Improving Supply Chain Performance and Managing Risk Under Weather-Related Demand Uncertainty

Daniel Dumke — Wed, 09 Nov 2011 16:18:00 +0000

The demand of many products is connected to the weather patterns during and before the selling season. Ice cream can be best sold during warm summers, of course. But also other food products or clothes exhibit weather dependent demand pattern.

This article by Chen and Yano (2010) has a look at improving contracting between a manufacturer of a product with weather dependent demand and its retailer. One is for sure: uncertain demand causes negative effects for the whole supply chain, and should be handled as such. The full paper can be downloaded here.

Method

The author utilize the classic news vendor setting with one manufacturer (M) and one retailer (R) to focus on the analysis of their relationship.

M is set as the focal company. Due to several reasons (e.g. long lead times, seasonal products) M wants to offer weather related rebates for the retailers.

Model

The sales process is modeled in three steps:

M designs the contract and offers it to R

R decides on the ordering quantity

Depending on the weather and the quantity ordered, payouts are made from M to R

A weather rebate is an alternative to other supply contracts that manufacturers might use to induce retailers not just to order greater […] quantities, but also to order them well in advance of the selling season. Such inducements fall into two broad categories: (1) early-season incentives that reduce the retailer’s financial obligation for any given purchase or commitment level and (2) end-of-season concessions paid by manufacturers when demand is weak.

In this case the authors only have a look at the second type.

Without loss of generality, the authors focus on the temperature as weather index and design the payoff depicted in figure 1, where t is the measured temperature, t* is the “strike”-temperature and K is the payoff.

Figure 1: Suggested Contract for Weather Rebate (Chen and Yano, 2010)

Results

The results show how the effects of the demand risks for the manufacturer can be mitigated in such a setting:

Choice of strike temperature (t*) and rebate function
t* has an huge effect on the risk sharing between the two parties. So in combination with adjusting the payoffs K, it is possible to cater to different degrees of risk aversion between the manufacturer and the retailer

Additionally risks can be hedged by using weather derivatives
Tapping the growing market of derivative products on weather allows to offset the risks of such a contract by buying a corresponding weather certificate.

Conclusion

The authors suggest a very flexible contracting scheme to optimize the distribution of risks between a manufacturer and retailer.

[The authors also see] very significant side benefits that the weather rebate offers (e.g., no auditing of leftover inventory at the retailer that would be required in the case of buy-back contracts or markdown allowances).

But, on the other hand the authors admit that the difficulty of the negotiation process might be increased since many more parameters (rebate, weather indices,…) have to be agreed upon.

Reference:

Chen, F.Y., & Yano, C.A. (2010). Improving Supply Chain Performance and Managing Risk Under Weather-Related Demand Uncertainty Management Science, 56 (8), 1380-1397 DOI: 10.1287/mnsc.1100.1194

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mitigation

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weather

demand side

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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