. In the presentcase, the risks along with their reachability, antecedent and intersection set as well asresulting levels are shown in Table III. The process (as described in step (4) of ISMmethodology) is completed in ten iterations.

3.4 Development of digraph and formation of ISM

Based on the reachability matrix, a conical matrix (lower triangular format) isdeveloped by arranging the elements according to their levels (Table IV).

Based on the conical reachability matrix, the initial digraph including transitivelinks is obtained. After removing indirect links, the final digraph is obtained. Next,the elements descriptions are written in the digraph to call it the ISM (Figure 3).

The developed ISM has no cycles or feedbacks. Elements are related in purehierarchical pattern.

3.5 MICMAC analysisIdentification and classification of the various supply chain risks are essential to developthe ISM under study. Comparing the hierarchy of risks in the various classifications(direct, indirect, potential) leads to rich source of information. MICMAC is an indirect

classification method to critically analyze the scope of each element. The objective of theMICMAC analysis is to assess the driving power and dependence of supply chain risks(Mandal and Deshmukh, 1994; Saxena and Sushil, 1990). In Table II, the sum along therows and the columns indicates the driving power and dependence, respectively.

All elements are divided into four groups of risks (autonomous, dependent, linkageand independent). Group I includes autonomous elements that have weak driver powerand weak dependence. Group II consists of dependent elements that have weak driverpower and strong dependence. The third group includes linkage elements that have


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21

20

19

18

17

16

15

14

13

12

11

10

9

8

7

6

5

4

3

2

1

1.Long-term

production

downtimes

A

A

A

O

O

O

O

O

O

O

O

O

O

O

O

O

A

O

O

A

2.ITbreakdown

O

A

A

O

O

O

O

O

O

O

O

O

O

X

O

V

O

O

V

3.Short-term

productiondowntimes

A

A

A

O

O

O

O

O

O

O

O

O

O

O

O

O

A

A

4.Capacityvariances/bo

ttlenecksonthesupplymarket

A

A

A

O

O

O

O

O

O

A

O

O

A

A

A

A

O

5.Dependencyonsupplier(i.e.oligopoly)

O

A

O

O

O

O

O

O

O

O

O

O

O

O

O

O

6.Delayindelivery

A

A

A

O

O

O

O

O

O

A

A

A

A

A

A

7.Long-term

production

downtimes

A

A

A

O

O

O

O

O

O

O

O

O

O

A

8.ITbreakdown

O

A

A

O

O

O

X

O

O

O

O

O

V

9.Short-term

productiondowntimes

A

A

A

O

O

O

O

A

A

A

A

A

10.Theft

A

A

O

O

O

O

A

O

O

A

V

11.Poordeliveryquality

O

O

O

A

A

A

A

A

O

A

12.Lackofsufficientequ

ipment,staff,transport/

warehousecapacity

A

A

A

A

A

A

A

O

O

13.Haulingclaim

O

A

O

O

O

A

A

A

14.Poorqualityofstaff

O

O

O

O

O

O

V

15.ITbreakdown

O

A

A

O

O

O

16.Poorperformanceofsubcontractors

A

A

O

A

A

17.Lackofsufficienttransportcapacities

A

A

A

A

18.Generalstaffshortage

X

O

O

19.Terroristattacks

O

O

20.Naturaldisasters

O

21.Employeestrikes

Table I.Structural self-interactionmatrix

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both strong driving and dependence power. In group IV, all independent elements areclustered that have strong driving power, but poor dependence power. Figure 4 showsthe classification of the analysed risks based on their driving power and dependence.

3.6 Fuzzy MICMAC analysisThe analysis can be further improved by considering the strength of relationshipsinstead of the mere consideration of relationships so far. By strength of relationship, wemean the strength of risk is impact (given its occurrence) on risk js probability ofoccurrence. This strength of impact can be defined by qualitative consideration on a0-1 scale, as shown in Table V.

These values are superimposed on the initial reachability matrix from step (4) inISM methodology. The resulting fuzzy direct relationship matrix is shown in Table VI.

