Pricing during Disruptions: A Cause of the Reverse Bullwhip Effect
Shanghai Jiao Tong University (SJTU) - Antai College of Economics and Management
Zuo-Jun Max Shen
University of California, Berkeley - Department of Industrial Engineering & Operations Research (IEOR)
Lawrence V. Snyder
Lehigh University - Department of Industrial and Systems Engineering
April 7, 2009
When supply disruptions occur, firms want to employ an effective pricing strategy to reduce losses. However, firms typically don't know precisely how customers will react to price changes in the short term, during a disruption. In this paper, we investigate three different pricing strategies, which we call naive, one-period correction (1PC), and regression, each of which makes progressively more sophisticated assumptions about customer behavior. We prove that strategies that appear to be more sophisticated may in fact lead to reduced profits and greater volatility. In particular, the 1PC pricing strategy produces a more volatile demand process and smaller revenue than the naive one does. Moreover, when customer behavior is sufficiently strategic, the customer order process under the 1PC pricing strategy is more volatile than the capacity process, a phenomenon known as the reverse bullwhip effect (RBWE). As supply disruptions become longer or more severe, the magnitude of the variability difference between the customer's orders and the capacity increases under the 1PC strategy but decreases under the naive one, while the firm's revenue decreases under both strategies. Furthermore, although the regression pricing strategy is a more advanced approach, it leads to smaller profit and greater customer's order variability than the naive pricing strategy (but the opposite when compared to the 1PC strategy). We conclude that the naive pricing strategy is superior to either of the more sophisticated ones, both in terms of the firm's profit and the magnitude of the customer's order variability in the supply chain as a whole.
Number of Pages in PDF File: 39
Keywords: bullwhip effect, reverse bullwhip effect, supply uncertainty, modeling error
Date posted: April 8, 2009
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