Knowledge that Transforms

This article investigates a setting where the retailer invests costly effort to shape demand, such as reducing demand fluctuation through demand–control effort or increasing demand levels with demand–promotion effort. We explore the preference bias between these two effort types and examine the impact of such a bias on profit performance. The experimental findings reveal that actual effort investments exhibit a significant preference bias for promotion effort in the low-profit condition, while no significant preference bias is observed in the high-profit condition. This behavioral pattern can be captured by a reference-dependent behavioral model incorporating the retailer's optimism level in the reference point. Additional analyses, such as robustness experiments and model extensions, provide further support for the promotion-effort bias in the low-profit condition. Our analysis presents a comprehensive understanding of demand-shaping effort preferences and extends the application of the reference-dependence framework. It provides insights for managers in identifying potential biases and mitigating profit loss in demand-shaping activities.

Consider a single server that should serve a given number of customers during a fixed period. The appointment scheduling problem (ASP) determines the schedule of planned appointments that minimizes some cost function that accounts for both the cost of idle times and the cost of waiting. When service time distributions are fully specified, the ASP presents a much-investigated computationally challenging stochastic program. When service time distributions are only partially specified, one can apply distributionally robust optimization (DRO) to find the schedule that minimizes costs in worst-case circumstances. We assume that only the mean, mean absolute deviation and range of the service times are known and develop a DRO method that finds the optimal (mini–max) schedule. For independent service times, the min–max problem becomes nonlinear and difficult, if not impossible, to solve exactly. Existing DRO methods for ASP with partial information (such as mean and variance), therefore, consider relaxations that allow correlations between service times. Such relaxations have major repercussions, as the worst-case scenario will then be highly correlated. Our method thus deals with independent service times and finds a robust schedule as the solution to a linear program. We identify several new structural features of optimal robust schedules. We also apply the method to model extensions including sequencing and alternative objective functions.

In this article, we study a shelf-stock allocation problem in a data-driven setting where the demand distribution is unknown. The retailer needs to decide which commodities to place on each level of the shelf (location decision), and determine the stock level for each selected commodity at the shelf (stock decision) that can be adjusted based on updated demand prediction, under constraints on allocation and capacity, so as to maximize total expected profit. Key issues of the problem include demand endogeneity on vertical-location effect and the demand ambiguity that may arise from the ever shorter life cycles of commodities and other complicated determinants. We address the problem in a rolling-horizon fashion and develop a two-phase data-driven robust optimization model leveraging a decision-dependent Wasserstein metric that incorporates time-series demand forecasts and captures vertical-location effect under demand ambiguity. We derive tractable solution schemes for the model, in which the phase-I model determines the optimal location decision by solving one instance of mixed-integer linear program, while the phase-II model admits an analytical solution for updating the optimal stock. Furthermore, we discuss the sensitivity implications on how the ambiguity aversion impacts the optimal stock, and prove the consistency of the model solution under regularity condition on the underlying demand process that well justifies the asymptotic performance of the proposed data-driven approach. Finally, we extend our model to incorporate substitution effect, and conduct sufficient numerical experiments with real-life data that demonstrate the performance of our model.

We empirically examine the effect of customers’ social capital on the financial performance of suppliers. Social capital, as captured by the strength of secular norms and the density of social networks in local geographical regions, reflects social influences surrounding corporate headquarters. Because customers’ social capital could constrain their opportunistic behaviors and improve supply chain collaboration through its influence on managers’ moral values and external monitoring, we hypothesize and find a positive association between customer social capital and supplier profitability. The association is more pronounced when customers reside closer to suppliers, have more short-horizon incentives, and possess stronger bargaining power. Collectively, our findings suggest that social capital arising from institutions in local areas may mitigate customer opportunism and highlight its real effects that spill over to suppliers’ operational performance.

Social technologies have enabled the emergence of online platforms that provide offline service consultations and recommendations. In this environment, economic inefficiency arises when customers are not fully aware of their horizontally differentiated preferences. With its expertise or data dominance, a platform can be more informed about customers’ hidden preferences. We focus on an instrumental social technology, that is, targeting, which is a type of data-driven personalized information provision to manipulate customers’ beliefs about service quality. We propose a Hotelling model wherein customers are sensitive to the delays for service while making Bayesian belief updates based on a platform’s recommendations. When customers self-select their favorite service, their choices impose negative externalities through congestion and welfare loss. Our results indicate that service recommendations allow customers to navigate toward the more appropriate service, thus improving matching efficiency, reducing congestion costs, and enhancing aggregate customer welfare. We further identify the role of “information transparency” and study how the platform should strategically release information by making personalized service recommendations to customers. Interestingly, when a customer-centric platform maximizes aggregate customer welfare, we identify the “value of opaqueness” by strategically withholding service recommendations from a subset of customers and notice that this effect is more pronounced for a profit-seeking platform. Our results offer a better understanding of information transparency policies in the joint design of service recommendation systems and pricing mechanisms.

This paper investigates a risk-averse firm's investment strategy in real-time event-monitoring technologies coupled with dynamic disruption response decisions. The firm does not know how long the disruption will last, and the event-monitoring solutions provide the firm with a quicker update regarding the length of the disruption. When the firm receives an update, it has the flexibility to revise its initial response action. We model a two-stage stochastic program to study this problem where risk aversion is modeled in the form of a Service-at-Risk constraint. Our paper makes four important contributions. First, it characterizes the optimal recourse strategies given an update and identifies the conditions when the firm benefits from the recourse. Second, we characterize the optimal investment and initial response strategies where we find that the investment behavior is non-monotone. Third, our paper shows an interesting impact of risk aversion on the investment decision such that the firm may benefit from investing in event monitoring at moderate degrees of risk aversion, but not necessarily at low or high degrees of risk aversion. Fourth, we find that a firm's benefit from event monitoring is more profound when the capacity of contingency response is moderately limited.

