Please use this identifier to cite or link to this item:

Supply chain contracting and salesforce compensation under asymmetric information

Authors Yang, Ruina
Issue Date 2012
Summary This thesis consists of three supply chain and salesforce compensation problems with contracting issues under asymmetric information. The first problem is concerned with contracting involving two competing suppliers and one retailer. In stage 1, suppliers announce the contracts. The retailer, who has superior information, decides to sign contracts. The suppliers purchase raw materials. In stage 2, the retailer sets prices to optimize profit. We evaluate the performances of two-part tariff and quantity discount contracts when the two products are independent, which suggests the information rent is higher under quantity discounts than two-part tariffs. Additionally, we compare these two contracts in terms of supply chain profit, information rent and suppliers’ expected profits when the two products are imperfect substitutes. Both analytical results and numerical experiments are provided. The second problem is concerned with compensation scheme involving one firm and two competing salespersons. In stage 1, the firm announces the plans. The salespersons, who have superior information, simultaneously and independently decide which plans to sign. The firm determines production quantity and the salespersons make effort decisions. In stage 2, the sales and the payments are realized. We study a quota-based plan and investigate the impacts of the quota and the intensity of competition. The results suggest a higher quota is advantageous to the firm but disadvantageous to the salespersons. Furthermore, the firm’s expected profit and the salespersons’ profits may not decrease monotonically with the intensity of competition. The third problem is concerned with compensation plan in a firm-salesperson setting under asymmetric information. We employ a geometric approach to investigate the optimal scheme, using the notion of nonlinear plan. Numerical experiments show the optimal scheme performs better than the forecast-based plan, which is a special case of nonlinear plan.
Note Thesis (Ph.D.)--Hong Kong University of Science and Technology, 2012
Language English
Format Thesis