Financial Distress of Suppliers: Causes, Management, and · PDF file · 2014-03-19Financial Distress of Suppliers: Causes, Management, and Consequences Christoph Bode Department of

Financial Distress of Suppliers:Causes, Management,

and Consequences

Christoph BodeDepartment of Management, Technology, and Economics

Swiss Federal Institute of Technology Zurich

Stephan M. WagnerDepartment of Management, Technology, and Economics


CAPS Research

2012

Financial Distress of Suppliers:Causes, Management, and Consequences

Christoph BodeDepartment of Management, Technology, and Economics


Stephan M. WagnerDepartment of Management, Technology, and Economics


CAPS Research

2012

Copyright © 2012 Institute for Supply Management™and W. P. Carey School of Business at Arizona State University.

All rights reserved.Contents may not be reproduced in whole or in part without the express permission of CAPS Research.

We would like to acknowledge the support of the executives from the firms whoparticipated in this research project. In the interest of confidentiality, the names ofthe firms and the individual executives are not listed here. These executives tooktime from their busy schedules to openly share their perspectives and experiencesrelated to this focus study's topic and to discuss the results. Without exception, theircooperation and interest in our research project proved essential to the success ofthis project.

We would also like to thank Denis Hübner, Moritz Peter, Boris Zaremba, SencerAkcan, and Carlos Duran for their support in this research project.

Finally, without the financial support of CAPS Research in Tempe, Arizona, thisstudy would not have been possible. We thank the staff at CAPS Research, withspecial recognition to Dr. Phillip L. Carter, executive director, for his encouragementand support for this project, and Debbie Maciejewski, research manager, for herpatience and assistance.

ISBN 0-945968-87-6

2 Financial Distress of Suppliers: Causes, Management, and Consequences

Acknowledgements

Acknowledgements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2Table of Contents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3Index of Figures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5Index of Tables. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6Executive Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

Key Findings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7Recommendations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8

Chapter 1: Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10Chapter 2: Financial Distress of Suppliers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

General Terminology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12The Buying Firm's Perspective: the Risk of Supply Chain Disruptions . . . . . . . 13

Supply chain risk . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13Supply chain disruptions and risk sources. . . . . . . . . . . . . . . . . . . . . . . . . . 14Supply chain risk management . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15

What Causes the Financial Distress of Suppliers? . . . . . . . . . . . . . . . . . . . . . . . 15Creation of competition among unqualified suppliers . . . . . . . . . . . . . . . . . 16Power influences . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16Opportunistic behavior . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17

Chapter 3: The Process of Managing the Financial Distress of Suppliers . . . . . . . . 18Scanning. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19

Content of scanning. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19Process of scanning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21Information-processing needs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22

Interpretation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22Action. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23

Buffering . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23Bridging . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23

Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23Chapter 4: Research Design and Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25Chapter 5: Case Study Findings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26

Scanning and Interpretation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26Action. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30

Chapter 6: Determining a Buying Firm’s Behavior . . . . . . . . . . . . . . . . . . . . . . . . . 33Soft Factors: Trust and Risk Orientation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33Hard Factors: Resource Dependence and Salvage Power . . . . . . . . . . . . . . . . . 33Scanning. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34

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Interpretation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35Action. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37Financial Consequences . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38

Chapter 7: Conclusions and Recommendations . . . . . . . . . . . . . . . . . . . . . . . . . . . 40References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42Appendix A: Interviewed Firms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48Appendix B: Detailed Description of Methodology. . . . . . . . . . . . . . . . . . . . . . . . . 51Appendix C: Calibration of Default Probabilities and Volatilities . . . . . . . . . . . . . . 52


Figure 1 Conceptual Framework . . . . . . . . . 18

Figure 2 Scanning Overview. . . . . . . . . . . . . 19

Figure 3 Amount of Information as a Function of Scanning Scope and Intensity . . . . . . . . . . . . . . . . . . . . . 22

Figure 4 Scanning Types . . . . . . . . . . . . . . . . 26

Figure 5 Preferred Scanning Behaviors . . . . . 27

Figure 6 Scanning Matrix . . . . . . . . . . . . . . . 28

Figure 7 Action Types . . . . . . . . . . . . . . . . . . 29

Figure 8 Preferred Action Strategies . . . . . . . 30

Figure 9 Learning Types . . . . . . . . . . . . . . . . 30

Figure 10 Cluster Dendogram . . . . . . . . . . . . 32

Figure 11 Scanning Behaviors. . . . . . . . . . . . . 34

Figure 12 Triggering Action . . . . . . . . . . . . . . 35

Figure 13 Interpretation Errors. . . . . . . . . . . . 36

Figure 14 Urgency of Signals and Stages of aSupplier Crisis . . . . . . . . . . . . . . . . 37

Figure 15 Salvage Power as Inhibitor to Actions . . . . . . . . . . . . . . . . . . . . . . 38

Figure 16 The Risk Management-Cost Smile . . . . . . . . . . . . . . . . . . . . . . . 39

Figure 17 Calibration of PDs and Their Volatilities. . . . . . . . . . . . . . . . . . . . 52

Index of Figures

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Financial Distress of Suppliers: Causes, Management, and Consequences6

Index of Tables

Table 1 List of Interviewed Firms . . . . . . . . 25

Table 2 Summary of Cross-Case Analyses . . . . . . . . . . . . . . . . . . . . . 30

CAPS Research 7

Executive Summary

The early identification of supplier default risk and themanagement of financially distressed suppliers havebecome important issues for buying firms. Manyanecdotes highlight how sudden supplier financialdefaults can result in severe losses in sales, production,and reputation and damage relationships withdownstream customers. To deal with this specific risk,buying firms need organizational capabilities tocorrectly detect a supplier running into financialdistress, to devise appropriate remedial actions, and togather such experiences in repositories of organizationalknowledge for future actions.

The primary focus of this study was to examine howbuying firms deal with existing suppliers that facefinancial distress. More specifically, we sought tocompare buying firms’ behaviors in scanning theirsupply bases for financially distressed suppliers,interpreting warning signals, reacting to financialdefaults, and learning from such experiences. Toaddress these issues, we reviewed the extant literature,developed a conceptual framework of the managementof financial distress of suppliers, and interviewedmanagers from 18 firms in Europe, the Middle East,North America, and Latin America. The participatingfirms produced and marketed various products andservices, including automotive (passenger cars andcommercial vehicles), electronics, food, industry goods,and services. The results provide both theoretical andpractical contributions.

Key Findings

We found significant differences among firms in termsof how information about distressed suppliers isscanned and interpreted, how responses are devised,and how firms learn. Based on these observed

differences, we were able to obtain insights into thefactors that determine buying firms’ behaviors.

All of the investigated firms were concerned about therisk of financially distressed suppliers, but the level ofconcern varied. Some industries, particularly theautomotive industry, were more concerned than others.This is certainly not surprising, and underscores thatfirms learn from negative experiences. Indeed,particularly those buying firms that experienced moresevere problems due to distressed suppliers in the pastshowed more interest in this specific risk than thosethat had experienced none or only smaller problems inthis regard. However, in several cases we also found asignificant gap between the willingness and the abilityto pursue advanced approaches to the management ofdistressed suppliers. Several firms confirmed that theywant to improve their approach to dealing withdistressed suppliers, but that they face organizationalbarriers (e.g., company culture) or lack resources (interms of time, expertise, and funds) that limit theirabilities to do so.

None of the investigated firms uses a “one-size-fits-all”solution for dealing with the risk of supplier defaults. Forexample, none of the investigated firms scans all of itssuppliers with the same level of scope and intensity. Infact, all of the investigated firms realize that supply chainrisk management comes at a cost and, consequently, thatthere is a trade-off between taking the risk andproactively managing the risk. For this reason, scanning,interpretation, and action are almost always — more orless deliberately — tailored to specific relationships (e.g.,criticality of the supplier). Still, we observed somerecurring patterns in the risk management process.

With regard to the scanning of suppliers and theinterpretation of warning signals, we classified the firmsinto three categories:

• Reactors, who wait for operative signals or explicitsupplier requests before the financial distress isacknowledged

• Observers, who do not only take operative signalsinto account, but also carefully track marketrumors

• Guards, who run advanced monitoring routinesthat allow them to scan their suppliers with highscope and high intensity

In terms of responses (actions) to distressed suppliers,our analysis revealed three different types of firms:

• Substitutors, who have a preference for replacingand substituting suppliers once they run intofinancial problems

• Cooperators, who pursue a selective strategy thatsupports important suppliers by relaxing paymentterms or granting short-term credits but thatsubstitutes non-critical suppliers in financialdistress situations

• Partners, those who have established long-termrelationships with few suppliers and devisedelaborate action plans whereby they would onlyswitch suppliers if it is completely unavoidable

Finally, with regard to learning, we determined fourclassifications. The first two are applied to firms thatactively try to learn from negative experiences withdistressed suppliers; the latter two do not.

• Adaptors use their past experiences with distressedsuppliers to slightly modify existing processes toimprove future reactions, i.e., single-looplearning.

• Evolvers who are open for more drastic changes ofoverall rules, norms, strategies, or structures as aconsequence of severe negative experiences, i.e.,double-loop learning.

• Lucky ones are those to whom severe supplierdefault situations have never occurred.

• Ignorers are those who neither draw conclusionsfrom past events nor do they see any possibility toimprove.

Findings from our cross-case analysis suggest thatparticularly inter-firm trust, resource dependence, riskorientation (i.e., a buying firm’s general awareness andconsciousness of, concerns about, and seriousnesstoward supply chain disruptions), and salvage power(i.e., the ability of a buying firm to salvage a specificfinancially distressed supplier) are the factors that shapea buying firm’s behavior and risk managementapproaches. In contrast to trust and risk orientation,which are arguably rather “soft factors,” dependenceand salvage power pose hard restrictions to the buying

firm’s room for maneuvering. In a nutshell, a buyingfirm’s increased trust in a supplier reduces the firm’smonitoring efforts and, if responses are elicited, sets itspreferences toward more supportive and collaborativeactions (bridging). Low levels of trust, however, renderthe buying firm more suspicious and cautious.Dependence also makes a buying firm more cautious interms of scanning, interpretation, and action. In case ofa financial distressed supplier, dependence pushes abuying firm into supporting (bridging) actions. Thestronger the buying firm’s risk orientation, the moreimportance it attaches to the issue of financiallydistressed suppliers and the more pronounced its needfor stability is. Consequently, this trait makes buyingfirms more intrusive in their scanning efforts and moreresponsive in their actions. Finally, salvage power is animportant rationale buying firms use to determine theiractions. If a buying firm disposes of any possibility tosalvage a distressed supplier (e.g., due to differentials infirm size — “ant vs. elephant”), the firm is pushed intosupplier switching behavior, even in situations wheredependence and trust would suggest cooperative andsupporting behavior.

Recommendations

• Supplier defaults can cause severe supply chaindisruptions. Consequently, buying firms are urgedto tackle these risks just as vigorously as theytackle financial and other business risks.

• The risk of supplier default can be measured bythe probability of default and exposure (resourcedependence).

• As in any other risk management setting, there isa trade-off between taking the risk and spendingresources on risk management. Consequently,buying firms have to carefully evaluate how muchrisk and cost they are willing to take or incur forreducing the risk of supplier defaults.

• There is no “one-size-fits-all” solution for dealingwith the risk of supplier defaults. It all dependson the probability of default and the individualexposure.

• The number of suppliers to be scanned and thescope and intensity of scanning increase a buyingfirm’s information processing needs. To avoidinformation overload and ensure economical useof resources, purchasing managers have tocarefully determine how much information theyneed to mitigate their risk of financially distressedsuppliers. Specifically, this involves determiningthe required accuracy of information processing(i.e., “How accurate do we need to be?” ”Does itmatter if we are using a rule of thumb?” “Is thecost of making an interpretation error so high that


gathering more data is warranted, to avoid suchan error?”)

• Resource dependence not only increases a buyingfirm’s exposure, but also may force the buyingfirm into expensive bridging actions to salvage adistressed supplier. Thus, important suppliers(where there is a high level of dependence)should be monitored thoroughly (high scope andhigh intensity) and action plans prepared inadvance.

• Good supplier relationships can have a costimpact, because a high level of trust decreases theuncertainty of the scanning process. For criticalsuppliers, buying firms should attempt to buildrelationships based on trust and relationalexchange.

• Buying firms might not always possess enoughpower to support or even salvage a distressed keysupplier. In these cases it is particularly importantthat an action plan be prepared that detailssolutions to avoid supply chain disruptions.

• Non-critical suppliers should be monitored on aselective base and less frequent level to ensure anefficient use of resources.

• Documentation systems help to translate tacit datainto explicit knowledge and can preventorganizations from repeating errors and incurringcosts.

9CAPS Research

Managers around the world have become increasinglyconcerned with the risk of possible disruptions in theirsupply chains. In particular, the early identification ofsupplier default risk and the mitigation andmanagement of financially distressed suppliers havebecome important issues for buying firms (Carter &Giunipero, 2010; Murphy, Schweinberg, & Pope,2008). For example, a quick look into current annualreports, for example, from Adidas, Apple, Procter &Gamble, U.S. Steel, and Volkswagen, indicates thatsupplier defaults (insolvencies or bankruptcies) areconsidered a major risk in many industries. A largebody of literature published has depicted examples andcase studies of financially distressed suppliers and oftheir serious impact on the affected buying firms (e.g.,Sheffi, 2005). In addition, several studies haveconsidered the risk of supplier defaults in analyticalmodels to understand how this specific risk affectscontracting (Babich, Burnetas, & Ritchken, 2007;Swinney & Netessine, 2009; Wagner, Bode, & Koziol,2009, 2011). Supplier defaults can result in severelosses in sales, production, and reputation; they can alsodamage relationships with downstream customers(Grewal, Johnson, & Sarker, 2007). The UK-basedautomotive OEM (original equipment manufacturer)Land Rover, for example, found itself in serious troubleafter UPF-Thompson, the single supplier of chassisframes for its Discovery model, suddenly andunexpectedly filed for bankruptcy in 2001. KPMG,which represented the UPF-Thompson’s receivers,exploited the single source relationship and virtuallyheld Land Rover for ransom demanding £35 million toresume production (Lester, 2002).

