Knowledge that Transforms

We study the optimal pricing and replenishment decisions in an inventory system with a price‐sensitive demand, focusing on the benefit of the inventory‐based dynamic pricing strategy. We find that demand variability impacts the benefit of dynamic pricing not only through the magnitude of the variability but also through its functional form (e.g., whether it is additive, multiplicative, or others). We provide an approach to quantify the profit improvement of dynamic pricing over static pricing without having to solve the dynamic pricing problem. We also demonstrate that dynamic pricing is most effective when it is jointly optimized with inventory replenishment decisions, and that its advantage can be mostly realized by using one or two price changes over a replenishment cycle.

We study a continuous‐review acquisition problem, in which the raw material price follows a discrete‐state Markov process and demand is compound Poisson. We show that one optimal policy is of the order‐up‐to type. Under our mean reversion and time continuity conditions, we further show that the order‐up‐to levels are decreasing at the current price level. At the same time, our computational study verifies that both conditions are indispensable for the monotonicity result. The study also hints at the connection between discrete‐ and continuous‐state price processes.

We consider a make‐to‐order manufacturer that serves two customer classes: core customers who pay a fixed negotiated price, and “fill‐in” customers who make submittal decisions based on the current price set by the firm. Using a Markovian queueing model, we determine how much the firm can gain by explicitly accounting for the status of its production facility in making pricing decisions. Specifically, we examine three pricing policies: (1) static, state‐independent pricing, (2) constant pricing up to a cutoff state, and (3) general state‐dependent pricing. We determine properties of each policy, and illustrate numerically the financial gains that the firm can achieve by following each policy as compared with simpler policies. Our main result is that constant pricing up to a cutoff state can dramatically outperform a state‐independent policy, while at the same time achieving most of the increase in revenue achievable from general state‐dependent pricing. Thus, we find that constant pricing up to a cutoff state presents an attractive tradeoff between ease of implementation and revenue gain. When the costs of policy design and implementation are taken into account, this simple heuristic may actually out‐perform general state‐dependent pricing in some settings.

We review, classify, consolidate, and synthesize the contributions to the expanding field of e‐business that have been published in Production and Operations Management. We classify e‐business research in the following four categories: (1) e‐auctions, (2) radio frequency identification, (3) e‐business system design, and (4) competition, conflict, collaboration, and coordination (C 4 in e‐business). We identify important research themes, research methodologies, and research techniques within each of these categories. We also provide directions for future research and discuss the managerial implications of the e‐business research reported in our paper.

Designing incentive contracts that constructively guide employee efforts is a particularly difficult challenge in novel innovation initiatives, where unforeseen events may occur. Empirical studies have observed a variety of incentive structures in innovation settings: “time and material contracts” (compensation for executing orders), “downside protection” (target‐driven incentives with protection from unexpected risks), and “upside rewards” (additional remuneration for pursuing opportunities). This paper develops a model of incentives in presence of unforeseen events and offers a theoretical prediction of which of the empirically observed incentive structures should be used under which circumstances. The combination of three key influences drives the shape of the best incentive contract. First, the presence of unforeseeable uncertainty, or the occurrence of events that cannot possibly be foreseen at the outset. These may force a change in the project's plan, making pure target setting insufficient. Second, fairness concerns dictate that the employee's expected compensation cannot be shifted downward by unforeseen events, because it would cause demotivation, hostility, and defection. Third, management may not be able to observe the detailed actions of the employee (moral hazard) nor whether a positive or negative unforeseen event has occurred (asymmetric information).

Large numbers of new products introduced annually by manufacturers may strain the relationship between retailers and manufacturers regarding assortments carried by retailers. For example, many retailers in the grocery industry will agree to broaden their assortments only if the manufacturer agrees to pay slotting fees for the new products. We investigate the role played by slotting fees in coordinating the assortment decisions in a supply chain. To do so, we study a single‐retailer, single‐manufacturer supply chain, where the retailer decides what assortment to offer to end customers. Double marginalization results in a discrepancy between the retailer's optimal assortment and the assortment that maximizes total supply chain profits. We consider a payment scheme that is analogous to slotting fees used in the grocery industry: the manufacturer pays the retailer a per‐product fee for every product offered by the retailer in excess of a certain target level. We show that, if the wholesale price is below some threshold level, this payment scheme induces the retailer to offer the supply‐chain‐optimal assortment and makes both parties better off.

