Vendor managed inventory models for items with perfect and imperfect quality

Abstract

Supply chain management (SCM) has been considered as a competitive strategy for integrating suppliers and customers with the objective of improving responsiveness and flexibility of manufacturing organizations. A key issue in SCM is how to entice all parties to cooperate and coordinate their decisions and activities in order to optimize the chain-wide performance. Various types of coordination mechanisms exist in practice such as vendor managed inventory, quantity discount, credit option, etc. In recent years, vendor managed inventory (VMI) has been recognized as one of the most successful practices that encourages coordination among different entities in supply chains. In fact, VMI was popularized after the successful partnership between Wal-Mart and Proctor & Gamble ( P&G) in 1985. Since then, other companies such as Shell Chemical, HP, Campbell Soup, and Johnson & Johnson have adopted the same approach. The benefits of implementing VMI are very significant and can be summarized as a reduced inventory costs and a better response to market changes which in turn improves the customer service levels. In VMI literature two issues are overlooked; the first of which is the contract between the vendor and retailer and the second one is the quality of items received by the vendor. The objective of this thesis is to develop six VMI models for a two layer supply chain. The first two models are related to a single-retailer and a multi-retailer case. The third and fourth models extend the first and the second models respectively to the situation where the items are of imperfect quality. The last two models generalize the third and forth by considering a finite screening rate. Decentralized and centralized scenarios are considered for each of the six models. Moreover, the surplus in profit generated by centralization is distributed among the vendor and retailers according to a weighting scheme such that no party gains less profit than that generated by decentralized VMI model. Also, optimal solutions are characterized by finding all Karush-Kuhn-Tucker (KKT) points of the profit functions. Based on these KKT points, algorithms are devised to solve the models. Moreover, sensitivity analysis is conducted to show the effect of key parameters of the models on the optimal solution.