The need to manage supplier default risk has beenfurther fueled by the recent economic crisis (Blome &Schoenherr, 2011; Finley, 2009). Liquidity has becomea scarce resource nowadays; particularly since 2008,banks and other financial intermediaries tightened their

credit and loan policies making it more difficult andmore costly for firms to raise the cash needed for theday-to-day operations. For example, in the automotivesupplier industry — where constant price pressuresfrom the buying automotive OEMs had erodedsuppliers’ financial health — the economic downturntriggered a large number supplier defaults (e.g.,Contech in February 2009, Visteon in May 2009). In2005, Moody’s rated 54 global auto suppliers (fromNorth America, Europe, and Asia) and assignedinvestment-grade to 33 percent of the suppliers, butspeculative-grade to 67 percent. In 2009, however, froma total of 59 global auto suppliers, only 24 percentmaintained investment-grade while 76 percent wererated speculative. The outlook was even worse: For 1 percent it was positive, for 34 percent it was stable,and for 65 percent it was negative (Harrod, 2009).Despite the rebound of the world economy in 2010,many automotive suppliers have been facing problemsin shifting gears and providing the supplies for theincreasing demand. The German automotive supplierHonsel, for example, although busily working in fiveshifts per day to supply automotive OEMs such asBMW or Volkswagen, had to file for bankruptcy in2010 because the supplier divested too many assetsduring the financial crisis. This example highlights that,even in a sound economic market environment, the riskof supplier defaults remains a major issue, and supplierfinancial risk management remains a topic of enduringimportance (Murphy, et al., 2008).

Unlike banks or other types of creditors, buying firmsare usually not interested in predicting the likelihood oftheir suppliers’ default, because they want to preservethe relationships. Therefore, this study focuses onsupplier financial distress, a condition which normallyprecedes supplier default by some period of time(Hertzel, Li, Officer, & Rodgers, 2008). For example,receiving a 12-month early warning of impending

Chapter 1: Introduction


supplier difficulties can provide buying firms theopportunity to either adapt their sourcing strategies(e.g., from single to multiple sourcing) or to financiallysupport the distressed supplier in order to keep thesupply chain going (Milne, 2009). Thus, implementinga risk-oriented supply base management approachmight lead to competitive advantage, as buying firms donot only compete on their sales markets for customers,but also on their procurement markets for capablesuppliers. The existing literature on supply risks andtheir management has generated very valuable insights,yet, despite the quite large body of literature, we knowremarkably little about how firms can effectively andproactively avoid or manage distressed suppliers.Specifically, there is little theoretical support to explainhow buying firms manage financially distressed suppliers.The purpose of this focus study is to help fill this gap.We will not focus on specific tools for proactive supplierfinancial and operational risk management. For anexcellent overview of these specific topics, the interestedreader is referred to the 2010 CAPS Research focusstudy Supplier Financial and Operational Risk Management,by Carter and Giunipero. Our focus lies on theinvestigation of the causes of financial distress situations,their overall management, and how buying firms learnfrom experiences with distressed suppliers. To date, weknow of no research that has comprehensivelyaddressed these issues in the context of supplierfinancial distress management.

To this end, we conducted a thorough review of thesupply chain risk management literature and developeda conceptual framework that is grounded in severaltheoretical perspectives, including organizationalinformation processing and learning perspectives. Priorto starting the empirical study, the conceptualframework was discussed with purchasing managersand researchers in the field of supply chain management.The results, obtained from interviews with managersfrom 18 different firms, provide providing boththeoretical and practical contributions.

11CAPS Research

General Terminology

Failure is an intrinsic part of organizational life and haslong attracted scholarly attention in economics andorganizational research (Anheier, 1996). Many attemptshave been made to craft theories that explain why firmsfail. A prominent approach is to view failure as a resultof a crisis situation. The organizational crisis phenomenonstarted to receive specific attention in the managementliterature in the 1960s and 1970s (Fink, S.L., Beak, &Taddeo, 1971; Hermann, 1963; Smart & Vertinsky,1977; Turner, 1976), although initial interest had begunin the early 1930s when scholars tried to developmanagerial implications based on the investigation offailing firms and the underlying causes (e.g., Fleege-Althoff, 1930). Today, the literature on this topic spansthe fields of management, economics, organizationalbehavior, psychology, political science, and sociology.(For a review, see Pearson & Clair, 1998.) As aconsequence of this long-standing research tradition,there are now numerous definitions of the termorganizational crisis and an abundance of descriptions ofits attributes and dimensions (e.g., Hermann, 1963;Kovoor-Misra, Clair, & Bettenhausen, 2001; Mannarelli,Roberts, & Bea, 1996; Milburn, Schuler, & Watman,1983; Pauchant & Douville, 1993; Pearson & Clair,1998). Basically, corporate crises constitute unplannedand unintentional processes of limited duration andinterference with an ambivalent outcome (Billings,Milburn, & Schaalman, 1980; Hermann, 1963). Anorganizational crisis endangers the existence of an entireorganization through the derogation of certain goals.

Closely related to an organizational crisis is financialdistress, a condition which normally precedes a failureby some period of time. In contrast to a financiallyhealthy firm (that disposes of enough liquidity to coverits obligations), a firm is considered financiallydistressed when it has problems in collecting (or does

not even possess) sufficient cash flow to meet thepayments on its debt (i.e., financial obligations) (Ross,Westerfield, & Jaffe, 2010), specifically with regard to“hard” contracts (i.e., those that are enforceable by law).Hence, the distressed firm is on the verge of beingforced to breach its debt contracts. Once the firm is atthe point it does not repay its debt and other liabilitieswhen they become due for payment, a financial defaultoccurs. Technically, this means only that the firm hassuspended or halted some payments. If the firm is trulyunable to repay its debt, it enters into an insolvencyprocess, where the goal is to address the collectivesatisfaction of the outstanding claims against its assets(United Nations Commission on International TradeLaw, 2005). This does not mean that the firm is legallybankrupt (i.e., that the market value of its assets is lessthan its liabilities; the firm has a negative net worth). If a firm files for protection of a bankruptcy act,restructuring actions are likely to be triggered as thefirm might lose control rights to the firm’s creditors. The bankruptcy process can be initiated by the ownersof the firm, who have their “right to default,” oralternatively by its creditors. Liquidation is reached whenthe firms sells its assets to the public. Normally it takesplace when the financial distress is so acute thatreorganization is not feasible, and the assets of the firmare worth more on liquidation than throughreorganization.

The central idea of the crisis perspective is that a firmgoes through several stages that may or may notultimately result in financial default — depending onhow well the firm is able to manage the crisis situation.In particular, the corporate crisis research emphasizesthat crises also brings about opportunities. Indeed,crises often create positive entrepreneurial and learningopportunities (which, from the perspective of thebuying firm, we will address in more detail in the nextsection). For example, Max Frisch, a famous Swiss

Chapter 2: Financial Distress ofSuppliers


novelist, said with respect to crisis: “Crisis is a status ofproductivity. You only need to eliminate the taste of acatastrophe.”

There are multiple escalation framework forunderstanding how organizational crises (e.g., Fink, S.,1986; Meyer, 1982; Turner, 1976) or financial distress(e.g., Lau, 1987) evolves. Although there are manyunderlying similarities in these models, they tend to usedifferent labels for the stages they describe. Fink (1986),for example, proposed a four-stage process model thatbegins with (1) the prodromal stage in which earlywarning signals (e.g., stakeholder issues) mightforeshadow a serious financial distress situation. Then, atriggering event (e.g., a bad credit rating) sets off (2) theacute (or emergency) stage that creates loss and ofteninvolves uncertainty, stress, limited or fragmentedinformation, and demands swift decision-making(Billings, et al., 1980). If the firm survives, (3) thechronic stage is the phase where the firm recovers fromthe crisis. Subsequently, the firm might return tonormality in (4) the resolution stage when it makesdecisions regarding tactical (short- and mid-term) orstrategic (long-term) change.

The Buying Firm’s Perspective: the Risk ofSupply Chain Disruptions

Of the numerous risks that buying firms are exposed to,supply chain risks arise from the interconnected flows ofmaterials, information, and funds in inter-organizationalnetworks (supply chains). Already in the 1980s, Kraljic(1983) and Treleven and Schweikhart (1988) stressedthat global supply chains are inherently susceptible torisky events. However, it was not until recently that theinterest in this phenomenon, both from scholars andpractitioners, has grown. A large body of literature hasdepicted examples and case studies of events thatdisrupted supply chain and transportation operationsand of the serious impact on the involved firms (e.g.,Sheffi, 2005). Arguable, there are at least two factorsthat fueled the recent interest: (1) the current businessenvironment is characterized by discontinuity, highvelocity of change, and permanent risk for unexpectedadverse events, and (2) many modern supply chainshave become relatively sensitive to exogenous shocks.Hamel and Välikangas (2003) conjectured that “theworld is becoming turbulent faster than organizationsare becoming resilient” (p. 52).

Indeed, over the last 15 years, almost all industries havewitnessed a remarkable change in their businessenvironments, due to increased competition and theglobalization of markets (D’Aveni & Gunther, 1995). At

the level of the individual firm, this has resulted in amassive pressure to make intra-firm business processesand inter-firm supply chains either more efficient ormore responsive and agile. In an attempt to meet thischallenge and to adapt to the changed businessenvironment, many firms decided to outsource oroffshore large portions of their manufacturing activities,to source in low-cost countries, to reduce inventoriesand slack in their intra-firm processes, to streamlinetheir supply bases, or to collaborate more intensivelywith their supply chain partners (Christopher & Peck,2004; Fisher, 1997; Hult, Ketchen, & Slater, 2004; Lee,2002, 2004; Yoshino & Rangan, 1995). Certainly, manyof the modern supply chain management initiatives,when properly used, can make supply chain operationseither more efficient or more responsive and agile instable environments — however, this might not holdtrue in more dynamic or turbulent ones. Therefore, thereis a call for caution: Many of these initiatives have notonly created more complex supply chains, but also ahigher degree of dependence among supply chainentities, which ultimately has increased the vulnerabilityof supply chains to unforeseen disruptions that canoccur anywhere in the supply chain. Interconnections insupply chains may cause and propagate disruptions thatinfluence the performance of one firm to also affect othermembers of the supply chain (Narasimhan & Talluri,2009). Research in the field of organizational science hassupported this reasoning, finding that due to theircomplex, tightly coupled, and technology-orientedprocesses, firms are becoming more prone to accidentsand disruptions (Lin, Zhao, Ismail, & Carley, 2006).

For this reason, buying firms are urged to tackle supplychain risks just as vigorously as they tackle financial andother business risks (Christopher & Lee, 2004; Elkins,Handfield, Blackhurst, & Craighead, 2005; Tomlin,2006). Several recent publications have attempted toadvance the conceptual clarity of the terms used in thedomain of supply chain risk management (e.g.,Craighead, Blackhurst, Rungtusanatham, & Handfield,2007; Manuj & Mentzer, 2008a, 2008b; Rao &Goldsby, 2009; Ritchie & Brindley, 2007). In thefollowing, we provide a brief overview of this literature.

Supply chain riskIn essence, the phenomenon under investigation in thesupply chain risk literature is the negative outcomeresulting from adverse events that occur in supply chainoperations, but usually two interrelated terms aredistinguished: supply chain risk and supply chaindisruptions.

Supply chain risk is the predominant theme in theliterature and often applied as a catchall concept for awide range of events, situations, potential threats, or

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uncertainties. Risk, however, is a construct with manydefinitions that vary by application, academic field (e.g.,decision theory, finance, insurance, management,marketing, psychology), and situational context (Yates& Stone, 1992). There are many different disciplinaryvoices, speaking in different languages, to differentissues and different audiences. But, because risk is aconceptual abstraction (it does not exist per se), thereare no right or wrong definitions — only more or lessappropriate ones for a specific context. Having a closerlook at the various streams of literature, one canidentify two major perceptions of risk: (1) risk as bothdanger and opportunity and (2) risk as purely danger(Mitchell, 1995). First, risk can be conceived as thefluctuations (variability) around the expected value(mean) of a measure. In other words, risk is equatedwith variance and therefore has both a downside (loss)and an upside (gain) potential. This is connected to thegeneral economic problem that one has to assume moreof this risk if one wants to achieve higher rates ofreturn. Second, and in contrast, most dictionariesdescribe risk as the threat of injury, damage, or loss(e.g., McKechnie, 1983). The notion that risk hasprimarily negative consequences seems more consistentwith the human perception than with the mean-variance approach. Several studies that have empiricallyinvestigated the risk perception and propensity ofindividuals have found support for this. March andShapira (1987), for example, examined the ways inwhich managers perceive and react to risk, andconcluded that the majority tends to exaggerate thedownside potential of risk. Likewise, MacCrimmon,Wehrung, and Stanbury (1986) found empirical supportthat managers do not consider variance to be risk, butthat they are rather concerned about the chances ofloss.

