Conceptual Model- Copy in Quantification Folder

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P r d i n g s ofthe 2004 IEEElntsrnatlonal Confironce on Robotics h Automatlon

New Orleans, U Aprll20M

A Conceptual and Analytical Framework for theManagement of Risk in Supply Chains

Roshan GaonkarThe Logistics Institute- Asia Pacific

Nation al University of SingaporeSingapore- 119260

E-mail: [email protected]

Abstrucf- In this paper, we develop a framework to classifysupply chain risk management problems and approaches forthe solutionof these problems. We arguethat risk managementproblems need to be handled at three levels strategic,operational and tactical. In addition, riskwithin the supplychain might manifest itselfin the form of deviations,disruptions and disasters. To handle unforeseen events in thesupply chain there aretw o obvious approaches:(1) to designchains with built in risk-tolerance and(2) to contain thedamage once the undesirable event bas occurred. Both of theseapproaches requirea clear understandingof undesirable eventsthat may take placein the supply chainand also tbe associatedconsequences and impacts from these events. We can then focusour effortson mapping out the propagation of eventsin thesupply chain due to supplier non-performance,and employ ourinsight to develop tw o mathematical programming basedpreventive models forstrategic level deviationand disruptionmanagement. The rust model, a simple integer quadraticoptimization model, adapted from the Markowitzmodel,determines optimal partner selectionwith the objective ofminimizing both the operational cost andthe variability of to taloperational cost. The second model, a simple mixed integerprogramming optimization model, adaptedfrom the credit riskminimization model, determines optimal p artne r selection suchthat the supply shortfall is minimized evenin the face ofsupplier disruptions. Hence, both of these models offer possibleapproaches to robust supply chain design.

Keywords-SupplyChain Risk Management, Risk Management,SupplyChain Management, Partner Selection, Supplier PortfolioOptimization

I. INTRODUCTIONManufacturing supply chains today tend to be globalin

nature, comprising of complex interactions and flowsbetween tens, even hundreds and thousands of com panies andfacilities geographically distributed across regions andcountries. Such chains are currentlyin operation in a varietyof industries suchas electronics, automotive, aerospace, etc.Despite their complexity, most manufacturing supply chainsare structurally similar. The member companies in a typicalmanufacturing supply chain network include the suppliersand their suppliers, assembly plants, distributors, retailers,inbound and out bound logistics providers and financinginstitutions. In fact under the intense competitive scenarioprevalent today, competition is no longer between companies

N. iswanadhamThe Logistics Institute- Asia Pacific

National University of SingaporeSingapore- 119260

E-mail [email protected]

hut between supply chain networks with similar productofferings, serving the same customer.

The winning supply chain networks .are usuallycharacterized by the presence of dominant organizations (alsocalled channel masters) such as Dell, GM, Sun or Nike thatpossess strong domain knowledge, design, brand andmarketing capabilities, around which they congregate.Furthermore, these supply chains are able to achieve a highlevel of efi cien cy through the sophisticated use of pervasiveinformation and logistics networks that hold the supply chaintogether, and facilitate the easy movement of information andgoods throughout the chaii. Given their influence andconsequentially therole of contro ller within the supply chain,the channel masters a re typically responsible fo r supply chainplanning incorporating[l]:

Selectionof appropriate partners to form the supplychain based on market requirements.

Synchronization of activ ities between selected partnersfor optimal profit.

In a perfect world, the plans generated by the channelmaster would allow all the partners to synchronize theiractivities and business processes leading to greaterefficiencies and profits for everyone.For e.g. componentswould arrive at the assembler site on time for production tostart, adequate inventory of all components would beavailable before production and demand would bedeterministically predictable. However in the practical worlduncertainty rules. Consequentially, sales routinely deviatefrom forecasts; components are damaged in transit;production yields fail to meet plan; and shipments are heldupin customs. In truth, schedule executionas per plansgenerated by supply chain planning is just a myth.