According to Zimmermann (1991), there are three types of fuzzy compositions inorder to determine the strength of the fuzzy indirect relation from element i to j:max-min, max-product and max-average. In the context of this research, the max-min

composition is the most suitable, since the fuzzy relations represent the strength ofrelations. That means, that the minimal strength has to be the maximum of all possibleminimal impacts fromitoj. If the fuzzy relations represent the probability of relations,the max-product approach seems to be the most suitable. In order to obtain indirectrelationships, the fuzzy direct relationship matrix is modified based on thecomputational steps given in Yenradee and Dangton (2000). The resulting direct andindirect fuzzy relationship matrix with driving power and dependence is given inTable VII.

Elements 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21Drivingpower

1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1

2 1 1 1 1 * 0 1 1 * 1 1 * 1 * 1 * 1 * 1 * 0 1 * 0 0 0 0 0 0 133 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 14 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 25 1 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 36 0 0 1 * 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 37 0 0 1 * 1 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 48 1 * 1 1 * 1 0 1 1 1 1 1 * 1 * 1 * 1 * 0 1 0 0 0 0 0 0 139 0 0 1 * 1 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 4

10 0 0 1 * 1 * 0 1 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 611 0 0 1 * 1 * 0 1 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 512 0 0 1 * 1 0 1 0 0 1 1 1 1 0 0 0 0 0 0 0 0 0 713 0 0 1 * 1 * 0 1 * 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 514 1 * 1 * 1 * 1 * 0 1 * 1 * 1 * 1 1 * 1 1 * 1 1 1 0 0 0 0 0 0 14

15 1*

1*

1*

1*

0 1*

1*

1 1*

1 1 1 1 0 1 0 0 0 0 0 0 1316 0 0 1 * 1 * 0 1 * 1 0 1 * 1 * 1 1 1 0 0 1 0 0 0 0 0 917 0 0 1 * 1 * 0 1 * 0 0 1 * 1 * 1 1 1 * 0 0 1 1 0 0 0 0 1018 1 * 0 1 * 1 * 0 1 * 1 * 0 1 * 1 * 1 1 1 * 0 0 1 1 1 0 0 1 1419 1 1 1 1 0 1 1 1 1 1 * 1 * 1 1 * 0 1 1 * 1 0 1 0 0 1620 1 1 1 1 1 1 1 1 1 1 1 * 1 1 0 1 1 1 0 0 1 0 1721 1 0 1 1 0 1 1 0 1 1 1 * 1 1 * 0 0 1 1 1 0 0 1 14Dependence 10 6 20 18 2 17 9 6 15 12 13 11 11 1 6 6 5 2 1 1 2

Table IFinal reachabili

matrix with drivinand dependence pow


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4. Discussion and conclusionFigure 4 clearly shows that the highest driving power lies with terrorist attacks (20),natural disasters (19) and employee strike (21), which all fall in the category of externalrisks. The occurrence of external risk cannot be influenced by risk management, whichmakes it even more important to asses the relationship of those risks to other supply

chain risks. Short-term production downtimes at own facilities (3), capacityvariances/bottlenecks on the supply market (4) and delay in delivery (6) wereclassified as highly dependent risks followed by short-term production downtimes ofsupplier (9). The operating levels of the focal company depend on these riskssubstantially. They have minor driving power and are at the top of the ISM hierarchy.Besides, assigning high priority to these risks, the management should understand thedependence of these risks on lower level risks, in achieving the SCRM goals andobjectives. But that means, putting high priority to the linkage risks (group III), too.

Elements Reachability set R(si) Antecendent set A(si) Intersection Level

1 1 1, 2, 5, 8, 14, 15, 18, 19, 20, 21 1 I2 1, 2, 3, 4, 6, 7, 8, 9, 10, 11, 12, 13,

14, 15

14 14 VIII

3 1, 2, 3, 4, 6, 7, 8, 9, 10, 11, 12, 13,15

2, 8, 14, 15, 19, 20 2, 8, 15 I

4 3 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14,15, 16, 17, 18, 19, 20, 21

3 II

5 3, 4 2, 4, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15,16, 17, 18, 19, 20, 21

4 II

6 1, 3, 5 5, 20 5 III7 3, 4, 6 2, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16,

