Effects of promotion cost sharing policy with the sales learning curve on supply chain coordination.
ABSTRACT Promotional activity has become increasingly more common in the service industry. This research considers a two-echelon multiple-retailer distribution channel under retailers’ promotional efforts and the sales learning curve. The competition between retailers arises from substitution effect due to shortages. This paper incorporates the idea of the sales learning curve into the promotion cost. The objective is to solve the retailers’ promotion and replenishment decisions under retailer competition and promotional effort with the sales learning curve. The current study considers promotion cost sharing as a mechanism to achieve coordination. The model shows that keeping the fractions of promotion cost sharing within an appropriate range increases profits for all parties. This work also discusses how retailer competition and the sales learning curve affect channel decisions and profits. Concepts from retailer competition and the sales learning curve, along with numerical studies on a few interesting cases help deliver several important managerial insights. These results should be a useful reference for managerial decisions and administrations.
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ABSTRACT: This paper investigates the issue of channel coordination for a two-stage supply chain with one retailer and one manufacturer. The demand is influenced by the retailer's sales effort and manufacturer's quality improvement efforts. We found that using the traditional two-part tariff contract alone cannot coordinate the supply chain well. Joining the two-part tariff contract with the quality effort cost sharing model remains ineffective in managing the two-stage supply chain. To effectively coordinate the channel members, we propose an innovative supply chain contract that integrates the endeavors of the manufacturer and the retailer. We identify the optimal level of retail sales effort, optimal level of quality-improvement effort and optimal supply chain profit. Sensitivity analyses are conducted to examine the impacts of changes in the costs of sales effort and quality effort on the performance of the supply chain.International Journal of Production Economics 12/2013; 146(2):745–755. DOI:10.1016/j.ijpe.2013.09.004 · 2.08 Impact Factor
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ABSTRACT: In this study, we first investigate the impact policy makers have when they set a threshold value of energy saving levels. We examine the impact on energy saving level and price of environmentally friendly products (EFP) decided by green supply chains within two different structures, i.e. vertical integration and a decentralized setting. Then, considering the tradeoff between energy savings and profits made by supply chains, we analyze decisions of the policy makers. In addition, we investigate the coordination of a supply chain by using the common wholesale pricing and profit sharing (WPPS) schemes and a lump sum transfer contract. A numerical example is used to illustrate the related issues. Observations are made, and managerial insights are indicated for the policy maker in setting threshold values of energy saving levels.Computers & Operations Research 12/2013; DOI:10.1016/j.cor.2013.11.020 · 1.72 Impact Factor
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ABSTRACT: This article deals with a decentralised service chain consisting of a service provider and a facility owner. The revenue allocation and service price are, respectively, determined by the service provider and the facility owner in a non-cooperative manner. To model this decentralised operation, a Stackelberg game between the two parties is formulated. In the mathematical framework, the service system is assumed to be driven by Poisson customer arrivals and exponential service times. The most common log-linear service demand and Weibull facility lifetime are also adopted. Under these analytical conditions, the decentralised decisions in this game are investigated and then a unique optimal equilibrium is derived. Finally, a coordination mechanism is proposed to improve the efficiency of this decentralised system.International Journal of Systems Science 01/2012; 44(10):1-12. DOI:10.1080/00207721.2012.670295 · 1.58 Impact Factor