Knowledge that Transforms

The journal is pleased to publish the abstracts of the six finalists of the 2013 Manufacturing and Service Operations Management Society’s student paper competition. The 2013 prize committee was chaired by Felipe Caro (UCLA Anderson School of Management, University of California, Los Angeles), Guillaume Roels (UCLA Anderson School of Management, University of California, Los Angeles), and Gabriel Weintraub (Graduate School of Business, Columbia University). The other committee members were Philipp Afèche, Vishal Agrawal, Gad Allon, Aydın Alptekinoğlu, Atalay Atasu, Göker Aydın, Opher Baron, Achal Bassamboo, Damian Beil, Omar Besbes, Kostas Bimpikis, Robert Bray, René Caldentey, Carri Chan, Li Chen, Xin Chen, Ying-Ju Chen, Soo-Haeng Cho, Laurens Debo, Nicole DeHoratius, Sarang Deo, Lingxiu Dong, David Drake, Wedad Elmaghraby, Pnina Feldman, Qi Feng, Srinagesh Gavirneni, Karan Girotra, Ming Hu, Tim Huh, Dan Iancu, Foad Iravani, Srikanth Jagabathula, Ganesh Janakiraman, Diwas KC, Eda Kemahlıoğlu-Ziya, Saravanan Kesavan, Sang Kim, Mirko Kremer, Harish Krishnan, Mümin Kurtuluş, Guoming Lai, Jun Li, Ilan Lobel, Ruben Lobel, Elisa Long, Lauren Lu, Victor Martínez-de-Albéniz, Toni Moreno, Hamid Nazerzadeh, Rodney Parker, Ali Parlaktürk, Margaret Pierson, Ramandeep Randhawa, Paat Rusmevichientong, Soroush Saghafian, Denis Saure, Sergei Savin, Nicos Savva, Nicola Secomandi, Kevin Shang, Max Shen, Melvyn Sim, Amitabh Sinha, Larry Snyder, Greys Sošić, Brad Staats, Xuanming Su, Ravi Subramanian, Robert Swinney, Alireza Tahbaz-Salehi, Brian Tomlin, Senthil Veeraraghavan, Gustavo Vulcano, Wenqiang Xiao, Nan Yang, Fuqiang Zhang, Jiawei Zhang, Yao Zhao, Leon Zhu, and Serhan Ziya. The 2013 prize winners are as follows: Two First Prizes Song-Hee (Hailey) Kim, Columbia University “ICU Admission Control: An Empirical Study of Capacity Allocation and its Implication on Patient Outcomes” Xuan Wang, New York University “Process Flexibility: A Distribution-Free Bound on the Performance of k-Chain” Finalists Dragos Florin Ciocan, Massachusetts Institute of Technology “Model Predictive Control for Dynamic Resource Allocation” Nitish Jain, INSEAD “Managing Global Sourcing: Inventory Performance” Anyan Qi, University of Michigan “Capacity Investment with Demand Learning” John Yao, Columbia University “Optimal Price and Delay Differentiation in Queueing Systems”

Manufacturing & Service Operations Management (M&SOM) depends on the volunteer work of many professionals who take the time to provide careful and constructive reviews of the manuscripts submitted to the journal. In fact, for regular submissions and revisions that were submitted in 2013, M&SOM received 624 reviews from 425 individuals. Remarkably, 51% of those reviews were completed on or before their due date, a figure that increases to 58% if you allow a one-day grace period. Due in large part to the responsiveness of our reviewers, M&SOM made 91.9% of its 397 manuscript decisions for 2013 regular submissions and revisions within 75 days and 100% within 90 days. While we deeply appreciate all those who served as reviewers for the journal in 2013, 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 2013 Meritorious Service Award to….

Each year Manufacturing & Service Operations Management (M&SOM) selects one paper for its Best Paper Award. This paper is deemed most deserving for its contribution to the theory and practice of operations management. Nominations for this award will be accepted until March 30, 2014.

I is a pleasure to announce that the 2014 Manufacturing & Service Operations Management Best Paper Award goes to Philipp Afeche for “IncentiveCompatible Revenue Management in Queueing Systems: Optimal Strategic Delay” for its contribution to theory and practice of operations management. The author will receive a $2,000 prize, contributed by the MSOM Society of INFORMS. Each year, all papers published in the journal during the past three years (2011–2013) are eligible for the award, and judging is done through a two-stage process. First, M&SOM Editor Steve Graves and a group of M&SOM associate editors select a small set of finalists from the nominations received. Second, an ad hoc committee, organized by the immediate past president of the MSOM Society, selects the best paper from among the finalists. This year, the ad hoc committee consisted of Charles Corbett, Jeremie Gallien, Amy Ward, and MSOM pastpresident Beril Toktay. They selected the winning paper from a set of five outstanding finalists. The winning paper studies the revenue-maximizing price and lead-time menu and scheduling policy in a queueing system when customers are heterogeneous with respect to their valuations and time sensitivity, and their type is not observable to the firm. A similar problem has been studied from the social optimality perspective. The novelty of this paper stems from the fact that it studies the problem from the point of view of revenue maximization. This makes a fundamental difference. From the social optimality perspective, it makes sense to consider only work-conserving scheduling policies (i.e., the server does not idle in the presence of a queue). This is not the case when the objective is revenue maximization: The firm may want to “strategically delay” the low-priority customers to make sure that the time-sensitive customers choose the high-price, fast-service option. The paper establishes the “strategic delay” concept and develops interesting managerial insights. The following reviewer commentary reflects points made by many of the reviewers:

It is a pleasure to announce that the 2013 Manufacturing & Service Operations Management Best Paper Award goes to Omar Besbes, Robert Phillips, and Assaf Zeevi for “Testing the Validity of a Demand Model: An Operations Perspective” for its contribution to theory and practice of operations management. The three authors will share a $2,000 prize, contributed by the MSOM Society of INFORMS.