Against the background of these two general views onrisk, several publications have defined the term supplychain risk. Both of these views have been discussed andapplied. For example, Jüttner, Peck, and Christopher(2003) followed the mean-variance notion and definedsupply chain risk as a “variation in the distribution ofpossible supply chain outcomes, their likelihood, andtheir subjective value” (p. 200). In contrast, Harland,Brenchley and Walker (2003), after discussing variouspossible definitions, concluded that supply chain risk isprimarily associated with the “chance of danger,damage, loss, injury, or any other undesiredconsequences” (p. 52). We believe that the latter notionof risk as purely negative resonates best with thebusiness reality of decision makers. Usually, managersconsider their goals, such as a certain turnover orproduction volume, not so much as a target point but aslower limits of half-open ranges, e.g., to achieve at leasta certain turnover or to spend less than a certain

amount of resources (budget). Hence, a goal deviationonly occurs when the defined thresholds are either notmet or exceeded. Neither “happy disasters” norsituations in which decision makers intentionally“gamble” are typically part of the scope of supply chainrisk management. Risk is conceived as the deviationfrom the expected value of one (or more) performancemeasure(s), resulting in negative consequences for thefocal firm. Supply chain risk is equated with thedetrimental consequences (i.e., negative performanceimpact) arising from a supply chain disruption. Theseconsequences can be either direct or indirect (i.e.,consequential loss), and can affect either majorperformance objectives (e.g., profit, company value, orcompany continuity) or minor performance objectives(e.g., reputation or customer satisfaction).

Supply chain disruptions and risk sourcesIn contrast to risk, a supply chain disruption is amanifested circumstance. It can be viewed as thecombination of (1) an unintended and unexpectedtriggering event that occurs somewhere in the supplychain or the supply chain environment, and (2) aconsequential situation that significantly threatens thenormal course of business operations of the focal firms.Obviously this is a broad definition that sets the stagefor a large, heterogeneous set of issues. Supply chaindisruptions can materialize from inside or outside asupply chain and can vary greatly in their magnitude,attributes, and effects. Consequently, their nature can behighly divergent. For instance, a delayed shipment ofnon-critical material is potentially a much less serioussupply chain disruption than is an eight-week laborstrike at an important international port. In attemptingto differentiate supply chain disruptions from otheradverse events in business (e.g., shocks on the financialmarkets), several studies have proposed classificationsof supply chain disruption in the form of typologiesand/or taxonomies. The derived categories of supplychain disruptions are usually labeled supply chain risksources, in terms of being a known source from whichsupply chain disruptions emerge with a certainprobability. Understanding the various supply chain risksources and their nature is very important becausedifferent risk sources demand different sets of riskmanagement activities. For example, Svensson (2000)named two supply chain risk sources (quantitative andqualitative), Jüttner (2005) delineated three (supply,demand, and environmental), and Manuj and Mentzer(2008a) proposed eight (supply, operational, demand,security, macro, policy, competitive, and resource).

This large set of supply chain risks also includes thethreat of financial instability of suppliers and theconsequences of supplier financial distress and default.Financial distress and default of a supplier are common


causes for supply chain disturbances and disruptions,which can have severe consequences for the buyingfirm, especially if a supplier is going out of business(Blome & Schoenherr, 2011; Finley, 2009; Wagner, etal., 2009). Obviously, such issues may negatively affectthe associated supply chains: The defaulting suppliermay no longer be able to purchase the requiredmaterials, pay its workforce, and deliver goods orservices to its customers (Hertzel, et al., 2008). Even if acorporate crisis has the potential for a positive outcome,the avoidance of supply chain disturbances triggered byfinancially distressed suppliers remains one of the mostchallenging tasks that supply chain risk managementcan be confronted with.

Supply chain risk managementSupply chain risk management seeks to identify, assess,and manage the risk of disruptions either in a proactiveor reactive fashion. In general enterprise riskmanagement can be defined as the “field of activityseeking to eliminate, reduce, and generally control purerisks” (Waring & Glendon, 1998, p. 3). While the exactterminologies vary from author to author, a systematicrisk management process usually comprises thefollowing stages: Risk identification (identification of allrelevant risks), risk assessment (analysis of probabilityand impact for each identified risk), risk treatment (riskmanagement in the narrow sense), and risk monitoring.The overall objective of this process is to determine,implement, and monitor an optimal combination ofmeasures to avoid, defer, reduce, or transfer all relevantrisks. The determined mix is considered to be optimal ifthe remaining amount of risk is in line with the firm’srisk preference and its corporate strategy.

A large body of literature has proposed measures andactivities of supply chain risk management (e.g., Chopra& Sodhi, 2004; Christopher & Peck, 2004; Elkins, etal., 2005; Johnson, 2001; Lee & Wolfe, 2003; Manuj &Mentzer, 2008a, 2008b; Martha & Subbakrishna, 2002;Rice & Caniato, 2003; Zsidisin, Melnyk, & Ragatz,2005). Tang (2006), for example, identified four areaswhere supply chain risk management activities can takeeffect: supply management, demand management,product management, and information management.Kleindorfer and van Wassenhove (2004) cited two typesof supply chain risk management activities: supply-demand coordination activities and activities formanaging disruption risks. Yet another possibility is todistinguish cause-oriented measures from effect-orientedmeasures. This classification can be explained inanalogy to a soccer player. Following cause-orientedrisk management, the soccer player can reduce or avoidthe possibility of getting seriously injured by avoidingdangerous tackling. Following effect-oriented riskmanagement, the soccer player can use protections (e.g.,

shin guards) to become more robust or can apply an icespray to alleviate injuries. Hence, cause-oriented actionsfocus on eliminating or preventing the causes of supplychain disruptions. In effect-oriented supply chain riskmanagement practices, a firm decides to bear certainrisks while attempting to limit or mitigate the negativeconsequences of supply chain disruption when theyoccur. Such risk management efforts are effective whenoperations are sustained or quickly resumed, andorganizational and external stakeholder lossesminimized. In this context, one often encounters theterm “resilience,” i.e., the ability of a supply chain (or afirm in the supply chain) to recover and to bounce backinto the original or desired state after being stressed bya supply chain disruption. In practice, however, thedistinction between cause-oriented and effect-oriented isnot always this simple and clean, due to the fact that aparticular initiative can contribute to both categories.Either way, risk management requires prior preparation.

What Causes the Financial Distress ofSuppliers?

Despite the large body of literature, there is still a lackof a coherent theoretical underpinning for failures andthe causes thereof. Anheier (1996, p. 956), for example,concluded that “the phenomenon of failure inorganizations is too multifaceted” to support a “grandtheory of organizational failures.” The crisis of a firmmay be the result of a mix of many different causesranging from ineffective management to competition.Anheier (1996) arranged the causes in two groups. Thefirst group consists of internal (or endogenous) factors.From this viewpoint many crises are caused, forexample, by poor corporate decisions, mismanagement,disputes and in-fighting, and lack of organizationalslack. The second group consists of the external (orexogenous) factors. This group includes the externalcauses of failures such as decline in available resourcesand organizational carrying capacity, increasedcompetition, changes in niche dimensions the firmoperates in, economic downturns, and events such asnatural catastrophes.

Among the large set of external causes are also issuesthat pertain to the behavior and conduct of thecustomers and buying firms. Buying firms are importantstakeholders that affect the success of a supplier firm.Behaviors, decisions, and sourcing strategies of buyingfirms can be important sources of supplier financialdistress. For this reason, buying firms should payattention to how they contribute to their suppliers’financial distress. Although supplier bankruptcy is animportant topic, especially in light of the recent

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economic downturn, there is limited literature toexplain the causes of financial distress of suppliers.Existing studies point out three main causes:

• Creation of competition among unqualifiedsuppliers

• Power influences• Opportunistic behavior

Creation of competition among unqualifiedsuppliersProcurement costs account for a large share of anindustrial firm’s total costs (up to 70 percent).Therefore, cost savings in purchasing increases a firm’soverall profitability. For this reason, in today’s market,squeezing suppliers is a major focus of the buying firm.In order to decrease supplier margins and achieve thelowest prices, purchasing managers play suppliers offagainst each other. However, if a firm includesunqualified suppliers into the bidding process, thequalified suppliers will be forced to drop their prices.The suppliers that are already locked in to the buyingfirm may decrease prices even below the profitabilitylevel and, thereby, risk the suppliers’ financial health. Inthe worst case, this price-pushing strategy can result insupplier bankruptcies. For example, in the early 1990s,the purchasing head of General Motors (GM) generatedsignificant savings for GM by starting price wars amongunqualified suppliers. However, that resulted in twonegative long-term consequences for GM. Oneconsequence was that the financial health of suppliersdramatically worsened, and the other consequence wasthat some of the GM suppliers started selling their bestideas to GM competitors (Laseter & Sharma, 2010;Maloni & Benton, 2000).

Power influencesPower distribution among members of the supply chainis an important factor in determining actions of supplychain members. Following Emerson (1976), power isthe ability of one firm to influence the intentions andactions of another firm. A firm that is more powerfulthan another firm can contribute to the less powerfulfirm’s bankruptcy or financial distress. For example, thisis the case if the more powerful firm pressures the lesspowerful firm to take actions that are beneficial to themore powerful firm, but at the time harm the lesspowerful firm’s financial health. In the scope of thisresearch, the power differential between buyer (source)and supplier (target) and its effects on suppliers areinvestigated.

A study of Maloni and Benton (2000) provides athorough view of power influences on supply chainsand their consequences. They focus on the U.S.automobile industry, where the number of buyers is

relatively low compared to number of suppliers.Therefore, the buyers are much more powerful thansuppliers. In order to explain power influences insupply chains, i.e., how a supply chain memberexercises power over another supply chain member, sixtypes of power bases are introduced: reward, coercive,expert, referent, legitimate, and legal legitimate.

Reward and coercive, also known as carrot and stickrespectively, are well-known types of power bases. Theyrepresent the capability of buyers to reward the supplier(e.g., increasing business volume or sharing the benefitsfrom cost reductions) or penalize the supplier (e.g.,decreasing business volume or not sharing the benefitsfrom cost reductions).

Another type of power base is expert power, which isthe recognition by suppliers of buyers as informationand expertise holders. In that case, buyers take the leadin products and processes.

Referent power refers to a supplier’s desire to berecognized in association with a particular buyer. Forexample, Chrysler approached its suppliers in a morerelational way by the Extended Enterprise program andoffered mutually beneficial cost reductions (Dyer,1996).

Legitimate power refers to a situation in which thesupplier believes that the buyer retains the natural rightto influence. Hence, the buyer exercises power over thesupplier without legal legitimization, for example,constituted by a formal contract.

The last power base, legal legitimate power, implies thatthe supplier is under control of the buyer by a formalcontract.

Maloni and Benton (2000) divide power bases into twogroups: mediated and non-mediated power bases.Mediated power refers to the efforts or threats to controland manipulate the target by using reward, coercive,and legal legitimate power bases. Non-mediated powersare not specifically aimed to directly manipulate thetarget and include expert, referent, and legitimate powerbases. Studies about the use of power in channelrelationships point out that usage of different powerbases leads to diverse and contrasting results in buyer-supplier relationships. For example, Brown, Lusch, andNicholson (1995) showed that when a buying firm usesmediated powers, it lowers the commitment from thesupplier; conversely, the use of non-mediated powerraises commitment. In the same vein, other empiricalstudies suggest that the use of non-mediated powerbases has a significant positive impact on commitment,trust, and cooperation levels in buyer-supplier


relationships (Hunt, Mentzer, & Danes, 1987; Maloni &Benton, 2000). In addition, these studies found thatthere is a positive relationship between the strength ofbuyer-supplier relationships and performance of allmembers of the supply chain. Conversely, the receivedresults suggest that the use of mediated power basesharms the relationship between buyers and suppliers. Insum, prior studies of power usage in buyer-supplierrelationships suggest that the use of coercive, reward,and legal legitimate power may hurt the relationshipand decrease the cooperation between buyer andsupplier. Poor relationships with buyers and lack ofcooperation harm the performance of the supplier andworsen its financial health accordingly.

Opportunistic behaviorOpportunism refers to a behavior (e.g., the behavior ofa buying firm) of taking advantage of opportunities andcircumstances regardless of potential negativeconsequences for other parties involved (e.g., thesupplier). When a party is in a relationship withanother party and it invests in relationship-specificassets that have less or no value outside therelationship, the investing party becomes weaker againstopportunistic behavior of the other party (Rindfleisch &Heide, 1997). In the context of this focus study, weinvestigate how opportunistic buyer behavior canpotentially contribute to supplier financial distress.

Buvik and Grønhaug (2000) state that asset specificityincreases the dependence of a supplier on the buyer.Specific investments made by the supplier lock it in tothe relationship. If the buyer acts opportunistically bypushing prices down or negotiating higher serviceperformance, the supplier will be under high pressureto accept the demands of the buyer because thesupplier’s specific assets cannot be utilized for othercustomers. Consequently, opportunistic behavior bybuying firms leads to financial difficulties for suppliersand potential supplier bankruptcies.

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In this chapter, we develop a conceptual framework(shown in Figure 1) that explains how buying firmsmanage the financial distress of suppliers; thisframework guides our subsequent empirical study. Wefocus on ongoing buyer-supplier relationships and noton the initial supplier selection situation.

