Knowledge that Transforms

We develop an analytical framework for studying the role capacity costs play in shaping the optimal differentiation strategy in terms of prices, delivery times, and delivery reliabilities of a profit‐maximizing firm selling two variants (express and regular) of a product in a capacitated environment. We first investigate three special cases. The first is an existing model of price and delivery time differentiation with exogenous reliabilities, which we only review. The second focuses on time‐based (i.e., length and reliability) differentiation with exogenous prices. The third deals with deciding on all features for an express variant when a regular product already exists in the marketplace. We subsequently address the integrative framework of time‐ and‐price‐based differentiation for both products in a numerical study. Our results shed light on the role that customer preferences towards delivery times, reliabilities and prices, and the capacity costs (absolute and relative) have on the firm's optimal product positioning policy.

We study conflict and cooperation issues arising in a supply chain where a manufacturer makes products which are shipped to customers by a distributor. The manufacturer and the distributor each has an ideal schedule, determined by cost and capacity considerations. However, these two schedules are in general not well coordinated, which leads to poor overall performance. In this context, we study two practical problems. In both problems, the manufacturer focuses on minimizing unproductive time. The distributor minimizes customer cost measures in the first problem and minimizes inventory holding cost in the second problem. We first evaluate each party's conflict, which is the relative increase in cost that results from using the other party's optimal schedule. Since this conflict is often significant, we consider several practical scenarios about the level of cooperation between the manufacturer and the distributor. These scenarios define various scheduling problems for the manufacturer, the distributor, and the overall system. For each of these scheduling problems, we provide an algorithm. We demonstrate that the cost saving provided by cooperation between the decision makers is usually significant. Finally, we discuss the implications of our work for how manufacturers and distributors negotiate, coordinate, and implement their supply chain schedules in practice.

We address the problem of determining the optimal retailer order quantities from a manufacturer who makes new products in conjunction with ordering remanufactured products from a remanufacturer using used and unsold products from the previous product generation. Specifically, we determine the optimal order quantity by the retailer for four systems of decision‐making: (a) the three firms make their decisions in a coordinated fashion, (b) the retailer acts independently while the manufacturer and remanufacturer coordinate their decisions, (c) the remanufacturer acts independently while the retailer and manufacturer coordinate their decisions, and (d) all three firms act independently. We model the four options described above as centralized or decentralized decision‐making systems with the manufacturer being the Stackelberg leader and provide insights into the optimal order quantities. Coordination mechanisms are then provided which enable the different players to achieve jointly the equivalent profits in a coordinated channel.

This paper proposes simple methods for staffing a single‐class call center with uncertain arrival rate and uncertain staffing due to employee absenteeism. The arrival rate and the proportion of servers present are treated as random variables. The basic model is a multi‐server queue with customer abandonment, allowing non‐exponential service‐time and time‐to‐abandon distributions. The goal is to maximize the expected net return, given throughput benefit and server, customer‐abandonment and customer‐waiting costs, but attention is also given to the standard deviation of the return. The approach is to approximate the performance and the net return, conditional on the random model‐parameter vector, and then uncondition to get the desired results. Two recently‐developed approximations are used for the conditional performance measures: first, a deterministic fluid approximation and, second, a numerical algorithm based on a purely Markovian birth‐and‐death model, having state‐dependent death rates.