Pricing in Times of Disruption

Daniel Dumke — Wed, 10 Aug 2011 16:13:00 +0000

Many articles, including my own research show, that companies tend to focus largely on risk mitigation measures concerning the supply side. Only little is done to include demand side risks or demand side measures into the mitigation of supply chain risks. The study “Pricing During Disruptions: A Cause of the Reverse Bullwhip Effect” focusses on optimal pricing measures during a disruption. And so it helps to close the gap a little bit.

You can download a preprint of today’s paper at SSRN.

Reverse Bullwhip Effect

During a disruptions demand can change quickly. Due to a real or felt shortage customers are likely to order more than they actually need. This effect can be described by the Reverse Bullwhip Effect:

Whenever there is a perceived shortage of supply, it amplifies as it propagates down the supply chain. In fact, forward and reverse bullwhip effects often act as a system. If you start with a sudden upturn in demand, it gets amplified as it goes upstream, which creates a perceived shortage that amplifies as it propagates downstream. This creates a panic and downstream consumers overstate their demand, which amplifies again as it goes upstream. In turn, a greater scarcity is felt, and so on. Each of these effects feeds the other.

Method

The authors establish a mathematical model of a two tier supply chain, containing a single manufacturer (M) and a single (aggregated) demand (C). The process is established as follows:

Before the interaction, the manufacturer is hit by a disruption with a decreasing effect on capacity. The recovery takes place slowly over the course of several periods. The following steps are executed in each period:

M realizes its current capacity

M sets a price

C orders a specific number of products according to its predetermined behavior

The manufacturer has three price setting strategies at its disposal:

Naive: The price is set only based on the capacity and the long term demand curve of the customer, any short term behavior of the customer is ignored
One Period Correction: The deviation between the long term demand and the actual order of the customer is taken into account for the pricing decision.
Regression pricing: A regression analysis between set price and customer orders is included into the decision making process of the manufacturer.

The customer’s order behavior also includes historical prices. Two demand curves are specified, a long term demand curve, where the demand is linear in price, and a short run demand curve where the price change is included as well (figure 1).

Figure 1: Short Term Demand Behavior (Rong et al. 2009)

Results

The results show that the one period correction strategy results in a more volatile customer ordering process and lower revenues than both the naive and regression pricing strategies. Also in these terms the regression pricing strategy performs

worse than the naive strategy. The Reverse Bullwhip Effect is shown to occur in the disruption setting and almost always leads to reduced revenues.

Conclusion

From my point of view the study emphasizes four things:

All strategies involve active price changes by the company. Even though the naive strategy sounds very “lazy” it still involves using the available knowledge to optimize the profits of the manufacturer, including adapting prices.

The limited knowledge of the manufacturer is the major obstacle in the reduction of the Reverse Bullwhip Effect and improved revenues.

As for the Bullwhip Effect itself, the effects might be even worse with longer supply chains

An open question for me would also be how a more realistic (i.e. slower) price setting algorithm would affect the strategies.

Reference:

Rong, Y., Shen, Z.-J. M., & Snyder, L V. (2011). Pricing During Disruptions: A Cause of the Reverse Bullwhip Effect SSRN

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Tags:

demand

disruptions

mitigation

risk

demand side

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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