Knowledge that Transforms

Native American-owned enterprises commonly use preference hiring to promote tribal self-sufficiency and include a population historically excluded from the workforce. Based on interviews with tribal casino executives, we describe how this socially important practice creates operational challenges and how some casinos have addressed these. First, we show that Native American population density and customer satisfaction at tribal casinos are negatively correlated. While managers acknowledge the possibility of some bias against Native Americans, they emphasize the importance of training for tribal enterprises to succeed in their use of preference hiring. We then explore how tribal preference affects workforce recruitment and retention. Finally, we present challenges faced by these organizations in day-to-day capacity planning. Enabled by the unique legal circumstances and ownership structure of tribal enterprises, Native American preference hiring represents an extraordinary commitment to including disadvantaged workers and offers lessons for other organizations seeking to promote diversity and inclusion.

Frequently releasing a new product generation has become a common practice to sustain sales over time, thus accurately forecasting the sales trajectory of each product generation plays a vital role in the short-, medium-, and long-term planning of a firm. Classic multigeneration diffusion models do not incorporate within-generation repeat purchases, making them unusable for product lines with high rates of such purchases. Concentrating on technology products, we develop a multigeneration sales model to fill this void. We demonstrate that the new model can be used for predictive and prescriptive analytics. Our empirical results show that the new model estimates and forecasts sales more accurately than a state-of-the-art benchmark model that does not account for within-generation repeat purchases, underscoring the importance of incorporating repeat purchases. Furthermore, we use two different versions of our model to examine market entry timing under two main strategies, that is, (i) a phase-out transition strategy in which firms continue to sell the old generation after the release of the new generation, and (ii) a total transition strategy in which firms discontinue the old generation after the introduction of the new generation. Our results indicate that the repeat purchases rate determines whether it is optimal to expedite or delay the new product launch, underscoring the importance of incorporating repeat purchases in market entry strategies.

We present a data-driven prescriptive framework for fair decisions, motivated by hiring. An employer evaluates a set of applicants based on their observable attributes. The goal is to hire the best candidates while avoiding bias with regard to a certain protected attribute. Simply ignoring the protected attribute will not eliminate bias due to correlations in the data. We present a provably optimal fair hiring policy that depends on the protected attribute functionally, but not statistically. The policy does not set rigid quotas, and does not withhold information from decision-makers. Both synthetic and real data indicate that the policy can greatly improve equity for underrepresented and historically marginalized groups, often with negligible loss in objective value.

We develop a novel statistical method to estimate customer choice among a firm’s portfolio of offerings when the firm cannot directly observe customers who choose not to purchase any product. This censored demand problem is prevalent in many industries such as hotels, airlines, and retail. Although several methods have been proposed to address this problem, they require some level of data aggregation across arrivals and/or choice sets, which results in information loss and potentially biased estimates. Therefore, they have limited applicability in an environment where the prices of a firm’s portfolio of offerings vary over time and sometimes even across different customers. Our proposed method combines several desirable properties, which makes it a better fit for realistic datasets where the available choice sets or attributes of the products in the choice sets change over time. We consider two additional types of information for identification of our model parameters: (1) additional mild assumptions on the customers’ utility function, and (2) external information about a firm’s market share. We then develop a robust estimation procedure that accounts for inaccuracies in either information type and let the data determine the best approach. Through Monte Carlo simulations, we show that our approach provides promising predictions of customer choice behavior when compared with other generally used methods and clearly outperforms those methods in scenarios where the product prices change frequently over time. Utilizing a real hotel transaction dataset provided by Oracle Labs, we further illustrate the improved estimation accuracy of our method compared to benchmark methods. Relative to existing approaches for estimating customer choice-based models, our proposed methodology better suits environments employing dynamic pricing and personalized offering practices, such as hospitality or online retailing.

This research investigates how to sustain process improvement (PI) initiatives in healthcare. Despite the abundant knowledge on PI, we know very little on why and how PI sustains over time. This is particularly true in the context of heterogeneous frontline teams of physicians and nurses with different operational characteristics such as attrition rates, skill sets, training, and goals. Our study builds a model that simulates the mutual, dynamic interaction of these stakeholders with each other and with processes with different characteristics in the presence of endogenous management prioritization. We build confidence in the model by calibrating it to data from PI teams at several family medicine clinics at a large US Midwestern healthcare system. The computational experiments performed using data from these teams yield several important insights for theory and practice. First, we find that process metrics, which are leading metrics related to the day-to-day functioning of the units, and outcome metrics, which are lagging metrics often tied to the unit's bottom line, are both critical for the functioning of the PI teams. Emphasizing one over the other can reduce sustainability while a mixed case in which one group of employees is incentivized to leading and another group to lagging can create a regime that is more effective in many scenarios. Second, we find that management interventions that improve short-term PI gains generally also reduce the likelihood of long-run sustainment. Third, we find that attrition and turnover among the team members affect sustainability of PI initiatives and reducing turnover for one class of employee, which attends lagging metrics, facilitates sustainment while reducing it for the other class, which attends leading metrics, can hinder sustainment. We conclude by discussing these implications to the practice of PI among healthcare teams.

