Knowledge that Transforms

The journal is pleased to publish the abstracts of the six finalists of the 2006 Manufacturing and Service Operations Management Society’s student paper competition. The 2006 prize committee was chaired by Erica Plambeck (Stanford University). The other committee members were: Naren Agrawal (Santa Clara University), Aydin Alptekinoglu (University of Florida), Mor Armony (New York University),Yossi Aviv (Washington University in St. Louis), Yehuda Bassok (University of Southern California), Fernando Bernstein (Duke University), Damian Biel (University of Michigan), Kathryn Caggiano (University of Wisconsin), Felipe Caro (University of California, Los Angeles), Jiri Chod (Boston College), Francis de Vericourt (Duke University), Laurens Debo (Carnegie Mellon University), Nicole DeHoratius (University of Chicago), Vinayak Deshpande (Purdue University), Cheryl Druehl (University of Maryland), Don Eisenstein (University of Chicago), Feryal Erhun (Stanford University), Jan Fransoo (Eindhoven University ), Jeremie Gallien (Massachusetts Institute of Technology), Vishal Gaur (New York University), Manu Goyal (University of Maryland), Stephen Graves (Massachusetts Institute of Technology), Teck Ho (University of California, Berkeley), Xinxin Hu (Indiana University), Stelios Kavadias (Georgia Institute of Technology), Eda Kemahlioglu Ziya (University of North Carolina), Gurhan Kok (Duke University), Harish Krishnan (University of British Columbia), Martin Lariviere (Northwestern University), Retsef Levi (Massachusetts Institute of Technology), Christoph Loch (INSEAD), Víctor Martínez de Albéniz (Universidad de Navarra), Rodney Parker (Yale University), Georgia Perakis (Massachusetts Institute of Technology), Ioana Popescu (INSEAD), Justin Ren (Boston University), Guillaume Roels (University of California, Los Angeles), Glen Schmidt (University of Utah), Amar Sapra (University of Florida), Sergei Savin (Columbia University), Nicola Secomandi (Carnegie Mellon University), Kevin Shang (Duke University), Max Shen (University of California, Berkeley), Enno Siemsen (University of Illinois), Greys Sosic (University of Southern California), Richard Steinberg (Cambridge University), Xuanming Su (University of California, Berkeley), Ravi Subramanian (Georgia Institute of Technology), Julie Swann (Georgia Institute of Technology), Terry Taylor (Columbia University), Christian Terwiesch (University of Pennsylvania), Brian Tomlin (University of North Carolina), Tunay Tunca (Stanford University), Senthil Veeraraghavan (University of Pennsylvania), Owen Wu (University of Michigan), Xiaowei Xu (Rutgers University), Assaf Zeevi (Columbia University), Fuqiang Zhang (University of California, Irvine), Shaohui Zheng (Hong Kong Institute of Science and Technology). The 2006 prize winners are as follows: First Place Itay Gurvich, Columbia University “Cross-Selling in a Call Center with a Heterogeneous Customer Population” Second Place Sarang Deo, University of California, Los Angeles “Cournot Competition Under Yield Uncertainty: The Case of the U.S. Influenza Vaccine Market” Vivek Farias, Stanford University “Dynamic Pricing with a Prior on Market Response” Finalists Ozgun Caliskan Demirag, Georgia Institute of Technology “The Effects of Customer Rebates and Retailer Incentives on Manufacturer Profitability and Sales” Sang-Hyun Kim, University of Pennsylvania “Performance Contracting in After-Sales Service Supply Chains” Chris Lee, University of Pennsylvania “Optimal Initiation of the Dialysis Therapy”

Each year Manufacturing & Service Operations Management (M&SOM) formally recognizes outstanding service to the journal’s scholarly mission by granting the Meritorious Service Award to those individuals who have done a significant amount of high-quality service by consistently writing timely, unbiased and thoughtful reviews. While we deeply appreciate all those who served as reviewers for the journal in 2006, we especially recognize the following individuals for their outstanding contribution toward the success of M&SOM.

In the early 1990s, research began to show that the Japanese production theory, which espouses reduction of machine setup time as a sure way to improve production performance, may be limited. Specifically, it was found that reduction in mean setup times without any change in variance can, paradoxically, increase waiting time and work in process (WIP) in a cyclic production system. Setup time variance was demonstrated to play a central role because of the paradoxes it produced with the resulting harm to effective capacity. Subsequently, explicit formulas were derived for determining whether adding fixed forced idle time (but holding variance constant) would reduce waiting time and, if so, the optimal amount of idle time to add. However, research to date has offered little guidance to reduce setup time variance to improve waiting time. We show that a greater reduction is achievable by adding a variable idle time that is a nonincreasing function of setup time and thereby reduce the combined setup time variance. We provide explicit procedures for finding the optimal variable idle time as a function of setup time when the latter follows any finite discrete distribution. We also show how to implement our policy and show that our approach can improve waiting time even when other currently known approaches cannot.

