Knowledge that Transforms

A regular feature of Manufacturing & Service Operation Management, “In This Issue” briefly describes each issue's articles and highlights their contributions.

A regular feature of Manufacturing & Service Operations Management, “In This Issue” briefly describes each issue's articles and highlights their contributions.

A regular feature of Manufacturing & Service Operation Management, “In this issue…” briefly describes each issue's articles and highlights their contributions.

A regular feature of Manufacturing & Service Operation Management, “In this issueℓ” briefly describes each issue's articles and highlights their contributions.

As is our tradition at the journal, we are pleased to publish the extended abstracts from the winners of the 2003 MSOM Society Student Paper Competition. We do this to celebrate the achievements of these young scholars and provide you with the opportunity to learn about their work in more detail. The 2003 prize committee was chaired by Professor Gerard Cachon from the University of Pennsylvania. The other committee members were: Dan Adelman (University of Chicago), Narendra Agrawal (Santa Clara University), Yossi Aviv (Washington University), Rene Caldentey (New York University), Wedad Elmaghraby (Georgia Institute of Technology), Noah Gans (University of Pennsylvania), Roman Kapuscinski (University of Michigan), Pinar Keskinocak (Georgia Institute of Technology), Constantinos Maglaras (Columbia University), Joe Mazzola (Georgetown University), Serguei Netessine (University of Pennsylvania), Michael Pinedo (New York University), Erica Plambeck (Stanford University), Nils Rudi (University of Rochester), Sergei Savin (Columbia University), Kevin Shang (Duke University), Terry Taylor (Columbia University), Christian Terwiesch (University of Pennsylvania), Brian Tomlin (University of North Carolina), Tunay Tunca (Stanford University).

We consider a simple game in which strategic agents select arrival times to a service facility. Agents find congestion costly and, hence, try to arrive when the system is underutilized. Working in discrete time, we characterize pure-strategy Nash equilibria for the case of ample service capacity. In this case, agents try to spread themselves out as much as possible and their self-interested actions will lead to a socially optimal outcome if all agents have the same well-behaved delay cost function. For even modest sized problems, the set of possible pure-strategy Nash equilibria is quite large, making implementation potentially cumbersome. We consequently examine mixed-strategy Nash equilibria and show that there is a unique symmetric Nash equilibrium. Not only is this equilibrium independent of the number of agents and their individual delay cost functions, the arrival pattern it generates approaches a discrete-time Poisson process as the number of agents and arrival points gets large. Our results extend to the case of time varying preferences. With an appropriate initialization, the results also extend to a system with limited capacity. Our model lends support to the traditional literature on managing service systems. This work has generally ignored customers strategically choosing arrival times. Rather it is commonly assumed that customers seek service according to some well-behaved process (e.g., that interarrival times follow a renewal process). We show that assuming Poisson arrivals is an acceptable assumption even with strategic customers if the population is large and the horizon is long.

Professional market advisory services provide specific advice (advisory programs) to grain producers on how to market their commodities, and assist them in their efforts to manage price risk. Previous studies analyzed the effectiveness of individual services and could find only weak evidence that these services help farmers improve their returns beyond market prices. The present study applies portfolio theory to determine an efficient combination of advisory programs using a nonlinear integer programming framework. The optimization results provide some evidence that an efficient portfolio provides significantly greater risk and return benefits compared to individual programs and external benchmarks. In a holdout period analysis, efficient portfolios have superior average return performance, but they fail to dominate the relevant benchmarks in terms of mean and variance. However, these out-of-sample results should be considered cautiously, given the small number of observations available.

We analyze planning and scheduling of multiproduct batch operations in the food-processing industry. Such operations are encountered in many applications including manufacturing of sorbitol, modified starches, and specialty sugars. Unlike discrete manufacturing, batch sizes in these operations cannot be set arbitrarily, but are often determined by equipment size. Multiple batches of the same product are often run sequentially in “campaigns” to minimize setup and quality costs. We consider a multiproduct, single-stage, single-equipment batch-processing scheme and address the problem of determining the timing and duration of product campaigns to minimize average setup, quality, and inventory holding costs over a horizon. We formulate the deterministic, static version of this problem over an infinite horizon. We show that, in general, a feasible, finite, cyclic solution may not exist. We provide sufficient conditions for the existence of a finite cycle, use single-product problems to provide lower bounds on the costs for the multiproduct problem, and use them to test heuristics developed for this problem. Next, we modify this formulation to incorporate fixed cycles that may be necessary due to factors such as product obsolescence, perishability, or contracts with customers. We do this by allowing for disposal of excess stock so that finite cycles are always feasible, though they might not be optimal; we also develop bounds and heuristic solution procedures for this case. These methods are applied to data from a leading food-processing company. Our results suggest that our methods could potentially reduce total annual costs by about 7.7% translating to an annual savings of around $7 million.

This paper studies service-delivery design in settings where firms engage in value-creation activities that have the objective of generating additional revenue from customer interactions. The paper provides a general modelling framework to analyze the ties between market segmentation decisions, incentives, and process performance in such service-delivery systems. The firm is modelled as a single-server queue, in a principal-agent framework. Customers have different value-generation potentials whose realizations are observed by the server but not by the manager of the firm. The manager determines a market segmentation scheme given an overall customer value-generation profile, which divides customers into two groups (high and low), and also determines a service level for each segment. The server decides which of the two available service levels (high and low) to provide for each customer, given a compensation scheme offered by the manager. The optimal market segmentation decision, optimal service-level choice, and a set of optimal linear incentive contracts that enable their implementation are characterized. The robustness of these strategies is explored with respect to model parameters and assumptions. It is shown that a market segmentation scheme that combines revenue generation concerns with their process implications is essential for success. Characteristics of appropriate incentive schemes are identified.

In this paper we extend forecast band evolution and capacitated production modelling to the multiperiod demand case. In this model, forecasts of discrete demand for any period are modelled as bands and defined by lower and upper bounds on demand, such that future forecasts lie within the current band. We develop heuristics that utilize knowledge of demand forecast evolution to make production decisions in capacitated production planning environments. In our computational study we explore the efficiency of our heuristics, and also explore the impact of seasonality on demand and availability of information updates.