Our framework is rooted in the open systemsperspective (Katz & Kahn, 1978). We draw on literaturethat views firms as information-processing systems(Galbraith, 1974; Hult, et al., 2004; Thompson, 1967;Tushman & Nadler, 1978), on resource dependencetheory (Pfeffer & Salancik, 1978), and on theorganizational learning literature (Argyris & Schön,1978, 1996; Huber, 1991). The information-processingliterature suggests that organizational responses toenvironmental events are shaped by subsequent

information-processing activities (Barr, 1998; Dutton,Fahey, & Narayanan, 1983; Isabella, 1990). Thus,considering the theory from this literature will enablean improved understanding of the processes that are atwork during the management of distressed suppliersand how they motivate the formation of an action.Specifically, the information-processing literaturesuggests that a buying firm manages distressed suppliersaccording to an information-processing process thatconsists of four subsequent stages: Scanning,interpretation, action, and learning. In turn, resourcedependence theory will be used to help gain anunderstanding of the content of the response. Finally,the organizational learning perspective provides a lensto understand how buying firms learn from experienceswith distressed suppliers.

Chapter 3: The Process of Managingthe Financial Distress of Suppliers


Figure 1Conceptual Framework

Scanning

The scanning stage is concerned with the collection ofinformation about the suppliers’ financial health.Usually, suppliers send out a train of warning signalsbefore they enter financial distress or the default process(Altman, 1968; Platt & Platt, 2002, 2009). If thesesignals are properly picked up (timely detection) andacted upon (correct interpretation), many problems canbe contained or even averted. For this reason, scanningis an extremely important element of any successful riskmanagement process.

As shown in Figure 2, we suggest to distinguishbetween the content of the scanning effort (i.e., whatinformation a buying firm collects) and the process ofthe scanning effort (i.e., how a buying firm collectsinformation).

Content of scanningAccording to the credit risk literature there are twoaspects that determine the supplier default risk that abuying firm faces: (1) the supplier’s probability of default,a measure of financial health, and (2) the buying firm’sexposure (more precisely exposure-at-default). Eachcomponent can be evaluated via qualitative andquantitative approaches (Carter & Giunipero, 2010). Inaddition to these two parameters, newer literature alsosuggests that there is a third issues to consider, namely(3) supplier default dependencies (Wagner, et al., 2009,2011). Buying firms may attempt to obtain informationfor these three measures during the scanning process.

(1) Probability of defaultThe probability of default refers to the probabilitythat a default of a suppliers will occur within afixed time span, usually one year. Various sourcesand approaches can be used to derive indicatorsfor the probability of supplier default:

• Financial statement analysis. A starting pointfor quantitative analyses is publicly availableaccounting and financial statement data(corporate disclosures). Specific financialstatements allow for simple assessment of asupplier’s liquidity, activity, debt, andprofitability using financial metrics and creditrisk scores. Starting with the seminal works ofBeaver (1966) and Altman (1968), numerousaccounting-based bankruptcy predictionmodels were developed. Altman’s Z-score, alinear combination of balance sheet ratios, forexample, can be calculated based on a smallnumber of financial ratios, such as the liquidityratio and the leverage ratio (calculated on thebasis of balance sheet and P&L statements).Due to their ease of use, accounting-basedcredit scores still enjoy widespread application.However, because they depend on the numbersfound on balance sheets, updates occur ratherinfrequently (i.e., they take a “rear-windowview”) and some are known to have a bias(Shumway, 2001). Also, while these scoresmight be proxies for a supplier’s probability ofdefault, they are not probabilities in a technicalsense.

• Analyses based on credit and market data.Beyond the relatively simple scoring methods,the credit risk literature offers more advancedstructural and reduced-form credit risk modelsthat provide a sophisticated way to estimate afirm’s probability of default. In structuralmodels, the asset pricing model by Merton(1974) is used as a theoretical foundation tolink a firm’s probability of default to thevariability in the firm’s asset value. Theprobability of default is modeled as thelikelihood that the asset value of the firm dropsbelow a certain critical threshold (within a

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Figure 2Scanning Overview

fixed time horizon) (Lando, 2004). Forexample, the well-known KMV structuralmodel (originally developed by Kealhofer,McQuown, and Vasicek, now owned andoffered by Moody’s) defines the probability ofdefault in the context of such an asset valueprocess. In contrast, reduced-form modelsutilize bond prices or credit spreads as inputs.Under the assumption that markets areefficient in pricing debt, the price of a bondshould be equal to the risk-adjusted presentvalue of the cash flows that are expected tooccur in the future. Jarrow and Turnbull(1995), for example, proposed an intensity-based model based on corporate zero-couponbond prices.

• Use of third-party reports and ratings. Abuying firm can also use the services of ratingsfrom rating agencies such as Fitch, Moody’s,Standard & Poor’s, or Dun & Bradstreet.Rating agencies measure the credit worthinessof a firm and assign a credit rating expressed ina letter system (e.g., Aaa or B-). These letterratings can be easily translated into aprobability of default by using historic defaultfrequencies (Bluhm, Overbeck, & Wagner,2002). Appendix C presents a procedure forthis simple calibration. Obviously, oneadvantage of alternative is that it requires lesseffort with regard to data collection andanalysis. Previous research also suggests thatcredit ratings provide relatively goodapproximations for firms’ probabilities ofdefault (Wang, 2004). A major disadvantage ofratings, however, is that rating agencies usuallyconsider only larger firms. Many firms aredeemed too small to be rated by the bigagencies and smaller rating agencies often useambiguous rating methods. Ratings face thedownside of infrequent updating, too.Furthermore, another disadvantage of ratings isthat they are not transparent, because thedetails of the underlying methodology are oftenprivate. The recent financial crisis has alsohighlighted that credit ratings may not alwaysbe reliable.

• Active tracking of supplier performance.Many indicators can only be obtained via directinquiries or interactions with the supplier(Carter & Giunipero, 2010). Depending ontheir power in the relationship, buying firmsmay be able to persuade suppliers to discloseinformation (e.g., via supplier surveys) on theircurrent and forward-looking financial health,such as sales expectations, days payablesoutstanding, fluctuations in headcount, changes

in the top management team, product recalls,or major technology changes. These data can beused to develop a qualitative proxy for asupplier’s probability of default, but historicalperformance data is required to spot significantchanges. However, a supplier will notnecessarily disclose all information about itsfinancial health. Thus, obtaining additionalinformation is crucial. Local, regional, and/ornational trade associations often serve asclearinghouses for credit information that issupplied and made available to member firms.It is also sometimes possible for a buying firm’sbank to obtain credit information from thesupplier’s bank. Some firms also use monitoringservices (e.g., Dun & Bradstreet) that provideinformation about the health of firms. Finally,information can be obtained from marketrumors or media reports.

(2) Exposure/resource dependenceIn the finance industry, exposure measures theextent to which a creditor may be exposed to acounterparty in the event of default. This notioncan be directly transferred to a buying firm. Abuying firm’s exposure results from the need tosafeguard its supply. In the event of a supplierdefault, a buying firm must act to remainoperative, which involves monetary costs. Thus,exposure is directly related to the buying firm’sdependence on the corresponding supplier.Dependence is a core property of any exchangerelationship and is determined by the importanceof the exchanged resource, the discretion overthe resource allocation, and the extent to whichthere are alternatives for the resource (Emerson,1962; Pfeffer & Salancik, 1978). Being highlydependent on the exchange partner suggests thatthe focal firm needs to maintain the relationshipin order to achieve its desired goals and that itwould incur high switching costs if it had toreplace the relationship (El-Ansary & Stern,1972; Frazier, 1983b).

Exposure is usually difficult to estimate inquantitative, i.e., monetary, terms. There are onlya few situations where the exposure can beestimated explicitly by switching costs. For somecommodities, for example, spot markets mayexist so that a buying firm can switch from acontracted supplier to the spot market to satisfyits demand (Haksöz & Kadam, 2009). In such asetting, the exposure is the difference betweenthe spot market unit price and the contract unitprice multiplied by the volume of the contract.However, purchased items are often customer-specific, and the underlying manufacturing and


logistics processes are the result of intensivecollaboration between the buying firm and thesupplier; as such, alternative spot markets withprice information do not exist. A more general,albeit non-monetary, approach is to identifydeterminants of exposure (such as complexity ofthe purchased items, availability of alternativesuppliers, ease of supplier switching) and toderive a quantitative measure for exposure (on ascale other than monetary value).

(3) Supplier default dependenceUsually, one observes positive defaultdependence between two firms, which meansthat if one firm defaults, the other firm may havea higher probability of defaulting. Empiricalresearch indicates that this is a commonphenomenon and by no means negligible (Zhou,2001). Wagner, et al. (2009) reported from theGerman automotive industry that defaultdependency among suppliers often exists and canresult in significant detrimental consequences forthe buying OEMs. There are specific reasons toassume positive default dependence in supplierportfolios. First, it has been observed that —from the buying firm’s perspective — somesuppliers maintain relationships with othersuppliers in the buying firm’s supplier portfolios(Choi, Wu, Ellram, & Koka, 2002; Wu & Choi,2005). Suppliers may work together closely,exchange ideas, and even engage in joint ventureprojects in cooperative supplier-supplierrelationships. Being linked so closely may resultin comparable strategic and operative actions andbehaviors of the supplier firms. Second, suppliersoften face similar challenges. In the automotivesupplier industry, for example, almost allsuppliers are exposed to powerful customers.Suppliers are not only pressed by the automotiveOEMs to constantly cut costs, but also to investheavily in innovation and to support OEMs intheir new product development activities. Thelatter causes increased production and R&Dcosts. In addition, automotive suppliers are facedwith volatile prices of raw materials that mayhave a high impact on their profitability (West,Frey, & Hendker, 2005). These are specificautomotive-supplier-industry examples ofcommon or correlated risk factors that are oftenmentioned in the finance literature (Chava &Jarrow, 2004). These factors suggest that buyingfirms should also consider default dependencieswhen scanning their suppliers.

Process of scanningThe scanning process itself can differ significantly basedon the type of buying firm and the type of the

relationship under investigation. Specifically, thescanning may vary in scope and intensity.

(1) Scope. Scope refers to the breadth and depth ofthe information and indicators that a buying firmscans to obtain information about a supplier’sfinancial health.• Breadth defines the set of different indicators

the buying firm is interested in. If the scanningprocess is broad, the buying firm investigates alarge set of different default measures andwarning signals, e.g., ownership structure,profitability, operational changes (e.g.,fluctuations of staff), and technology position.

• Depth refers to the amount of data that thebuying firm collects to inform the set ofindicators. For each indicator the scanningbuying firm may choose different levels ofanalytic depth. For example, to obtaininformation about a supplier’s technologyposition, the buying firm might use bothqualitative to quantitative indicators from e.g.,third-party ratings/reports, financial statements,news articles, and rumors

(2) Intensity. Intensity refers to the frequency andintrusiveness of the scanning process. Buyingfirms may use many instruments to collect data.Surveys, observations through personal contacts,agents, balance sheets, or income statements aresome of the instruments that a buyer can use toaggregate information. Frequency of datacollection also varies between firms.• Frequency: Buying firms can review their

suppliers’ financial position on a monthly,quarterly, semi-annually, or annually basis —or only if needed, i.e., after a triggering eventsuch as a change in ownership status, a newcontract, new information about risk factors.As the frequency of information collectionincreases, the intensity also increases.

• Intrusiveness: Buying firms vary largelybetween intrusive and non-intrusive behavior(Daft & Weick, 1984). Intrusive firms arevigilant toward the environment, behaveproactively and assertively, and strive to learnfrom their experiences. They act upon, ratherthan react to, environmental events. Incontrast, non-intrusive firms accept theenvironment as a given, interpret theenvironment within narrow limits, arereluctant to engage in active information-searchprocesses and are slow to respond toenvironmental events. Thus, intrusiveness is anactive, forceful behavior where the buying firmactively searches the environment forinformation. For example, an intrusive

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behavior would be plant visits or usingintelligence or monitoring services to activelyobtain indicators for a supplier’s health. Incontract, non-intrusiveness refers to a passivebehavior where the buying firms use only“receptors to sense whatever data happen toflow by the organization” (Daft & Weick, 1984,p. 288).

Information-processing needsObviously, scope and intensity of the scanning processaffect a firm’s information processing needs. As thescope and intensity of monitoring increase, theinformation processing needs of the firm increases:f(scope, intensity) = information.

As shown in Figure 3, if the scope and the intensivenessare too high, the information interpretation process willthen be overloaded. Even the most risk-conscientiousbuying firm cannot scan all suppliers with great scopeand great intensity. Firms must therefore weigh theamount of information needed versus the time andexpense required. To this end, buying firms usually useportfolio approaches to prioritize suppliers and toallocate their information processing capabilities. Asupplier portfolio perspective can help firms classifysuppliers and derive groups of suppliers, which can besubjected to similar scanning approaches.

Interpretation

In the second stage, interpretation of the gatheredinformation takes place, because the raw data receivedduring the scanning process will not directly makesense. The main issue in the interpretation process isthe determination of the risk and the decision ofwhether or not actions are necessary.

As discussed in the previous section, the key measurefor the financial health of a supplier is the probability ofdefault. From a technical perspective, however, thisprobability of default represents a random variable thatfollows a certain distribution, e.g., a normaldistribution. So, practically speaking, there is anexpected value for the probability that a supplier willdefault within a certain finite time horizon and someuncertainty (variance). The central problem for thebuying firm is now to assess the overall risk based onthe expected probability of default, the involveduncertainty, and its exposure. The assessment ofuncertainty is closely related to the urgency suggested bythe scrutinized indicators and the reliability of thesources.

(1) UrgencyThe organizational crisis perspective suggests thatsuppliers start to send out signals from thebeginning of their crisis — even way before theoccurrence of a financial distress situation oreven default. Obviously, these warning signals arenot equal in the urgency they convey. Earlywarning signals such as change in ownershipmight be good indicators to signal financialdistress, but the time until default and, thus, thebuying firm’s reaction time and room tomaneuver are still large. In addition, earlywarning signals are usually associated with more“noise” and, thus, more uncertainty. For thisreason, a firm that receives an early warningsignal might decide to wait until the signalbecomes clearer.

