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Negative Default Dependencies in Supplier Networks

Daniel Dumke — Mon, 18 Jun 2012 12:45:08 +0000

If people talk about disruptions and network effects within the supply chain, the associations are most often negative.

The picture of an automotive/just-in-time supply chain comes to mind, where a small screw from a distant supplier did not get delivered in time and all production processes within the whole network suddenly come to an involuntary halt.

But on the other hand there are companies profiting from these smaller and larger disruptions: competition.

To analyze these effects we have a look at the consequences of negative default dependence between suppliers. The full paper can be found here.

Default dependence and method

Empirical research on corporate defaults in the finance literature indicates that corporate defaults often cluster in time and that the default of a company is frequently affected by the defaults of other companies. […]

In the automotive industry there are several reasons why positive default correlation may exist in supplier networks. First, automotive suppliers face similar challenges, such as large and powerful customers who force suppliers constantly to cut costs and invest heavily in R&D or the volatile prices of raw materials. It is likely that the automotive suppliers have to suffer from the consequences of these challenges in a similar way or are reacting in a comparable manner to cope with them. Second, suppliers may maintain relationships with other suppli- ers and share “technical and explicit information as well as tacit information” and “work together closely, exchange ideas, and even engage in joint venture projects.” Being linked so closely may result in comparable strategic and operative actions and behavior of the supplier firms. The consequence is that decisions that lead to financial problems are likely to be taken by both suppliers that are linked through close supplier–supplier relationships.

However there are also reasons/situation in which default-events might be negatively correlated:

First, after a supplier default, customers might shift business to another supplier in the network.

Second, the default of a supplier can result in lay-offs and the competitor will be able to hire more and qualified staff.

Finally, due to the reduced number of alternative sources, the buying firm may become more dependent on the surviving supplier who, through the gained power, may be able to incur higher profit margins and, thus, gain in financial stability.

The authors use copula-functions.

Financial data to calculate default probabilities for a case study are derived from Datastream (Thomson Reuters). This data was used to calculate the individual default intensities.

For the worldwide 100 largest suppliers to the automotive OEMs in 2005 that were included in the Datastream database, we extracted the necessary data required for specifying and adjusting our model.

Figure 1 shows the default intensities for selected companies.

Figure 1: Company Characteristics (Wagner et. al, 2011)

The default dependencies were calculated using numerical results of a simulation.

Results and implications

The authors draw three conclusions from the results of their analysis.

First, our estimation of default intensities of selected first-tier suppliers in the automotive industry supports the concerns raised in the literature about the financial stability of automotive suppliers. Supplier default intensities above 5% are disquieting for the respective automotive OEMs.

Second, the simulation results depict that negative default dependence among suppliers in a supplier network has consequences for the survival probabilities of the entities in the network. The higher the individual default intensity of a supplier, the stronger the effect of negative default dependence on its survival probability after the default of the other supplier. […] for example, the portfolio with low default intensity suppliers demonstrated to increase the survival probability of the second supplier by 2.7% and the portfolio with high default intensity suppliers by 15.4% (in comparison to the independence case).

Third, in addition to the dependence level, the dependence structure, reflected in our model by the choice of copula, is an important factor for modeling default dependence in a supplier portfolio.

The following management implications can be given:

Purchasing managers should be aware that negative default dependence between suppliers may exist and take this into account for their sourcing decisions. A better understanding of the randomness and relatedness of supplier defaults internal to the supplier network can help firms to plan for uncertainty, take proactive measures to reduce risk (e.g., switch a supplier), and achieve better, less variable outcomes.

Firms should preferably establish relation- ships with suppliers that have low default intensities, and with suppliers that will benefit from the default of their competitors – given that the default of the competitor will not significantly shift the power in the buyer–supplier relationship

Conclusion

Not only surviving competitors can potentially profit from the default of its contestant, but also their clients may profit.

This research shows that interdependencies – no matter if positive or negative – have to be analyzed and should included in the decision making process.

One has to keep in mind though, that Wagner et al.‘s results heavily rely on their method to estimate the default dependencies within the supplier portfolio.

This might induce additional uncertainty in the form of model risk.

Reference:

Wagner, S., Bode, C., & Koziol, P. (2011). Negative default dependence in supplier networks International Journal of Production Economics, 134 (2), 398-406 DOI: 10.1016/j.ijpe.2009.11.013

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Survey on Risk Handling in the Manufacturing Industry

Daniel Dumke — Mon, 08 Nov 2010 14:38:00 +0000

A current report by KPMG shows that international manufacturers are increasingly considering effects of supplier selection on cost and potential risks. Nearly 200 companies participated globally in the survey. The full report can be downloaded here at KPMG, I just summarized the major findings.

Results

Reintegration: Every third manufacturer has reintegrated formerly outsourced processes into his own organization again. Furthermore about 40% are considering reintegration to lower cost and risks.
Single Sourcing: About 40% of the companies want to reduce the number of suppliers in the next two years. About the same number stated that they are planning to design their contracts with longer time horizonts
Disruptions: Companies are increasing their activities on contingency planning in case supply is disrupted.
Risk Aversion: The authors conclude that manufacturers may be avoiding many risks completely thereby reducing their chances as well.

Conclusion

From a research point of view these results can be viewed as the basis for further in depth analysis. In a footnote I found an interesting remark, that the complete study has been conducted by an external contractor: the Economist Intelligence Unit. I am not really sure if this increases or decreases the credibility and validity of the results.

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Perception of Supply Risk

Daniel Dumke — Fri, 16 Jul 2010 16:06:00 +0000

One of the most published supply risk researchers is George A. Zsidisin. In his 2003 article he describes the characteristics of inbound supply that affect the perception of risk.

