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Linking risk management practice and strategies to performance

Daniel Dumke — Mon, 03 Sep 2012 13:41:29 +0000

Agility and robustness are the concepts of choice if you want to reduce supply chain risks. In their upcoming paper (Dealing with supply chain risks: Linking risk management practices and strategies to performance) Andreas Wieland and Carl Marcus Wallenburg analyze the effects of both of these concepts on customer value and business performance.

Since Andreas is a colleague of mine at the “Berlin University of Technology” he kindly provided me with a heads up.
Actually, the paper has been named the most exiting paper of 2012 so far by the International Journal of Physical Distribution & Logistics Management.
Here is what the IJPDLM has to say about it:

Probably the most important topic today in global distribution and SCML is the governance of risk across relationships. Andreas Wieland and Carl Marcus Wallenburg have written a very interesting manuscript titled, “Dealing with supply chain risks: Linking risk management practices and strategies to performance.” What managers need is the ability to justify when to and to not spend money on monitoring and governance practices. To build justification, a value understanding must be developed. This manuscript opens the door for expanded research in this area, while also generating interesting practical outcomes. Researchers should view this manuscript as a call for future research in IJPDLM.

There is also a short video summary of the article.

Methodology

The effects of supply chain risk management (SCRM) on the performance of a supply chain remain unexplored. It is assumed that SCRM helps supply chains to cope with vulnerabilities both proactively by supporting robustness and reactively by supporting agility. Both dimensions are assumed to have an influence on the supply chain’s customer value and on business performance. This research is aimed at providing clarity by empirically testing these hypotheses and scrutinizing the findings by the means of case studies.

The authors employ two empirical methods to assess this question:

First, a survey was conducted with 270 manufacturing companies from Europe.
Second, these findings were then complemented by six case studies to explore the survey findings further and extend on them.

Figure 1 shows the process of the method selection.

Figure 1: Research Concept (Wieland and Wallenburg, 2012)

Building hypothesis: agility and robustness

The authors analyze preventive and reactive strategies (figure 2).

Figure 2: Robustness and Agility to reduce Supply Chain Risks (Wieland and Wallenburg, 2012)

Based on the existing literature and research gaps the authors form the following hypotheses as a foundation for their research:

Supply chain risk management
- H1a: Supply chain risk management has a positive effect on agility.
- H1b: Supply chain risk management has a positive effect on robustness.
Supply chain’s customer value
- H2a: Agility has a positive effect on the supply chain’s customer value.
- H2b: Robustness has a positive effect on the supply chain’s customer value.
Business performance
- H3a: Agility has a positive effect on business performance.
- H3b: Robustness has a positive effect on business performance.
- H4: Business performance is positively influenced by the supply chain’s customer value.

Results

The findings of the survey are shown in figure 3.

Figure 3: Resulting links between Research Concepts (Wieland and Wallenburg, 2012)

The hypotheses 1a and 1b are both supported, which reinforces the notion that agility and robustness are indeed used as measures in supply chain risk management.
Both measures also have a positive impact on the supply chain’s customer value.
On the other hand the link between agility and business performance cannot be supported for agility and only weakly supported for business performance.

The results of the case studies expand on these results. Figure 4 shows the case study participants.

Figure 4: Case Study Participants (Wieland and Wallenburg, 2012)

In general, the cases revealed that all companies strive to be both agile and robust in order to utilize the specific advantages of each approach.

Interestingly, “the case studies show that robustness is rather required on the supplier side (i.e. upstream in a supply chain). For instance, multiple suppliers are helpful, if the quality of a component is low or a supplier has a high insolvency risk. This finding first of all implies that supplier-related risks tend to be more predictable as otherwise a proactive approach would not be feasible and effective.”

The following propositions are made as a summary of the case interviews:

P1: Realizing agility is an effective supply chain approach to deal with customer-related risks.
P2: Realizing robustness is an effective supply chain approach to deal with supplier-related risks.
P3: To be effective, the degree of agility and robustness needs to fit to the overall competitive strategy.

