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My hard drive just crashed. Well, one of two I use to store all a my private media. The disks were arranged as RAID 0. And the 0 already indicates that there is zero redundancy. So the data is gone.

Sure there is a backup, so nothing is really lost. Getting up and running again still will take at least until Sunday. The first thing I did was ordering a new disk on amazon. As soon as it arrives I will have to recreate my raid and play back all of the data. A tedious and long process, since that’s something you don’t do every day…

So my lessons learnt: Never rely on a single weak link, especially if you are not at least prepared to fill the gap.

By the way. Since early 2011 I always buy the same disk by Samsung (HD204UI 2TB). And that’s the price development since then:
January, 2011: 80.39 EUR
July, 2011: 62.99 EUR
August, 2011: 60.99 EUR
June, 2012: 105.17 EUR

As you can see the price nearly doubled after the Thailand flooding.
But now let’s see what others did this week…

  • Apparently Apple is trying hard to defend its top position in supply chain rankings: AppleInsider reports that inspections have increased in number as well as in their depth. ( AppleInsider)
  • Leaders in a new category: According to Market Watch Marsh Rick Consulting has been named best supply chain risk consulting services provider. ( MarketWatch)
  • If you want to read more on Marsh, just have a look at yesterday’s post at Enterra Insights, where Stephen DeAngelis discusses a recent press release by Marsh and his stand on supply chain insurance and alternatives. ( Enterra Insights).

Enjoy your weekend!

Originally posted by Daniel Dumke at
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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.

Company profiles
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


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.


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

Originally posted by Daniel Dumke at

This was a slow first week, after my vacation. I am still waiting for some feedback on my dissertation. Regarding my job-hunt: I sent applications to several interesting companies and now I am looking forward to their feedback.

The following articles I found worth reading this week.

  • Short summary, with some impressions of the NOFOMA 2012 logistics conference. ( Interorganisational)
  • Scott Byrnes describes his impressions of the Gartner Supply Chain Conference in Palm Desert, CA, citing resilience as one of the major topics this year. ( Supply Chain Visibility)
  • The Guardian has an article this week on child labor in supply chains. Concluding that: “Eliminating child labour and improving conditions within our supply chains must be a collaborative process with all stakeholders taking on responsibility”. ( Guardian)
  • Andreas from SCM research writes about “Real options in supply chain management”, highlighting a study which shows potential advantages of real-option-models. Another article on this topic can be found here in the blog. ( SCM Research)

I hope enjoy your weekend.

Originally posted by Daniel Dumke at

We are back from Norway. I had a great time there. The first week stayed at a small cabin at the Vindafjord (first picture) later we visited Jotunheimen National Park (second picture), Oslo and Bergen.
Overall I was really surprised by the magnificent fjords, mountains and nice people.

Our cabin at the Vindafjord
Figure 1: Cabin at the Vindafjord

Mountains in Jotunheimen National Park
Figure 2: Landscape at the Jotunheimen National Park

There were only two longer articles I read during that time and you find the links to them below.

  • Enterra Insights discusses aspects of supply chain visibility to reduce supply chain costs and conclude: “The supply chain winners of the future may largely be the ones that have more information at their disposal, and use that information more smartly than their competitors”. ( Enterra Insights)
  • Vivek Sehgal talks about the impact of Business Strategy on the Supply Chain of the company. ( Supply Chain Musings)

Originally posted by Daniel Dumke at
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One basic assumption in risk-aware supply chain design is the notion that the design of the supply chain actually has an impact on the vulnerability of the supply chain.
This question has been analyzed about six years ago in a broad empirical study by Wagner and Bode.


The authors use a rather large sample of companies in Germany. Overall nearly 5000 supply chain professionals were asked to participate and 760 actually took part in the study.
Most sample companies had an industrial focus (72% versus service (20%) and trade (9%)).
This study is founded on a similar sample as this other study by Wagner and Bode analyzing the impact of risks on supply chain performance.


The author focus on some supply chain design variables, which supposedly increase supply chain vulnerabilities. Figure 1 shows the assumed relationship between those drivers of supply chain vulnerability and three supply chain risk categories.

The relationship between drivers of supply chain vulnerability and supply chain risk
Figure 1: Concept: Relationship between Design and Risk

The authors propose the following hypothesis, which are then tested using the empirical data:

  • H1: The higher the drivers of supply chain vulnerability, the higher the level of demand side risk a firm faces.
  • H2: The higher the drivers of supply chain vulnerability, the higher the level of supply side risk a firm faces.
  • H3: The higher the drivers of supply chain vulnerability, the higher the level of catastrophic side risk a firm faces.


The results show that all hypothesis are supported by the findings of the authors. However the design factors/vulnerabilities only explain part of the observed supply chain risks (7% for H1, 13% for H2, 3% for H3).
Demand side risk was influenced by strong customer dependence and strong supplier dependence.
Supply side risk was influenced by supplier dependence, single sourcing and global sourcing.
Lastly, catastrophic risk was impacted by the degree of global sourcing.

The authors draw the following conclusions:

First, the supply chain vulnerability variables in our model explain a rather small portion of the variance in the risk arising from demand side risk sources. It is a low but not astonishing value since the majority of the vulnerability variables focuses on the upstream supply chain. However, the results reveal that customer dependence increases demand side risk. This finding indicates that firms that are dependent on some customers are exposed to a higher risk of suffering from the detrimental effects of demand volatility and poor downstream information. This could be because of order batching or limited possibilities of demand pooling. […] This leads to the hypothesis that, beyond the investigated variables, there are several additional aspects both internal and external to the supply chain that determine a firm’s exposure to supply chain risk.

Second, risk derived from supply side risk sources is elevated by supplier dependence, single sourcing and global sourcing. Supplier dependence obviously amplifies the threat from poor quality, supply shortages, sudden demise of one of these suppliers, and poor logistics performance. Although this argumentation also applies to single sourcing, the single sourcing approach seems to be less hazardous than general dependence on some suppliers. This is because single sourcing is usually aligned with a closer relationship that might absorb some of the supply side risk.

Third, when it comes to risk from catastrophic risk sources it has to be taken into consideration that the sample data was collected in Germany which has been a very “calm” place with regard to disasters. Here, it is solely global sourcing that is a significant factor that exposes firms to higher risk from catastrophes. The implementation of a global sourcing strategy stretches the supply chain geographically which ultimately means more peril points for the information and material flow. The robustness and resilience of regional or national supply chains is usually higher. Surprisingly, the study shows that supplier dependence decreases the risk exposure to catastrophes. Again we would argue that this is because of lack in supply flexibility. Firefighting against the consequences of catastrophic events might be more successful with the ability to quickly adjust the supply.


I would argue that it is always hard to measure risk consistently in a qualitative study. People are likely to evaluate the same risk quite differently, which might lead to unclear results.
Furthermore the low impact of these specific design variables emphasizes the view that there are many more factors (internal and external) that impact the exposure to supply chain risk.


Wagner, S.M., & Bode, C. (2006). An Empirical Investigation into Supply Chain Vulnerability Experienced by German Firms Erich Schmidt Verlag, 79-96

Originally posted by Daniel Dumke at