Sridhar Seshadri

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 .

The COVID-19 pandemic accelerated the adoption of digital platforms across various sectors, notably in education and healthcare, with remote learning and social media emerging as pivotal tools for communication and crisis management. Social networks played a crucial role in disseminating critical information, combating misinformation, and fostering community engagement. Recent research underscores the significance of social media in shaping public behavior towards adopting protective measures against COVID-19, yet quantifying its precise impact remains challenging due to the complexity of social relationships and diverse information sources. Multimodal data generated by social media platforms presents opportunities for more insightful Machine Learning (ML) models, but also poses technical challenges in data integration and interpretation. Leveraging crowdsourcing, we organized a data science competition aimed at forecasting COVID-19 positivity rates and identifying factors influencing its spread using infection and social media data. The competition facilitated collaborative problemsolving and provided actionable insights for public health communication and policy-making. This study outlines the competition structure, methodologies employed by participants, key findings, and implications for future pandemics and public health crises.

We propose two perspectives on the shift from U.S. domestic manufacturing to Asia in 1990–2011: production cost arbitrage and the management of supply-demand mismatch. In our model, a firm facing demand uncertainty decides between investing in domestic or overseas production capacity. The model predicts greater investment overseas when the cost arbitrage is high, switching cost is low, demand volatility is high, and the systematic risk in demand is above a certain threshold. Empirically, we observe strong support for the cost arbitrage motive in 1990–2000 and the risk management motive in 2001–2011, i.e., after China’s entry into the WTO. We estimate that investing into risk mitigation could have saved more than 400,000 U.S. manufacturing jobs.

Accepted by: Konstantinos Nikolopoulos The COVID 19 pandemic forced supply chain managers to explore different ways to cope with rapid changes in supply, manufacturing, distribution and demand. The lessons learnt from that experience is that flexibility in responding to demand and modularity must be planned at every stage. Along with planning, we argue that execution becomes challenging and is equally important to consider when making plans. We illustrate with a broad category of flexibility and modularity, dual sourcing, and how management mathematics can be used to manage these systems and understand the cost of execution. Dual sourcing has been used to manage the trade-off between cost and responsiveness by firms and has received considerable attention in academic literature. It is known that except in special cases, the optimal sourcing policy does not have an easy structure that is practically appealing and can be used by managers. Over the last decade and half, researchers have developed various management mathematics techniques and analyzed the performance of heuristic policies. This paper presents a discussion of the results in a few key papers related to the dual-sourcing inventory management problem and recent distribution free results in asymptotic regions. The asymptotic regimes considered include systems where the lead-time from the slower supplier is significantly higher than that from the closer, faster supplier and conditions where the unit cost of procurement is significantly higher compared to the unit cost of carrying inventory. These regimes represent different conditions about how valuable or costly using the faster supplier is and illustrate the value of simple heuristic policies and characterize the cost of these heuristics. The key learnings are that optimal ordering decisions may be robust to misspecification of demand distribution and managers only need summary statistics, such as the average and standard deviation of demand to determine the order quantities from the different suppliers. Managers could also consider ways to roll out new planning and control systems for managing multiple suppliers.

In this paper, we compare the effects of forecasting demand using individual (disaggregated) components versus first aggregating the components either fully or into several clusters. Demand streams are assumed to follow autoregressive moving average (ARMA) processes. Using individual demand streams will always lead to a superior forecast compared to any aggregates; however, we show that if several aggregated clusters are formed in a structured manner, then these subaggregated clusters will lead to a forecast with minimal increase in mean-squared forecast error. We show this result based on theoretical MSFE obtained directly from the models generating the clusters as well as estimated MSFE obtained directly from simulated demand observations. We suggest a pivot algorithm, which we call Pivot Clustering, to create these clusters. We also provide theoretical results to investigate sub-aggregation, including for special cases, such as aggregating demand generated by MA(1) models and aggregating demand generated by ARMA models with similar or the same parameters.

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.

In this paper we compare the effects of forecasting demand using individual (disaggregated) components versus first aggregating the components either fully or into several clusters. Demand streams are assumed to follow autoregressive moving average (ARMA) processes. Using individual demand streams will always lead to a superior forecast compared to any aggregates, however we show that if several aggregated clusters are formed in a structured manner then these subaggregated clusters will lead to a forecast with minimal increase in mean-squared forecast error. We show this result based on theoretical MSFE obtained directly from the models generating the clusters as well as estimated MSFE obtained directly from simulated demand observations. We suggest a pivot-algorithm, that we call Pivot Clustering, to create these clusters. We also provide theoretical results to investigate sub-aggregation, including for special cases such as, aggregating MA(1) streams and ARMA streams with similar or same parameters.

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.