Conclusion

From my point of view, the nomination as most exciting paper of 2012 (so far) seems to be highly justified.
The nomination as well as the high survey response rate of nearly 20% highlights the importance of supply chain risk management in the current research and practice.
Beside the results, I especially liked the elaborate description of the methodological foundation both for the survey and the case study research.

I also found some points with the potential for improvement:
First, the survey results show that there is no link between agility and business performance. On the other hand, the authors claim that the hypothesis on the linkage between robustness and business performance can be supported. However this happens on a very thin foundation the confidence level for this interpretation is quite low with a error 1 probability of nearly 10%.

Second, a key conclusion of the paper is that robustness should be used for supply-side risks, agility for demand-side risks. However this result originates only from the case study interviews. From a formal point of view I would have wished that it would have also be supported by the survey results.

Third, there is a clear disadvantage to using only concepts (like agility and robustness) in one’s research, since those do leave a lot of room for further questions: What robustness measures are really improving the value for the customer? Are there also agility strategies which should be employed for supply side risks?

But these are only minor points, I really recommend you to read the paper: You can download the full (pre-publication) article here. Click one of the following links, if you want to learn more about robustness or agility.

Reference:

Wieland, A., & Wallenburg, C.M. (2012). Dealing with supply chain risks: Linking risk management practices and strategies to performance International Journal of Physical Distribution & Logistics Management, 42 (10)

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Optimizing Efficiency-Robustness Trade-Offs in SC-Design

Daniel Dumke — Mon, 16 Apr 2012 15:04:18 +0000

Efficiency is about trade-offs. Effectiveness is about achieving a goal, making it happen no matter what.
But in reality resources are scarce and efficiently reaching a goal is nearly as important as reaching it at all.

Low-risk supply chains often contradict the efficiency demands of the company’s stakeholders.
Robust strategies, which reduce risks while keeping performance up, are still the holy-grail of supply chain risk management.
Simple strategies are not able to accomplish this goal, only a extensive redesign of the supply chain (as for example using the postponement strategies) may indeed be able to reduce risks while keeping performance up.

How these trade-offs can be optimized according to the goals of the company is the topic of the 2011 paper: “Optimizing efficiency-robustness trade-offs in supply chain design under uncertainty due to disruptions” by Shukla, Lalit and Venkatasubramanian.

Model and robustness metric

The authors use a mathematical model to implement their robustness metric, which “is based on expected losses incurred due to network failures. It defines efficiency and robustness in terms of operational cost and expected disruption cost (EDC), respectively. The EDC is defined in terms of loss of opportunity cost incurred due to not meeting demand on time after a disruption has occurred.”

Decision variables of the mixed-integer linear model are:

the assignment of the warehouse to the manufacturing center and
the assignment of the warehouse to the customer.

The objective function is defined as the weighted sum of efficiency and robustness. Efficiency is defined in terms of OC of the supply chain and robustness is defined in terms of the EDC [figure 1]:

Figure 1: Objective Function (Shukla et al., 2011)

The authors use secondary data to build a case study with scenarios from the current US.
Figure 2 shows an extract of the demand numbers used.

Figure 2: Aggregated Demand as Input for the Model (Shukla et al., 2011)

Figure 3 contains details of the risks experienced by the respective warehouse locations.

Figure 3: Warehouse Distances and Risk Data (Shukla et al., 2011)

Four case studies are conducted, I picked number one as an example and refer you to the paper for further details.

Case study 1 deals with node failure or failure of warehouses for functional products. Since most of the warehouse locations are far apart we assume that failures are independent of each other and multiple failures can occur simultaneously. The probability of failure of a warehouse depends on the region in which the warehouse is located.

Results

Figure 4 shows the efficient supply chain design for the first case study and figure 5 the corresponding results for more robust results.

Figure 4: Case Study: Efficient Supply Chain Design (Shukla et al., 2011)

Figure 5: Case Study: Robust Supply Chain Design (Shukla et al., 2011)

The authors state that:

The resulting supply chain is much more reliable in the long term since we have shown that a significant amount of robustness can be built into the system without compromising a lot on efficiency.