Because supply chain performance is inherentlyunpredictable and chaotic, supply chain practitioners oftenmust seek safety mechanisms to protect against unforeseenevents. Significant effo rts are expended to expedite orders,tocheck order status at frequent intervals, to deploy inventoryjust-in-case andto add safety margins to lead times, amongseveral other creative ways to counter the occurrence ofunforeseen events. These time and material inventories alongwith limited communications among the partners hide theproblems until they lead to serious consequences. Whilst riskbas always been present in the process of reconciling supply

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with demand, there are a number of factors, which haveemerged in the last decadeor so, which might be consideredto have increased the level of risk. These include a focus onefficiency rather than effectiveness; the globalization ofsupply chains; focused factories and centralized distribution;the trend towards ou tsourcing; reduction o f the sup plier base;volatility of demand; lack of visibility and controlprocedures.As a result, it has become extremely importantfor channel mastersto employ risk management tools in themanag ement of their supply chains.

However, the existingERF', SCM, EA1 and otherB2 Bsolutions are designedto improve efficiency of the supplychains and notto enhance their reliability or robustnes s underuncertainty. Some of the vendors offer partial solutions tothis problem under the name of Supply Chain EventManagement (SCEM). These offerings include track andtrace, supply ch a i visibility and alert messaging solutions[2], which merely notify the human operator of unexpectedoccurrences and leave himto resolve the issue. In such ascenario, there is a critical need for a framework and for

. ' suitable tools that would allow companies and m anagersto ,better understand the presence and significance of various

types of risks and allow themto manage it better; In thispaper we attemptto address these needs from the perspectiveof a channel master.

..' A . Previous Work

:In a very general sense, research from high reliability

organizations(HROs), netivorked organizations, and inter-organizational systems is relevant in the study of supplychain reliability, trust and risk [See3 and 41. Some of theresearch .withinthis area focuses on risk management in a,special breed of Organizations, called virtual organizations,which are also a collection of compinies under independentownership that come together for a common purpose suchasfighting forest firesor mitigating the risk of oil spills.However, in terms of directly relevant work in the area ofsupply chain risk management, Paulsson[5 ] provides agoodS i e y of the recent literature in the field. She % [6] provides.a dual sou rcing approach to handle supply risks in the face ofunforeseen events in the supply chain brought about byinternational terrorism: In' addition, there are a fewcommercial s o h a r e solutions~ and technologyimplementations to manage supply chain exceptions andevents [2]. In[7], one of the authorshas developed a methodbased on. process capability indicesto minimize lead-timevariance minimization. Despite these publications,since thearea.of supply ch aii risk management is an emerging area of

~ research, there are limited perspectives, theoretical modelsand frameworks addressing the area. We wishto provide.exactly such a theoretical basis in this paper.

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B. Organizafion of this Pap erIn this paper, we present a conceptual framework for the

classification of supply chain ri s k and associated approachesto handling them. Inparticular, we focus on the design ofrobu st su pply chains at the. strategic level throug h theselectionof suppliers that minimize the variab ility of supp lychain p erformance in terms of cost andoutput. n this manner

we are ableto build robustness into the supply cham at theplanning stage itself. In section2, we present a conceptualframework for the classification of supply chain risks anassociated approachesto building robustness in the supplychain. In section3, we develop models for supply chain riskmanagement at the strategic level. In section 4, we sharsome of OUT computational results and obsenrations andfmally we conclude in section5 with a discussion on the

possibilities for future work.

11. CONCEPTUAL FRAMEWORK O APPROACHUPPLYCHAINRISK PROBLEMS

A .

Based on its nature, uncertainty in the supply chain maymanifest itself in three broad forms- deviation, disruptionand disaster- s explained below.

A deviation is said to'hav e occurred whenone or more parameters, such as cost, demand, lead-timeetc., within the supply chain system stray from their expecteor mean value, without any changesto the underlying supplychain structure.

Examules o f deviations:

1. Variations in demand.2. Variations in supply.3.

4.

A disruption occurs when the secturepfthe supp ly chain system is radically transformed, through thnon-availability of certain production,warehousing anddistribution .facilitiesor transportation options duetounexpected events &used by hu minor nature.

