Knowledge that Transforms

We study the newsvendor problem when consumers are heterogeneous either in their valuations of the newsvendor's product, in their valuations of an outside option available to them, or in both valuations. In this context, we observe that the outside option, which represents the value that a given consumer associates with choosing not to purchase the newsvendor's product, may be interpreted as a search cost. Taking into consideration whether consumers' valuations differ on either one dimension of heterogeneity or on both dimensions, we develop a framework for classifying newsvendor models that incorporate demand‐management effects. In particular, we show that this framework includes both the newsvendor model with price‐dependent demand and the newsvendor model with endogenous demand as special cases. In addition to making a conceptual contribution by developing and drawing insights from this framework, we make technical contributions by providing more general sufficient conditions under which the underlying optimization problems are well behaved.

The replacement of an existing product with a new one presents many challenges. In particular, uncertainties in a new product introduction often lead to extreme cases of demand and supply mismatches. This paper addresses inventory planning decisions for product upgrades when there is no replenishment opportunity during the transition period. We allow product substitution: when a company runs out of the old product, a customer may be offered the new product as a substitute. We show that the optimal substitution decision is a time‐varying threshold policy and establish the optimal planning policy. Further, we determine the optimal delay in a new product introduction, given the initial inventory of the old product.

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.

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.

For nearly all call centers, agent schedules are typically created several days or weeks before the time that agents report to work. After schedules are created, call center resource managers receive additional information that can affect forecasted workload and resource availability. In particular, there is significant evidence, both among practitioners and in the research literature, suggesting that actual call arrival volumes early in a scheduling period (typically an individual day or week) can provide valuable information about the call arrival pattern later in the same scheduling period. In this paper, we develop a flexible and powerful heuristic framework for managers to make intra‐day resource adjustment decisions that take into account updated call forecasts, updated agent requirements, existing agent schedules, agents' schedule flexibility, and associated incremental labor costs. We demonstrate the value of this methodology in managing the trade‐off between labor costs and service levels to best meet variable rates of demand for service, using data from an actual call center.

This study investigates the role of organizational structure in facilitating the development of mass customization (MC) capability in various manufacturing settings. Specifically, three dimensions of organizational structure are considered—flatness, centralization, and employee multifunctionality. We model organizational structure as a second‐order factor whose value is captured on a mechanistic‐organic continuum, where the organic form is characterized by a flat, decentralized structure with a wide use of multifunctional employees. We propose that a positive relationship exists between the organic organizational structure and MC capability. Additionally, building upon contingency theory, we argue that this positive relationship is moderated by mass customizer type—full mass customizers, which customize products at the design or fabrication stage of the production cycle, versus partial customizers, which customize products only at the assembly or delivery stages. Based on a study of 167 manufacturing plants from three industries and eight countries, we find that, for the overall sample, organic structure plays a significant role in enabling firms to pursue MC capability. However, an analysis of full versus partial mass customizers shows that the positive impact of organic structure on MC capability is statistically significant only for full mass customizers, not for partial mass customizers.

Although white‐collar work is of vast importance to the economy, the operations management (OM) literature has focused largely on traditional blue‐collar work. In an effort to stimulate more OM research into the design, control, and management of white‐collar work systems, this paper provides a systematic review of disparate streams of research relevant to understanding white‐collar work from an operations perspective. Our review classifies research according to its relevance to white‐collar work at individual, team, and organizational levels. By examining the literature in the context of this framework, we identify gaps in our understanding of white‐collar work that suggest promising research directions.

Few papers have explored the optimal reserve prices in the name‐your‐own‐price (NYOP) channel with bidding options in a multiple channel environment. In this paper, we investigate a double‐bid business model in which the consumers can bid twice in the NYOP channel, and compare it with the single‐bid case. We also study the impact of adding a retailer‐own list‐price channel on the optimal reserve prices. This paper focuses on achieving some basic understanding on the potential gain of adding a second bid option to a single‐bid system and on the potential benefits of adding a list‐price channel by the NYOP retailer. We show that a double‐bid scenario can outperform a single‐bid scenario in both single‐channel and dual‐channel situations. The optimal reserve price in the double‐bid scenario is no less than that in the single‐bid case. Furthermore, the addition of a retailer‐own list‐price channel could push up the reserve prices in both single‐bid and double‐bid scenarios.

We investigate resource‐constrained project scheduling with stochastic activity durations. Various objective functions related to timely project completion are examined, as well as the correlation between these objectives. We develop a GRASP‐heuristic to produce high‐quality solutions, using so‐called descriptive sampling. The algorithm outperforms existing algorithms for expected‐makespan minimization. The distribution of the possible makespan realizations for a given scheduling policy is also studied.

We use a real‐options approach to analyze investments in process improvement. We develop a simple, stochastic model of a firm making investment decisions in process improvement. Our analysis offers several interesting insights into investments in process improvement. First, early investment in process improvement results in valuable knowledge, which helps increase the value of the option to invest in process improvement in future periods. This may motivate a firm to invest in process improvements as early as possible. Second, it may be optimal for a firm to stop investing when such investments do not create enough value in the later stages of the investment horizon. Third, although one would expect the state of a firm's process relative to that of other firms to impact a firm's decision to invest in process improvement, this study finds that the impetus is conditional and identifies these conditions. Finally, in such an environment, the delay of investment in process improvement incurs an opportunity cost for a firm, and we show that the traditional net present value rule must incorporate this opportunity cost and the knowledge‐induced change in future option values to lead to a correct investment decision.