Conclusion

So there is no free lunch and it seems inevitable to sacrifice some of the efficiency to gain robustness, but this paper shows that in the case studies with only small cost increases risks can effectively be reduced based on supply chain design changes.
So if your customers value reliability this might be the right approach for your chain.

Reference:

Shukla, A., Lalit, V., & Venkatasubramanian, V. (2011). Optimizing efficiency-robustness trade-offs in supply chain design under uncertainty due to disruptions International Journal of Physical Distribution & Logistics Management, 41 (6), 623-647 DOI: 10.1108/09600031111147844

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Exception Handling for Robust Supply Chain Design

Daniel Dumke — Wed, 07 Dec 2011 18:33:00 +0000

This article considers the design of robust supply chains from the viewpoint of exception handling. Disruptions of the supply chain happen all the time. Smaller disruptions like quality issues are part of the daily business; but a look at the recent ten years shows that large disruptions, happen more often as well (think of terrorist attacks or earthquakes). “Thus, exception management is an important issue in global supply chain networks.”

Basics and definition

If one accepts the fact, that exception cannot be eliminated completely there are two ways to treat them: preventive and interceptive. Figure 1 shows different exception management strategies.

Figure 1: Exception Management Strategies (Gaonkar and Viswanadham, 2003; click to zoom)

The authors define a supply chain exception as the opposite to the classical seven-Rs of logistics:

We can use this description to define a supply chain failure or exception occurring whenever the supply chain deviates from any one of the […] required specifications – either in terms of delivering the wrong product, in the wrong quantity, in the wrong condition, at the wrong place, at the wrong time, at the wrong cost and to the wrong customer.

Exception management

In this case exception management is addressed “at the strategic level through the preventive selection of supply chain partners that mitigate risk in the network.”

In the first step the consequences of a given exception (here: supplier non-performance) are analyzed. The authors use a Cause-Consequence-Diagram for this end (figure 2).

Figure 2: Cause and Consequence Diagram for one Example (Supplier Non-Performance) (Gaonkar and Viswanadham, 2003; click to zoom)

Then, “given the probability of occurrence of the initiating event, which is supplier non-performance, and the probabilities for the various intermedialy events, we can calculate the probability of occurrences for each of the end states or outcomes. Furthermore, each of these end states may result in different levels of supply shortfalls and financial cost. Hence, given the probability of each end state and the supply shortfall or financial cost for each end state, we can calculate the expected shortfall or financial risk for the non-performance of a given supplier.”

If repeated for each supplier, the supplier with the least expected impact can be identified.

Model

To implement their approach the authors develop a small linear mixed-integer model.

The model was formulated in Microsoft Excel and solved using the Solver add-in. The model was solved for a problem with a single manufacturer (located in the US), dealing with 5 suppliers. The probabilities of supplier disruption for all the suppliers (individually and in various combination) were considered as given [the first part is shown in figure 3]. The relation cost was taken as $5000 and the quantity required by the manufacturer was 520 units.

Figure 3: Scenario / Probability Table (Gaonkar and Viswanadham, 2003)

Conclusion

The presented approach makes it easy to include uncertainty into the supplier selection process. Considering the moderate efforts necessary for implementing the supplier assessment and implementing the model, I would like to read more about the effectiveness in a real business situation.

Reference:

Gaonkar, R., & Viswanadham, N. (2003). Robust Supply Chain Design: a Strategic Approach for Exception Handling Proceeings of the 2003 IEEE International Conference on Robotics & Automation, 1762-1767

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Robust Strategies for Mitigating Disruptions

Daniel Dumke — Wed, 25 May 2011 14:51:00 +0000

There are several scientific research centers on supply chain risks in the US (as around the world): The east coast has several researcher on this topic e.g. with Yossi Sheffi from the MIT, in the center with Joseph Fiksel from the Center for Resilience at the Ohio State University and on the west coast Christopher Tang from the UCLA or Hau L. Lee from Stanford University.

I wanted to write about this article, by Christopher and Tang, for some time now.

History teaches us

After a major disruption there are many firms who severely suffer and take a long time to fully recover. On the other side of the spectrum there are a few companies that continue to satisfy their customers demand, and thus use these capabilities to retain and acquire new customers in the process.

