We consider a supply chain in which a retailer faces a stochastic demand, incurs backorder and inventory holding costs and uses a periodic review system to place orders from a manufacturer. The manufacturer must fill the entire order. The manufacturer incurs costs of overtime and undertime if the order deviates from the planned production capacity. We determine the optimal capacity for the manufacturer in case there is no coordination with the retailer as well as in case there is full coordination with the retailer. When there is no coordination the optimal capacity for the manufacturer is found by solving a newsvendor problem. When there is coordination, we present a dynamic programming formulation and establish that the optimal ordering policy for the retailer is characterized by two parameters. The optimal coordinated capacity for the manufacturer can then be obtained by solving a nonlinear programming problem. We present an efficient exact algorithm and a heuristic algorithm for computing the manufacturer's capacity. We discuss the impact of coordination on the supply chain cost as well as on the manufacturer's capacity. We also identify the situations in which coordination is most beneficial. © 2008 Wiley Periodicals, Inc. Naval Research Logistics, 2008

Scheduling Advertising on Cable Television Advertisement scheduling is a daily essential operational process in the television business. Efficient distribution of viewers among advertisers allows the television network to satisfy contracts and increase ad sale revenues. Ad scheduling is a challenging multiperiod, mixed-integer programming problem in which the network must create schedules to meet advertisers’ campaign goals and maximize ad revenues. Each campaign must meet a specific target group of viewers and a unique set of constraints. Moreover, the number of viewers is uncertain. To solve this problem, S. Souyris, S. Seshadri, and S. Subramanian develop and implement a practical approach that combines mathematical programming and machine learning to create daily schedules. According to standard business metrics and the small integer programming gap, these schedules are of high quality. Using their methods, leading networks in the United States and India experience a 3% to 5% revenue increase, which translates to about $60 million annually for one prominent user.

Bank credit access allows firms to borrow funds from banks and other financial institutions. Although theories have been developed on how bank financing affects firms’ operational decisions, empirical investigations in operations management (OM) literature remain limited. This study examines firms’ inventory management strategies in response to the enhanced credit lines from the adoption of interstate bank branching laws, which introduced staggered access to bank credit for operational firms. By merging multiple datasets with credit line information from 10-K SEC filings, we constructed a panel data set covering 1990–2005. Utilizing a difference-in-differences (DID) approach, we find that improved bank credit access leads to a 6% faster inventory turnover, rather than an increase in inventory investment. This outcome appears to stem from short-term increases in capacity investments leveraging their enhanced bank credit and increased use of trade credit, alongside long-term improvements in infrastructure and capital intensity. While these developments are more pronounced for small firms in concentrated markets, their advantage translates into increased competition, negatively impacting the market position and profitability of larger firms. Additionally, in a broader supply chain context, focal firms experience faster inventory turnover when their major customers access more bank credit. The effect is especially prominent for large suppliers in competitive markets, driven by increased sales volumes. This research enhances understanding of the extensive impact of bank credit on operational management and offers insights for policymakers about the diverse effects of bank regulations across different market players and sectors. This paper was accepted by J. George Shanthikumar, data science. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2024.08465 .

Can educational institutions open up safely amid COVID-19? We build an epidemiological model to investigate the strategies necessary for institutions to reopen. The four measures that are most relevant for in-person opening are: (i) wide-spread rapid testing, possibly saliva-based, (ii) enforcement of mask wearing, (iii) social distancing, and (iv) contact tracing. We demonstrate that institutions need to test at a relatively high level (e.g., at least once every week) in the initial phases of reopening. Contact tracing is relatively more important when the positivity rate from random testing is relatively low, which is likely during the initial phases. A Bayesian adaptive testing strategy based on positivity rates can help institutions optimally manage the costs and risks of reopening. Finally, we provide empirical estimates of some of the educational institutions opening up experience and comment on mitigation strategies. We also provide an estimate of locational effects of institution on mitigation strategies. This paper contributes to the nascent literature on combating the COVID-19 pandemic and is especially relevant for large-scale organizations. This work is motivated and guided by the SHIELD program of UIUC.