Knowledge that Transforms

The journal is pleased to publish the abstracts of the six finalists of the 2007 Manufacturing and Service Operations Management Society's student paper competition.

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 2007 M&SOM received 457 reviews from 272 individuals. Remarkably, 53% of those reviews were submitted on or before their due date, a figure that increases to 60% if you allow a one-day grace period. Due in large part to the responsiveness of our reviewers, M&SOM processes 95% of manuscripts within 90 days. While we deeply appreciate all those who served as reviewers for the journal in 2007, some individuals have distinguished themselves by reviewing many manuscripts and with each manuscript by writing a timely, unbiased, and thoughtful review. In recognition of their outstanding service provided to support the journal's scholarly mission, M&SOM grants the 2007 Meritorious Service Award to…

We introduce and analyze a model that explicitly considers the timing effect of intertemporal pricing—the concept, found in practice, that demand during a sale is increasing in the time since the last sale. We present structural results that characterize the interaction between the decision to hold a sale and the inventory-ordering decision. We show that the optimal inventory-ordering policy is a state-dependent base-stock policy; however, the optimal pricing policy can be quite complicated due to both the value and the cost of holding inventory and delaying sales. In our computational analysis, we find that compared to a fixed-price policy, we see an average gain in profit of almost 5% from optimally varying promotion and inventory decisions accounting for intertemporal demand, and we find that this potential profit gain increases as demand variability decreases. We also develop a heuristic based on deterministic pricing and find that it performs well relative to the optimal policy.

We study how rework routing together with wage and piece-rate compensation can strengthen incentives for quality. Traditionally, rework is assigned back to the agent who generates the defect (in a self-routing scheme) or to another agent dedicated to rework (in a dedicated routing scheme). In contrast, a novel cross-routing scheme allocates rework to a parallel agent performing both new jobs and rework. The agent who passes quality inspection or completes rework receives the piece rate paid per job. We compare the incentives of these rework-allocation schemes in a principal-agent model with embedded quality control and routing in a multiclass queueing network. We show that conventional self-routing of rework cannot induce first-best effort. Dedicated routing and cross-routing, however, strengthen incentives for quality by imposing an implicit punishment for quality failure. In addition, cross-routing leads to workload-allocation externalities and a prisoner's dilemma, thereby creating the greatest incentives for quality. Firm profitability depends on demand levels, revenues, and quality costs. When the number of agents increases, the incentive effect of cross-routing reduces monotonically and approaches that of dedicated routing.

Check processing institutions are being forced to downsize their workforce to cut cost and improve the efficiency of their operations as a result of continued growth of electronic payments, a consequence of the increasing popularity of debit/credit cards and use of online banking. For these institutions, these events are making more urgent the decision of how to staff a check-clearing house to trade off efficiency and the expected costs associated with the risks of delayed checks, which include fraud and float costs. In this paper, we discuss how a team of executives at a major commercial bank (CB) and Carnegie Mellon University students and faculty engaged in conducting a model-based study of the CB check-clearing operations. This project culminated in the development of a simulation optimization model to systematically analyze the nature of the highlighted risk efficiency trade-off at CB. The firm used the model recommendations to obtain operations downsizing guidelines for its senior managers during the implementation of a strategic workforce reduction program at their check-clearing house. The managerial insights from the team analysis, and the specific model-based recommendations, enabled CB executives to balance risk and efficiency while planning the reduction of their check-processing workforce.

This paper presents two methods to solve the production smoothing problem in mixed-model just-in-time (JIT) systems with large setup and processing time variability between different models the systems produce. The problem is motivated by production planning at a leading U.S. automotive pressure hose manufacturer. One method finds all Pareto-optimal solutions that minimize total production rate variation of models and work in process (WIP), and maximize system utilization and responsiveness. These Pareto-optimal solutions are found efficiently in polynomial time with respect to total demand by an algorithm proposed in the paper. The other method relies on Daniel Webster's method of apportionment for production smoothing, which produces periodic, uniform, and reflective production sequences that can improve operations management of the JIT systems. Finally, the paper presents the results of a computational experiment with the two methods.

Many service providers offer customers the choice of either waiting in a line or going offline and returning at a dynamically determined future time. The best-known example is the FASTPASS ® system at Disneyland. To operate such a system, the service provider must make an upfront decision on how to allocate service capacity between the two lines. Then, during system operation, he must provide estimates of the waiting times for both lines to each arriving customer. The estimation of offline waiting times is complicated by the fact that some offline customers do not return for service at their appointed time. We show that when demand is large and service is fast, for any fixed-capacity allocation decision, the two-dimensional process tracking the number of customers waiting in a line and offline collapses to one dimension, and we characterize the one-dimensional limit process as a reflected diffusion with linear drift. The analytic tractability of this one-dimensional limit process allows us to solve for the capacity allocation that minimizes average cost when there are costs associated with customer abandonments and queueing. We further show that in this limit regime, a simple scheme based on Little's Law to dynamically estimate in line and offline wait times is effective.

Generalizing earlier work on staffing and routing in telephone call centers, we consider a processing network model with large server pools and doubly stochastic input flows. In this model the processing of a job may involve several distinct operations. Alternative processing modes are also allowed. Given a finite planning horizon, attention is focused on the two-level problem of capacity choice and dynamic system control. A pointwise stationary fluid model (PSFM) is used to approximate system dynamics, which allows development of practical policies with a manageable computational burden. Earlier work in more restrictive settings suggests that our method is asymptotically optimal in a parameter regime of practical interest, but this paper contains no formal limit theory. Rather, it develops a PSFM calculus that is broadly accessible, with an emphasis on modeling and practical computation.

Motivated by the ease with which online customers can bid simultaneously in multiple auctions, we analyze a system with two competing auctioneers and three types of bidders: those dedicated to either of the two auctions and those that participate simultaneously in both auctions. Bidding behavior is specified and proven to induce a Bayesian Nash equilibrium, and a closed-form expression for the expected revenue of each auctioneer is derived. For auctioneers selling a single item, partial pooling—i.e., the presence of some cross-auction bidders—is beneficial to both auctioneers as long as neither one dominates the market (e.g., possesses more than 60%–65% of the market share). For multi-item auctions, pooling is mutually beneficial only if both auctioneers have nearly identical ratios of bidders per items for sale; otherwise, only the auctioneer with the smaller ratio benefits from pooling. Pooling's impact on revenue decreases with the number of bidders, suggesting that popular auction sites need not be overly concerned with mitigating bidding across auctions.