17, 18, 19, 20, 216 IV

8 1, 2, 3, 4, 6, 7, 8, 9, 10, 11, 12, 13,15

2, 8, 14, 15, 19, 20 2, 8, 15 VIII

9 3, 4, 6, 7 2, 7, 8, 14, 15, 18, 19, 20, 21 7 IV

10 3, 4, 6, 9, 10, 11, 12 2, 8, 12, 14, 15, 16, 17, 18, 19, 20, 21 12 VI11 3, 4, 6, 9, 10, 11 2, 8, 10, 12, 14, 15, 16, 17, 18, 19, 20,21

10 V

12 3, 4, 6, 9, 13 2, 8, 13, 14, 15, 16, 17, 18, 19, 20, 21 13 VII13 3, 4, 6, 9, 11 2, 8, 10, 11, 12, 14, 15, 16, 17, 18, 19,

20, 2111 V

14 1, 2, 3, 4, 6, 7, 8, 9, 10, 11, 12, 13,15

2, 8, 14, 15, 19, 20 2, 8, 15 IX

15 3, 4, 6, 9, 10, 11, 12, 13, 16 16, 17, 18, 19, 20, 21 16 VIII16 3, 4, 6, 9, 10, 11, 12, 13, 16, 17 17, 18, 19, 20, 21 17 VIII17 3, 4, 6, 9 2, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17,

18, 19, 20, 219 IX

18 1, 3, 4, 6, 7, 9, 10, 11, 12, 13, 16, 17,18, 21

18, 21 18, 21 X

19 1, 2, 3, 4, 6, 7, 8, 9, 10, 11, 12, 13,15, 16, 17, 19

19 19 X

20 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,15, 16, 17, 20

20 20 X

21 1, 3, 4, 6, 7, 9, 10, 11, 12, 13, 16, 17,18, 21

18, 21 18, 21 XTable III.Levels of supply chainrisks

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The MICMAC analysis indicates that these are IT-related risks (2), (8) and (15),respectively, poor performance of subcontractors (16) and lack of transport capacities(17) which both originate from 3PL provider and finally theft (10), poor delivery quality(11), lack of sufficient equipment, staff or transport/warehouse capacity (12) andhauling claim (13) which are in the first-tier suppliers sphere. These linkage risks have

relatively strong driving power as well as strong dependence. Therefore, these form themiddle level of the model. Though the lower level risks induce or affect these risks,these also have significant driver power to influence some other risks, which are at thetop of the model.

Another insight from driver power and dependence figure is that dependency onsuppliers (5) is the only autonomous risk. Autonomous risks are weak drivers andweak dependents and do not have much influence on the system.

Further insights can be gained from the ISM model shown. First of all, the graphdepicts the risks and their dependencies. In this configuration, the ISM model,in contrast to the digraph built from the initial reachability matrix, is clearly arrangedbut still contains all dependencies. The initial reachability matrix indicates only thedirect relationships between any two elements. By building the ISM model, plenty of

these edges can be removed while the information is still represented by a set ofindirect dependencies. Thereby, the complexity of the visualization is reduced. So thismapping of inter-relationships is a useful method for supply chain risk managers toevaluate supply chain risks and learn about the impact chains of these risks. It can alsobe used to communicate and explain these dependencies within the company andwithin the supply chain, to enable an effective management which deals with the mostimportant risks, not only from a company perspective, but primarily from an overallsupply chain perspective.

Elements 1 3 4 5 6 7 9 11 13 10 12 2 8 15 16 14 17 18 19 20 21

1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 03 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

4 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 05 1 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 06 0 1 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 07 0 1 1 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 09 0 1 1 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0

11 0 1 1 0 1 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 013 0 1 1 0 1 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 010 0 1 1 0 1 0 1 1 0 1 0 0 0 0 0 0 0 0 0 0 012 0 1 1 0 1 0 1 1 0 1 1 0 0 0 0 0 0 0 0 0 0

2 1 1 1 0 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 08 1 1 1 0 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0

15 1 1 1 0 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 016 0 1 1 0 1 0 1 1 1 1 1 0 0 0 1 0 0 0 0 0 0

