trade-off

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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Resilience or Lean SCM - This Week in Supply Chain Management (15 / 2012)

Daniel Dumke — Fri, 13 Apr 2012 12:43:22 +0000

Finally I was able to hand in my dissertation to my professor. I hope to get his feedback soon.
This week I found several interesting articles, just pick those you find useful.

Articles

Russ Banham wrote about “Reducing Disruption in the Global Supply Chain” summarizing some strategies and case studies (Wall Street Journal)
Enterra Insights presents an overview on the trade-offs between lean and resilient principles. (Enterra Insights)

Already tweeted

These are the tweets from this week. You can follow me on twitter.

RT @Industrialmfg: Factors Impacting Risks in Today’s Supply Chains http://t.co/sDoD5nX4
RT @benscm: @SCRMBlog Social Engagement for Supply Chain Improvement http://t.co/mLspDy7k
Supply chain interdependency creates new risks http://t.co/T4qXUVV3

Enjoy your weekend!

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Customer Service or Cost? Optimization of the Supply Chain Design

Daniel Dumke — Mon, 28 Feb 2011 14:09:00 +0000

In several articles I talked about competing objectives to the classical cost reduction goals (eg. with responsiveness or with reducing uncertainty).

Shen and Daskin (2005) explore the “Trade-offs Between Customer Service and Cost in Integrated Supply Chain Design”. The goal is to find ways to simultaneously improve customer service and reduce operating cost.

Modell

The authors model a three tier supply chain, with a supplier, distribution center, and retailer.
The following cost factors are considered: Facility Location, Inventory, divided into working inventory and safety stock, and distribution cost.
Since the goal is to find design decisions which lead to reduced cost and improved service the customer service is measured by the fraction of all demands that are within a specific distance to the distribution center. To make the cost and service requirements comparable Shen and Daskin have to introduce a weighing factor, in this case for the uncovered demand. In practice this factor represents the cost equivalent of any unmet demand, including lost sales, but also effects of negative reputation due to bad service, etc.

Solution

Shen and Daskin use an genetic algorithm to solve the network design problem. A genetic algorithm uses several concepts which follow the genetic selection metaphor. Operators in genetic algorithms are, for example: preservation of elites, mutations and expansions. Using this they reach optimal or near optimal solutions.

Management Insight

The authors gain two major insights from their experiments:

The cost difference between the cost-minimization solution and the service-maximization solution can be quite large.
Significant improvements in customer service can often be achieved at relatively little cost.

Conclusion

Shen and Daskin found the trade-off they promised in the title. At least for most parts of such a trade-off curve it is impossible to make service improvements without adding cost. Even though the insights somehow follow general intuition of increasing marginal cost for any trade-off, it stands as an important finding.
But as usual with modelling, it is important what factors you consider and which you leave out. There very likely could be positive effects on costs if the customer reaction to varying service levels had been considered.

You also already realized that the service measure used is probably not perfect as well. Most companies I talked to are using a more dimensional measure, which includes the fact that also customers out of a specific range can still be serviced.

Furthermore the authors could have used specific methods for calculating the weights of the service consideration (eg. using AHP, analyzed here).

Reference:

Shen, Z., & Daskin, M. (2005). Trade-offs Between Customer Service and Cost in Integrated Supply Chain Design Manufacturing Service Operations Management, 7 (3), 188-207 DOI: 10.1287/msom.1050.0083

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