Knowledge that Transforms

The journal is pleased to publish the abstracts of the six finalists of the 2015 Manufacturing and Service Operations Management Society’s student paper competition. The 2015 prize committee was chaired by Goker Aydin (Kelley School of Business, Indiana University), Karan Girotra (INSEAD) and Sameer Hasija (INSEAD). The other committee members were: Philipp Afeche, Vishal Agrawal, Aydin Alptekinoglu, Atalay Atasu, Opher Baron, Bob Batt, Omar Besbes, Kostas Bimpikis, Robert Bray, Rene Caldentey, Andre Calmon, Carri Chan, Xin Chen, Ying-Ju Chen, Soo-Haeng Cho, So Yeon Chun, Florin Ciocan, Nicole DeHoratius, Sarang Deo, Lingxiu Dong, Pnina Feldman, Santiago Gallino, Srinagesh Gavirneni, Itai Gurvich, Jonathan Helm, Ming Hu, Dan Iancu, Foad Iravani, Srikanth Jagabathula, Fikri Karaesmen, Diwas Kc, Saravanan Kesavan, Bora Keskin, Sang Kim, Song-Hee Kim, Mirko Kremer, Harish Krishnan, Mumin Kurtulus, Guoming Lai, Cuihong Li, Jun Li, Ilan Lobel, Ruben Lobel, Lauren Lu, Alex Mills, Toni Moreno, Anton Ovchinnikov, Rodney Parker, Ali Parlakturk, Alfonso Pedraza Martinez, Ramandeep Randhawa, Paat Rusmevichientong, Soroush Saghafian, Ozge Sahin, Burhaneddin Sandikci, Nicola Secomandi, Melvyn Sim, Amitabh Sinha, Milind Sohoni, Greys Sosic, Brad Staats, Robert Swinney, Alireza Tahbaz-Salehi, Gustavo Vulcano, Gabriel Weintraub, Owen Wu, Wenqiang Xiao, Nan Yang, Zhibin Yang, Fuqiang Zhang, Jiawei Zhang, Yao Zhao, Karen Zheng, and Leon Zhu. The 2015 prize winners are as follows: First Prize: Bike-Share Systems: Accessibility and Availability Ashish Kabra, INSEAD Second Prize: Procurement Mechanisms for Differentiated Products Daniela Saban, Stanford University Finalists (in alphabetical order according to the author’s last name): Online and Offline Information for Omnichannel Retailing Fei Gao, University of Pennsylvania Optimal Purification Decisions for Engineer-to-Order Proteins Tuğçe Martağan, Eindhoven University of Technology Public Relative Performance Feedback in Complex Service Systems: Improving Productivity through the Adoption of Best Practices Hummy Song, Harvard University Impact of Electricity Pricing Policies on Renewable Energy Investments and Carbon Emissions Şafak Yücel, Duke University

The continued success of Manufacturing & Service Operations Management (M&SOM) depends on the volunteer work of many professionals who take their precious time to provide careful and constructive reviews of the manuscripts submitted to the journal in a timely manner. On behalf of M&SOM, Editor-in-Chief Christopher Tang would like to express his deepest gratitude to all those who served as reviewers for the journal in 2015. Among all reviewers, 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 2015 Meritorious Service Award to….

For many early-stage entrepreneurs, hiring the first employee is a critical step in the firm’s growth. Doing so often requires significant time and monetary investments. To understand the trade-offs involved in deciding when to hire the first employee and how hiring differs in entrepreneurial settings from more established firm settings, we present a simple growth model that depends on two critical inputs for revenue generation: the entrepreneur’s time and money. We show that without hiring, the entrepreneur’s time eventually becomes more valuable than money in contributing to the firm’s growth. In that context, the value of the employee is driven by how much relief he provides to the entrepreneur. We characterize the optimal timing of hiring in terms of the firm’s cash position and how the firm is affected if it requires an upfront fixed investment in time and/or money. We find that the upfront investment in time needed for hiring cannot be converted to an equivalent upfront investment in money and that mistiming hiring can be very costly, especially when these upfront investments are high.

To set inventory service levels, suppliers must understand how changes in inventory service level affect demand. We build on prior research, which uses analytical models and laboratory experiments to study the impact of a supplier’s service level on demand from retailers, by testing this relationship in the field. We analyze a field experiment at the supplier Hugo Boss to determine how the supplier’s inventory service level affects demand from its retailer customers. We find increases in historical fill rate to be associated with statistically significant and managerially substantial increases in current retailer orders (i.e., demand, not just sales). Specifically, a one percentage point increase in fill rate, measured over the prior year, is associated with a statistically significant 11% increase in current retailer demand, controlling for other factors that might affect retailer demand. We explore the drivers of this demand increase, including changes in retailer assortment and order frequency. We discuss features of a retail buyer’s decision context identified through our field work that may explain the magnitude of the relationship we observe.