(2) Reliability of the sources/quality of the dataDifferent sources may have different reliabilities.A market rumor that a supplier faces somedifficulties is a less trustworthy signal than asupplier asking for changes in the paymentpolicy.


Figure 3Amount of Information as a Function of Scanning Scope and Intensity

Based on the assessed information, the buying firmconstructs or modifies its beliefs about the risk involvedin the current situation and decides how to proceed.The interpretation process may lead to insights thatdelegitimize a state that had previously been consideredto be acceptable (Greening & Gray, 1994; Meyer, 1982).The information processing perspective suggests that afirm only responds if the interpreted informationexceeds a certain response-justifying threshold (Cyert &March, 1963; Huber & Daft, 1987). More specifically,the buying firm compares its updated beliefs about theinherent risk in the supplier relationship with its riskappetite and goals. The latter can be viewed as athreshold of action. If the firm concludes that there is adiscrepancy, it will be motivated to bring its resourceprovision back in line with its expectations. If the risk isbelieved to be below the threshold, the buying firmdoes not act and accepts the current situation.

Action

Based on the results of the interpretation process, thefocal firm determines its response to the distressedsupplier. We use resource dependence theory tounderstand the basic types of responses.

Resource dependence theory has two major tenets: (1) afirm’s essential need for scarce external resources createsa dependence on its exchange partners, which results inpotential sources of adversity and vulnerability for thefirm, and that (2) firms strive to minimize thisdependence, which is tantamount to maximizing theirpower (Pfeffer, 1981). A clear mandate for decision-makers derives from the second statement, but there issome managerial discretion over how to achieve thegoal of reduced dependence (Oliver, 1991; Pfeffer &Salancik, 1978). Given that the focal firm strives todetermine ways to reduce its risk with respect to theexchange relationship, resource dependence theorysuggests two viable response strategies: buffering andbridging (Cook, Shortell, Conrad, & Morrisey, 1983;Fennell & Alexander, 1987; Meznar & Nigh, 1995;Scott & Davis, 2007; Thompson, 1967). As long asresource dependencies exist, buffering and bridging maybe pursued irrespective of specific industry constraints.Collectively, the two strategies exhaust the set ofpossible active responses. Neither buffering nor bridgingstrategies are “good” or “bad” per se. Either of the twomay be effective, depending on the specific context.While buffering and bridging are independent concepts,they are not mutually exclusive and may act ascomplements to each another. For example, the firmmight decide to start searching for an alternative source,while simultaneously attempting to support thedistressed supplier with financial subsidies.

BufferingThe response of buffering aims to reduce or eliminatethe firm’s external resource dependencies in order toachieve higher levels of autonomy for the firm(Galbraith, 1973; Thompson, 1967). Its direction isexternal to the current exchange relationship, becausethe firm explores new alternatives to reduce theimportance of, and its exposure to, the currentexchange relationship. In general, buffers are any meansthat serve both to insulate the firm from its exchangepartner and to mitigate the detrimental consequences ofdisturbances that the relationship may confer. A typicalbuffering approach is to terminate the relationship withthe distressed supplier and to switch to an adequatealternative source, which is associated with switchingcosts. Switching costs may include expenses for searchand development of an appropriate supplier, additionalpurchasing costs, as well as possible losses due to theintermittent disruption of supply. Another method ofbuffering is to design modularized or componentizedproducts that may help increase tolerance of theexchange partner (Tang, 2006).

BridgingThe response of bridging aims to manage the externalresource dependencies by reinforcing the collaborativeties with the exchange partner or by enlarging thecontrol and influence over it (Aldrich, 1979; Katz &Kahn, 1978). For distressed suppliers, this will usuallymean salvaging the supplier (at least for a transitionperiod), which is associated with financial subsidies. Byemploying a mix of coercive (“Do this or else!”) andnon-coercive (rewards) influence strategies, the firmmay modify or manipulate the relationship more or lessformally (Frazier, 1983a). This modification can resultin forming relationships with influential individuals inthe other firm, cooptation (Selznick, 1949), or verticalintegration (Ulrich & Barney, 1984). Collaborativestructures or initiatives with the exchange partner, suchas a joint risk management system, also assist inimproving the cohesiveness. In addition, bridging maybe associated with more or less intrusive scanningapproaches such as monitoring or intensifyinginformation exchanges (Pfeffer & Salancik, 1978; Scott& Davis, 2007).

Learning

Norrman and Jansson (2004) describe how the Swedishtelecommunications equipment provider Ericsson, inthe wake of a supply chain disruption (Latour, 2001),reassessed and radically changed its supply chain riskmanagement processes and organizational culture.Today, Ericsson is considered to have a world-classsupply chain risk management system (Norrman &

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Jansson, 2004). The case of Ericsson suggests thatsupply chain disruptions can be viewed as anexperience-based learning process (Levitt & March,1988) that can expose the latent flaws andvulnerabilities of internal and external structures,processes, and systems (Meyer, 1982; Turner, 1976),thus helping the affected firms to understand theproblems and improve their strategies for the future(Milburn, et al., 1983; Nathan & Kovoor-Misra, 2002).The lean management philosophy, for example,deliberately uses this learning effect in a preemptivemanner by systematically reducing operational slackwith the intention of revealing and eliminatinginefficient processes (Womack, Jones, & Roos, 1990).Supplier defaults are usually considered as negativeevents. However, this view overlooks the positivelearning potential in these experiences. For example,after the subprime crisis, banks were forced to revisitand adapt their risk management processes. In a similarvein, buying firms can learn from prior experienceswith distressed suppliers and improve their scanning,interpretation, and action processes.

Organizational adaptation and learning have beenexamined from a wide variety of theoretical perspectives(for a review, see Argote, 1999), one of them being theexperience-based learning perspective, which holds thatorganizational learning is a process of gatheringexperience and drawing inferences from this historicalexperience in repositories of organizational knowledgefor future actions (Cyert & March, 1963; Levitt &March, 1988). At its most basic level, learning createsthe potential for behavioral change (Huber, 1991). Thisimplies that learning is not necessarily connected withchange but merely the potential to change.

Much of the literature on organizational learningrecognizes that infrequent, hazardous experiences, suchas supplier defaults, can be a valuable source oforganizational learning and, as a consequence, thesource of competitive advantage (e.g., Nathan &Kovoor-Misra, 2002; Sitkin, 1992). Sitkin (1992)emphasizes the “transformational nature” of failure andunanticipated external challenges. Particularly higher-level learning (i.e., more drastic changes of overall rules,norms, strategies, or structures with a long-termimpact) often needs an extreme external stimulus (Fiol& Lyles, 1985). This stems from the idea that suddenhazardous environmental events force an organization toquestion existing schemes and structures and to unlearnhabitual behaviors (Hedberg, 1981; Nystrom &Starbuck, 1984). Moreover, such events seem to open awindow of opportunity during which organizationalinertia can be overcome (Meyer, 1982).

Organizational learning is a social process, as manyindividuals are involved in the learning and sharecertain knowledge together through interaction. This isthe primary distinction between organizational andindividual learning. Organizations make use of sharedknowledge through the codification of the knowledgeinto a social system (Sinkula, 1994). For example, anorganization translates its learning and behaviors intoprocess standards, mission statements, or organizationalroutines. This ensures that each individual in anorganization has access to the knowledge and behavesin the given and defined way. Consequently, anorganization can only learn if it codifies its learning andtranslates it into procedures. According to Levitt andMarch (1988), if a learned experience is not transferredto other persons directly, “the lessons of history arelikely to be lost through turnover of personnel” (p.328).

In literature, the simplest form of learning is referred toas single-loop learning (Argyris & Schön, 1978). Thisform of learning involves low risk because onlyincremental changes in behavior are added to an alreadyused routine. Thus, the aim of single-loop learning is toimprove upon the initial situation without drasticallychanging it. The more complex form of learning isdouble-loop learning, which contributes (in contrast tosingle-loop learning) to long-term organizationalchanges. The first step toward double-loop learning isdescribed as unlearning. An unlearning organizationeliminates old logic in order to make room for newideas, logics, or solutions. This first step occurs on anindividual level (individual learning) before it reachesan organizational level. Unlearning takes a firstimportant role as it helps and supports a firm in itsefforts to reorient norms, values, or behaviors throughthe consistent change of individual cognitive structures.Thus, unlearning is a prerequisite on the way to double-loop learning (Klein, 1989). The second step afterunlearning is substituting. If an organization unlearns thepresent way of doing something and substitutes it witha new way or form of doing it, the organization moveson the double-loop learning route. It’s important to note that only the combination of (1) unlearning and(2) substitution can create a long-term learning success(Sinkula, 1994).


Based on the proposed framework, we investigatedseveral firms to understand how they dealt withsuppliers in financial distress. In the interest ofgenerating theory, the data collection followed atheoretical sampling strategy and was based on in-depth, semi-structured interviews with specialists in theareas of procurement, supply, and supply chainmanagement. The regions chosen this study were:Europe, the Middle East, North America, and LatinAmerica. In some cases, the respondents were notresponsible for the entire firm’s purchasing/supply areasbut rather a specific business unit. However, these

business units had their own P&L responsibilities andown purchasing organizations. The profile of therespondents ranged from mid-management (e.g.,managers or specialists), to senior and top management(e.g., head of purchasing, COO). Our main focus wason large manufacturing firms. However, in order to getan extended review we included some wholesale andservice provider firms. In sum, a total of 18 firms wereinterviewed. Table 1 shows the overall demographicinformation. More details on the employedmethodology are provided in Appendix B of this report.

Chapter 4: Research Design andMethodology

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Firm Interview Total Revenues1

(Alias)Industry Headquarters

location Employees (million US$)AntennaCo Electrical equipment USA Mexico 11,500 $1,800AutomotiveCo Automotive OEM Germany Germany 260,000 $130,000BeverageCo Food and beverages Greece Switzerland 42,000 $8,800BreweryCo Food and beverages England Switzerland 70,000 $27,000BusCo Automotive OEM Turkey Turkey 1,200 $340CementCo Construction material Turkey Turkey 1,000 $540DefenseCo Defense Germany Mexico 21,093 $7,000ElectronicsCo Electrical equipment Germany Switzerland 335,000 $100,000FashionCo Fashion Italy Italy 7,000 $1,300HealthcareCo Pharmaceuticals USA Mexico 116,000 $60,000LogisticsCo Logistics services USA USA 3,000 $2,000PowerCo Electrical equipment Switzerland Switzerland 130,000 $30,000RadiatorCo Automotive supplier Turkey Turkey 700 N/ARetailerCo Industrial Retailing The Netherlands Colombia 280 $20SustEnergyCo Industrial services Japan Switzerland 8,000 $3,500TurbinesCo Electrical equipment France France 93,500 $27,000VanCo Automotive OEM Turkey Turkey 10,000 $4,900WirelessCo Wholesale Mexico Mexico 80 $3

Table 1List of Interviewed Firms

1Based on exchange rates as of Dec. 31st, 2011.

Below is a summary of the findings from the cross-caseanalysis. First, our findings about different scanning andinterpretation techniques are presented, followed byfindings related to the different actions taken, andfinally findings of how organizational learning takesplace after situations of supplier financial distress. Asmost firms do not distinguish between the scanningprocess (where selected data are collected) and theinterpretation process (consisting of sense- and decision-making based on the collected information), wecombine the discussion of the two first steps.

Scanning and Interpretation

The firms’ scanning and interpretation efforts can beclustered around three characteristic behaviors, whichleads to three types of firms labeled as reactors,observers, and guards. Figure 4 provides an overview ofthe identified types.

The distribution among them is uneven. We found 39percent of responding forms belonging to the reactorsgroup, 22 percent to the observers, and 39 percent tothe guards. The reactors consist solely of subsidiesrepresenting all sectors except automotive, construction,and food processing.

All of the interviewed firms use qualitative indicators tomonitor their active suppliers’ financial situation.Likewise, all firms track the operational performance oftheir suppliers, as they consider minor operationalproblems such as order delays, order cancellations, orquality issues as possible signals of financial distress.BeverageCo, for example, argues that “deviation inquality can be interpreted as a direct consequence offinancial problems, as the supplier might reduce thequality of their materials to decrease its costs.”Particularly, requests for changing payment terms andexplicit support request by the suppliers are commonlyconceived as strong signals of a more acute financial

Chapter 5: Case Study Findings


Figure 4Scanning Types

distress situation. Reactors are those firms that rely ononly such relatively late signals. Many of the firms thatfit to this classification are overseas subsidiaries. One ofthe representatives stated: “Evaluating suppliers in morecomplex ways is a headquarters task.”

Some firms go beyond these late signals of escalation,without implementing sophisticated monitoringsystems. For example, AntennaCo and ElectronicsCotrack market rumors and attempt to forecast possiblesupplier defaults based on this information. BeverageCoand RadiatorCo rely on their “strict accreditationprocess,” which RadiatorCo supports by regular suppliersite visits, while ElectronicsCo employs additional third-party rating agencies. We label firms applying thesemeasures observers.

