Knowledge that Transforms

Manufacturing & Service Operations Management M&SOM) depends on the volunteer work of many professionals who take the time to provide careful reviews of the manuscripts submitted to the journal. In fact, in 2009 M&SOM received 553 reviews from 289 individuals. Remarkably, 58% of those reviews were submitted on or before their due date, a figure that increases to 67% if you allow a one-day grace period. Due in large part to the responsiveness of our reviewers, M&SOM made 96% of its 343 manuscript decisions within 75 days and 99% within 90 days. While we deeply appreciate all those who served as reviewers for the journal in 2009, some individuals have distinguished themselves by reviewing several manuscripts and with each manuscript by writing a fair, critical, and constructive review in a timely fashion. In recognition of their outstanding service provided to support the journal's scholarly mission, M&SOM grants the 2009 Meritorious Service Award to…

The journal is pleased to publish the abstracts of the six finalists of the 2020 Manufacturing and Service Operations Management Society’s student paper competition. The 2020 prize committee was chaired by Vishal Agrawal (Georgetown), Feryal Erhun (University of Cambridge), and Jun Li (University of Michigan). The judges were Adem Orsdemir, Antoine Desir, Anton Ovchinnikov, Anyan Qi, Arian Aflaki, Arzum Akkas, Ashish Kabra, Bin Hu, Bob Batt, Bora Keskin, Can Zhang, Carri Chan, Chloe Kim Glaeser, Daniel Lin, Eduard Calvo, Ekaterina Astashkina, Elena Belavina, Elodie Adida, Enis Kayış, Ersin Korpeoglu, Fabian Sting, Fang Liu, Fanyin Zheng, Fei Gao, Florin Ciocan, Gah-Yi Ban, Gizem Korpeoglu, Guihua Wang, Guillaume Roels, Guoming Lai, Hessam Bavafa, Hummy Song, Ioannis (Yannis) Stamatopoulos, Ioannis Bellos, Iris Wang, Itir Karaesmen, Jiankun Sun, Jiaru Bai, Jing Wu, Joann de Zegher, Joel Wooten, John Silberholz, Jose Guajardo, Kaitlin Daniels, Karen Zheng, Ken Moon, Kenan Arifoglu, Lennart Baardman, Leon Valdes, Lesley Meng, Linwei Xin, Luyi Gui, Luyi Yang, Mary Parkinson, Mazhar Arikan, Michael Freeman, Ming Hu, Morvarid Rahmani, Mumin Kurtulus, Nan Yang, Necati Tereyagoglu, Nektarios Oraiopoulos, Nikos Trichakis, Nil Karacaoglu, Nitin Bakshi, Niyazi Taneri, Nur Sunar, Olga Perdikaki, Ovunc Yilmaz, Ozan Candogan, Ozge Sahin, Panos Markou, Pascale Crama, Pengyi Shi, Pnina Feldman, Qiuping Yu, Renyu (Philip) Zhang, Robert Bray, Ruslan Momot, Ruxian Wang, Saed Alizamir, Safak Yucel, Samantha Keppler, Santiago Gallino, Serdar Simsek, Seyed Emadi, Shiliang (John) Cui, Shouqiang Wang, Simone Marinesi, So Yeon Chun, Song-Hee Kim, Soo-Haeng Cho, Soroush Saghafian, Stefanus Jasin, Suresh Muthulingam, Suvrat Dhanorkar, Tian Chan, Tim Kraft, Tom Tan, Tugce Martagan, Velibor Misic, Weiming Zhu, Xiaoshan Peng, Xiaoyang Long, Yasemin Limon, Yehua Wei, Yiangos Papanastasiou, Ying-Ju Chen, and Zumbul Atan.

In this paper, we investigate the optimality of myopic inventory replenishment policies in a periodic-review single-echelon system, with nonstationary, correlated, stochastic demand and cost, and nonincreasing stochastic prices. Using the single-unit decomposition approach, we provide certain general conditions on the demand and cost processes under which a myopic policy is optimal. Under these conditions, the optimal policy is a myopic state-dependent base-stock policy, which can be expressed in closed form as a base-probability policy. Specifically, the order associated with a given customer should be placed if and only if its arrival probability within the leadtime is higher than a threshold. Our results generalize earlier conditions for the optimality of myopic policies. Namely, we show that myopic policies can be optimal even when the demand is correlated or stochastically decreasing.