Examulesof disruutions:

1. Disruptionsin production (Taiwan earthquake resulted indisruption of IC chip production, Component productiofor disrupted dueto a fue in T oyota's. supplier's factoryin M exico resulting in d ownstream factory shutdown)

2. Disruptions in supply (Meat-supply was disrupted duetospread of foot-and-mouth disease in England).

3. Disruptions in logistics(US port shutdown disrupted thetransportation of componentsfromAsia to theUS)

Disaster: A disaster is 'd ef ie das a temporaryirrecoverableshutdownof the su pply cham network duetounforeseen catastrophic system-wide disruptions.

Exam ules of disasters:

I. Terrorist Action (The entire US economy wastemporarily shutdown dueto the downturn in consumerspending, closure of international borders and sh utd owof production facilities in the aftermath of theterroristattacks on the1 * of September2001.)

In general, it is possibleto design supply chains that arerobust enoughto profitably continue operations in the face ofexpected deviations and unexpected disruptions. However,

Classificationof SC Risk Problems

Deviation:

Variations in procurement, production and logisticcosts.Varia tions in transportation and p roduction lead-times.

Disruption:

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Supply side

I

B. Mathematical Progamming ModelsWithin the context of the broad classification of risk

management issues suggested above a number of differentanalytical and computational methods can be employed todesign robust supply chains, mathematical programmingbeing on e of the most preferred tools amongstthem.

Mathem atical planning modelscan be employed to selectand schedule processes and partners such that the overallsupply chain is by design robust to intemal and externalstimuli. In particular, portfolio optimiition modelscommonly applied in fmance can be used to select a portfolioof suppliers such that the total supply chain cost variabilityand the consequences from supplier non-performance arewithiin manageable limits, as demonstrated in the latersection s of this paper.In addition, recent work in the area ofrobust optimization can also be used to generate supp ly chainsolutions that maintain their optimality under minordeviations in environmental conditions.

C. Basics of Uncer tainty ManagementTo better manage the uncertainties in the supply chain it

is necessary to identify the exceptions that can occur in thechain , estimate the probabilities of their occurrence map out

Delayor unavailability of materials fromsuppliers, leading to a sho rtageof inputs

the chain of immediateand delayed consequ ential eveuts thatpropagate through the chain and quantify their impact.In thiscontext, it becomes important to identify the possibleexceptionsin a supply chain and their consequences beforeproceeding to the development of analytical models.

I ) Failure o r Dismption ModesIn a supply chain exceptions can occur at various uodes-

on the supply side, demand side, during transport orinstorage -an d dueto a variety of different causes. There couldbe failures of power and communicationsor employeestrikes. There is also a risk of breach of trust by partners, byoutside elements. It is not possible to list all of them but w ehave the following possible modesof disruption.

TABLE U. EXA~PLESF FAILURER DISRUPTIONODES

Modeof IDescription I

PlanningLevel

Type of ExampleEvent

and outbound transportationto movegoods due to carrier breakdownor

Breaches infreight orpartnerships

Failed

of plants, warehouses and of ic ebuildings.Violation of th e integrityof cargoes,products (can be due either to $eftortampering with c riminal purpose, e.g.smuggling weapons inside containers)orcompany proprietary information.Failure of inform ation and

Communications

Wild demandfluctuations

communication infrastructure due to line,computer hardwareor software failures

or v h sattacks, leading to th e inabilityto coord inate operations and execute

Sudden loss of demand dueto economicdownturn, com pany bankruptcies, war,

Ietc. IIn this paper, we specifically study supplier non-

performance, in termsof the co mplete failure of a supplier todeliver components to the manufactureror the inability of thesupplier to deli ver components at the promised price.

2) Cause-Consequence DiagramsCause-consequencediagrams or event trees are tools

commonly used in reliability analysis to study the overallimpact of a particular failure on the en tire system. Based on

the supply chain configuration, we can develop cause-consequence diagrams for each failure described above.However, given our interest in developing models forsupplier selection, we employ cause-consequence diagramsto s pecifically analyze the effect of su pplier non-performanceon the supply chain andto estimate the associated shortfallsin supply.[9]

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Given the probability of occurrence of the initiatingevent, which is supplier non-performance, and theprobabilities for the various intermediary events, we cancalculate the probability of occurrences for each of the endstatesor outcomes. Furthermore, each of these end states mayresult in different levels of supply shortfalls and financialcost. Hence, given the probability of each end state and thesupply shortfallor financial cost for each end state, we cancalculate the expected shortfallor financial risk for the no n-performance of a given supplier. Suchan analysis can berepeated for each su pplier, and the least risky supplier can beidentified as the one whose non-performance results in theleast expected supply disruptionor least expected fmancialloss.

