Mechanism Design for Multi-layer Supply Chains

Abstract

In this dissertation, we consider a three-layer supply chain with a manufacturer, a reseller, and a salesperson. In the manufacturer's target market, the sales outcome is jointly determined by the market condition and the salesperson's sales effort. While the salesperson can privately observe both, the manufacturer observes none. If the manufacturer contracts with the salesperson directly, it faces a mixture of the adverse selection and moral hazard problems. Because the reseller has a closer contact to the market and the salesperson, she can better monitor the two pieces of private information. Therefore, the manufacturer may delegate the sales responsibility to the reseller to indirectly mitigate the inefficiency caused by information asymmetry. As different resellers have different monitoring expertise, the manufacturer faces a supply chain construction problem by deciding which reseller to delegate to. Unlike traditional two-layer principal-agent problems in which the principal searches for the optimal direct monitoring functions, this dissertation studies the optimal strategy of indirect monitoring. Using the mechanism design approach and extending the traditional principal-agent model, we discuss the manufacturer's reseller selection strategy and the embedded salesforce compensation problem faced by the reseller. When the reseller can either observe the market condition or the sales effort, we show that the manufacturer should delegate to the latter one. We then study the reseller's resource allocation problem when the reseller can estimate the two aspects, both imperfectly, subject to a fixed budge constraint. When the reseller can only estimate the market condition, we show that the manufacturer's expected profit is convex on the reseller's accuracy. Collectively, our results deliver insights to manufacturers in selecting their reselling partners with various monitoring abilities and answers how the reseller's monitoring functions affect the supply chain performance

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