Knowledge that Transforms

In this article, we investigate the interplay between returns policy, pricing strategy, and quality risk. We define quality risk as the possibility of product misfit, defect, or unconformity with the consumers’ perception. These notions of quality risks differ in return policy restriction, residual values, and whether it is possible to unambiguously reduce the probability of mismatch. Using a stylized two‐segment market setting, we demonstrate that consumer returns are offered only when the high‐segment consumers incur a higher hassle cost, and both the quality risk and the valuation of the low segment are moderate. Moreover, it is possible to wisely design the returns policy that eliminates all inappropriate returns. Furthermore, the seller with a high‐quality risk may offer a refund that exceeds the selling price, which provides a theoretical ground and specific operating regime for the satisfaction guaranteed policy used in some e‐tailers. In contrast, when the quality risk is relatively low, further improvement on mitigating the quality risk may not necessarily benefit the seller. Finally, we observe that the restocking fee may be non‐monotonic in product quality; thus, a more generous returns policy does not necessarily indicate a lower quality risk.

In models of optimal decision making, assumptions about managerial behavior are often made with the hope that the prescriptions offered by these models will be effective in practice, even if actual behavior occasionally strays from these assumptions. However, recent revenue management (RM) research has demonstrated what appear to be systematic deviations from normative models of decision making. These deviations can even be observed in relatively simple RM contexts. We suggest that technical errors in capacity allocation decisions are linked to issues such as arousal and stress associated with state conditions of RM tasks. Our study goes beyond existing findings by considering behavioral phenomena in concurrent task settings, where the decision maker is faced with managing decisions for more than one product or service. Physiological measures of eye dilation and blink rate are used as markers of arousal and stress in subjects engaged in RM tasks. Our analysis shows that physiological responses are indeed associated with both the state conditions of RM tasks and the number of capacity blocks managed concurrently by an individual. Deviations from modeled decision making appear to be significantly dependent upon these physiological responses. We conclude with a discussion of implications for further research and practice.

Previous work has considered the simultaneous (as opposed to sequential) optimization of a maintenance policy and a production policy in a multi‐product setting with random yield and product mix constraints. One of the sequential approaches to which the simultaneous approach is compared is a so‐called first‐come‐first‐served (FCFS) approach, i.e., an approach that generates randomized production policies that do not depend on the deterioration state of the machine. However, the model formulation for this approach does not generate policies consistent with this FCFS notion. Therefore, we present a revised FCFS model and analyze its performance using an existing experimental design. The results suggest that previous work overestimates the degree to which a FCFS approach is suboptimal, and underestimates the value of simultaneously optimizing the maintenance and production decisions. Lastly, we conduct additional experiments which suggest that the joint impact of using both simultaneous optimization and a deterioration dependent production policy is quite significant.

Bucket brigade order picking is a method for retrieving orders from a storage rack where workers follow a fixed sequence and dynamically adjust to variability in work content along the rack. The method is simple and has been shown to provide superior performance in many applications. In this article, we analyze how the location in which products are stored in the rack affects throughput. We identify conditions where storage decisions have a large impact on throughput (e.g., a 20% increase in productivity) and conditions where the impact is minimal. Conditions associated with high impact are high variation in worker skill, high variation in SKU volume, and a moderate level of walking‐to‐picking work content per pick list.

Make‐to‐order (MTO) manufacturers must ensure concurrent availability of all parts required for production, as any unavailability may cause a delay in completion time. A major challenge for MTO manufacturers operating under high demand variability is to produce customized parts in time to meet internal production schedules. We present a case study of a producer of MTO offshore oil rigs that highlights the key aspects of the problem. The producer was faced with an increase in both demand and demand variability. Consequently, it had to rely heavily on subcontracting to handle production requirements that were in excess of its capacity. We focused on the manufacture of customized steel panels, which represent the main sub‐assemblies for building an oil rig. We considered two key tactical parameters: the planning window of the master production schedule and the planned lead time of each workstation. Under the constraint of a fixed internal delivery lead time, we determined the optimal planning parameters. This improvement effort reduced the subcontracting cost by implementing several actions: the creation of a master schedule for each sub‐assembly family of the steel panels, the smoothing of the master schedule over its planning window, and the controlling of production at each workstation by its planned lead time. We report our experience in applying the analytical model, the managerial insights gained, and how the application benefits the oil‐rig producer.

