Negative Default Dependencies in Supplier Networks

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.