The literature on mass customization generally focuses on the tradeoff between higher revenues from better matching customer preferences with product specifications, and higher costs of offering a broader—possibly fully customized—product line. Less well understood is the tradeoff between the increased ability to precisely meet customer preferences and the increased leadtime from order placement to delivery often associated with customized products. In this paper, we use a locational customer choice model to formulate a firm's integrated product line design problem that involves variety, leadtime (or inventory), and pricing decisions. We propose a dynamic programming based solution procedure that amounts to solving a shortest path problem on an acyclic network, and derive some structural results on the optimal product line design. We find that unimodal preferences generally result in hybrid product lines, with standard products clustering around the mode and custom products covering the tails, in contrast with the all-custom or all-standard product lines that are optimal under uniform preferences. We also numerically examine how the firm should adjust its leadtime and variety in response to changes in parameters such as customer dispersion and operational scale. We find that the tradeoff between leadtime and variety is sometimes nonintuitive and complex.

This paper analyzes the trade-off between (demand) substitution costs and (production) changeover costs in a discrete-time production-inventory setting using a two-product dynamic lot-sizing model with changeover, inventory carrying, and substitution costs. We first show that the problem is polynomially solvable and then develop several insights into the behavior of such systems and identify strategies for effectively managing them. A key driver for the extent of substitution is the ratio of changeover cost to the substitution cost associated with mean demand. The interaction between changeovers and substitution is most prominent when this ratio is neither too high nor too low. Furthermore, the value of this ratio also influences the length of an appropriate rolling horizon; an increase in the value of the ratio signals an increase in the length of a near-optimal rolling horizon. We identify a complementary relationship between substitution and changeover costs: When the changeover cost is large, it is better to invest in reducing the substitution cost and vice versa. As the holding cost of the substitutable product increases, substitution is (respectively, changeovers are) utilized more when the changeover (respectively, substitution) cost is large.

This paper studies the problem of purchasing and allocating copies of movies to multiple stores of a movie rental chain. A unique characteristic of this problem is the return process of rented movies. We formulate this problem for new movies as a newsvendor-like problem with multiple rental opportunities for each copy. We provide demand and return forecasts at the store-day level based on comparable movies. We estimate the parameters of various demand and return models using an iterative maximum-likelihood estimation and Bayesian estimation via Markov chain Monte Carlo simulation. Test results on data from a large movie rental firm reveal systematic underbuying of movies purchased through revenue-sharing contracts and overbuying of movies purchased through standard (nonrevenue-sharing) ones. For the movies considered, our model estimates an increase in the average profit per title for new movies by 15.5% and 2.5% for revenue sharing and standard titles, respectively. We discuss the implications of revenue sharing on the profitability of the rental firm.

Dell's supply chain for desktops involves Asian vendors shipping components by sea to several U.S. plants. Although suppliers are responsible for shipping enough inventory to meet total needs across all production sites, Dell can reroute and expedite their shipments while in transit, and also transfer on-hand inventory in order to balance supply across sites. This paper describes the development, implementation, and impact of the process and optimization-based control system now used by Dell to address this supply-routing challenge for its U.S.-bound monitors. In a first phase, Dell created a new job definition focused solely on supply routing and implemented a supporting visualization tool. In a second phase, a decision support system relying on a mixed-integer programming formulation was implemented, overcoming two main challenges: (i) the estimation of shortages as a function of expected inventory, accounting for actual forecast quality; and (ii) the estimation of a meaningful shortage cost. This new methodology is estimated to have reduced Dell's inventory-repositioning costs for monitors by about 60%.

We consider the operational scheduling or dispatching problem of assigning servicemen to service requests that arrive in real time. The objective is to optimize responsiveness, i.e., to minimize waiting in excess of a promised response time. We study how responsiveness is influenced by modeling decisions and solution methods that arise when solving the dynamic problem by repeatedly solving real-time problems. Most results are derived using a set-partitioning based solution approach, which is shown to perform best among considered alternatives. Our research is based on a large-scale real-life application regarding roadside service assistance.