Knowledge that Transforms

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 Operations 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 Operations Management, “In This Issue” 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 2004 MSOM 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 2004 prize committee was chaired by Professor Phil Kaminsky (University of California, Berkeley). The other committee members were: Naren Agrawal (University of Santa Clara), Hyun-Soo Ahn (University of Michigan), Damian Beil (University of Michigan), Fernando Bernstein (Duke University), Izak Duenyas (University of Michigan), Wedad Elmaghraby (Georgia Institute of Technology), Jeremie Gallien (Massachusetts Institute of Technology), Teck Ho (University of Pennsylvania), Seyed Iravani (Northwestern University), Ananth Iyer (Purdue University), Eric Johnson (Dartmouth College), Roman Kapuscinski (University of Michigan), Pinar Keskinocak (Georgia Institute of Technology), Anton Kleywegt (Georgia Institute of Technology), Özalp Özer (Stanford University), Georgia Perakis (Massachusetts Institute of Technology), Alan Scheller-Wolf (Carnegie Mellon University), Sridhar Seshadri (New York University), Max Shen (University of California, Berkeley), David Simchi-Levi (Massachusetts Institute of Technology), Jay Swaminathan (University of North Carolina), Terry Taylor (Columbia University), Beril Toktay (INSEAD), Scott Webster (Syracuse University), and David Wu (Lehigh University).

In this article, we analyze a decentralized supply chain consisting of a supplier and two independent retailers. In each order cycle, retailers place their orders at the supplier to minimize inventory-related expected costs at the end of their respective response times. There are two types of lead times involved. At the end of the supplier lead time, retailers are given an opportunity to readjust their initial orders (without changing the total order size), so that both retailers can improve their expected costs at the end of respective retailer lead times (the time it takes for items to be shipped from the supplier to the retailers). Because of the possibility of cooperation at the end of supplier lead time, each retailer will consider the other’s order-up-to level in making the ordering decision. Under mild conditions, we prove the existence of a unique Nash equilibrium for the retailer order-up-to levels, and show that they can be obtained by solving a set of newsboy-like equations. We also present computational analysis that provides valuable managerial insight for design and operation of decentralized systems under the possibility of partial cooperation.

We develop a model and analyze reverse information sharing, a growing business practice in supply chain management in which a manufacturer shares information about supply with a retailer. We model the manufacturer as a production queue with finished goods warehouse, the retailer as an inventory location, and other customers as an external demand stream. In our model, the manufacturer allows the retailer access to inventory status at the warehouse. To take advantage of this new information, the retailer changes from a single-level base-stock policy to a two-level, state-dependent base-stock policy. We provide an exact method for computing performance and develop a procedure for evaluating optimal policy. We demonstrate the impact of the new policy on the manufacturer and other customers. Numerical computations lead to insights about the value of information to the retailer, and to guidelines for the manufacturer on sharing information.

This paper provides a model of the competitive newsvendor problem in which there is price competition following the inventory decisions. Using the biform game formalism of Brandenburger and Stuart (2004), the price competition is modeled by considering the core of the induced cooperative game. Such an analysis allows price competition to be modeled without a priori assumptions about price-setting power or pricing procedures. The paper shows that with no uncertainty, the inventory decision is equivalent to the capacity decision in Cournot competition. With uncertainty, the analysis again reduces to Cournot competition if the demand uncertainty is characterized by an appropriately constructed, expected demand curve. The results highlight the critical role of the fixed-price assumption in newsvendor models.

One way to organize workers that lies between traditional assembly lines, where workers are specialists, and craft assembly, where workers are generalists, are “bucket brigades.” We describe how one firm used bucket brigades as an intermediate strategy to migrate from craft assembly to assembly lines. The adoption of bucket brigades led to a narrowing of tasks for each worker and thus accelerated learning. The increased production more than compensated for the time lost when workers walk back to get more work, which was significant in this implementation. To understand the trade-offs in migrating from craft to assembly lines, we extend the standard model of bucket brigades to capture hand-off and walk-back times.

Motivated by the incentive programs that have been offered by energy companies under tight market conditions in the past few years, we consider a production control problem in which time alternates randomly between peak and nonpeak periods. During peak periods, the energy supplier offers the manufacturing firm—the energy user—an incentive program to reduce its energy usage by shutting down its production facility. Participation in the incentive program, however, is totally voluntary, and the user firm is rewarded for each unit of time that it participates in the program. We consider two problems that face the manufacturing firm. The first is whether it is worth shutting down production to participate in the incentive program when it is offered, and in which part (portion) of the peak period the firm should participate. The second problem is how the firm should decide on its production policy in both the peak and nonpeak periods in the presence of such an incentive program. In this paper, we provide simple models to give insight into the nature of these problems. Two cases are studied. In the first case, the peak duration is assumed to be exponentially distributed, and in the second, its length becomes known at the beginning of a peak period. In both cases, the occurrence times of the peak periods are uncertain. We characterize the optimal production and shutdown policy for both the peak and nonpeak periods. We also study the effect of seasonality on the optimal control policies.