Pricing and Design of After-Sales Service Contract: The Value of Mining Asymmetric Sales Cost Information

Yanfei Lan (), Zhibing Liu () and Baozhuang Niu
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Yanfei Lan: College of Management and Economics, Tianjin University, Tianjin 300072, P. R. China
Zhibing Liu: College of Mathematics and Physics, Huanggang Normal University, Hubei 438000, P. R. China

Asia-Pacific Journal of Operational Research (APJOR), 2017, vol. 34, issue 01, 1-25

Abstract: In this paper, we study a pricing and after-sales service contract design problem, where a retailer purchases products from a manufacturer and then sells to the consumers. The sales cost is the retailer’s private information and might be mined by the manufacturer via advanced learning algorithms and related big data techniques. We first develop a crisp equivalent model, based on which the optimal contracts and the supply chain parties’ profits under asymmetric information are derived. We show that, compared with the optimal wholesale price and after-sales service level with symmetric information, asymmetric cost information makes the wholesale price distorted upward when the retailer’s sales cost is low. When the retailer’s cost is high, the after-sales service level is distorted downward. We characterize the manufacturer’s loss and the retailer’s gains due to asymmetric sales cost information. This helps the manufacturer make the investment decision of big data techniques. Interestingly, we find that the retailer might voluntary disclose the sales cost information, which results in a win-win situation for the manufacturer and the retailer. This makes the manufacturer less favor big data techniques. Finally, we conduct extensive sensitivity analysis with respect to the retailer’s sales cost, the consumer’s sensitivity to the retailer’s after-sale service level, and the fraction of high-type retailers in the market.

Keywords: After-sales service contract; wholesale pricing; asymmetric sales cost information; big data techniques (search for similar items in EconPapers)
Date: 2017
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Citations: View citations in EconPapers (6)

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DOI: 10.1142/S0217595917400024

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