Brian T. Tomlin

The Power of Flexibility for Mitigating Supply Chain Risks

Daniel Dumke — Wed, 26 Oct 2011 13:31:00 +0000

This article sheds light on the question of how much flexibility is necessary to secure the supply chain against disruption risks.
The paper reviewed today takes a closer look at three supply chain risks: supply, process and demand risks (figure 1).

Figure 1: Selected Risks for further Analysis (Tang and Tomlin, 2008)

Strategies

The authors focus on the short term agility to reduce risk and chose five strategies, which are analyzed in depth (figure 2).

Figure 2: Strategies for risk reduction using Flexibility (Tang and Tomlin, 2008)

The strategies are backed by several case studies each.
For all five strategies the authors build a different model. First stands the description of the scenario, second is the development of the model which fits the scenario and is able to deploy the strategy. The last step is the description of the results.

Supply risks

Flexibility via multiple suppliers
Multiple alternative suppliers can reduce the supply cost risks, since the company gains the flexibility to purchase from the company with the lowest prices.
The first model is based on a case by Intercon Japan and Li and Fung. One ordering company is confronted with supply cost risk. The price of the identical goods is random and can vary between $5 and $15. The authors calculate the profit for the single up to a five (optional) supplier case and conclude that profits rise with increasing number of suppliers, whereas the marginal increase declines (figure 3).

Figure 3: Profit vs. Number of Supplier to reduce Supply Cost Risks (Tang and Tomlin, 2008)

Flexibility via a flexible supply contract
Flexible supply contracts allow the ordering firm to adjust its predetermined order at short notice usually within a limited quantity range.
This model is based on a case with Canon, HP and Best Buy. Demand is random, the purchasing contracts between buyer and seller are negotiated in the beginning of a period. But the actual ordered quantity can be adjusted between u% less or more than the originally agreed quantity. Calculating the profits for increasing flexibility u% gives the results shown in figure 4.

Figure 4: Profit vs. Contract Flexibility (Tang and Tomlin, 2008)

Process risks

Flexibility via flexible manufacturing processes
Flexible manufacturing requires a plant to be adaptable to produce a multitude of different products.
In this model process flexibility is modeled by the number of products (h) which can be produced by one plant. Different forms are shown in figure 5.

Figure 5: Different Flexibility Systems (Tang and Tomlin, 2008)

The calculations shows that increased process flexibility can also increase profits in an uncertain scenario (figure 6).

Figure 6: Profit vs. Degree of Process Flexibility (Tang and Tomlin, 2008)

Demand risks

Flexibility via postponement
Further flexibility is gained by postponing product differentiation decisions until the latest possible point during the production process.
In this model two products are sold at a market, demand for them is uncertain. Postponing product differentiation (shown in figure 7). Calculating profits for two different random processes (Independent and Identically Distributed (IID) and Random Walk (RW) Model), shows that also increase in postponement leads to increasing profits (figure 8).

Figure 7: Scheme for Product Postponement (Tang and Tomlin, 2008)

Figure 8: Profit vs. Postponement Flexibility (Tang and Tomlin, 2008)

Flexibility via responsive pricing
Case studies show the value of price adaptability to steer customer demand.
In this model the company is able to postpone the pricing decision for the selling season until the time t. Up to this point in time the company is therefore able to observe demand. Timing flexibility increases as t increases. From this it can also be shown that increasing flexibility leads to better profits with uncertain demand. Figure 9 has the results.

Figure 9: Profits vs. Pricing Flexibility (Tang and Tomlin, 2008)

Conclusion

The authors took some effort to generate five different models to analyze the flexibility cases. Nonetheless, I would judge the models to be quite specific for the cases, which is good for the research, but also poses the question: Will these results hold true in any case? For any company and supply chain?
There are other results (e.g. by Tomlin und Wang, 2005, which indicate that flexibility might not be the right choice for risk averse companies, which might be better of by choosing a dedicated strategy instead)

But if you keep this caveat in mind this work is a strong proponent of flexibility as a tool to increase resilience in an uncertain environment.

Reference:

Tang, C., & Tomlin, B. (2008). The power of flexibility for mitigating supply chain risks International Journal of Production Economics, 116 (1), 12-27 DOI: 10.1016/j.ijpe.2008.07.008

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Mitigation or Contingency Strategies against Disruptions

Daniel Dumke — Wed, 16 Feb 2011 10:42:00 +0000

Uncertainty can be categorized in continuous risk, more slowly changing patterns, and disruptions, which describe abrupt changes in a system.

Tomlin (2006) investigates the question which supply chain strategies perform best when dealing with the later.

Mitigation vs. Contingency Planning

From case studies analyzed in literature the author first deducts disruption management strategies used in practices. The summary can be seen in figure 1.

Figure 1: Strategies for Managing Disruptions (Tomlin, 2006)

Mitigation strategies are distinguished from contingency strategies by the time when an action is performed. Often most of the cost occur at the same time.

Mitigation Strategies, are planned and executed before the risk occurs (eg. increasing inventory levels).

Contingency Strategies, are planned beforehand but executed after the risk occurs (eg. power generator after a power failure).

Optimal Strategies

Figure 2 finally summarizes the strategies which are tested in Tomlin’s model.

Figure 2: Available Strategies in the Model (Tomlin, 2006)

To test the strategies the author develops a mathematical model. The model shows that Supplier uptime [%] and the expected disruption length have a major impact on the chosen strategy. Figure 3 therefore shows optimal strategies depending on those parameters. The results vary for different flexibility levels.

