Knowledge that Transforms

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.

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.

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.

In this paper we examine the impact of different types of advance demand information on firm profit and on the benefits of resource flexibility. Specifically, we consider a firm that must choose capacities of resources that will be used to satisfy stochastic demand for multiple products, where demands follow a multivariate normal distribution. Prior to the capacity decision, the firm receives information revealing either the total volume of demand across products or the mix of demand between products. We examine two different scenarios: a dedicated resource setting with product-specific resources and a common resource scenario with one flexible resource. For both scenarios we derive the distribution of the (possibly imperfect) volume or mix demand signal, as well as the conditional distributions of demand given the particular signal. We explore the impact of either type of information on optimal capacities and profit. We find that commonality and volume information are strategic complements—so that it is more valuable to obtain volume information in settings with a common resource. On the other hand, commonality and mix information are strategic substitutes. Moreover, we find that mix and volume information themselves are complements in systems with dedicated resources. Having either type of information is valuable in reducing uncertainty for each individual product demand, but having both of them together provides information on two different dimensions, allowing for a much greater reduction in demand uncertainty. In systems with a common resource, however, the two types of information are substitutes. Because volume information is well aligned with commonality (both focus on total demand), such information already provides much of the value that can be obtained—having mix information adds limited additional value.

Many factors introduce the prospect of changes in the demand environment that a firm faces, with the specifics of such changes not necessarily known in advance. If and when realized, such changes affect the delicate balance between demand and supply and thus current prices should account for these future possibilities. We study the dynamic pricing problem of a retailer facing the prospect of a change in the demand function during a finite selling season with no inventory replenishment opportunity. In particular, the time of the change and the postchange demand function are unknown upfront, and we focus on the fundamental trade-off between collecting revenues from current demand and doing so for postchange demand, with the capacity constraint introducing the main tension. We develop a formulation that allows for isolating the role of dynamic pricing in balancing inventory consumption throughout the horizon. We establish that, in many settings, optimal pricing policies follow a monotone path up to the change in demand. We show how one may compare upfront the attractiveness of pre- and postchange demand conditions and how such a comparison depends on the problem primitives. We further analyze the impact of the model inputs on the optimal policy and its structure, ranging from the impact of model parameter changes to the impact of different representations of uncertainty about future demand.

Inpatient staffing costs are significantly affected by nurse absenteeism, which is typically high in U.S. hospitals. We use data from multiple inpatient units of two hospitals to study which factors, including unit culture, short-term workload, and shift type, explain nurse absenteeism. The analysis highlights the importance of paying attention to heterogeneous absentee rates among individual nurses. We then develop models to investigate the impact of demand and absentee rate variability on the performance of staffing plans and obtain some structural results. Utilizing these results, we propose and test three easy-to-use heuristics to identify near-optimal staffing strategies. Such strategies could be useful to hospitals that periodically reassign nurses with similar qualifications to inpatient units in order to balance workload and accommodate changes in patient flow. Although motivated by staffing of hospital inpatient units, the approach developed in this paper is also applicable to other team-based and labor-intensive service environments.