Name
Abstract | ||
|---|---|---|
When supply disruptions occur, firms want to employ an effective pricing strategy to reduce losses. However, firms typically do not know precisely how customers will react to price changes in the short term, during a disruption. In this article, we investigate the customer's order variability and the firm's profit under several representative heuristic pricing strategies, including no change at all (fixed pricing strategy), changing the price only (naive pricing strategy), and adjusting the belief and price simultaneously (one-period correction [1PC] and regression pricing strategies). We show that the fixed pricing strategy creates the most stable customer order process, but it brings lower profit than the naive pricing strategy in most cases. The 1PC pricing strategy produces a more volatile customer order process and smaller profit than the naive one does. Although the regression pricing strategy is a more advanced approach, it leads to lower profit and greater customer order variability than the naive pricing strategy (but the opposite when compared to the 1PC strategy). We conclude that (i) completely eliminating the customer order variability by employing a fixed pricing strategy is not advisable and adjusting the price to match supply with demand is necessary to improve the profit; (ii) frequently adjusting the belief about customer behaviors under imperfect information may increase the customer's order variability and reduce the firm's profit. The conclusions are robust to the inventory assumption (i.e., without or with inventory carryover) and the firm's objective (i.e., market clearance or profit maximization). |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1111/deci.12494 | DECISION SCIENCES |
Keywords | DocType | Volume |
Bullwhip effect, Modeling error, Order variability, Pandemic, Supply uncertainty | Journal | 53 |
Issue | ISSN | Citations |
4 | 0011-7315 | 0 |
PageRank | References | Authors |
0.34 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xiaojing Feng | 1 | 0 | 0.34 |
| Ying Rong | 2 | 101 | 8.03 |
| Zuo-Jun Max Shen | 3 | 479 | 34.75 |
| Lawrence V. Snyder | 4 | 448 | 31.03 |