The rise of online marketplaces has raised customer expectations regarding customization and lead time. It poses significant challenges to manufacturing firms and prompts a move from make-to-stock to a more flexible make-to-order system. Compared to make-to-stock settings, make-to-order systems cannot smooth fluctuations in demand using available stock. While viewing dynamic pricing as a useful strategy to balance supply with demand, many manufacturing firms can also create capacity flexibility. In that scenario, system costs could be cut by managing capacity and demand simultaneously. In this paper, we consider a make-to-order production environment with base and surge capacity as well as the ability to adjust product pricing. Our main focus is on operational decision-making, assuming that the base capacity and surge capacity are fixed, but activating the surge capacity incurs a setup cost. Initially, we propose a stochastic control model to reflect this complex decision problem. However, our initial model leads to an intractable dynamic programming problem. To overcome this, we convert the problem to a more tractable diffusion control problem. This approach helps to reveal the conditions under which utilizing flexible capacity is more advantageous than relying solely on fixed capacity. When flexible capacity is advantageous, we provide a solution to the diffusion control problem that can guide optimal capacity and price adjustments. We discover an interesting interplay between capacity adjustment and dynamic pricing. In particular, we find that the price, which aims at reducing congestion, may not monotonically increase with the congestion level when capacity adjustments incur a fixed cost.

Scholars in management science and operations management (MS and OM) continue to make significant contributions to the notion of “doing good with good operations.” Impressively, the MS and OM literature has developed several pro-social sub-streams, such as healthcare operations, sustainability, and nonprofit operations; however, to the best of my knowledge, there are only two studies in the top MS and OM journals that mention LGBTQ+-related terms in their abstracts, keywords, or introductions (one appeared in 1989 and the other in 2021). The LGBTQ+ community is an integral part of society, and the field has significant potential to impact the lives of its members economically and socially. MS and OM scholars could pay greater attention to research problems at the interface of operational decision-making and the LGBTQ+ community, which I term “rainbow operations.” This study advances LGBTQ+ diversity, equity, and inclusion within the MS and OM literature by invoking several existing studies and showcasing how similar state-of-the-art techniques and tools can be used to answer interesting, rich, and impactful research questions concerning rainbow operations. I present motivating examples and supporting statistics, discuss related work by MS and OM scholars, and suggest several avenues for future research around the following three themes: LGBTQ+ clients in service delivery settings, LGBTQ+ employees in contemporary workplaces, and LGBTQ+ community in global supply chains. My goal is to inspire MS and OM scholars to think more broadly about our discipline and offer valuable operations-related perspectives on research problems of relevance to the LGBTQ+ community.

In this essay, our analysis takes important insights on diversity and inclusion from the behavioral literature but critically contextualizes them against the reality of humanitarian operations. Humanitarian operations are characterized by system immanent diversity, particularly between local and expatriate aid workers, who not only bring valuable different perspectives to the table but also differ along multiple dimensions of diversity into a so-called diversity faultline. Such a faultline, however, provides fertile ground for continued conflict resulting in relational fractures and, ultimately, inefficient collaboration. While, in theory, inclusion could help overcome the negative effects of faultlines, in practice, the time pressure for humanitarian organizations to quickly respond to disasters makes it effectively impossible to engage in it. Against this background, we argue, humanitarian organizations should take preemptive action before disaster strikes. Specifically, we posit that the pre-disaster phase presents an opportunity to engage in inclusion in order to cultivate relational resilience between local and expatriate aid workers. Such resilience would enable them to not only better weather the inevitable relational fractures during a disaster response (and thus stay more functional throughout), but also quickly realign with each other in the post-disaster phase. We conclude with a set of concrete recommendations for practicing inclusion in the pre-disaster phase.

This article studies the impact of a competing firm's substantial expansion in product variety, achieved through modularity and delayed differentiation, on the sales and inventory of the focal firm in the market. Intuitively, the firm's sales are expected to decrease when a competitor increases its product variety, as existing consumers are drawn to the greater range of competing products, resulting in a substitution effect. However, we also emphasize a simultaneous spillover effect, where the dramatic increase in a competitor's product variety can expand the overall market for all firms (including the focal firm) by attracting new consumers to the market. The net impact thus depends on the tradeoff between the substitution effect and spillover effect, forming an empirical question. To explore this tradeoff and quantify the net impact of a competitor's substantial increase in product variety on the focal firm's sales and inventory, we exploit the launch of Coca-Cola's Freestyle dispenser that offers over 120 beverage products via modularity and delayed differentiation. Our analysis focuses on the treatment effects of the launch of Freestyle on changes in the focal firm's sales quantity, sales dispersion, and inventory levels. Contrary to common intuition, we find that the focal firm's total sales remain unchanged following the launch of Freestyle dispensers, but its sales dispersion increases by 2.2%, which in turn leads to a 1.0% increase in inventory levels at distribution centers. The unchanged total sales and increased sales dispersion imply a redistribution of sales among the focal firm's stock keeping units (SKUs). By analyzing data at the SKU level and the business customer level, we uncover the mechanism behind this sales re-allocation: a 1.9% increase in the sales of the focal firm's long-tail products at the expense of its star product sales. Meanwhile, we identify a 0.9% increase in sales to retailers and entertainment locations, while sales to restaurants decline. These empirical results help disentangle the substitution and spillover effects on the focal firm's sales resulting from its competitor's substantially increased product variety. Managerial implications derived from these findings are discussed.