Finally, guards have implemented sophisticatedmonitoring systems, which include several of thefollowing: accreditations, financial analysis, and thecollection of in-depth qualitative data through regularaudits. Many representatives agreed to the following:“Our staff regularly visits our suppliers and thusreceives formal and informal information.” Also thesefirms might employ some external agencies to evaluatetheir suppliers’ financial strength. PowerCo andTurbinesCo follow the headcount and staffing policy oftheir suppliers and consider layoffs as an importantindicator for financial problems. Furthermore, they arewell aware of the utilization rates of their core suppliers,as they “know the monthly capacity [of our suppliers]and how it is booked,” as one of the respondentsexplained. Nevertheless, for the sake of saving costs,even the guards do not apply such sophisticated andresource-demanding monitoring to non-critical

suppliers. Some also refrain from monitoring globalfirms acting in monopolistic or oligopolistic markets,where bridging and buffering strategies would not bepossible. As CementCo explained: “As there are only acouple of huge firms that control fuel supply globally,we do not monitor our fuel suppliers, although theyrepresent 45 percent of our purchasing volume.”Figure 5 shows the preferred scanning behavior of thefirms classified as guards.

Just as the level of scanning varies among the firms, theinterpretation techniques to deal with the variance ofthe signals, and action thresholds also vary. The guardsdefine explicit triggers that will initiate actions even incases where the risk for a supplier’s default is still low.For strategic and critical suppliers, the trigger might beas seemingly mundane as the loss of a key employee, asCementCo explained. Further triggers might be theexceeding of a certain threshold in financial ratios orratings. Initial actions in such situations can focus on amore accurate monitoring of the respective supplier.One interviewed supply manager stated that afterrecognizing an acute default threat of a critical supplier,the management board of the firm evaluates the riskand decides the future proceedings. Other firms withless sophisticated scanning and interpretation processesrecognize the risk of a default by late signals, such asdelivery delays, and will react late (maybe too late),which might lead to a severe supply chain disruptionand cause high expenses.

Figure 6 shows the differences with regard to scanningintensity and scanning scope among the groups. Allautomotive OEMs were classified as guards. In total weallocated seven firms to this category, which also

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Figure 5Preferred Scanning Behaviors

includes firms from the construction, industrial, andfood processing sectors. The observers category containsonly four firms, one automotive supplier, one from foodprocessing, and two from the electronics sector. Thereactors consist of the seven remaining firms —predominantly from the service sector, among them thesmallest of our sample. Although it might be assumedthat firm size dictates scanning efforts, our researchshows that scanning intensity has a stronger correlationto business sector.

Action

After a situation of (potential or current) supplierfinancial distress is recognized, different actions can becarried out to reduce the probability of default and/orthe exposure to disruptions. We observed that thealternatives chosen are more dependent upon thebusiness sector of a firm rather than its size. Weidentified three characteristic action patterns. Wecategorized 38 percent of our sample firms assubstitutors; we categorized 28 percent as cooperators;and we categorized 33 percent as partners. Figure 7gives an overview of the three identified action types.

First, the substitutors represent firms, which, due todifferent reasons, have predominantly arm’s lengthsupplier relationships that involve little dependence(Dyer & Singh, 1998). Due to the lack of relationalelements and low switching barriers, they would rather

substitute a supplier than support it. Usually they sourcestandard products in competitive markets, particularlyspot markets. In our sample, all representatives of thisgroup belong to the service industries and the foodprocessing sector. “We have a low resource dependenceon our suppliers. Thus, we will terminate our businessrelationship and blacklist a supplier when it causedhigh-impact disruptions,” said BreweryCo. There aresome exceptions to this approach for well-performingsuppliers, such as adapting payment terms to assist them,as LogisticsCo and others affirmed. Generally, thisconduct can be considered buffering (Bode, Wagner,Petersen, & Ellram, 2011). Some of the firms wouldcancel open orders and transfer them to another supplier,but (possibly) return to the supplier once the financialsituation was stabilized.

Second and in contrast to the substitutors, thecooperators rely on strong relationships with portions oftheir supplier base. These relationships arecharacterized by a higher level of dependency and trust.Due to the relational exchange, the cooperators firmsfreely agree to adapt payment terms to assist theirpreferred suppliers. AntennaCo gives a good example ofthis approach: “Though we pay special attention onhaving a highly diversified supplier base, we want tooffer an equal win-win relationship to all of ourbusiness partners — even for small businesses.”Furthermore, ElectronicsCo is willing to provide short-term credits to their strategic suppliers. Nevertheless, ifa supplier is non-critical or it is not expected to


Figure 6Scanning Matrix

overcome the financial crisis, the cooperators wouldrather substitute with a healthy supplier. As CementCoexplained: “Although we did not expect a supplier toovercome its financial crisis, we supported it by payingwages in order to keep our production running. Assoon as an alternative supplier was found, we stoppedthe support for the distressed one.” Thus, cooperatorspursue a selective strategy. We observed manufacturingfirms from different sectors and sizes in this category.

Third, some firms — which we call partners — possesssophisticated contingency or action plans that theyactivate if they face a supplier in financial distress. Allautomotive firms belong to this classification, as well asHealthcareCo and PowerCo. These firms establishedlong-term relationships with relatively few, accreditedsuppliers, which they demand to be “honest andtransparent, if they are facing any problems,” asHealthcareCo explained. Both the level of trust and thelevel of dependence are high. In case of a financialdistress situation, they would adapt payment terms,provide credits, and also support with knowledge transfer(e.g., through their in-house consulting departments).

To safeguard themselves, some of these firms are usingmodular products, which enable them to be flexible withregard to suppliers. To avoid supply chain disruptions, afirm would buy the raw materials necessary to process itsorders, relieving the supplier from upfront investments.An interviewee stated: “Though we support our suppliersafterward, we cannot always avoid a default. Even if theproduction will be continued, usually operations will befully stopped for 48 hours. If we expect the worst case —an insolvency — we extend our stock of items sourcedfrom the struggling supplier, in order to keep ourproduction lines running without interruptions.”

Although these firms are following a bridging strategy toavoid supplier defaults, they admit that sometimes theywould substitute even a critical supplier: “One of oursuppliers has been going through financial problems forseveral years. Several times a consortium of OEMs savedit from bankruptcy. When we learned about anadditional shareholders conflict, we decided to developan alternative supplier. One year later this companyfiled insolvency due to the conflict and other OEMssuffered seriously, while we could continue ourproduction.”

Figure 8 illustrates the strategy preferences forcooperators and partners.

Learning

Our cross-case analyses revealed four different types oflearning in terms of gaining experiences with distressedsuppliers. The observed learning forms can becategorized into single- or double-loop learning,discussed above. Furthermore, we identified twocategories where no learning took place. Our findingsabout the different learning behaviors are summarizedin Figure 9. We consider 11 percent of our sample firmsas lucky ones, 28 percent as ignorers, 22 percent asadaptors, and 39 percent as evolvers.

Two of the interviewees — the lucky ones — stated thattheir firms had not yet faced a supplier default.Nevertheless, these firms have established scanningprocesses. RadiatorCo states that it has developed anaction plan and would rather try to avoid the default ofa supplier than develop a new one, while BreweryCo

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Figure 7Action Types

would distinguish according to the importance of thesupplier.

The second group, where no learning took place, arethe ignorers. These firms did not change their behaviorsafter negative experiences with distressed suppliers, nordid they implement knowledge management systems tocollect and store acquired knowledge. Stated inhibitors

to learning are firm culture, lack of knowledge managementsystems, or the lack of time outside the everyday routine.“We are so busy with our daily business that we do nothave time for reporting procedures,” the RetailCorepresentative complained. All five firms that belong tothis group also neglect their scanning process and areconsidered by us to be reactors.


Figure 8Preferred Action Strategies

Figure 9Learning Types

Third, adaptors have implemented a knowledgemanagement system and use their experiences to adjusttheir procedures (e.g., improving contract terms, supplierselection profiles, scanning, and interpretation procedures)either event-based or periodically. All four firms in thisgroup also established sophisticated scanning processesand belong to the guards with respect to scanning.BusCo stated an example of incremental learning: “Weimproved our scanning process by adding or refiningindicators.” This adaptive learning process can beclassified as single-loop learning according (Argyris &Schön, 1978).

Lastly, evolvers can be found among all scanning andaction types. These firms run a formalized knowledgemanagement system, spreading the experiences amongtheir employees to prevent errors from recurring. Afterthey face a serious problem with a supplier, theyperform in-depth analyses (e.g., top management businesscase discussions). “We have an internal online database toshare experiences about disruptions leading to smallconsequences. However, if a supplier’s financial distressleads to a severe impact, a detailed business case isprepared and discussed by the management board,”explained AutomotiveCo. By carrying out suchintrospective techniques they are prepared to applymajor changes. This involves the unlearning of acceptedroutines and the implementation of new behaviors,meeting the definition of double-loop learning (Argyris& Schön, 1978).

Summary

In our cross-case analyses we were able to derive severaltypes of behavior for each of the three stages. When theresults are summarized, as illustrated in Table 2, a fewoverarching patterns can be explored.

In particular, the first stage (scanning/interpretation)and the last stage (learning) of our information-processing framework seem to be significantlycorrelated (Spearman � = 0.54, Kendall’s � = 0.49). Inother words, certain behaviors in the scanning/interpretation stage co-occur with certain behaviors inthe learning stage. Specifically, firms that act as guardsduring the scanning/interpretation stage tend to act asadaptors during the learning stage. This seems quiteplausible because guards, i.e., firms that use alreadysophisticated monitoring processes during scanning/interpretation, might only want to incrementallyimprove their well-established risk management systems(adaptor). Interestingly, however, firms that take a morepassive observer position during the scanning/interpretation stage seem to be willing to radically evolvetheir risk management procedures in the learning stage,when they see the necessity. Finally, firms that behave asreactors during the scanning/interpretation stage alsoshow little interest in learning (ignorers) from negativeexperiences.

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Table 2Summary of Cross-Case Analyses

TypesFirm

Scanning & (Alias)

IndustryInterpretation

Action Learning

AntennaCo Electrical equipment Observer Cooperator EvolverAutomotiveCo Automotive OEM Guard Partner AdaptorBeverageCo Food and beverages Observer Substitutor EvolverBreweryCo Food and beverages Guard Substitutor Luck oneBusCo Automotive OEM Guard Partner AdaptorCementCo Construction material Guard Cooperator AdaptorDefenseCo Defense Reactor Cooperator IgnorerElectronicsCo Electrical equipment Observer Cooperator EvolverFashionCo Fashion Reactor Substitutor IgnorerHealthcareCo Pharmaceuticals Reactor Partner EvolverLogisticsCo Logistics services Reactor Substitutor EvolverPowerCo Electrical equipment Guard Partner EvolverRadiatorCo Automotive supplier Observer Partner Luck oneRetailerCo Industrial Retailing Reactor Substitutor IgnorerSustEnergyCo Industrial services Reactor Substitutor IgnorerTurbinesCo Electrical equipment Guard Cooperator EvolverVanCo Automotive OEM Guard Partner AdaptorWirelessCo Wholesale Reactor Substitutor Ignorer

The middle stage (action) seems to be somewhat lesscorrelated with the other stages. Still, when adding thisstage into the analysis and using cluster analysistechniques (for categorical data), three differentoverarching groups of firms can be identified: (1) Guard– Partner – Adaptor; (2) Observer – Cooperator – Evolver;and (3) Reactor – Substitutor – Ignorer. Figure 10illustrates this finding. The first group is formed by thethree automotive firms in our sample (AutomotiveCo,BusCo, VanCo), the second consists of AntennaCo andElectronicsCo, our representatives of the electronicsindustries, and the third group is formed by FashionCo,RetailerCo, SustEnergyCo, and WirelessCo.


Figure 10Cluster Dendrogram

Our qualitative research revealed that four factors havea particularly strong influence over a buying firm’sscanning, interpretation, and action behaviors: Trust,risk orientation, resource dependence, and salvage power. Inthis chapter, we present these four factors. They can bedistinguished into soft or hard factors. Whereas softfactors determine the firm’s attitude, hard factors haverather exogenous constraints. Subsequently, we showhow and whether they come into play in the differentstages of the information-processing process.

Soft Factors: Trust and Risk Orientation

We found that buying firms are not unbiasedinformation processors. In fact, they differ significantlyin their modes of interpretation, i.e., the manner inwhich they process, manipulate, and ultimately utilizeinformation gathered from the environment. Trust in theexchange partner and the buying firm’s risk orientationwith regard to the risk of supplier defaults are twomajor factors that affect the buying firm’s information-processing behavior and preferences during themanagement of distressed suppliers.

(1) TrustTrust in an exchange partner can be described asan expectation or a belief that the exchangepartner will honor its commitments (is credible)and will behave with good intentions (isbenevolent) (Doney & Cannon, 1997; Ganesan,1994). Trust is considered to be central toexplaining a firm’s interpretation of and behaviortoward its exchange relationships. Low levels oftrust imply that, during a relationship’s tenure,the buying firm has not (yet) experiencedpositive effects from the exchange partner’sconduct.

(2) Risk orientationFirms show different levels of concern regardingthe risk of supplier defaults. One mightconceptualize this as a continuum with one endrepresenting firms that are very concerned aboutthis risk and the other end representing firmsneglecting risks. In particular, prior experienceswith supplier defaults have a strong effect on thisfactor.

Hard Factors: Resource Dependence andSalvage Power

In contrast to the soft factors trust and risk orientation,the hard factors of resource dependence and salvage powerwill restrict a buying firm’s action alternatives to acertain set of options, even if it prefers anothersolution.

(1) Resource dependenceResource dependence of a firm is the“vulnerability in a firm’s external resourceprovisions.” If a firm depends extensively on theresources of its suppliers, and the amount ofsuppliers they can source from is small, then thisfirm is considered to have high resourcedependence, and vice versa.

(2) Salvage powerThe size and financial strength of a buying firmplace hard structural constraints on its options tosalvage a distressed supplier and may excludesome specific capital-intensive actions.

