Supply-chain coordination under an inventory-level-dependent demand rate
ABSTRACT In this paper, we consider coordination issues of a distribution system composed of a manufacturer and a retailer. The manufacturer offers a single product to the retailer and the demand for the product at the retailer's end is stock dependent. We focus on three aspects of the resulting supply chain. First, we discuss the manufacturer-Stackelberg game structure to determine how the manufacturer sets the wholesale price of the product and how the retailer in turn determines the order quantity. We assume that both the parties share relevant cost information. Then we develop a simple profit-sharing mechanism that would ultimately achieve perfect channel coordination. Finally, the manufacturer is provided with a quantity discount scheme to induce the retailer to increase the order quantity so as to maximize the manufacturer's profit. We show that this discount scheme also achieves the perfect coordination of the whole channel. Numerical examples are used to illustrate the models.
- SourceAvailable from: Sujit Kumar De
Article: An EOQ model with backlogging[Show abstract] [Hide abstract]
ABSTRACT: This paper deals with a new approach of linguistic dichotomous fuzzy variables for a classical backordered EOQ (Economic Order Quantity) model with PE (Promotional Effort) and selling price dependent demand rate. In practice, we have observed that the demand rate during a shortage period decreases with time. Based on these assumptions, we have developed a cost minimization problem (a crisp model) by trading off setup cost, inventory cost, backordering cost and cost for promotional effort. Then, we have studied a fuzzy model by considering the coefficient vectors as pentagon fuzzy numbers associated with some co-ordinates. Defuzzification is made with the help of the center-of-gravity method followed by a ranking index and the Euclidean distance of the objective function. Considering a numerical example, phi- (ϕ-)coefficients have been computed for each method and a decision is made according to the natural characteristics of the decision variables. Finally, conclusions are drawn, explaining the justification of the model.International Journal of Management Science and Engineering Management. 01/2015;
- International Journal of Systems Science 12/2014; · 1.58 Impact Factor
- [Show abstract] [Hide abstract]
ABSTRACT: We consider the problem of pricing and alliance selection that a dominant retailer in a two-echelon supply chain decides when facing a potential upstream entry. The two-echelon supply chain consists of a dominant retailer, an incumbent supplier and an “incursive” vendor, where both the incumbent supplier and “incursive” vendor sell substitutable products to the common market through the dominant retailer. Our objective is to discuss whether the dominant retailer should sell the “incursive” vendor's products and, if so, how the dominant retailer strategically selects the alliance structure to maximize his/her own profit. We also present how all the members make their pricing decisions and analyze the impact of competitive intensity between two products on their pricing strategies after the entry of the vendor in possible alliance settings. Our results show that: (1) the introduction of the upstream vendor always benefits the retailer, and more interestingly, benefits the incumbent suppler in many cases, too; (2) in this paper, we define the competitive ability as the price dominance of one player over another when both are competing for the same customer market, if the price competition between the incumbent supplier and the “incursive” vendor is relatively fierce, the dominant retailer should ally with the one who has a relatively strong competitive ability rather than the other who has a relatively weak competitive ability; otherwise, he/she should ally with both upstream members. Finally, using numerical examples, we analyze the impact of different parameters and provide some management insights.European Journal of Operational Research 05/2015; 243(1). · 1.84 Impact Factor