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 2008 M&SOM received 514 reviews from 277 individuals. Remarkably, 59% of those reviews were submitted on or before their due date, a figure that increases to 66% if you allow a one-day grace period. Due in large part to the responsiveness of our reviewers, M&SOM made 98.5% of its 317 manuscript decisions within 90 days. While we deeply appreciate all those who served as reviewers for the journal in 2008, 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 2008 Meritorious Service Award to…

The fields of statistics and econometrics have developed powerful methods for testing the validity (specification) of a model based on its fit to underlying data. Unlike statisticians, managers are typically more interested in the performance of a decision rather than the statistical validity of the underlying model. We propose a framework and a statistical test that incorporate decision performance into a measure of statistical validity. Under general conditions on the objective function, asymptotic behavior of our test admits a sharp and simple characterization. We develop our approach in a revenue management setting and apply the test to a data set used to optimize prices for consumer loans. We show that traditional model-based goodness-of-fit tests may consistently reject simple parametric models of consumer response (e.g., the ubiquitous logit model), while at the same time these models may “pass” the proposed performance-based test. Such situations arise when decisions derived from a postulated (and possibly incorrect) model generate results that cannot be distinguished statistically from the best achievable performance—i.e., when demand relationships are fully known.

We propose an extension of the competitive newsvendor model to investigate the impact of quick response under competition. For this purpose, we consider two retailers that compete in terms of inventory: customers that face a stockout at their first-choice store will look for the product at the other store. Consequently, the total demand that each retailer faces depends on the competitor's inventory level. We allow for asymmetric reordering capabilities, and we are particularly interested in the case when one of the firms has a lower ordering cost but can only produce at the beginning of the selling season, whereas the second firm has higher costs but can replenish stock in a quick response manner, taking advantage of any incremental knowledge about demand (if it is available). We visualize this problem as the competition between a traditional make-to-stock retailer that builds up inventory before the season starts versus a retailer with a responsive supply chain that can react to early demand information. We provide conditions for this game to have a unique pure-strategy subgame-perfect equilibrium, which then allows us to perform numerical comparative statics. We confirm that quick response is more beneficial when demand uncertainty is higher or exhibits a higher correlation over time. We also find that the competitive advantage from quick response is larger when facing a slow response competitor, and interestingly, asymmetric competition can be desirable to both competitors.

Two single-product firms with different quality levels and fixed limited capacities engage in sequential price competition in an essentially deterministic model where customers have heterogeneous valuations for both products. We develop conditions under which the leader (she) can take strategic advantage of her limited capacity by pricing relatively low, purposefully creating shortages and leaving some leftovers for the follower (him) to feast on, avoiding direct competition. The extent to which the leader benefits in this Leftovers Equilibrium depends on operational variables such as the capacity levels of the two firms and the sequence in which customers arrive at the market. We spell out the details for three different known arrival sequences within a specific subset of plausible fixed-capacity levels. The follower's strategic shadow price can be positive even when not all his capacity is used, and the leader's can be negative when all her capacity is used. We illustrate that Leftovers Equilibria can arise when some of our assumptions are relaxed.

In some supply chains, materials are ordered periodically according to local information. This paper investigates how to improve the performance of such a supply chain. Specifically, we consider a serial inventory system in which each stage implements a local reorder interval policy; i.e., each stage orders up to a local base-stock level according to a fixed-interval schedule. A fixed cost is incurred for placing an order. Two improvement strategies are considered: (1) expanding the information flow by acquiring real-time demand information and (2) accelerating the material flow via flexible deliveries. The first strategy leads to a reorder interval policy with full information; the second strategy leads to a reorder point policy with local information. Both policies have been studied in the literature. Thus, to assess the benefit of these strategies, we analyze the local reorder interval policy. We develop a bottom-up recursion to evaluate the system cost and provide a method to obtain the optimal policy. A numerical study shows the following: Increasing the flexibility of deliveries lowers costs more than does expanding information flow; the fixed order costs and the system lead times are key drivers that determine the effectiveness of these improvement strategies. In addition, we find that using optimal batch sizes in the reorder point policy and demand rate to infer reorder intervals may lead to significant cost inefficiency.

We study a profit-maximizing firm providing a service to price and delay sensitive customers. We are interested in analyzing the scale economies inherent in such a system. In particular, we study how the firm's pricing and capacity decisions change as the scale, measured by the potential market for the service, increases. These decisions turn out to depend intricately on the form of the delay costs seen by the customers; we characterize these decisions up to the dominant order in the scale for both convex and concave delay costs. We show that when serving customers on a first-come, first-served basis, if the customers' delay costs are strictly convex, the firm can increase its utilization and extract profits beyond what it can do when customers' delay costs are linear. However, with concave delay costs, the firm is forced to decrease its utilization and makes less profit than in the linear case. While studying concave delay costs, we demonstrate that these decisions depend on the scheduling policy employed as well. We show that employing the last-come, first-served rule in the concave case results in utilization and profit similar to the linear case, regardless of the actual form of the delay costs.

We study cross-selling operations in call centers. The following questions are addressed: How many customer-service representatives are required (staffing), and when should cross-selling opportunities be exercised (control) in a way that will maximize the expected profit of the center while maintaining a prespecified service level target? We tackle these questions by characterizing control and staffing schemes that are asymptotically optimal in the limit, as the system load grows large. Our main finding is that a threshold priority control, in which cross-selling is exercised only if the number of callers in the system is below a certain threshold, is asymptotically optimal in great generality. The asymptotic optimality of threshold priority reduces the staffing problem to a solution of a simple deterministic problem in one regime and to a simple search procedure in another. We show that our joint staffing and control scheme is nearly optimal for large systems. Furthermore, it performs extremely well, even for relatively small systems.