In the following section, we are going to discuss howthese factors influence a buying firm’s conduct. Thishappens on the level of a single buyer-supplierrelationship, whereas the classifications we provided

Chapter 6: Determining a Buying Firm’sBehavior

33CAPS Research

during the foregoing case discussion describe thegeneral conduct of the firms.

Scanning

First, we found that risk orientation and salvage powerwill determine the general willingness and possibilitiesof a firm to conduct a scanning process, i.e., firms thatrealize they cannot salvage a specific supplier (salvagepower) or firms that show rather little interest insupplier default risk per se (risk orientation) have littleincentive to monitor the supplier’s financial health.Secondly, given the boundaries set by these two factors,trust and dependence will substantially affect thescanning processes applied for specific suppliers. Asshown in Figure 11, based on the level of trust andresource dependence four typical scanning behaviors canbe observed.

In general, firms choose a wider scope and a higherscanning frequency when they are dependent on aspecific supplier. For example, we found that firmsallocate more resources to scan sole suppliers, whose

default might have severe consequences, than they do toother suppliers. At the same time, trust affects thebuying firm’s intrusiveness. In cases with high trust,buying firms have experience-based confidence that thesupplier will provide accurate information.

(1) Minimal scanning (low dependence, high trust):Due to the low level of dependence and thestrong belief that the corresponding suppliers arebenevolent, firms reduce their scanning efforts toa minimum (i.e., low scope and low intensity).

(2) Suspicious scanning (low dependence, lowtrust): Given low trust in and low dependence onthe supplier, the scanning firm’s behavior will beprincipally governed by its belief that theexchange partner is not reliable. Buying firms tryto minimize their scanning efforts, but focus ondata sources other than the supplier itself. Theymight also show more intrusiveness.

(3) Trust-but-verify scanning (high dependence,high trust): When a firm is highly dependent ona supplier, even with a high level of trust,information obtained only by the supplier mightbe insufficient. Although the firm has a good


Figure 11Scanning Behaviors

Suspicious

■ Scope:

– Breadth: narrow

– Depth: low

■ Intensity:

– Frequency: low

– Intrusiveness: active

Vigorous

■ Scope:

– Breadth: wide

– Depth: high

■ Intensity:

– Frequency: high

– Intrusiveness: active

Minimal

■ Scope:

– Breadth: narrow

– Depth: low

■ Intensity:

– Frequency: low

– Intrusiveness: passive

Trust-but-verify

■ Scope:

– Breadth: wide

– Depth: high

■ Intensity:

– Frequency: high

– Intrusiveness: passive

TR

US

T

low

high

low DEPENDENCE high➞

➞

relationship with the supplier, it wants to ensurethat the supplier is healthy and employs “trust-but-verify” strategies.

(4) Vigorous scanning (high dependence, lowtrust): The most resources are spent for scanningif the supplier is important but not consideredtrustworthy. The belief that the supplier will notact in a benevolent way and, thus, will notprovide accurate information about its financialhealth, requires the use of alternative sources anda more intrusive scanning behavior.

Interpretation

Basically, the interpretation stage can be understood as adynamic hypothesis test in a changing environment.Based on steadily updating information, the buyingfirms periodically reassess the situation. Meanwhile,during the escalation of supplier distress, the set ofapplicable alternatives diminishes. The signals receivedin this environment are stochastic and represent theexpected probability of supplier default. A high trustlevel within a relationship increases the clearness of the

signals, what is tantamount to a lower deviation of thestochastic variable of the signal. The threshold fortaking action is determined by the dependence on aspecific supplier and the general risk orientation of thefirm. The threshold is higher for less importantsuppliers and lower for critical suppliers: A buying firmwill react rather proactively for important supplierswhose default could cause a severe supply chaindisruption. Furthermore, a highly risk-orientated firmwill prefer to act proactively in order to ensure smoothoperations. The closer a supplier gets to the point ofdefaulting (increasing urgency), the clearer the signalsbecome and the easier it gets for the buying firm tounderstand these signals. Once the confidence intervalof the signal crosses the threshold, action is triggered tocope with the potential default. This is illustrated inFigure 12. Summing up, in the interpretation stage,buying firms have to determine whether or not thereceived information suggests that a supplier will enterthe financial distress stage or not and if any actions shallbe taken. Causes and effects are often ambiguous anddifficult to understand; thus, interpreting the signals ofsupplier distress is complex.

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Figure 12Triggering Action

Default

FinancialHealth

Probability of Default t = 0 t t = D

Threshold

Act

Igno

re

Trus

t

Trus

t

�

Sig

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Num

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of a

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ativ

es

Dep

ende

nce,

Ris

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atio

n

Action Trigger

FinancialDefault

Set of Valid Alternatives�

Due to this complexity, the interpretation stage is proneto two errors, as shown in Figure 13. (1) The firm canfalsely reject a signal as irrelevant (similar to Type Ierror: reject the null hypothesis when the nullhypothesis is actually true); or (2) falsely overreact to asignal that points to a financial problem that doesn’texist (similar to Type II error: fail to reject the nullhypothesis when the null hypothesis is actually false).Hence, in determining its response, the firm faces adilemma between being overcautious (reduction of Type Ierror) and inattentive (reduction of Type II error).

Firms that are more risk-oriented tend to be moreprecautionary. This might be the result from of thefirm’s own experiences. Previously experienced supplierdefaults and the severity of their consequences play animportant role for risk orientation. For example, abankruptcy of a sole supplier in the past leads a buyingfirm to behave more proactively to avoid impendinghypothetical supplier defaults. Nevertheless, several ofthe interviewed managers confirmed that in cases ofdoubt, they always seek additional information toimprove the state of knowledge and avoid Type II error.Upon receiving weak signals that a supplier might be indifficulties, these firms first act is to gain a betterunderstanding of the problem and to identify theunderlying root causes.

Figure 14 provides an overview of the warning signalsthat were named by the interviewed managers. The

warning signals are sorted according to the type of crisisthat they could foreshadow. The crises are sortedaccording to their urgency.

Action

Once a firm decides to act, it can basically choosebetween the two alternatives buffering and bridging. Wefound that the four factors — trust, risk orientation,dependence, and salvage power — will jointlydetermine the way a firm will act, as shown in Figure15. Highly risk-oriented firms prepare action plans andexecute those, while unprepared firms will have to reactad-hoc in a distress situation. Nevertheless, the riskorientation will not affect the action strategy that will beapplied. The motivation to act will be channeled intobuffering and bridging by the factors trust and resourcedependence on the supplier. More specifically, resourcedependence pushes the buying firm to pursue abridging strategy. Particularly when a supplier is the solesupplier of a certain good of high importance to thebuying firm, the buying firm will tend to support thesupplier in order to sustain its operations. In contrast, ifa good is broadly available in the market and searchingcosts are low, the buying firm will more likely apply abuffering strategy and substitute the supplier.Analogous, a firm will consider it rather worthy tocontinue a strong (long-term) relationship, implying ahigh level of trust and commitment by both partners. In


Figure 13Interpretation Errors

contrast, it will tend to substitute an unreliable partner.Often the degree of trust and dependency correlates. Sotrust and dependence will determine the preferredstrategy to be applied by a firm. But, the buying firm’ssalvage power imposes a constraint on the focal firm’sset of options. It restricts the buying firm’s ability topursue a bridging strategy to a maximum degreedepending on the buying firm’s own financial condition.Buying firms with a low level of salvage power havefewer possibilities to salvage or even support thesupplier. Hence, the option to pursue a bridgingstrategy with the aim to salvage a supplier is limited.

Learning

Unlike the previous stages, learning is less affected bythe four aforementioned factors, but more so bycompany culture and prior experiences. Some firmsdevelop working environments favoring organizationallearning, where experience is shared among many

individuals through different interactions. Nevertheless,without relevant prior experiences, buying firms facedifficulties in making sense of signals and indetermining the form and the strength of a response. Itseems that prior experience acts as an information filterthat suppresses the irrelevant information and enhancesthe relevant information. Having encountered supplierdefaults, buying firms may have developed dedicatedrules and routines for dealing with distressed suppliers.Moreover, experienced firms may have more completeinformation on the set of options available to stabilize asupplier and should have confidence in using the entireresponse repertoire, because it has a better understandingof the response-outcome relationship. If the focal firm isfamiliar with the interpretation of signals fromdistressed suppliers, the interpretation and theresponses become increasingly effective. In contrast,firms that have been exposed to fewer supply chaindisruptions lack relevant knowledge and are unfamiliarwith interpreting these events. Hence, these firms facedifficulties in determining adequate responses. A lack of

37CAPS Research

Figure 14Urgency of Signals and Stages of a Supplier Crisis

knowledge leads to the application of simple “rules ofthumb,” which leads to a risk of overreacting or “under-reacting.”

Based on prior experiences, some firms have developedand established monitoring systems that they reviewand adjust regularly. Thus, we conclude that a firm’sculture fostering knowledge management will lead tobetter monitoring, interpretation, and action techniques.

Financial Consequences

The discussed risk management process affects a firm’scost base and thus also has profit impact. Having saidthat, it is in a firm’s best interest to minimize the totalcost of risk management activities, which can bedescribed by the sum of costs caused by (possible)disruptions and costs spent to prevent these. These twofactors, obviously, imply a trade-off. The costs caused bya supplier default cannot be predicted precisely, as theyare prone to too many factors and have to be modeledas a random variable. In contrast, the risk managementcosts, including expenditures on monitoring,interpreting, actions, and learning processes can beassumed as deterministic. By increasing the costs of riskmanagement, more disruptions can be avoided, andthus the total cost of disruptions will decrease.Nevertheless, the risk management costs face decreasingeconomies of scale. A total minimum of riskmanagement costs can be determined, as illustrated inFigure 16.

In order to achieve an optimal alignment, a firm mustbalance the expenditure on the four stages of the riskmanagement process. For the scanning stage, aperiodical categorization of the supplier portfolioensures an optimal allocation of resources to the criticalsuppliers, without creating waste by monitoring non-critical suppliers. During the interpretation stage, highertrust increases the clearness of the distress signal andthus reduces the probability of an error. This isbeneficial to the cost basis due to two reasons. First, thethreshold to act can be set higher and thus expenditureof unnecessary costs avoided; second, the probability ofthe case of an unpredicted supplier default reduces.Finally, when action is taken the strategy applied mustfit the importance and credibility of the supplier. If abuying firm can substitute suppliers on the spot market,disruption costs will be quite low and correspondinglybridging activities shall be limited. Contrary, when thebuying firm will face a heavy impact from a supplierdefault it is advisable to be prepared in the event of apossible disruption. If a firm cannot salvage a supplier,it shall prepare alternative suppliers to activate in caseof the default of the preferred supplier.

The cross-case analysis revealed that some of the buyingfirms did consider the financial impact of the riskmanagement activities, while other did not. Weobserved different permutations of buying firm conductduring risk management stages. Nevertheless, twocharacteristic patterns emerged, which also form twoextremes of the conduct continuum. First, allautomotive OEMs are guards, partners, and adaptors.This implies that they successfully identify their


Figure 15Salvage Power as Inhibitor to Actions

Dependence

Trust

Buffering

Bridging

Salvage Power

dependence on their suppliers and are aware of theirsituation and adapt to it. The other end of thecontinuum is formed by the buying firms we classify asreactors, subsitutors, and ignorers. All representatives ofthis group belong to service industries and have a lowresource dependence on their suppliers. Additionally,some of the firms are the smallest of our sample; it canbe concluded that their salvage power is accordinglylow. Thus this conduct can also be considered asstrategically aligned. Within the continuum some firmsalso achieve strategic fit, while others do not.

39CAPS Research

Figure 16The Risk Management-Cost Smile

The default of a supplier can lead to a severe negativeimpact on the individual buying firm and the supplychain. Procurement managers should pay heightenedattention to the overall situation of their suppliers;because of the current critical economic situation,financial distress is very common and can affect anyfirm. The signals can be particularly subtle and scarce.Thus, it is of great importance for firms to monitor theirsuppliers using both qualitative and quantitativemechanisms, as that is the best approach for managingsuppliers risk management. This allows firms to protecttheir supply chains from the risks of defaultingsuppliers by applying different measures. In the firstplace, equitable supplier relationships enable thesuppliers to maintain financial health. Also, buyingfirms should 1) avoid creating an overly high level ofcompetition, particularly between its unqualifiedsuppliers, 2) use their power in a prudent way, and 3) avoid opportunistic behavior with its locked-insuppliers.

Nevertheless, even following these measures, thefinancial distress of a supplier is not avoidable in everycase. Our research shows how firms prepare themselvesfor such cases. The monitoring of financial data andqualitative signals, such as losses of key employees orquality issues, allows firms to anticipate futuredevelopments with regard to their suppliers. When asupplier’s default is expected different actions are carriedout by the buying firms. Sometimes it is advantageousto substitute a supplier, particularly if the supplier isnon-critical or the buying firm does not possesssufficient salvaging power to stabilize the supplier. If asupplier is critical for a firm, the preferred response to afinancially distressed supplier will be to support it byadapting payment terms or through provision of credits.In addition, a firm also might transfer knowledge andhelp its suppliers to operate profitable.

After the experience of a supplier default, the majorityof buying firms will use the gained knowledge to adapttheir purchasing and supplier monitoring processes.

We found that the factors trust, risk orientation,dependence, and salvage power influence buying firms’approaches to managing financially distressed suppliers.Thus, several managerial implications andrecommendations can be deduced from our research:

• Supplier defaults can cause severe supply chaindisruptions. Consequently, buying firms are urgedto tackle these risks just as vigorously as theytackle financial and other business risks.