We examine when and how task division improves performance for inventory planning. Specifically, we consider a decentralized inventory management context with two interdependent subtasks: preparing a forecast and setting a service level. Using a behavioral experiment, we reveal that the task challenge moderates the relationship between task division and performance. Our findings indicate that task division improves performance when the task becomes more challenging, such as under high demand uncertainty. It facilitates counteracting behavior, where individuals adjust their decisions to counterbalance their partner's errors, leading to more stabilized final decisions. We identify this counteracting behavior as a critical mechanism driving the benefits of task division, mainly when subtasks are interdependent. We demonstrate the robustness of our findings by examining an egalitarian system, where decision-making authority is shared among team members, and a hierarchical system, where decision control resides entirely with one team member.

Offering bespoke products is the cornerstone of many competitive manufacturing strategies. The challenge for production control is allocating materials to individualized orders before they can be released to the shop floor. This requires joint evaluation of material inventory and workload control. However, these topics have been studied in isolation for <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll"> <mml:mn>40</mml:mn> </mml:math> years, under the presumption of a hierarchical control design. This traditionally had the practical advantage of a reduced need for real-time system information. With the advancements in Industry 4.0, control functions can now be integrated. We amalgamate knowledge from both disciplines, present hierarchical and integrated production control designs, and formulate material inventory policies tailored to pulled order release. Using simulation, we show that a decentralized and integrated production control design drastically reduces cost (at least 15%) and improves delivery performance in various settings.

Hate crimes, which stem from prejudiced attitudes, have a distributionally detrimental impact on societal stability. Although inter-group contacts are potentially an effective means for reducing prejudice and subsequently decreasing the number of hate crimes, scholars have recently recognized the possibility of negative contacts that might actually amplify prejudice. As a result, the question of whether intergroup contacts truly possess the ability to effectively decrease hate crime numbers remains inconclusive. In addition, prior contact research primarily relies on laboratory experiments because the establishment of intergroup contacts in a field setting is challenging. Examination of the effectiveness of intergroup contacts hence merits further investigation in a real-world setting. In this article, we propose that ride-hailing services, which naturally connect individuals from different backgrounds, offer an avenue to facilitate intergroup contacts in practice, which could potentially reduce prejudice and the volume of hate crimes. Leveraging the staggered introduction of this technology into counties in the United States, we conducted a series of analyses to empirically evaluate the contact effects in the open field. Our analysis reveals a notable decrease in the number of hate crimes (particularly a 5.75% reduction in racial hate crimes) after the introduction of ride-hailing services. These findings remained consistent across various robustness tests. Additional moderation analysis suggests that the increased interaction between different groups, facilitated by ride-hailing services, is the most likely explanation for the observed decrease in hate crimes. We further conducted an extensive survey involving real ride-hailing drivers and passengers. The results from our survey provide direct evidence that ride-hailing services create natural and constructive environments where positive interactions and mutual understanding can develop among diverse groups of people. This, in turn, helps mitigate prejudice and hate crimes within society, as observed in our analysis. This study not only extends the existing body of literature on contact theory but also sheds light on how modern technologies can play a pivotal role in curtailing hate crime, yielding both theoretical and practical implications.

E-commerce packages are notorious for their inefficient usage of space. More than one-quarter volume of a typical e-commerce package comprises air and filler material. The inefficient usage of space significantly reduces the transportation and distribution capacity increasing the operational costs. Therefore, designing an optimal set of packaging box sizes is crucial for improving efficiency. We present the first learning-based framework to determine the optimal packaging box sizes. In particular, we propose a three-stage optimization framework that combines unsupervised learning, reinforcement learning, and tree search to design box sizes. The package optimization problem is formulated into a sequential decision-making task called the box-sizing game. A neural network agent is then designed to play the game and learn heuristic rules to solve the problem. In addition, a tree-search operator is developed to improve the performance of the learned networks. When benchmarked with company-based optimization formulation and two alternate optimization models, we find that our ML-based approach can effectively solve large-scale problems within a stipulated time. We evaluated our model on real-world datasets supplied by a large e-commerce platform. The framework is currently adopted by a large e-commerce company across its 28 fulfillment centers, which is estimated to save the company about 7.1 million USD annually. In addition, it is estimated that paper consumption will be reduced by 2,080 metric tons and greenhouse gas emissions by 1,960 metric tons annually. The presented optimization framework serves as a decision support tool for designing packaging boxes at large e-commerce warehouses.

This article provides a comprehensive overview of the advancements and applications of operations research (OR) and management science (MS) in inventory management described by the Franz Edelman Award finalists from 1985 to 2023. The research presents the transformative potential of OR/MS in addressing complex inventory management challenges across various industries. Through an in-depth examination of the methodologies and solutions employed by these studies, we highlight the strategic implementations of several OR/MS techniques, including simulation-optimization, deep learning and advanced forecasting, dynamic pricing and yield management, and stochastic modeling. The analysis reveals the tangible benefits realized, such as optimized inventory levels, reduced costs, improved profits and revenue, and improved customer satisfaction. This research underscores the critical role of inventory process optimization and risk mitigation in navigating uncertainties and demand fluctuations. The managerial insights derived from these initiatives provide a roadmap for practitioners seeking to implement advanced OR/MS methodologies in real-world inventory management. The findings also encourage the adoption of innovative methods to enhance the operational efficiency and competitiveness. Through this exploration, we aim to stimulate further innovation and research in the evolving field of inventory management and to celebrate the achievements in applied analytics brought forth by the Franz Edelman Award finalists.