Consider a manufacturer who mass customizes variants of a product in make‐to‐order fashion, and also produces standard variants as make‐to‐stock. A traditional manufacturing strategy would be to employ two separate manufacturing facilities: a flexible plant for mass‐customized items and an efficient plant for standard items. We contrast this traditional focus strategy with an alternative that better utilizes capacity by combining production of mass‐customized and standard items in one of two alternate spackling strategies: (1) a pure‐spackling strategy, where the manufacturer produces everything in a (single) flexible plant, first manufacturing custom products as demanded each period, and then filling in the production schedule with make‐to‐stock output of standard products; or (2) a layered‐spackling strategy, which uses an efficient plant to make a portion of its standard items and a separate flexible plant where it spackles. We identify the optimal production strategy considering the tradeoff between the cost premium for flexible (versus efficient) production capacity and the opportunity costs of idle capacity. Spackling amortizes fixed costs of capacity more effectively and thus can increase profits from mass customization vis‐à‐vis a focus strategy, even with higher cost production for the standard goods. We illustrate our framework with data from a messenger bag manufacturer.

We consider an online retailer's joint pricing and contingent free‐shipping (CFS) decisions in both monopoly and duopoly structures, which is an important marketing‐operations interface problem. We begin by investigating the impacts of a retailer's decisions on consumers' purchase behaviors, and show that the CFS strategy is useful to acquire the consumers with large order sizes. Then, we compute the probability of repeated purchases, and construct an expected profit function for an online retailer in the monopolistic setting. We find that the fixed shipping fees may have the largest impact on the retailer's profit among all shipping‐related parameters, and the retailer can benefit more from homogeneous markets than from heterogeneous ones. Next, we consider the competition between two retailers in the duopoly structure, and analytically show that, if two retailers have identical fixed and variable shipping fees, then their equilibrium decisions are equal. In order to numerically find a Nash equilibrium for two retailers, we develop a simulation approach using Arena and OptQuest. Our simulation‐based examples suggest that, as a result of the competition, the two retailers should decrease their profit margins but increase their CFS cutoff levels if they have the same fixed and also the same variable shipping fees.

The objectives of this research are first to empirically replicate Advanced Manufacturing Technology (AMT) utilization taxonomies identified in foregoing research, second to investigate the relationship between patterns of AMT utilization and manufacturing capabilities attainment, and third to explore differences in context, and performance across AMT groups. Theories of performance frontiers and capability progression provide the basis for our hypotheses. Data were collected from 224 U.S. manufacturing plants in industries considered to have potential utilizations of AMTs. A cluster analysis of the data yields a solution that closely resembles a previous AMT utilization taxonomy, including four groups labeled, respectively, as Traditionalists, Generalists, High Investors, and Designers. Significant manufacturing capability differences across these four groups indicate that plants that utilize a broader scope of AMTs enjoy a greater breadth of manufacturing capabilities. The implied capability attainment pattern is consistent with cumulative capability theory. However, the results suggest that cost capability is not included in the capability mix when broad‐based AMT utilization is the enabler of capability gains. A post hoc exploration of the AMT groups indicates significant differences in performance across the groups. Collectively, the results extend prior research by providing added insights into the possible rationale and impact of AMT utilization patterns.

This paper develops a novel framework to evaluate the integral performance of order picking systems with different combinations of storage and order picking policies. The warehousing literature on order picking mostly considers minimizing either elapsed time or distance as the sole objective, whereas warehouse managers in a supply chain have to look beyond single‐dimensional performance and consider trade‐offs among different criteria. Thus managers still need a unified and efficient framework to select a portfolio of appropriate order picking policies from a multi‐criteria and contextual perspective. Our framework—combining data envelopment analysis, ranking and selection, and multiple comparisons—provides an efficient methodology to simultaneously analyze several interrelated problems in order picking systems with multiple performance attributes, such as service levels and operational costs. We demonstrate our approach through comprehensive evaluations of order picking policies in three low‐level, picker‐to‐parts rectangular warehouses facing demand variations.