14 1 1 1 0 1 1 1 1 1 1 1 1 1 1 0 1 0 0 0 0 017 0 1 1 0 1 0 1 1 1 1 1 0 0 0 1 0 1 0 0 0 018 1 1 1 0 1 1 1 1 1 1 1 0 0 0 1 0 1 1 0 0 119 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 0 1 0 1 0 020 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 0 0 1 021 1 1 1 0 1 1 1 1 1 1 1 0 0 0 1 0 1 1 0 0 1

Table IVConical form

reachability matr


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Besides the potential as a structured and simple communication tool among supplychain risk managers, the ISM model also supports the chosen risk categorization shownin Figure 1. With an exception of the IT risks (2), (8) and (15), the resulting structureillustrates the same hierarchy of categories. On level I are only the focal companysprocess and control risks, followed by its supply risks (4)-(6). On level IV are the first-tier

Figure 3.ISM-based model

Long term

production downtimes

(focal company)

Capacity

variances/bottlenecks on

the supply market

Delay in delivery

Long term production

downtimes

(1st-tier supplier)

Short term production

downtimes

(1st-tier supplier)

Poor delivery quality(1st-tier supplier)

Hauling claim(3 PL)

Theft

(1st-tier supplier)

Lack of sufficient

equipment, staff,

transport/warehouse

capacity

(1st-tier supplier)

IT breakdown

(1st-tier supplier)

IT breakdown

(3 PL)

IT breakdown

(focal company)

Poor quality of staff

(3 PL)

Lack of sufficient transport

capacities

(3 PL)

Terrorist attacks

(external)

Natural desasters

(external)

Employee strikes

(external)

General staff

shortage

(3 PL)

Poor performance of

subcontractors

(3 PL)

Short term

production downtimes

(focal company)

Dependency on supplier

(i.e. oligopoly)

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suppliers process and control risks (7) and (9), followed by its supply risks (10)-(12).Finally, on level V and below are the 3PLs risks (13) and (14), followed by its resourcerisks (16)-(18). The external risks, which have a direct impact on all players, are at thebottom of this digraph. Risks (2), (8) and (15), i.e. IT breakdown on all levels (focalcompany, first-tier supplier, 3PL), are exceptions because these risks have an influenceboth on risks on higher levels (upwards the supply chain) as well as on lower levelswithin the hierarchy. Risk management should carefully assess the causes behind thoserisks which could be either technical or relationship related. The former involvesinterruptions in data communication. The latter deals with risks of not getting relevantinformation about the development within the partners company (e.g. forecast of

volume changes, availability of transport capacities).Although these results provide a lot of information about the dependencies between

supply chain risks, the ISM model cannot be used to identify a direct (critical) linkbetween two risks, that, when eliminated, would have no longer any effect. The resultingISM model shows overall impact chains but removes links, if the information is stillcontained, to keep track of the dependencies at the expense of detailed information aboutall direct links. Furthermore, the ISM model shows only that there is a connectionbetween two risks without any information if the impact of this connection is significant