111. PROBLEM FORMULATIONSOR STRATEGIC EVELRISK MANAGEMENT

With the above foundation in the basics of supply chainrisk management we now highlight the abov e approach bypresenting two representative models for strategic levelsupply chain risk management, from the perspectiveof thechannel master. W ith referenceto our classification presentedearlier the fust model falls under the class of strategic levelproblems for deviation management and the second fallsunder the classo f slmtegic level disruption managementmodels. Both m odels employ the preven tive approach to riskmanagement b ased .on the use of mathematical modelingtechniquesas described below.

1. Strategic-level Deviation Management M odel: Given theexpected costs and variab ility (deviation) of costs for a llsuppliers, the fust problem relatesto the-selection of anoptimal group of suppliers such that the expected costofoperating the entire sup ply .chain and the riskofvariations intotal supply chain costs is minimized.

Strategic-level Disruution Management Model: Given

the expected probabilities for various supplier dism ptionscenarios and the supply shortfalls under each of thesescenarios the objective for the manufacturer is to choosea set of suppliers that minimize the expected shortfallduring the op eration of the supply chain.

In addition, we make the assumptionthat the supply chainis distributed globally and each player within the chainhas itsown goals, policies and cultures. The channel master whooccupies a d omin ant position in the ch ain .has all theinformation on its partners, including cos ts and schedu lesofthe s uppliers, the logistics providers, etcto be able to make arational decision in the interest of m in ii zi n g risk.

A . Strategic Level Deviation Mana gement Model

We propo se an integer quadratic programming model forpartner selection that triesto minimize the ov erall cost impactfrom the deviation in supplier costs. Such a model will bevery useful to su pply chain owners and chann el masters. Themode l is an adaptationof the Markowitz model for financialportfolio management, for the purpose of managing aportfolio of suppliers. For this model, we define the impact interms of the risk as given by the d eviation of the total supplychain cost from its expected mean value. Given the exp ected

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costs and the variability of costs for all suppliers andmanufacturers the ob jective isto choose a set of sup pliers andmanufacturers that minimize the expectedcost of operatingthe entire supply chain and at the same time minimize therisk of variations in the total su pply cham cost. The selectioof these partners also considers the allocation of ordersbetween these selected partners. The mean costs andvariability of the costsfor each suppliercan be ob tained froman analysis of their historical performanceor by consideringthe probabilities of their non-performance and the associatecosts of handling the consequent impacts. Furthermore, duto the stochastic nature of events in the cause-consequencdiagram we can safely assume that in general the fmaloutcomes and associated costs o f supplier non-performancwill be normally distributed.

Identifiersm e M :Manufacturer identifier.iE I :Com ponen t identifier.S E S, :Supplier identifier amongst the set of suppliers fo

component i to a specific manufacturer m.

ParametersC :Meancost of the suuulv chain entityVNP

:Cost variability for i6e sup ply cba& entity.:Minimum number of entitiesto procure from.:Risk aversion parameter(0 < p < m ).Large va luesfor p emphasize risk minimization and small valuescost minimization.

VariablesX :Fraction of orders and hence costs allocated

between manufacturers.(0 < x < 1 ).Y :Fraction o f orders and hence costs allocated

between suppliers for a specific manufachuer.(0 < y< 1 ).:O if supply chain entity is not selected and1 if

selected.

