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AbstractWe present an expanded framework for the use of business analytics in projects. To the commonly used descriptive, predictive, and prescriptive analytics, we add comparative analytics, wherein we compare the performance of systems under different interventions. This framework provides a conceptual roadmap for the implementation of business analytics projects. We then demonstrate this framework using recent operations research literature on analytics in healthcare, summarizing papers focusing on one of these aspects. Next, we discuss queue mining as an example of theory and practice illustrative of these aspects. We conclude there is room for further work by operations researchers and management scientists within business analytics projects generally and the healthcare industry more specifically. We argue future work should consider both theory and practice, especially within prescriptive analytics projects, where analysis through the lens of operations research and management science is imperative. We provide some thoughts on the current and future state of operations research and management science in business analytics.

AbstractWe consider the problem of service network design: choosing the optimal number, locations, and service capacities of facilities, taking into account that facilities may have a finite or an infinite waiting room. Accordingly, our service measure is either the percentage of blocked customers or the percentage of customers who need to wait in line. The goal is to minimize the total cost, which consists of costs associated with traveling, blocking or queueing delay, service capacities, and operating (fixed) costs. We derive structural results when facilities are on a two‐node network, and then use them to study the problem for a general network. We prove that the cost of service capacity and the cost of blocking or queueing delay are independent of the number of opened facilities as long as all facilities are identical in terms of their design parameters c and K (or only c in the case of an infinite waiting room). We use our results to develop an efficient algorithm that solves the problem for general networks. Finally, to demonstrate the applicability of our results and tractability of our algorithm, we discuss as an example an industrial‐size problem, which considers the drive‐through operations of McDonald's in the Toronto metropolitan area.

ABSTRACTOur main objective is to investigate the influence of the bargaining power within a chain on its industry. As a building block, we first discuss the implications of bargaining within a single chain by considering an asymmetric Nash bargaining over the wholesale price (BW). We show that both Manufacturer Stackelberg (MS) and vertical integration (VI) strategies are special cases of the BW contract. We then develop the Nash equilibrium in an industry with two supply chains that use BW. We identify the profit‐maximizing (coordinating) bargaining power within this industry. We show that when a chain is not monopolistic, VI does not coordinate the chain and that the MS contract, where the manufacturer has all the bargaining power, is coordinating when competition is intense. We find that the main determinant of the equilibrium in mature industries is to respond well to the actions of the competing chain rather than to directly maximize the profit of each chain. That is, the equilibrium does not necessarily maximize the profit of the entire industry. While a coordination of the industry could then increase the profitability of both chains, such a coordination is likely against antitrust law. Moreover, if one chain cannot change its actions, the other chain may unilaterally improve its profitability by deviating from the equilibrium. Our results lead to several predictions supported by empirical findings, such as that in competitive industries chains will work "close to" the MS contract.

AbstractReference prices (RPs) are consumers' subjective perceptions of prices that have important influences on purchase decisions. The standard RP formulation, which defines RP as an exponentially weighted average of past prices, ignores a certain asymmetry in weights between the regime of a price decrease and that of a price increase, which can be observed by the demand trend during the few days after a price decrease or increase. Such oversight usually leads to overestimation in demand as we illustrate by empirical evidence. We introduce the novel concept of RP with exposure effect (RPEE) that captures such asymmetry in RP formulation by imposing a weight proportional to how much the price is exposed to consumers. The exposure effect can be measured by clickstream data that are available for most e‐retailing platforms. We develop a customer behavioral model that can explain the formation of standard RP, and extend it in a natural way to provide foundation to the use of RPEE, especially for products with few repeat purchases. We then establish empirically the extensive benefit of forecasting from RPEE for e‐retailers that sell thousands of products. We demonstrate that RPEE exhibits significant and consistent improvement over standard RP for products, with around
reduced weighted mean absolute percentage error.