Figure 4Driving power an

dependence diagra

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22

22 22

21 Group IV Group III 21

20 20

19 1918 18

17 17

16 16

15 15

14 14

13 13

12 12

11 11

10 10

9 9

8 8

7 7

6 6

5 5

4 4

3 3

2 Group I Group II 2

1 1

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22

Drivingpower

Dependence

34

6

9

1

10

11

7

152

20

1418

19

21

17

16

12

13

5

8

Strength of impact No Weak Medium Strong Very strong

Numerical value 0 0.25 0.5 0.75 1Table V

Fuzzy scales of impa


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Elements

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

21

1

1

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

2

0.75

1

1

0

0

0.5

0

0.25

0

0

0

0

0

0

0

0

0

0

0

0

0

3

0

0

1

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

4

0

0

0.75

1

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

5

0.5

0

0.75

0

1

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

6

0

0

0

0.5

0

1

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

7

0

0

0

0.5

0

1

1

0

0

0

0

0

0

0

0

0

0

0

0

0

0

8

0

0.25

0

0.25

0

1

0.75

1

1

0

0

0

0

0

0.5

0

0

0

0

0

0

9

0

0

0

0.25

0

0.75

0

0

1

0

0

0

0

0

0

0

0

0

0

0

0

10

0

0

0

0

0

0.5

0

0

0.5

1

0.25

0

0

0

0

0

0

0

0

0

0

11

0

0

0

0

0

1

0

0

0.75

0

1

0

0

0

0

0

0

0

0

0

0

12

0

0

0

0.5

0

0.75

0

0

0.75

0.25

1

1

0

0

0

0

0

0

0

0

0

13

0

0

0

0

0

0

0

0

0.75

0

0

0

1

0

0

0

0

0

0

0

0

14

0

0

0

0

0

0

0

0

0.25

0

0.5

0

0.75

1

0.5

0

0

0

0

0

0

15

0

0

0

0

0

0

0

0.25

0

0.25

0.5

0.25

0.25

0

1

0

0

0

0

0

0

16

0

0

0

0

0

0

0

0

0

0

0.25

0.25

0.5

0

0

1

0

0

0

0

0

17

0

0

0

0

0

0

0

0

0

0

1

1

0

0

0

0.5

1

0

0

0

0

18

0

0

0

0

0

0

0

0

0

0

0.25

1

0

0

0

1

1

1

0

0

0.25

19

0.75

0.25

0.75

0.5

0

0.5

0.75

0.25

0.75

0

0

0.25

0

0

0.25

0

0.25

0

1

0

0

20

0.75

0.75

1

0.75

0.5

1

0.75

0.75

1

0.25

0

0.5

0.25

0

0.75

0.25

0.25

0

0

1

0

21

0.75

0

1

0.25

0

0.5

0.75

0

1

0.25

0

0.25

0

0

0

0.25

1

1

0

0

1

Table VI.Initial fuzzy directrelationship matrix

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Elements

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

21

D

rivingpower

1

1

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

1

2

0.75

1

1

0.5

0

0.5

0.25

0.25

0.25

0.25

0.25

0.25

0.25

0

0.25

0

0

0

0

0

0

5.75

3

0

0

1

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

1

4

0

0

0.75

1

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

1.75

5

0.5

0

0.75

0

1

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

2.25

6

0

0

0.5

0.5

0

1

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

2

7

0

0

0.5

0.5

0

1

1

0

0

0

0

0

0

0

0

0

0

0

0

0

0

3

8

0.25

0.25

0.5

0.5

0

1

0.75

1

1

0.25

0.5

0.25

0.25

0

0.5

0

0

0

0

0

0

7

9

0

0

0.5

0.5

0

0.75

0

0

1

0

0

0

0

0

0

0

0

0

0

0

0

2.75

10

0

0

0.5

0.5

0

0.5

0

0

0.5

1

0.25

0

0

0

0

0

0

0

0

0

0

3.25

11

0

0

0.5

0.5

0

1

0

0

0.75

0

1

0

0

0

0

0

0

0

0

0

0

3.75

12

0

0

0.5

0.5

0

1

0

0

0.75

0.25

1

1

0

0

0

0

0

0

0

0

0

5

13

0

0

0.5

0.5

0

0.75

0

0

0.75

0

0

0

1

0

0

0

0

0

0

0

0

3.5

14

0.25

0.25

0.5

0.5

0

0.75

0.25

0.25

0.75

0.25

0.5

0.25

0.75

1

0.5

0

0

0

0

0

0

6.75

15

0.25

0.25

0.5

0.5

0

0.5

0.25

0.25

0.5

0.25

0.5

0.25

0.25

0

1

0

0

0

0

0

0

5.25

16

0

0

0.5

0.5

0

0.5

0

0

0.5

0.25

0.25

0.25

0.5

0

0

1

0

0

0

0

0

4.25

17

0

0

0.5

0.5

0

1

0

0

0.75

0.25

1

1

0.5

0

0

0.5

1

0

0

0

0

7

18

0.25

0

0.5

0.5

0

1

0.25

0

0.75

0.25

1

1

0.5

0

0

1

1

1

0

0

0.25

9.25

19

0.75

0.25

0.75

0.5

0

0.75

0.75

0.25

0.75

0.25

0.25

0.25

0.25

0

0.25

0.25

0.25

0

1

0

0

7.5

20

0.75

0.75

1

0.75

0.5

1

0.75

0.75

1

0.25

0.5

0.5

0.25

0

0.75

0.25

0.25

0

0

1

0

11

21

0.75

0

1

0.5

0

1

0.75

0

1

0.25

1

1

0.5

0

0

1

1

1

0

0

1

11.75

Dependence

5.5

2.75

12.75

9.75

1.5

14

5

2.75

11

3.75

8

6

5

1

3.25

4

3.5

2

1

1

1.25

Table VIFinal fuzzy relationsh

matr