Optimal Strategies (Example, Tomlin, 2006)

From the data at hand Tomlin draws the following conclusions:

Switching to a more reliable source is favored over inventory mitigation for less frequent but longer disruptions

Inventory mitigation may not be an attractive strategy when disruptions are rare but longer, since significant quantities of inventory need to be carried for extended periods

For a given percentage uptime, mitigation rather than contingent rerouting tends to be optimal if disruptions are rare

If volumes of the more reliable supplier are flexible, contingent rerouting (from the unreliable to the reliable supplier) can be a possible strategy

Contingent strategies can help reduce the firm’s cost when used as a component of the optimal disruption management strategy

For risk averse firms, mixed mitigation strategies (combining partial sourcing from more reliable sources and inventory mitigation) can be optimal

Conclusion

Tomlin ment his model not to be used for direct application in an business context, but to find out more about what factors influence optimal strategies for mitigating disruption risks. He finds that the most influential parameters are the supplier uptime and the expected disruption length, and those therefore have a great deal in defining the optimal supply chain strategy for a company.

Using these findings businesses can analyze and reevaluate their current strategies.

Reference:

Tomlin, B. (2006). On the Value of Mitigation and Contingency Strategies for Managing Supply Chain Disruption Risks Management Science, 52 (5), 639-657 DOI: 10.1287/mnsc.1060.0515

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Supply Chain Design: Capacity, Flexibility and Wholesale Price Strategies

Daniel Dumke — Wed, 24 Nov 2010 16:49:00 +0000

This is the forth 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 Brian Tomlin in 1999 as his doctoral thesis at the Massachusetts Institute of Technology, Cambridge, USA. It can be downloaded here at his homepage at his current employer (Tuck School of Business at Dartmouth). The title is:

Supply Chain Design: Capacity, Flexibility and Wholesale Price Strategies

Summary

Tomlin cumulates three distinct topics and analyzes them in depth in his dissertation. His core question is always the supply chain design, with demand uncertainty as the dominant risk.

Wholesale Pricing Strategies
Using a game theoretic model with two actors (supplier and manufacturer) he explores the best strategies for setting the wholesale price. The objective is to maximize the supply chain profit (think of it as a return on investment in the supply chain). He shows, that no matter if quantity-discount or quantity-premium pricing schedules are used, non-linear pricing schedules outperform linear pricing schedules.
Process Flexibility Strategies
He then tackles flexibility strategies in a multi-product, multi-stage supply chain environment. He first identifies two inefficiencies which only occur in a multi-stage environment, namely stage-spanning bottlenecks and floating bottlenecks. He develops a mathematical supply chain optimization model and based on the results he concludes, that “in supply chains with a large number of products or stages, additional flexibility is advisable, especially for stages in which the capacity is not much greater than the expected demand.”
Capacity Decision
With this chapter on capacity decision making in multi-product, multi-stage supply chains he walks on new grounds, since up to then only single-stage models have been analyzed. He uses two criterion for his analysis: service level and expected shortfall. The results of his analysis show that under optimal capacity allocation: for every stage must hold true: for a small increase in a stage’s capacity, the decrease in shortfall divided by the capacity cost should be the same for all stages.

Conclusion

I have to admit that I do not really like the segregated design of the thesis itself, analyzing one topic in depth usually should be sufficient for one dissertation. On the other hand the work by Tomlin on the three topics would probably also have sufficed for two or three separate dissertations as well.

Compared to other dissertations I read it was refreshing to see, that he did not spend (too) much time on making an in depth literature review beforehand but focussed very much on his own works.

Reference:

Brian T. Tomlin (2000). Supply Chain Design: Capacity, Flexibility and Wholesale Price Strategies Massachusetts Institute of Technology, Dissertation

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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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Design of Agile Supply Chains

Daniel Dumke — Tue, 29 Jun 2010 13:28:00 +0000

I already reviewed two other articles about agile supply chains. One on the role of distribution centers in supply chains and one on the migration from lean to agile supply chains.

But the question comes to mind if lean and agile supply chains have to be mutually exclusive or if it is possible to combine them. Christopher and Towill (2001) had a look at this question regarding the supply chain design.

Factors for Leanness / Agility

Several factors influence the design decision if a supply chain should be more agile or lean. Nowadays more often than not supply chains compete, not companies. This implies that not only companies must act market orient, but also supply chains.
Market orientation can be analyzed using the market winner / market qualifier concept. This also means that there are different foci of the lean and agile supply chain.

Correlation of Agile / Lean Supply Chains to Market Winners / Market Qualifiers

Strategies

Christopher and Towill mention three ways to combine the lean and agile paradigm in one supply chain:

80/20 approach
Usually in a supply chain with multiple products only a few of them make most of the overall volume (20% make 80% of the volume). Result: for the high volume products choose a lean approach, while for the low volume products an agile strategy might be better suited.
Decoupling point approach
Strategic inventory can act as a decoupling point within a supply chain. If you select this point wisely, it can be beneficial to use a lean strategy up to this point and an agile strategy after this point.
Base and surge demand
the strategies can also be combined if the demand pattern shows a base demand. This demand could be produced using a lean strategy, while the surge implements a more agile approach.
Base and Surge demand
Source: Christopher and Towill (2001)

Conclusion

It is important to see, that not only leanness and agility can be combined, but also the strategies mentioned above can be used complementarily.

Reference:

Christopher, M., & Towill, D. (2001). An integrated model for the design of agile supply chains International Journal of Physical Distribution & Logistics Management, 31 (4), 235-246 DOI: 10.1108/09600030110394914

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