We study a new dynamic order acceptance and assignment problem of a make-to-order manufacturing or service system that produces heterogeneous products or services using heterogeneous servers. Unlike traditional dynamic order acceptance and assignment problems, in our settings the system can strategically postpone acceptance and assignment decisions for orders in hand. The system does this while waiting for more profitable orders to come and/or more productive servers to become available, with the risk of losing orders in hand, low server utilization, and high waiting penalties. We formulate this problem as a stochastic dynamic program, characterize the structure of the optimal policy, and discuss managerial insights gained from the optimal policy. The paper also provides a methodological contribution by finding general structural properties and their preservation conditions, which can be reused in related future operations management studies.

We study a periodically reviewed multiechelon serial inventory system with a capacity constraint on the order quantity at each stage. The cost criterion we use to evaluate inventory policies for this system is the sum of the expected long-run average holding and shortage costs. It is well known that for this problem, characterizing the structure of the optimal policy and computing it are very difficult. We consider the use of echelon base-stock policies for our system (even though they are known to be suboptimal) and propose algorithms for finding base-stock levels that are easy to understand and implement. We derive bounds on the ratios between the costs achieved by our algorithms and the optimal costs (over all policies). For light-tailed demand distributions, our algorithms are shown to be asymptotically optimal in the sense that our bounds are close to one in high service-level environments. Our computational investigations reveal that our algorithms perform well even under modest service levels.

Motivated by innovative rental business models, we study a rental system with random loss of inventory due to use. We utilize a discrete-time model in which the inventory level is chosen before the start of a finite rental season, and customers not immediately served are lost. Demand, rental durations, and rental unit lifetimes are stochastic; sample path coupling allows us to derive structural results that hold under limited distributional assumptions. Considering different “recirculation” rules (i.e., which unit to select to meet a demand), we prove the concavity of the expected profit function and identify the optimal recirculation rule under two different models of a rental unit’s state: the number of times rented out or its condition. We develop two upper bounds on the number of lost rental units and two heuristics for the inventory decision. Numerical study shows the following: (1) accounting for rental unit loss can increase the expected profit by 7% for a single season; (2) the optimal inventory level in response to increasing loss probability is nonmonotonic; (3) both heuristics perform well; and (4) choosing the optimal recirculation rule over a commonly used policy can increase the profit-maximizing service level by up to six percentage points.

We examine a risk-averse distributor’s decision in selecting between bottled wine and wine futures under weather and market uncertainty. At the beginning of every summer, a fine wine distributor has to choose between purchasing bottled wine made from the harvest collected two years ago and wine futures of wine still aging in the barrel from the previous year’s harvest. At the end of the summer, after seeing weather and market fluctuations, the distributor can adjust its allocation by trading futures and bottles. This paper makes three contributions. First, we develop an analytical model to determine the optimal selection of bottled wine and wine futures under weather and market uncertainty. Our model is built on an empirical foundation in which the functional forms describing the evolution of futures and bottle prices are derived from comprehensive data associated with the most influential Bordeaux winemakers. Second, we develop structural properties of optimal decisions. We show that a wine distributor should always invest in wine futures because it increases the expected profit in spite of being a riskier asset than bottled wine. We characterize the influence of variation in various uncertainties in the problem. Third, our study empirically demonstrates for a large distributor the financial benefits of using our model. The hypothetical average profit improvement in our numerical analysis is significant, exceeding 21%, and its value becomes higher under risk aversion. The analysis is beneficial for fine wine distributors, as it provides insights into how to improve their selection in order to make financially healthier allocations.

For many entrepreneurs, time is a key constraint. They need to invest time to achieve growth, but also lose time because of recurring crises. We develop a simple stochastic dynamic program to model how an entrepreneur should prioritize between improving processes to reduce crises versus harvesting revenue or ensuring future growth. We show that it is initially optimal to prioritize process improvement: an entrepreneur should strive for high process quality early in the venture’s growth process. We numerically analyze a simple heuristic derived from this optimal policy and identify the conditions under which it is (or is not) effective. It performs near optimally except when process quality or revenue rate may deteriorate too fast or when the cost of process improvement or revenue enhancement is too high. Our work provides a theoretical foundation for the advice found in the popular entrepreneurship and time management literature to invest time now to save time later.

We use a unique empirical setting to investigate the spillover of quality knowledge across supply chains and to the examine contingencies that affect such spillover. We analyze the quality performance of 191 suppliers, who utilize the same facilities to manufacture similar products for two distinct businesses: one that makes cars and the other that makes commercial vehicles. From 2006 to 2009, the car business undertook 2,121 quality improvement initiatives at these suppliers, while the commercial vehicles business did not undertake any such initiatives. We find that the quality knowledge developed through the quality improvement initiatives undertaken by the car business does not easily spill over to benefit the commercial vehicles business. Quality knowledge spills over under three conditions: (1) when quality improvement efforts are focused on organizational members, as opposed to when they focus on routines or technology; (2) when quality improvement efforts focus on the output activities of suppliers, not when they focus on the input or in-process activities; and (3) when quality knowledge is developed at suppliers with low complexity in their operations. Our results provide insights on managing quality at shared suppliers.