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or negligible. To get more detailed information about the strength of the relationbetween risks, fuzzy ISM has been applied and subsequently, driver power anddependencies were derived. The main advantage of the fuzzy digraph is its clustering ofthe edges. The scale used is shown in Table V. In Figure 5, the fuzzy digraph contains all

edges, the direct and indirect dependencies, which have a very strong impact of 1.

Figure 5.Digraph based onreachability matrix withrelation strength 1

1 3

4

6

5

97

11

10

12

2 8 15 16

21

20

1714

1819

13

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The less significant edges are hidden to reduce the complexity. In this diagram, a supplychain risk manager can point out directly, which risks have significant impact on otherrisks and yet he can identify which linkage risks should be managed first to reduce apotential series of impacts on other, mostly downstream, risks. After having dealt with

the most significant dependencies in a next step, the edges with a relation strength of 1will be hidden and those with a strength of 0.75 will be shown. This approach ensures astructured treatment of the risks depending on the strength of impact on other riskswhile keeping the complexity in the diagram on a manageable level. This researchcontributes to the field of managerial decision-making literature as it emphasizes theusefulness of integrating ISM in the identification phase of SCRM.

5. Case studyTo test the above theoretical findings and ISMs applicability for practical use, two casestudies with a German industry and a trade company, respectively, were conducted. Theconclusions in the previous chapter were drawn from results of group discussions

between fellow researchers in the field of logistics and supply chain management. Thetwo case studies aimed to show the methodologys ability to structure supply chain risksthat originate within a real company or from one of this companys suppliers. On thisaccount, a questionnaire with preselected process, control, supply and environmentalrisks was handed to executives and senior managers with the two companies to collecttheir assessment of the risks dependencies.

In the first case study (company A), the ISM evaluation was conducted in awell-guided process, i.e. the participants had the opportunity to clarify the meaning ofevery risk during the assessment process and they were reminded by the moderator toconcentrate on the direct linkages between each pair of risks. As a result of the casestudy A, the analysis gave the participants a hierarchy as well as a digraph thatshowed the risk paths and their interdependencies. To conclude whether theascertained results showed practical use and were representative for company As risksituation, the digraph was discussed with the participants in a second round duringwhich the supply chain experts were asked to interpret the linkages and importance ofall risks from the questionnaire and draw a risk map based on their experience. Theresults of the second discussion with the supply chain experts (A) were positive andshowed great similarity between the risk map of the total risk situation based on expert

judgement and the outcome of the ISM algorithm.The second case study (company B) was less guided with the participants

answering the questionnaire after a brief instruction without any further support.Thus, the outcome of this second case study was less positive and showed severalcycles in risk linkages. Also the comparison with risk maps based on the experts

experience showed far less similarities than in case study A. A subsequent analysis ofthis result with the participants showed two reasons why the risk map and the digraphhad less in common than in the first case:

(1) the assessment of the dependencies between the risks did not focus solely onbidirectional linkages but also included knowledge of transitive connections;and

(2) the risks were described less specifically in their definitions without thepossibility to clarify the exact meanings during the evaluation process.


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Hence, the findings of the case studies can be summarised as described below:

. First, ISM was proven as a useful methodology to structure supply chain risks inan easy and distributed approach that can also be carried out in a step-by-stepprocess on several manufacturing stages.

. Second, the input to the algorithm (risks) has to be well-defined to give theparticipants an exact understanding of all risks that have to be assessed, i.e. thebetter the input to ISM is prepared the better the outcome and representationwill be.

