Year: 2025
Type: article
Abstract: To ensure high service quality, managers need to personalize treatment options and meet their customer demands. Our research is motivated by the need to better anticipate and prepare for that. We develop a generalizable framework that is the first to address two healthcare risk management goals: (1) identifying high risk and stable-demand customers and (2) predicting the medium-term demand for services of stable-demand customers. We also design a model-agnostic method for variable evaluation. It can rank predictors based on their global impact and highlight their effect on a model's local accuracy. In this research, we leverage a large electronic medical records' data set, which comprised 48,344 chronic kidney disease patients treated across geographically diverse Veterans Affairs regions. Our framework indicates that although only 1.3% of the examined individuals are high-risk patients, it can correctly identify 35% of them and highlight an additional 8.9% as having important demand implications. Identifying high-risk individuals can be used in (1) monitoring prioritization, (2) patients' motivation, and (3) patients' stabilization. Furthermore, our model accurately predicts the monthly need for care of stable-demand individuals up to 3 years into the future and outperforms popular statistical and data mining models. This information is especially critical for hospital management in identifying future hiring needs. Although lenient return policies can drive sales and customer loyalty, they have also resulted in enormous returns volumes and reverse logistics costs. Online retailers often feel compelled to offer free returns, but are then faced with numerous operational challenges, ranging from accurately forecasting returns volumes to identifying presales strategies to reduce the likelihood that a (costly) return occurs. In this research, we consider how the complementarity of the products within an order basket is related to consumer returns. By developing an understanding of the link between basket contents and returns, we can improve order-level returns forecasts, while also providing insights into the effect of basket recommendations on the expected return rate. We take a multimethod approach to this problem. First, we use a stylized model to generate theoretical predictions regarding how within-basket complementarity should influence return probability. Next, we propose a data-driven measure of complementarity, degree of copurchase (DCP), which is based on the machine learning concept of association rule and is implementable using standard retail sales data. Finally, utilizing a unique data set provided by a leading online specialty retailer, we implement the DCP measure and test the predictions of our analytical model. We find, as expected, that there is a decreasing relationship between within-basket complementarity and return probability. However, we also show that this decrease is convex, indicating that the return probability impact is more notable when the complementarity is increased from a lower base. Our results have practical implications for both reverse logistics planning and online product recommendations. Firms increasingly engage in mergers and acquisitions (M&As) to improve their network positions and enhance performance. Yet, how the fundamental structure of the merged supply base changes after the M&A and how the structural changes affect the performance are largely unknown. Building upon the strategy-structure-performance framework, this study investigates dual roles of supply base structural changes in M&A events, namely, as an outcome and as a moderator to post-M&A performance. In particular, we examine how the size and the country diversity of the merged supply base change after M&As, as well as how these structural changes affect the merged firm's operational performance. We conduct a difference-in-differences analysis by comparing firms that engaged in M&A events (treatment group) with those comparable firms that did not engage in any M&A events (control group). The results show that the merged firms tend to reduce the size of their supply bases after M&As. In addition, our results demonstrate that both supply base consolidation (i.e., reducing the number of suppliers) and country diversification (i.e., spreading suppliers across multiple countries) improve the merged firms’ post-M&A operational performance, an effect that becomes stronger for horizontal M&As. We also explore how the type of M&As (i.e., horizontal vs. nonhorizontal), industry relatedness between the two merging firms, and the purposes of M&As affect the post-M&A structural changes to the supply bases. This study helps inform supply managers the important roles of supply base in M&As to enhance post-M&A operational performance or to limit the synergies when not properly done. Electrifying transit bus networks (TBNs) has recently become a challenging problem that many public transport operators around the world are facing. Due to the limited driving range of electric buses, electric TBNs are more sensitive to operational delays and uncertainties. Moreover, the impact on sustainability is most profound when the buses are powered by renewable energy resources, which are often subject to intermittency and uncertainty. In this study, we tackle the complicated problem of planning charging schedules amid these various sources of uncertainty. We develop a real-time decision support system that uses real-time data, predictions, and mathematical optimization to update the charging schedules and mitigate the impact of operational uncertainties. Our results show that the online strategy can maintain higher reliability and renewable energy utilization levels compared to other charging strategies. The study has been carried out in cooperation with the public transport operator in Rotterdam in the Netherlands to assist them in their TBN transition process. Autonomous drones are no longer science fiction but are becoming reality. Prior studies have investigated how an autonomous drone can be used in conjunction with a parcel delivery truck, but they all restricted the drones’ launch/recovery sites to customer nodes visited by a truck. In practice, parcel carriers are considering the use of intermediate points (IPs), the sites found along the arcs connecting customer nodes, as drones' launch/recovery points. This means that the academic literature currently lags the industry practice. This article extends the previous works on truck-and-drone last-mile delivery by investigating the conditions under which the use of IPs is beneficial by using both theoretical and empirical approaches. Our results show that although using IPs is an effective concept that can save the cost (time) of package deliveries by 2.189% on average, the cost saving realized by using IPs can vary notably across carriers depending on their network characteristics. Specifically, our results suggest that the benefit of using IPs becomes high when a network has the following characteristics: (1) low customer density (a small number of customers served per square mile) and (2) large number of time-sensitive packages (subject to time window constraints). Based on these findings, we provide normative implications on if, when, where, and to what extent IPs can be beneficial for the truck-and-drone joint operations in last-mile logistics.
Source: Decision Sciences
DOI: 10.1111/deci.70004
Language: English
Subfield: Emergency Medical Services
Field: Health Professions
Domain: Health Sciences
Open Access status: bronze
OpenAlex ID: W4409239332