Knowledge that Transforms

We consider the problem of a retailer managing a category of vertically differentiated products. The retailer has to pay a fixed cost for each product included in the assortment and a variable cost per product sold. Quality levels, fixed, and variable costs are exogenously determined. Customers differ in their valuation of quality and choose the product (if any) that maximizes their utility. First, we consider a setting in which the selling prices are also fixed. We find that the optimal set of products to offer depends on the distribution of customer valuations and might include dominated products, that is, products which are less attractive than at least one other product, on every possible dimension. We develop an efficient algorithm to identify an optimal assortment. Second, we consider a setting in which the retailer also determines the selling prices. We show that in this case the optimal assortment does not include any dominated product and does not vary with the distribution of customer valuations when there is no fixed cost. We develop several efficient algorithms to identify an optimal assortment and optimally price the products. We also test the applicability of our methods with realistic data for two product categories.

Remanufacturing, long perceived as an environmentally friendly initiative, is supported by a number of governments. Yet, the assumption that remanufacturing is desirable to society has never been systematically investigated. In this paper, we examine the effectiveness and eco‐efficiency of remanufacturing in the personal computer and mobile phone industries. We investigate whether remanufacturing substantially reduces the environmental impact, as measured by cumulative energy demand (CED), generated over the life cycles (LCs) of these products, and the size of any reduction. We also examine the relative eco‐efficiency of remanufacturing compared with virgin manufacturing for these two products, where eco‐efficiency includes both willingness‐to‐pay (WTP) for the products as well as the energy consumed in producing the products. Our main findings are the following. One, remanufacturing is an effective way to reduce the total energy consumed during the LCs of personal computers and mobile phones, with one notable exception, when the life spans of remanufactured products are substantially shorter than the life spans of their new counterparts. Two, a remanufactured personal computer or mobile phone is not always more eco‐efficient than a corresponding new product. Three, the WTP for remanufactured personal computers and mobile phones, and consequently, their eco‐efficiencies, are a function of the prices of the correspondent new products at launch and years elapsed between launch and remanufacturing. Four, remanufactured units are sold at a discount relative to the price of new personal computers and mobile phones. Five, on the whole, the market for remanufactured mobile phones is more eco‐efficient than the market for new mobile phones. Six, the market for remanufactured computers is more eco‐efficient than the market for new computers. Lastly, because the group of remanufactured products is heterogeneous, not all remanufactured units are more eco‐efficient than the average new computer and mobile phone. We conclude with a discussion of the impact of our findings on European WEEE and WEEE‐like legislation.

Product quality and product warranty coverage are two important and closely related operational decisions. A longer warranty protection period can boost sales, but it may also result in dramatically increased warranty cost, if product quality is poor. To investigate how these two decisions interact with each other and influence supply chain performance, we develop a single‐period model with a supplier that provides a product to an original equipment manufacturer, which in turn sells it to customers. Customer demand is random and affected by the length of the product warranty period. Warranty costs are incurred by both the supplier and the manufacturer. We analyze two different scenarios based on which party sets the warranty period: manufacturer warranty and supplier warranty. Product quality is controlled by the supplier, and the manufacturer determines the ordering quantity. We analyze these decentralized systems and provide the structural properties of the equilibrium strategies. We also compare the results of centralized and decentralized systems and identify the conditions under which one system provides a longer warranty and better product quality than the other. Our numerical study further shows that, in decentralized settings, when the warranty period is determined by the firm sharing the larger proportion of total warranty costs, the supply chain can achieve greater system‐wide profit. Both parties can therefore benefit from properly delegating the warranty decision and sharing the resulting additional profit. We further design a supplier‐development and buy‐back contract for coordinating decentralized supply chains. Several extensions are also discussed.

There is a natural order to most events in life: Everything from learning to read to DNA sequences in molecular biology follows some predetermined, structured methodology that has been refined to yield improved results. Likewise, it would seem that firms could benefit by adopting and implementing technologies in some logical way so as to increase their overall performance. In this study of 555 hospitals, we investigate the order in which medical technologies are transformed into information technologies through a process of converting them from stand‐alone technologies to interoperable, integrated information systems and whether certain configurations of sequences of integration yield additional value. We find that sequence does matter and that hospitals that integrated foundational technologies first—which in this case are known to be more complex—tend to perform better. Theoretical and practical implications of this finding and others are discussed.