. Third, the participating experts have to be instructed to focus solely onbidirectional linkages between two risks. There must not be any transitivedependencies considered when the linkage between two risks is assessed.Otherwise, the algorithm will produce too many cycles and therefore will notderive a hierarchy based on the input.

. In addition, a moderated process proved to be more reliable than an assessmentbased on paper questionnaires only. Thus, a possibility for all participants topost questions and clarify their understanding of the risks has to be consideredin any application.

6. Practical use of ISM in SCRMIn this paper, an attempt has been made to apply ISM to uncover interdependencies ofsupply chain risks. The ISM creates conditions for rational decision making in thatcomplex issues are structured. The pairwise analysis of risks in a group of expertsfrom different functional areas encourages contributions from those who understandthe issues being discussed, but may not understand all issues related to the overall riskmanagement process. Thus, the present model will help to increase the awareness ofdecision makers, whilst assisting them to better understand the mutual influence

among different supply chain risks and the consequences this implies for decisionsabout risk mitigation strategies. Substantial discussion of the identified risks amongthe experts lead to significant learning about the inter-relationship and total riskexposure of the company or supply chain under study.

The ISM steps could be implemented in software. Input to this software would bethe list of identified risks. The decision about the pairwise relationship between therisks (step 2) can be done by using a voting screen (interface) that displays everyrelationship between risks upon the users have to vote. Use of such software will helpto keep track of all the relationships and to ensure that all necessary comparisons aremade, which form the basis for the SSIM. The following steps will be calculated andthe final graph will be drawn automatically. Furthermore, it reduces the chance ofhuman errors and shortens the overall process considerably. We propose to use such

an ISM software as a complementary tool with other risk management (identification)tools. Moreover, the software could also be used in a collaborative environment withsuppliers and customers to share risk information across company boundaries.

7. Future orientationThe application of ISM showed its practicability as an analysis and decision-supporttool in order to facilitate thorough understanding of a complex problem. The complexproblem studied was the inter-relationship of supply chain risks. The process

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of building an ISM develops subject matter knowledge throughout the discussion andanalysis. In the present work, only 21 risks have been used for modeling. More riskscan be identified to develop ISM. In addition to the identification of critical risks withhigh driver and dependence power (linkage risks) and chains of dependent events in

terms of the consequences, it is important to consider the probability. Using thisapproach, the first risk to manage would be the one with the highest probable impactonto the supply chain. Future research should integrate the probability in the modeland combine it with the results derived from fuzzy MICMAC in order to providedecision makers with a more precise basis for the effective allocation of riskmanagement resources. Moreover, the model has not been statistically validated.Testing of the validity of this model can be another area of future research. Structuralequation modeling has the capability of testing the validity of an already developedtheoretical model.

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About the authorsHans-Christian Pfohl studied Industrial Management and Engineering from 1962 to 1968 at theTechnische Universitat (TU) Darmstadt where he graduated as Dr rer. pol. From 1975 to 1982, heheld the Chair of Business Administration with responsibility for Organization and Planning,at the University of Essen. From 1982 until 2010, he held the Chair in Management & Logistics atthe TU Darmstadt. In 1996, he received an honorary doctorate degree from the University ofVeszprem, Hungary. Besides general management, logistics is Professor Pfohls main researchinterest. He contributes very actively through his basic research activities to new developmentsin general management and to the development of the logistics concept.

Philipp Gallus is a Research Associate and Doctoral student at the Chair of Management &Logistics. He studied Industrial Management and Engineering from 1999 to 2006 at the TUDarmstadt, where he graduated as Diplom-Wirtschaftsingenieur. He also holds a Masters degree

in Manufacturing Management from Linkopings Universitet in Sweden. His main research areasare supply chain risk management, logistics and transportation.

David Thomas is a ResearchAssociate and Doctoral student in the Supply Chain and NetworkManagement Group at the TU Darmstadt. He studied Economics and Computer Sciences from2001 to 2008 at TU Darmstadt, where he graduated as Diplom-Wirtschaftsinformatiker. His mainresearch areas are supply chain risk management and business intelligence.


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Interpretive Structural Modeling of Supply Chain Risks

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