We study a centralized inventory sharing system of two retailers that are replenished periodically. Between two replenishments, a unit can be transshipped to a stocked‐out retailer from the other. It arrives a transshipment time later, during which the stocked‐out retailer incurs backorder cost. Without transshipment, backorder cost is incurred until the next replenishment. Since the transshipment time is shorter than the time between two replenishments, transshipments can reduce the backorder cost at the stocked‐out retailer and the holding costs at the other retailer. The system is directed by a centralized inventory manager, who minimizes the long‐run average cost consisting of replenishment, holding, backorder, and transshipment costs. The transshipment policy is characterized by hold‐back inventory levels, which are nonincreasing in the remaining time until the next replenishment. The transshipment policy differs from those in the literature because we allow for multiple transshipments between replenishments, positive transshipment times, and backorder costs. We also discuss the challenges associated with positive replenishment time and develop upper and lower bounds of average cost in this case. Bounds are numerically shown to have an average gap of 1.1%. A heuristic solution is based on the upper bound and differs from the optimal cost by at most this gap.

This paper contributes to research on quality drivers in healthcare settings by examining the relationships between patient volume, teaching mission, and process quality in US hospitals. To develop a model that accurately assesses the impact of patient volume and teaching status on quality, we draw on three related research streams pertaining to the volume–quality relationship, the comparative quality of care in teaching and non‐teaching hospitals, and quality drivers in service institutions. We propose the impact of patient volume on process quality varies across hospitals with different teaching intensities. The test of this proposition uses a large data set that measures process quality for treatments for heart attacks and heart failures in all major US hospitals. Our results suggest that, as hospital teaching intensity increases, greater patient volume is associated with decreased process quality. Never before was such a relationship uncovered. This initial finding has important practical implications. First, the regionalization policy of hospitals should be re‐evaluated in light of their teaching function. Second, the root causes for the lower quality scores of large, high resident‐to‐bed ratio teaching hospitals, compared with smaller versions, must be found.

In this paper, we study a single‐product periodic‐review inventory system that faces random and price‐dependent demand. The firm can purchase the product either from option contracts or from the spot market. Different option contracts are offered by a set of suppliers with a two‐part fee structure: a unit reservation cost and a unit exercising cost. The spot market price is random and its realization may affect the subsequent option contract prices. The firm decides the reservation quantity from each supplier and the product selling price at the beginning of each period and the number of options to exercise (inventory replenishment) at the end of the period to maximize the total expected profit over its planning horizon. We show that the optimal inventory replenishment policy is order‐up‐to type with a sequence of decreasing thresholds. We also investigate the optimal option‐reservation policy and the optimal pricing strategy. The optimal reservation quantities and selling price are shown to be both decreasing in the starting inventory level when demand function is additive. Building upon the analytical results, we conduct a numerical study to unveil additional managerial insights. Among other things, we quantify the values of the option contracts and dynamic pricing to the firm and show that they are more significant when the market demand becomes more volatile.

Supply chain risk management (SCRM) is a nascent area emerging from a growing appreciation for supply chain risk by practitioners and by researchers. However, there is diverse perception of research in supply chain risk because these researchers have approached this area from different domains. This paper presents our study of this diversity from the perspectives of operations and supply chain management scholars: First, we reviewed the researchers' output, i.e., the recent research literature. Next, we surveyed two focus groups (members of Supply Chain Thought Leaders and International SCRM groups) with open‐ended questions. Finally, we surveyed operations and supply chain management researchers during the 2009 INFORMS meeting in San Diego. Our findings characterize the diversity in terms of three “gaps”: a definition gap in how researchers define SCRM, a process gap in terms of inadequate coverage of response to risk incidents, and a methodology gap in terms of inadequate use of empirical methods. We also list ways to close these gaps as suggested by the researchers.

In this article, we study the newsvendor problem with endogenous setting of price and quoted lead‐time. This problem can be observed in situations where a firm orders semi‐finished product prior to the selling season and customizes the product in response to customer orders during the selling season. The total demand during the selling season and the lead‐time required for customization are uncertain. The demand for the product depends not only on the selling price but also on the quoted lead‐time. To set the quoted lead‐time, the firm has to carefully balance the benefit of increasing demand as the quoted lead‐time is reduced against the cost of increased tardiness. Our model enables the firm to determine the optimal selling price, quoted lead‐time, and order quantity simultaneously, and provides a new set of insights to managers.