Journal of Operations Management最新文献

An abiding preoccupation for firms is understanding how customers value their products versus competitors' products. This is difficult to quantify and estimate from data as, even if competitor prices are public information, their sales are typically unobservable. However, in some industries, most prominently the hotel industry, third-party information brokers collect and supply aggregate competitor sales information. In the hotel industry, these reports from Smith Travel Research, popularly known as STR reports, are widely subscribed to. Hotels participate by reporting their sales information and, in turn, obtain access to marginal competitor sales data, in the form of daily occupancy percentage, albeit aggregated across groups and lengths-of-stay. Despite its availability, this data is not widely incorporated into revenue management estimation, likely due to the lack of robust models and methodologies. In this paper, focusing mainly on the hotel industry, we develop a constrained maximum likelihood method (constrained by moment conditions) to overcome the following significant challenges in estimation of a market share model with a competitor attractiveness factor: (i) competitor data is aggregated across multiple lengths-of-stay with varying demand characteristics; (ii) no-purchase data is unobservable, preventing tracking of customers who choose neither the focal firm's (we refer to as our) product nor the competitor's product; (iii) private (unobserved) group sales of competitors prior to retail sales reduce competitor capacity and influence their subsequent prices; and finally, (iv) maximizing the partial-information likelihood function is intractable. We first evaluate our method through Monte Carlo simulations on synthetic data generated under a generalized Nash competition model. In these simulations, our method accurately recovers the true parameters to a close degree in almost all cases, exploiting the marginal competitor data. Next, we apply the method to real-world hotel booking data and benchmark its performance against alternative approaches from the network tomography and revenue management literature.

Despite the immense potential of precision medicine to revolutionize healthcare, its integration into routine clinical practice remains a significant challenge. This study investigates the impact of Clinical Pharmacogenetics Implementation Consortium (CPIC) guidelines, which provide synthesized evidence and pharmacogenetics-based drug dosing recommendations, on the delivery of psychiatric care enabled by precision medicine. Specifically, we conduct a comprehensive evaluation of the effect of CPIC guidelines on serious drug-related adverse events and explore how drug characteristics related to therapeutic uncertainty, namely drug age and drug label warning, moderate this effect. Our findings suggest that the availability of CPIC guidelines is associated with almost a 25% decrease in serious drug-related adverse events in the context of psychiatric care delivery enabled by precision medicine. Furthermore, we find that the presence of drug label warning, as FDA-endorsed negative news on pharmacogenetics-related drug use, enhances the effect of CPIC guidelines and is associated with a further decrease in serious drug-related adverse events. Post hoc analysis reveals that CPIC guidelines with high strength of evidence are associated with a significant decrease in serious drug-related adverse events, while no such effect is observed for guidelines with low strength of evidence. These findings contribute to the nascent literature on integrating precision medicine into routine clinical practice, highlighting the consequential role of clinical guidelines in improving the effectiveness of psychiatric care delivery for individual patients enabled by precision medicine. In addition, by demonstrating how the development and implementation of robust clinical guidelines are central to minimizing serious drug-related adverse events, the findings have policy implications for minimizing the downside risks of psychiatric care delivery.

We address a trans-specialist learning and coordination question in pre-competitive manufacturing R&D networks: how do early-stage consortia develop products across “dissimilar” (where knowledge requirements are different, and not solely based within) specialized networks? A unique aspect of the R&D consortia is that they integrate knowledge across product, production, and supply chain domains. This paper uses network ethnography as the methodology—in combining social network analysis with ethnographic methods—while drawing on a 10-year dataset on the evolution of pre-competitive collaboration networks in pharmaceutical continuous manufacturing deploying digital technologies. Our analysis reveals mechanisms through which design rules for products and processes are developed and converged across product, production, and supply chain domains. Specifically, we show that design rules, which are both “set-based” and “trans-specialized”, are the key mechanisms that enable heterogeneous specialist stakeholders to exchange knowledge and facilitate the convergence of development efforts. Second, we highlight the roles of boundary spanners and institutional actors (i.e., academia and regulatory bodies) in steering dialogues towards the convergence of design rules in early-stage R&D settings. The theoretical implications of these findings are not only germane to pharmaceutical drug development networks but also to early-stage product and technology development networks at large.

Last mile operations (LMO), the processes involved in the critical last stage of delivering goods and services, have widespread relevance across major sectors of the economy, including retail, food services, healthcare, humanitarian services, energy distribution, telecommunications, public services, and others. These operations account for a significant portion of the costs, jobs, and economic output in these sectors. Global economic output involving last mile deliveries alone, for instance, is valued at $165 billion per year and is growing at about 10% per year (InsightAce Analytic 2024).

Recent decades have witnessed an acceleration in the rate of evolution of LMO (Agatz et al. 2024; Boutilier and Chan 2022; Boyer and Hult 2005; Dreischerf and Buijs 2022; He and Goh 2022; Lyu and Teo 2022). Technology-driven innovations have catalyzed profound changes in the planning, design, and execution of LMO, with significant implications for the economics of these operations. Extending the last mile to the final user has increased convenience, accessibility, and reliability. Zipline, for example, has introduced drones to safely deliver lifesaving products in remote communities (Ackerman and Koziol 2019). An increasing number of pharmacies in Europe and Africa have been equipped with smart lockers to allow 24/7 access to critical medicines (Gobir et al. 2024). Some innovations leveraging platforms based on smartphone apps have given small corner stores in neighborhoods in cities across Latin America the means to sell and deliver daily groceries and other household staples to local residents (Escamilla et al. 2021). Other innovations, leveraging artificial intelligence, have found applications in vehicle routing tools and warehouse and fulfillment automation (such as Ocado's system (Mason 2019)), track-and-trace systems that provide real-time communications and visibility into delivery processes (such as Instacart and Uber Eats), anticipatory shipping algorithms to move inventories to specific areas ahead of realized demand (Chen and Graves 2021), and integration tools with third-party services (successfully deployed by ClickPost and ShipEngine).

However, considerable challenges remain. For example, because of short time frames and high delivery volumes to many dispersed locations, LMO have little room for human error. Yet, since many firms tend to tap into low-skilled, temporary, or crowdsourced labor to provide these services, there is high variability in performance and worker availability. LMO are also expensive, due in part to rising labor costs, delivery failures, more demanding customers, and vehicle and parking restrictions. Although academic research in LMO has a long tradition in Operations Research (see e.g., Agatz et al. (2011), Otto et al. (2018), Boy