This paper studies a supply chain with one supplier and multiple heterogeneous retailers where base-stock policies are practiced. The supplier's performance, in terms of optimal cost, optimal stocking level, and implied service level, is investigated under different retailer replenishment sequences. We show that under many realistic demand distributions, it is optimal to sequence retailers according to ascending variability. Furthermore, if the retailers vary in magnitudes of their orders, then the optimal sequence is usually the one with descending magnitudes.

The classical scheduling literature considers many problems where a given set of jobs must be processed at minimum cost, subject to various resource constraints. The literature only considers the issue of revenue generation in a very limited way, by allowing a job to remain unprocessed and its revenue contribution to be lost. By contrast, we consider three diverse practical situations where efficient scheduling affects revenue in much more general and realistic ways. First, we study two make-to-order environments where efficient scheduling increases customer goodwill, thus stimulating demand in different ways. Second, we study two make-to-stock environments where efficient scheduling creates inventory, thus also stimulating demand in different ways. Third, we study new product markets where efficient scheduling leads to a company becoming the first mover, and thus acquiring a larger market share. In each case, we provide both a computationally efficient algorithm for scheduling and a proof that a much more efficient algorithm is unlikely to exist. For both the make-to-stock and make-to-order problems, we also describe heuristic approaches that are easy to implement, and we study their average performance. The results show that substantial benefits arise from considering the implications of efficient scheduling for revenue and net profit. The practical impact of our work is to demonstrate the importance of efficient scheduling, not only in controlling cost, but also in increasing revenue and net profit.

In Gorman and Kanet (2005) we introduced the Author Affiliation Index (AAI) and how it could be used to rate the quality of operations management-related journals. Since then, Olson (2005) surveyed operations management professors from top U.S. universities and reported their assessment of the quality of various operations management-related journals. This paper compares the results of these two studies and concludes that patterns of research publications match the surveyed opinions from top U.S. universities, or that researchers publish in journals they rate as top. We confirm our earlier finding that the AAI is comparable to surveys for assessing journal quality.

For many companies, inventory record inaccuracy is a major obstacle to achieving operational excellence. In this paper, we consider an inventory system in which inventory records are inaccurate. The manager makes inventory inspection and replenishment decisions at the beginning of each period. There is a cost associated with each inspection. If an inspection is performed, inventory records are aligned with physical inventory. The objective is to develop a joint inspection and replenishment policy that minimizes total costs in a finite horizon. We prove that an inspection adjusted base-stock (IABS) policy is optimal for the single-period problem. In the finite-horizon problem, we show that the IABS policy is near optimal in a numerical study. Under this policy, the manager performs an inspection if the inventory recorded is less than a threshold level, and orders up to a base-stock level that depends on the number of periods since the last inspection. The prevalent approach to deal with inventory inaccuracy in practice is to implement cycle-count programs. Based on the structure of the IABS policy, we propose a new cycle-count policy with state-dependent base-stock levels (CCABS). We show that CCABS is almost as effective as the IABS policy. In addition, we provide guidelines for practitioners to design effective cycle-count programs by conducting sensitivity analyses on the IABS policy. Finally, by comparing the costs associated with these policies and several benchmark systems, we quantify the true value of accurate inventory information, which may be provided by radio-frequency identification (RFID) systems.

This paper studies a buyback contract in the Stackelberg framework of a manufacturer (leader) selling to a price-setting newsvendor retailer (follower). Using an analytical model that focuses on a multiplicative demand form, we generalize previous results and produce new structural insights. A novel transformation technique first enables us to establish the unimodality of the profit functions for both channel partners, under relatively mild assumptions. Further analysis identifies the necessary and sufficient condition under which the optimal contract for the manufacturer (wholesale and buyback prices) is distribution free, i.e., independent of the uncertainty in customer demand. A specific instance of the above condition is also necessary and sufficient for a no-buyback contract to be optimal from the manufacturer's perspective. We then prove that the optimal performance of the decentralized channel for distribution-free buyback contracts depends only on the curvature of the deterministic demand part. In addition, some of the optimal decisions and relevant profit ratios for buyback contracts in our setting are shown to be identical to those for their deterministic price-only counterparts.