Supply Risk

The author defines supply risk as

the potential occurrence of an incident associated with inbound supply from individual supplier failures or the supply market, in which its outcomes result in the inability of the purchasing firm to meet customer demand or cause threats to customer life and safety.

He therefore includes the probability and significance of an adverse event.

Methodology

With the above mentioned definition and a literature review as a basis Zsidisin establishes a case study with seven companies from different industries (Aerospace, Computer, Electronics, Semiconductor).

The data from these sample companies is then analyzed and common patterns are revealed using open and axial coding analysis.

Findings

Three categories which affect risk perception are found (see figure below): item(/product)- , market- and supplier-characteristics.

The influences are pretty straight forward. Examples:

Item Characteristics: new products have a higher perceived risk than existing products
Market Characteristics: unstable marked prices lead to a higher perceived risk
Market Characteristics: the lower the number of qualified suppliers the higher the perceived risk
Supplier Characteristics: low quality levels with suppliers lead to higher perceived risk

Characteristics influencing Supply Risk (Source: Zsidisin 2003)

Conclusion

The proposed categories can be used by supply professionals who are assessing their risks. Caveat: The findings have been generated using case study research the framework therefore cannot guarantee theoretical completeness.

Reference:

Zsidisin, G. (2003). Managerial Perceptions of Supply Risk The Journal of Supply Chain Management, 39 (1), 14-26 DOI: 10.1111/j.1745-493X.2003.tb00146.x

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Disruption-Management Strategies for Short Life-Cycle Products

Daniel Dumke — Sat, 10 Jul 2010 07:05:00 +0000

In his 2009 paper Brian Tomlin analyzes strategies to mitigate disruption risks in a three echelon supply chain.

Setting

Focus in his research is a single company, with its suppliers and customers. The objective is to maximize expected utility, while demand and supply are uncertain. There are two products available which can be used as substitutes. The time horizon for the decision maker is one season where the products can be sold.

Three different sourcing structures are considered.

Different sourcing structures (Source: Tomlin 2009)

Variables / Attributes of the SC

The following attributes in the supply chain are used as free variables and can therefore be modified:

Suppliers
- Supplier reliability
- Correlation between supplier failures
- Proportion of order cost charged in the event of a failure
- Relative cost of emergency supplier
Products
- Contribution margin
- Demand uncertainty
- Demand correlation
- Substitutability
Firm
- Risk aversion of the decision maker

Strategies

The effects of three major adaption strategies on the risk exposure / expected profit are tested:

Supplier diversification
adding another supplier to lower the overall risk for failure
Contingent sourcing
adding a supplier which is only used in case the main supplier fails to deliver
Demand switching
shifting customer demand to a different product

There is also the possibility not to engage in any activity and accept the risks.

Results

It is shown that contingent sourcing is the preferred strategy to supplier diversification for the firm, if the failure probability of the supplier increases. Furthermore, demand switching is an effective strategy for controlling demand risk and therefore is the preferred strategy with low supply risk.

Higher levels of risk aversion lead to a preference for contingent sourcing over the other strategies.

Conclusion

Tomlin shows viable and effective strategies to increase profit (utility) in supply chain settings with disruption risk. He therefore calculates the expected profits in different situations and uses the results to give suggestion for situational optimal strategies to mitigate these risks.

Reference:

Tomlin, B. (2009). Disruption-management strategies for short life-cycle products Naval Research Logistics, 56 (4), 318-347 DOI: 10.1002/nav.20344

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Supply Chain Compliance for Risk Mitigation

Daniel Dumke — Wed, 24 Feb 2010 14:33:00 +0000

Supply Chain Compliance

Apple Inc. recently published their Supplier Responsibility Report (PDF) for 2009.

In this report Apple elaborates what measures they are taking to ensure a broad set of goals at the inbound side of their supply chain. They especially address possible problems at their asian suppliers.The Supplier Code of Conduct contains the following topics:

Labor and Human Rights (eg. fair treatment, working hours, wages)
Health and Savety (eg. emergeny prevention, ergonomics)
Environmental Impact (eg. wastewater management, hazardous substance management)
Ethics (eg. whistleblowing, protection of intellectual property)

Instead of just including the code within supplier contracts, Apple also monitors compliance through audits, and promotes training of the suppliers employees.

Risk Mitigation

Outsourcing is one possibility for a single company to mitigate some supply chain risks by moving the risks to the suppliers. The financial impact of some risks usually will be covered in the contract by the means of penalty payments for non-performance. Apple is making use of this measure for a long time already. The slogan “Designed in California Assembled in China” is printed on each Apple product.

But outsourcing cannot reduce all SC related risks to zero, since many still affect the company. In the following I would like to point out three measures from the Supplier Responsibility Report and their implications for supply chain risk:

Child labor, insufficient waste management,… not only pose an ethical problem, which must not be neglected, but of course also has implications for the public opinion about Apple and its products possibly annihilating the current campaigns for hazardous substance reductions.

Monitoring the suppliers management system can give early warnings for mismanagement at the supplier, possibly affecting the timeliness of deliveries.

Training of the suppliers employees might increase productivity, job satisfaction, therefore reducing quality issues.

Doing a cost benefit analysis on these topics would be myopic, since it neglects the ethical implications of the measures. On the cost side alone measures like training of the suppliers employees or the ban on child labor seem to be quite cheap.

Conclusion

From a research point of view it is important to integrate these efforts into a holistic SCRM approach. This also shows that supply chain (risk) management and ethical / sustainable behavior cannot be viewed separately in a global supply chain. The interactions between risks / opportunities on the one hand and ethical and sustainable behavior on the other are so complex, that both have to be included in the same model to make the best decision possible.

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