F

&&I

Minimize

M Ix x

m = l i =

+ p [ m =:Subjectto:

' m i MX Y,C,F,+ Z xmCmYm

s = l m = l

M

C x m F m = lm = ln

3

miZ y S F s = F m forall m E M & i E I

s = l

Fm 2 F, forall mE M & s E Sm i

...( )

. 2)

...( )

. 4)

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...( 5)Minimize

The objective of the model isto choose manufacturers andtheir suppliers and allocate order quantities between them ina manner such that the expected cost of operating the supplychaii is minimized and also the variability of the overallcosts is minimized as well.This is subjectto the consmintthat the selected set of manufacturers, between, them fulfillthe order (Eq. 2) and that the selected set of suppliers forthese manu facturers, between them, fulfill the demand for allcomponents(Eq. 3). Suppliers are part of the supply chaiionly when the manufacturers they supplyto are involved(Eq.4). Furthermore, there might be othe r policies that require aminimum number of manufacturersor suppliers to beengaged at each level of the chain for the sake of redundancyand greater reliability(Eq. 5 & Eq. 6).

B.With the probabilities for supplier non-performance and

knowledge of supply shortfalls under various resulting end-states (as ob tained from the cause-consequence diagram), wepropose a mixed integer-programming model for partnerselection that tries to minimize the overall impact on thesupply shortfall consequential from the exception of suppliernon-performance. Such a model will be very useful tomanu facturers, supply chain o wners and chann el masters whowant to incorporate robustness into their supply chains. Themodel is an adaptation of the credit risk m i n i i t i o n modelemployed in financial portfolio managem ent, for the purposeof managing a portfolio of suppliers.For this model, wedefine the impact in termsof the risk as given by theexpected shortfall in the total supply from its expected value.Given the expected probabilities for various exceptionscenarios and the supply shortfalls under each of thesescenarios the o bjective for the manufacturer is to choos e a setof suppliers that minimize the expected shortfall during theoperation of the supply chain.

IdentifiersS E S :Supplier identifier.ie I

Strategic Level Disruption Management Model

:Scenario (state) identifier.I is the set of all supplyscenarios (states), which is obtained asa mix of allcombinations of supplier non-performance eventsfor all the suppliers in the setJ.

ParametersKXi

5Cj :Capacity of supplier j.

:Quantity required by the manufacturer.:Quantity supplied by supplier i.:Cost of including supplierj in supply chain.

VariablesFiYi

: 0 if supplier is not selected and1 if selected.:Total supply shortfallto manufacturerin scenario i.

Subjectto:

x = F s * C s forali s E S ...( 9)S

The objectiveof the model isto choose suppliers suchthat the expected shortfall in supply, in the face of supplierdisruptions is minimized.This is subject to the constraint(Eq. 8) which c alculates the shortfall for each possible sup plyscenario. Also, the qnantity supplied by any supplier isdependent on its capacity and alsoon the decision whether ornot the supplier is included into the supply chain network(Eq. 9). When the supplier is included into the supply chaiinetwork his supplies are equivalent tohis capacity.

w . COMPUTATTONALRESULTSBoth the models described above were formulated in

Microsoft Excel and solved usin g the Solver add-in.

A. Strategic-Level Deviation Management ModelThis model was solved for a problem with5

manufacturers, dealing w ith5 suppliers each, for each o f thetwo components required in their manufacturing. The riskaversion factor was taken as25 and it was required thatatleast 2 manufacturers be selected for fu lfilling the orders.

COSTAND VARIANCE OF COST FOR EACH PARTNERABLEm.

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Mfg = Manufacturer; Sup= Supplierfg = Manufacturer; Sup= Supplier

Due to the non-linear nature of the problem, the finalsolution obtained depends very m uch onthe initial values ofthe variables. Moreover, the choice of manufacturers is themost critical decision since it also decides to a large extentthe choice of suppliers. Hence, the model was solved forvarious initial solutions correspondingto all the possiblecombinations of supplier selection. The optimal solutionobtained as a result is given below.