Defining robust strategies

Tang defines “robust” strategies as those which fulfill two objectives at once:

Help reducing costs or improve customer satisfaction during times without a disruption,
Support the firm during and after a disruption in continuing operations.

The goal therefore is to create a win-win situation where the investment into the strategy can pay of no matter if a disruption occurs or not.

Cases

Tang lists three cases to highlight some of the few companies that achieved that goal:

Nokia vs. Ericsson
I wrote about this case before, where Nokia and Ericsson were affected by the same disruption at a micro chip plant of a common supplier. Nokia was able to quickly adapt the product design and find another supplier, while Ericsson suffered sever financial consequences leading to the Ericsson / Sony “merger”.
Li and Fung
changed its supply plan in a flash to meet customer demand during a currency crisis.
Dell’s adaptive pricing strategy
to accomodate to supply shortages and still guide and satisfy customer demand.

Robust strategies

Building on these case the author selects and describes nine supply chain strategies that fit above mentioned definition (figure 1).

Figure 1: Robust Supply Chain Strategies (click to enlarge; Tang, 2006)

There are however challenges which have to be considered before implementing the strategies:

Cost versus benefits
These strategies probably make it easier to justify the investments due to their positive effects with or without disruptions, nonetheless there is still a tradeoff which has to be analyzed. Tang suggest to think of the remaining cost surplus as insurance premium.
Strategic fit
Not every strategy may fit for every company. For example Dell’s pricing strategy might be adaptable for airline tickets but not for heavy machinery with a new quote every day.
Proactive execution
Proactive strategies (e.g. rerouting of shipments after a port strike) can be better than reactive strategies (e.g. increasing stocks).

Conclusion

I like this article due to the practice oriented approach taken. But I am missing some clearer description of the ways to generate these strategies. Also, there is no consideration of the completeness of this listing. So I have to assume that it is probably a good starting point. And to my knowledge there are already several studies building on this paper.

As a side note: Nokia also may be a mixed example for a company as role model for good strategies. I would argue that either Nokia was lucky or had a very in-homogenous state of strategy execution: The mentioned disruption happened in 2000, exactly the same time where they should have laid ground for the new smart phone generations which are commonplace now from many manufacturers, but not Nokia.

Reference:

Tang, C. (2006). Robust strategies for mitigating supply chain disruptions International Journal of Logistics Research and Applications, 9 (1), 33-45 DOI: 10.1080/13675560500405584

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Supply Chain Design - Robust Planning with differentiated Supplier Selection

Daniel Dumke — Wed, 13 Oct 2010 13:28:00 +0000

This is the third contribution to my series on doctoral dissertations on Supply Chain Risk Management. An immense effort and dedication is spent on these works only to find the results hidden in the libraries. So the goal is raise interest in the research of my peers.

Author / Topic

This dissertation was written by Stephanie Freiwald in 2005 as her doctoral thesis at the University of Bochum. It was published by Peter Lang, Frankfurt a.M. and can be ordered here from amazon.deor your local library. The title can be translated as:

Supply Chain Design – Robust Planning with differentiated Supplier Selection

Summary

In her work Freiwald builds a mathematical model of a four echelon supply chain (supplier, producer, distributor / inventory, customer) starting with a literature review of existing models. Building up from there she first adds variables to include sophisticated criteria for the supplier selection extending the traditional price-only selection.

Robustness can be oriented towards the models optimality, reliability, result-robustness and goal robustness. So as the next step the author set the goal to include robustness to uncertainty and unsettled preferences of the decision maker to the model.

As a final step the author applies her model to an industrial case, with the goal to find optimal, robust solutions for an Supply Chain Design problem.

Conclusion

Stephanie Freiwald’s work stands apart in including multiple aspects of the supply chain design problem into one concise model for optimization without neglecting the implementation of such a “theoretical” model.

Reference:

Stephanie Freiwald (2005). Supply Chain Design – Robuste Planung mit differenzierter Auswahl der Zulieferer Peter Lang, Frankfurt a.M., Dissertation

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