• The risk of supplier default can be measured bythe probability of default and exposure (resourcedependence).

• As in any other risk management setting, there isa trade-off between taking the risk and spendingresources on risk management. Consequently,buying firms have to carefully evaluate how muchrisk and costs they are willing to take or spend forreducing the risk of supplier defaults.

• There is no “one-size-fits-all” solution for dealingwith the risk of supplier defaults. It all dependson the probability of default and the individualexposure.

• The number of suppliers to be scanned and thescope and intensity of scanning increase a buyingfirm’s information processing needs. To avoidinformation overload and ensure economical useof resources, purchasing managers have tocarefully determine how much information theyneed in order to mitigate their risk of financiallydistressed suppliers. Specifically, this involvesdetermining the required accuracy of informationprocessing (i.e., “How accurate do we need tobe?” ”Does it matter if we are using a rule ofthumb?” “Is the cost of making an interpretation

Chapter 7: Conclusions andRecommendations


error so high that gathering more data iswarranted, to avoid such an error?”)

• Resource dependence not only increases a buyingfirm’s exposure, but also may force the buyingfirm into expensive bridging actions to salvage adistressed supplier. Thus, important suppliers(where there is a high level of dependence) shallbe monitored thoroughly (high scope and highintensity) and action plans be prepared inadvance.

• Good supplier relationships can have a costimpact, because a high level of trust decreases theuncertainty of the scanning process. For criticalsuppliers, buying firms should attempt to buildrelationships based on trust and relationalexchange.

• Buying firms might not always possess enoughpower to salvage or even support a distressed keysupplier. Especially for these cases, an action planshould be prepared that points out solutions ofhow such supply chain disruption can beavoided.

• Non-critical suppliers should be monitored on aselective basis and on a less frequent level toensure an efficient use of resources.

• Documentations systems help to transfer tacit intoexplicit knowledge and can prevent organizationsfrom repeating errors and incurring costs.

While conducting this research, we presentedreasonable and appropriate answers to the causes ofsuppliers’ financial distress and how firms can copewith these events. We believe that if firms align theirsupply chain risk management activities according tothe recommendations they can successfully decreasetheir risk exposure and operate on an efficient costlevel.

41CAPS Research

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A P P E N D I XA

AntennaCo (Electrical equipment)This firm is a multinational telecommunicationscorporation headquartered in the United States, withUS$1.8 billion annual revenues. It has operations in theAmericas, Europe, and Asia Pacific, employing 11,500people globally. The business unit interviewed is theone that manufactures carrier wireless and broadbandsolutions located in Mexico. The interviewee was aninternational buyer.

AutomotiveCo (Automotive OEM)This firm, headquartered in Germany, is among the 10largest automotive OEMs with annual revenues ofUS$130 billion, is stock-listed, and employs 260,000people worldwide. The group operates with a globalcentralized procurement organization and hasapproximately 2,000 suppliers. In light of the currentcritical economic situation, its large size, and thediversity of its supplier base, AutomotiveCo created aspecial functional department within its headquarters tomanage risks to its supplier base. The interview was asenior risk and restructuring manager from thisdepartment.

BeverageCo (Food and beverages)This firm is one of the leading anchor bottlers of softdrinks in Europe, with annual revenues of US$8.8billion, is headquartered in Greece, operates in 28countries, and employs 42,000 people. It has acentralized procurement department located in Austria.The interview was conducted with the Head of Logisticsof a business unit in Switzerland that interacts with 350suppliers.

BreweryCo (Food and beverages)This firm is one of the top brewing and bottling firms inthe world, is headquartered in London, is stock-listed,has US$27 billion annual revenues, and has 70,000employees in 75 countries, covering all the continents.Recently, it created a brand new enterprise to manage all

the global procurement activities of the firm. Thisbusiness unit is located in Switzerland, where theinterview was conducted with one of its GlobalProcurement Specialists.

BusCo (Automotive OEM)BusCo is one of the major automotive manufacturers inTurkey. Since 1963, BusCo has been providing solutionsto its customers both in commercial and militarycapacities. BusCo operates with its nearly 1,200employees in a 552,000-square meter plant in Turkeyand generates US$340 million revenues. Currently,BusCo is one of the main exporters of the Turkishdefense industry with its small buses, trailers, and semi-trailers. The interview was conducted with the ChiefPurchasing Manager of BusCo.

CementCo (Construction material)CementCo is a leading cement producer in Turkey anda joint venture of two conglomerates. It has operationsin the Marmara, Aegean, and Black Sea regions ofTurkey. Ten percent of Turkey’s cement needs is met byCementCo, with global standards, environmentallyfriendly facilities, and an emphasis on service quality. Itutilizes state-of-the art technology to meet customerstandards. CementCo’s total domestic cement andclinker sales amounted to 4.6 million tons in 2010. Thefirm’s domestic and clinker exports reached 7.4 milliontons, with clinker and cement exports of 2.8 milliontons. Total revenue was US$540 million in 2010. TheHead of Purchasing, who is responsible for suppliermanagement, was interviewed.

DefenseCo (Defense)This firm is the second largest business unit of a globalaerospace and defense corporation. The business unitoperates in the defense communication systems industryand is headquartered in Germany. In 2010 it achievedUS$7 billion annual revenues, with a global footprint ofmore than 21,000 employees in more than 20 countries

Interviewed Firms

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A P P E N D I X Ain the Americas, Europe, and Asia Pacific. Weinterviewed the supply chain manager responsible forthe Latin American region headquartered in Mexico City.

ElectronicsCo (Electrical equipment)This firm is a large multinational conglomerate that isactive in many industrial segments, particularlyelectronic and electrical equipment. It has four divisionsdistributed among 190 countries, employs approximately335,000 people worldwide, is headquartered inGermany, and has annual revenues of US$100 billion.The business unit interviewed is located Switzerland,where the international headquarters of the BuildingTechnologies is located.

FashionCo (Fashion)This firm is one of the leading Italian fashion firms thatmanufacture shoes, suits, and male accessories, withannual revenues of US$1.3 billion, and is headquarteredin Italy. It has 560 boutiques worldwide and 15manufacturing plants within Spain, Switzerland,Mexico, and Turkey; approximately 7,000 people areemployed worldwide. We conducted our interview withone of the senior operations and logistics managers.

HealthcareCo (Pharmaceuticals)This is a Fortune 500, multinational firm thatmanufactures healthcare and pharmaceutical products,which are sold in more than 175 countries; it isheadquartered in the United States. The corporation isstructured in 250 subsidiary companies in 57 countriesthat, together, produce annual revenues of US$60billion and employ 116,000 persons worldwide. Weinterviewed one of the firm’s operations directorsresponsible for the Mexican market.

LogisticsCo (Logistics services)This is an American multinational logistics serviceprovider that offers services in transportation, trade,warehousing, and export and import logistics. They areheadquartered in Philadelphia, have annual revenues ofUS$2 billion, and operations in 120 countries, wherethey employ 3,000 people worldwide. We interviewed asenior supply chain manager in the United States.

PowerCo (Electrical equipment)This firm is headquartered in Switzerland and mainlyoperates in the industrial power and automationindustry. It has annual revenues of US$30 billion, isstock-listed, and has a presence in approximately 100countries operating with 130,000 employees globally. Itmanages its corporate procurement in a centralizedfashion, directly from Switzerland, with a global supplybase of 135,000. The interview was conducted with theHead of Supply Chain Management at the headquartersoffices in Switzerland.

RadiatorCo (Automotive)RadiatorCo is a leading manufacturer of engine coolingradiators, heater cores, condensers, cooling modules,and HVAC systems for motor vehicles. RadiatorCooffers an extensive range of products and supplies to awide variety of OEM customers, such as passenger carmanufacturers, light- and heavy-duty commercialvehicle manufacturers, agricultural machinery, andindustrial machinery. The firm was founded in 1966 inIstanbul. Today it has 700 employees and operates intwo plants. RadiatorCo is positioned to serve theTurkish automotive industry for both local and exportmarkets. Our interviewee was the purchasing managerof RadiatorCo.

RetailerCo (Industrial retailing)This firm is part of a Dutch holding company that ownsapproximately 50 firms specialized in trading, transport,retail, oil, financial services, and cash-and-carrywholesale. The annual revenues of the holding firm areUS$20 million; headquarters are in the Netherlands.The firm interviewed is part of the cash-and-carrywholesale activities in South America. The business unitis located in Colombia, where one of the senior buyerswas interviewed.

SustEnergyCo (Industrial services)This Swiss firm is part of a Japanese holding firm thatowns approximately 35 firms specialized inenvironmental systems, industrial plants, infrastructure,precision machinery, and process equipment. Theannual revenues of the holding firm are US$3.5 billionwith headquarters in Osaka, Japan. The firminterviewed is part of the Environmental BusinessGroup and is a sustainable energy solutions providerand contractor. Its offices are located in Switzerland,where the Chief Procurement Manager was interviewed.

TurbinesCo (Electrical equipment)This firm is a large multinational conglomerate in theindustrial electrical equipment industry (particularlypower and energy). It is headquartered in France, hasyearly revenues of US$27 billion, is stock-listed, andhas a global footprint of more than 70 countries,employing 93,500 persons globally. It has a globalcentral supply chain department, where procurementmanages 22,000 suppliers on a global scale. Theinterview was conducted with one of the firm’s categorymanagers.

VanCo (Automotive OEM)VanCo is a Turkish automotive production firm, whichis a joint venture between a large global automotiveOEM and Turkey’s largest conglomerate. It startedproduction in 1965. VanCo employs more than 10,000employees at four plants in Marmara region of Turkey


A P P E N D I XAand generates a turnover of US$4.9 billion. It is theleading commercial vehicle manufacturer in Turkey andhas a total cumulated export worth of more thanUS$19.8 billion. The interview was conducted with asenior purchasing manager.

WirelessCo (Wholesale)This is a mid-sized Mexican wholesale firm in thetelecommunications and wireless technology industry,which, with only 90 employees, produces annualrevenues of US$3 million. It operates its procurementdirectly from Mexico where it has business relationswith approximately 120 suppliers from Europe, theUnited States, Mexico, and Latin America. The personinterviewed is the CEO and Founder.

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A P P E N D I X B

ApproachBecause our research intention is to extend, develop,and build theory (Eisenhardt, 1989) and the focus is onthe real-world context in which the phenomena at handare nested in (Yin, 2009), we opted for an inductivecase study research design based on semi-structuredinterviews. Case study research focuses on understandingthe dynamics present within single settings.

Data CollectionFor the purpose of confidentiality, we refer to theinvestigated firm using generic aliases (e.g.,AutomotiveCo). Initial contact to the firms wasestablished via email. With the email was an attachmentthat introduced this study, described the overall aim,and explained the goals and dynamics of the interviews.All interviews were conducted between June 2011 andOctober 2011. To guide the interview, we developed asemi-structured instrument that consisted of two parts:(1) One part focused on the management of financialdistress in suppliers and the consequences in case ofdisruption, aiming to explore different financial distresscases, their development, their impact on buying firms’supply chains, and buying firms’ response actions. (2) The second part of the instrument focused onexploring the learned experiences of the buying firmsafter the financial distress in their suppliers, how theseexperiences were documented and shared withinorganizations, and suggestions for converting tacitknowledge to explicit knowledge.

Data AnalysisTo analyze the different cases’ similarities anddifferences, the data generated through the interviewswas subject to first-level and pattern coding for firstsummarizing, examining, and comparing segments ofdata and later grouping the phenomenon into categoriesfor developing insights (Miles & Huberman, 1994).

Later we performed a within-case analysis and, in asecond step, cross-case analysis. Within-case analysesaim at improving the familiarity of researcher with eachcase and tries to generate patterns from cases. Instead ofbuilding an external pattern and looking for it in cases,within-case analysis allows generating patterns fromcases. In this thesis, within-case analysis is applied byanalyzing each case in categories. Investigating the datain categories increases the speed of the second step ofdata analysis, cross-case analysis. Eisenhardt (1989)discussed several tactics that one can use to do a cross-case analysis. For the purpose of this study, we employone of them by dividing cases into categories andfinding out similarities or differences betweencategories. After identification of patterns across thecategories, propositions are suggested.

Detailed Description of Methodology


A P P E N D I XC

For the best rating classes (e.g., Aaa) defaults have notbeen observed. To obtain a value for the probability ofdefault (PD) for these ratings and to smooth outsampling errors from the historically observed data, athree-step calibration method is usually performed(Bluhm, et al., 2002).

1. For each letter rating class, compute the over-yearmean value of the default frequencies and thestandard deviation based on the yearly bonddefault data, such as Moody’s historical corporatedefault data from 1920 to 2009 (available fromEmery & Ou, 2010).

2. Plot the mean value of the default frequencies ofeach class against the numbered rating classes forinstance 1 (Aaa) to 7 (Caa-C). Because there is

empirical evidence that default frequencies growexponentially with decreasing creditworthiness, alogarithmic link is usually chosen to estimate theregression function.

3. Assign a PD using the estimates obtained from theregression analysis in step 2.

Using Moody’s historical default data from 1920 to2009, we obtained:

In(�) = 1.12x – 10.05 (R2 = 0.98)In(�) = 0.92x – 8.57 (R2 = 0.96)

for the default probabilities and their volatilities (xdenotes the rating class with Aaa ➝ 1, …, Caa-C ➝ 7).Figure 17 graphs the result.

Calibration of Default Probabilities and Volatilities

Figure 17Calibration of PDs and Their Volatilities

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Documents

Financial Distress of Suppliers: Causes, Management, and · PDF file · 2014-03-19Financial Distress of Suppliers: Causes, Management, and Consequences Christoph Bode Department of