TAB LE IV. COST A N DVARIANCE OF COST FOR EACH PARTNER

B. Strategic-Level Disruption ManagementModelThis model was solved for a problem with a single

manufachlrer (located inthe US), dealing with5 suppliers.The probabilities of supplier disruption for all the suppliers(individually and in various com bination) were considered asgiven. The relation cost was takenas $5000 and.the quantityrequired by the manufacturer was520 units. Supplier1(capacity:250) was assumed to be based in Ireland withdisruption possibilities due to Terrorist Attacks and UnionStrikes.The second supplier (capacity:250) is assumed to bein Taiwan with disruptions possibly resulting fiomEarthquakes and exposure to port closureson the US WestCoast. The third supplier (capacity:280) is a non-reliablesupplier basedifl Malaysiaand the fourth (capacity:340) areliable supplier in Singapore, both of whom are susceptibleto the risk resulting fiom closure ofUS ports. The fifthsupplier (capacity:250) is assumedto be a local supplier.Inaddition, probability of all the different supply scenariosbased on the locationof the suppliers was considered. Forexample, the probability that only supplier3 was disruptedwas assumed to be0.08 (much higherthan he probability ofdisruption for other suppliers, given the fact that the supplierwas non-reliable and basedin a countly less developed).Similarly the probabilityof suppliers 1,2 and 5 beingsimultaneously disrupted was taken to b e0.0045 due to thegreater susceptibilityof each o f them to natural and terroristdisasters. The probabilities for all supply scenarios (all

possible combinations of supplier disruptions) wereconsidered. The model was solved with the abovedata. Theoptimal selectionof suppliers included Suppliers4 & 5, withan objective value of10017.It might be noticed that thesetwo uppliers are the most reliable suppliers.

V. CONCLUSION

We have developeda conceptual frameworkfor theclassification of supp ly cham risks and associated approachesto handling them. In particular, we focus on the designofrobust supply chains, at the strategic level, that are resilient todeviations and disruptions that may occur at the sup plier end.Our analysis is based on the identification of unforeseenevents that may occu r at the supplier end propagate down thesupply chain leading to cost variability and supply shortfallsRobustness is build into our supply chain design by selectinga portfolio of suppliers that m inimize the variabilityof supplychain performance in terms of cost and output. The modelswe develop are preventive in nature and employmathematical programming tools.Our efforts here are anattempt to formu late and solve problems in the em erging areaof supply chain risk management. For example using ouralgorithm, the value of reliable suppliers and of ado pting dualsourcing strategies in a supply chain can be easilydetermined. Finally we may mention thatOU T mapping ofexceptions and their associated consequences can also beused to build decision support systems for exceptionmanagement.

REF EFC~SNcEs[ I ] Roshan Gaonkar, Dynamic Configuration and

Synchronizationof Collaborative E-Supply Chain Networks,PhD Dissertation, National University of Singapore,2003.

[2] Michael Bittner, E-Business Requires Supply Chain EventManagement,AMR Research, November2000.

[3] Martha Grabowski, Jason R.W. Memck,John R. Harrald,Thomas A. Mazzuchi andJ Rene van Dorp, Risk Modellingin Distributed, Large-Scale Systems, IEEE Transactions onSystems, Man and Cybernetics,Vol. 30, No. 6 November2000.

[4] Martha Grabowski and Karlene H.Robots, Risk Mitigationin Virtual Organisations, Organisation Science, Vol.10 No.6, Nov-Dec1999.

[ 5 ] Ulf Paulsson, ManagingRisksin SupplyChains - An ArticleReview, Presented at NOFOMA2003, Oulu, Finland, 12-13June 2003.

[6] Yossi Sheffi, SupplyChain Management underthe Threat ofInternational Texronsm, The InternationalJournal ofLogistics Management, Volume12, Number2, 1-11,2001.

[7] D. Garg,Y. Narahari, and N. Viswanadhm, Design ofSixSigma Supply Chains , to appear in IEEE Transactions onAutomation Science and Engineering(first issue), April2004.

[8 ] N. Viswanadham, Analysis of Manufacturing Enterprises-An Approach toValue Delivery Processes for CompetitiveAdvantage, Kluwer Academic, Boston,1999.

[9] Roshan Gaonkar and N. Viswanadham, RobustSupply ChainDesign: A Shategic Approach to Exception Handling,Proceedings of the ICRA 2003.

[IO] SaviTechnology, http://www.savi.com.[I 11 Vigilance,http://www.vigilanceinc.com [I21 Eventra,hnp://www.eventra.com

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