In this paper, we examine the suggested link between product architecture (i.e., the extent to which a product is modular vs. integral) and supply chain configuration (i.e., whether the product development is done internally by the manufacturer in an integrated supply chain or in collaboration with a supplier in a decentralized supply chain). Our model suggests that the choice of product architecture depends on firm, market, and product characteristics in addition to supply chain structure. In contrast to other studies, we find that the optimal mapping from architecture to supply chain structure is not always one‐to‐one. A decentralized supply chain may be associated with a more integral product when the technical collaboration penalty is not excessive and suppliers have significantly superior product development capabilities. Furthermore, in a decentralized supply chain, the nature of the relationship between the original equipment manufacturer and its supplier (adversarial or collaborative) plays a role in the choice of product architecture: modular architectures are more likely when the parties have adversarial relationships, while long‐term trust‐based relationships facilitate more integral product architectures.

Coordination efforts that access and align relevant cross‐functional expertise are regarded as an essential element of innovation success. In recent years, these efforts have been further augmented through complementary investments in information systems, which provide the technological platforms for information sharing and coordination across functional and organizational boundaries. Somewhat overlooked has been the critical mediating role of the intelligence gained through these efforts and capabilities. This study draws on the theory of complementarity to elaborate on the nature of this mediating concept. Theoretical predictions of the model are tested using instrument variable regression analysis of data collected from a sample of publicly traded US manufacturing firms. The findings suggest that the effects of both internal and external coordination on market intelligence and supply‐chain intelligence are moderated by the firm's information system capability. The effect of both types of intelligence quality on new product development performance was contingent with the effects being enhanced (attenuated) when the market conditions were dynamic (stable). The results are robust to common‐method bias, endogeneity concerns, and alternative estimation methods.

Variability in hospital occupancy negatively impacts the cost and quality of patient care delivery through increased emergency department (ED) congestion, emergency blockages and diversions, elective cancelations, backlogs in ancillary services, overstaffing, and understaffing. Controlling inpatient admissions can effectively reduce variability in hospital occupancy to mitigate these problems. Currently there are two major gateways for admission to a hospital: the ED and scheduled elective admission. Unfortunately, in highly utilized hospitals, excessive wait times make the scheduled gateway undesirable or infeasible for a subset of patients and doctors. As a result, this group often uses the ED gateway as a means to gain admission to the hospital. To better serve these patients and improve overall hospital functioning, we propose creating a third gateway: an expedited patient care queue. We first characterize an optimal admission threshold policy using controls on the scheduled and expedited gateways for a new Markov decision process model. We then present a practical policy based on insight from the analytical model that yields reduced emergency blockages, cancelations, and off‐unit census via simulation based on historical hospital data.

This study introduces a universal “Dome” appointment rule that can be parameterized through a planning constant for different clinics characterized by the environmental factors—no‐shows, walk‐ins, number of appointments per session, variability of service times, and cost of doctor's time to patients’ time. Simulation and nonlinear regression are used to derive an equation to predict the planning constant as a function of the environmental factors. We also introduce an adjustment procedure for appointment systems to explicitly minimize the disruptive effects of no‐shows and walk‐ins. The procedure adjusts the mean and standard deviation of service times based on the expected probabilities of no‐shows and walk‐ins for a given target number of patients to be served, and it is thus relevant for any appointment rule that uses the mean and standard deviation of service times to construct an appointment schedule. The results show that our Dome rule with the adjustment procedure performs better than the traditional rules in the literature, with a lower total system cost calculated as a weighted sum of patients’ waiting time, doctor's idle time, and doctor's overtime. An open‐source decision‐support tool is also provided so that healthcare managers can easily develop appointment schedules for their clinical environment.

Uncertainty in the duration of surgical procedures can cause long patient wait times, poor utilization of resources, and high overtime costs. We compare several heuristics for scheduling an Outpatient Procedure Center. First, a discrete event simulation model is used to evaluate how 12 different sequencing and patient appointment time‐setting heuristics perform with respect to the competing criteria of expected patient waiting time and expected surgical suite overtime for a single day compared with current practice. Second, a bi‐criteria genetic algorithm (GA) is used to determine if better solutions can be obtained for this single day scheduling problem. Third, we investigate the efficacy of the bi‐criteria GA when surgeries are allowed to be moved to other days. We present numerical experiments based on real data from a large health care provider. Our analysis provides insight into the best scheduling heuristics, and the trade‐off between patient and health care provider‐based criteria. Finally, we summarize several important managerial insights based on our findings.

Agrowing stream of environmental legislation enforces collection and recycling of used electrical and electronics products. Based on our experiences with producers coping with e‐waste legislation, we find that there is a strong need for research on the implications of such legislation from an operations perspective. In particular, as a discipline at the interface of systems design and economic modeling, operations focused research can be extremely useful in identifying appropriate e‐waste take‐back implementations for different business environments and how producers should react to them.