In this paper we describe a database of online airline prices collected from a major online travel agent and a low-cost carrier. The database provides detailed pricing data for all nonstop flights offered in a market. Data are provided for 42 domestic U.S. markets across a 28-day booking horizon for 21 departure dates. Each of the 42 markets is served by one or more low-cost carriers. These data can be used to investigate the evolution of prices and price dispersion for monopoly, duopoly, and oligopoly markets. The data can be used to create simulated data sets for benchmarking the performance of revenue management algorithms that consider competitors' prices. Analysis of the data may enable researchers to observe general patterns that would be useful to motivate research and/or teaching in revenue management. Data, as supplemental material, are available at http://dx.doi.org/10.1287/msom.2013.0466 .

We consider an inventory policy that expedites delivery times of open orders if the inventory level drops below a certain threshold. By expediting open orders, back orders can be reduced. Order expediting is costly, and we include various types of expediting costs in the model. We prove structural properties of the model and show how the optimal parameters of the expediting policy can be computed efficiently. The expediting policy is easy to implement and, for situations with variable expediting cost only, the structure of the policy is optimal. For situations with nonvariable expediting costs, the expediting policy that we consider is generally not optimal. The optimal policy can be computed by dynamic programming, but this approach is computationally feasible only for small-problem instances. We conduct numerical experiments that are based on data from the service division of a global equipment manufacturer to evaluate the performances of the expediting policy and the optimal policy. The results show that substantial cost savings can be achieved by order expediting and that the expediting policy realizes a great share of the cost-saving potential offered by order expediting.

We consider a two-echelon inventory system with a capacitated centralized production facility and several distribution centers (DCs). Both production and transportation times are stochastic with general distributions. Demand arrives at each DC according to an independent Poisson process and is backlogged if the DC is out of stock. We allow different holding and backlog costs at the different DCs. We assume that inventory at DCs is managed using the one-for-one replenishment policy. The main objective of this paper is to investigate the control of the multiechelon M/G/1 setting with general transportation times. To achieve this objective, we analyze several decentralized allocation policies including the first-come, first-served (FCFS), strict priority (SP), and multilevel rationing (MR) policies. For our analytic results, we assume no order crossing. We derive the cost function for a capacitated two-echelon inventory system with general transportation times under these policies. Our numerical examples show that the FCFS policy may outperform the MR policy, even though the latter has been shown to be better in the centralized setting. This suggests that in decentralized settings there is a need to focus on policies that prioritize customers when there is backlog. This focus is in contrast to the centralized settings, where inventory rationing policies that focus on prioritization when there is available inventory are effective. We therefore introduce and analyze the generalized multilevel rationing (GMR) priority policy. We compare the GMR policy with other policies and show that the GMR policy outperforms the three policies used in the centralized setting. We also compare the GMR policy with the myopic (T), longest queue first (LQF), and the optimal (when order crossing is allowed during the transportation time) policies. Our results show that when the uncertainty of the transportation times is low, the GMR policy outperforms the myopic (T) and LQF policies and that the gap between the optimal policy and the GMR policy is not high.

We consider service systems with a finite number of customer arrivals, where customer interarrival times and service times are both stochastic and heterogeneous. Applications of such systems are numerous and include systems where arrivals are driven by events or service completions in serial processes as well as systems where servers are subject to learning or fatigue. Using an embedded Markov chain approach, we characterize the waiting time distribution for each customer, from which we obtain various performance measures of interest, including the expected waiting time of a specific customer, the expected waiting time of an arbitrary customer, and the expected completion time of all customers. We carry out extensive numerical experiments to examine the effect of heterogeneity in interarrival and service times. In particular, we examine cases where interarrival and service times increase with each subsequent arrival or service completion, decrease, increase and then decrease, or decrease and then increase. We derive several managerial insights and discuss implications for settings where such features can be induced. We validate the numerical results using a fluid approximation that yields closed-form expressions.

This paper studies coordinated pricing and production decisions in an assemble-to-order system. We first show that unlike in make-to-stock systems, a state-dependent base-stock list-price policy is optimal. The optimal state-dependent base-stock levels and list prices may increase or decrease as demand backlogs increase, whereas demand backlogs always improve the optimal expected profit. Because the problem easily becomes intractable under general system settings, we next develop a simple heuristic policy. The heuristic policy decouples inventory replenishment, pricing, and component allocation decisions in a coordinated way. We provide a sufficient condition that ensures the optimality of the heuristic policy, and present a numerical study to demonstrate its performance when the condition is not met. The numerical study also shows how the performance of the heuristic policy is affected by various market and operational conditions, and by the structure of the assemble-to-order system. By focusing on the simple W-model, we show how the heuristic pricing decisions are made in response to changes in inventory levels and various cost parameters.