This paper examines how trade credit regulation influences supply chain relationships and network structure. Exploiting the implementation of France’s Loi de Modernisation de l’Economie (LME), which imposed strict limits on inter-firm payment durations, we employ a difference-in-differences design using granular global supply chain data. Contrary to the policy’s intended objective of stabilizing supply chains by alleviating supplier financial constraints, we find that mandated shorter payment terms significantly increase the likelihood of relationship termination, particularly for upstream links and low-margin suppliers. At the same time, the regulation stimulates the formation of new downstream relationships. These opposing forces lead to a structural reconfiguration of supply chains, resulting in shorter and flatter network structures. Our findings provide support for the incentive-provision view of trade credit, demonstrating that deferred payments function as a key relational governance mechanism rather than merely a source of short-term liquidity. When payment flexibility is constrained, firms strategically redesign their supply networks in response to weakened relational enforcement.

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.

In this paper, we present a decision support system used by an agricultural cooperative in the Indian states of Andhra Pradesh and Telangana to purchase, blend, sell, and store groundnut to maximize profits. The cooperative purchases raw groundnuts (input commodity) from its member-farmers, which is then processed to obtain multiple grades of groundnut seeds (output commodity). After the processing, two grades may be blended to obtain an intermediate grade to take advantage of arbitrage opportunities. The cooperative then sells some or all the output commodity in the spot market and carries the remaining inventory to the following period. It faces the problem of determining the quantity of input commodity to process and the quantity of different grades of output commodity to blend and, subsequently, sell in every period. The novel features of the problem are that the supply of input commodity is random every period, which is caused by the obligation of the cooperative to purchase all the supply that its members bring, and the option to blend grades to create intermediate ones. The possibility of blending over a multi-period planning horizon has not been examined yet in the operations management literature on commodities. We formulate the problem as a dynamic program over a harvest season and use the associated framework to identify structural properties of the optimal value function and optimal decisions. We show that the optimal value function is not separable in the input inventory and the output inventory vector. This makes the identification of the optimal solution challenging. In special cases, the value function may have a simpler form, for example, when blending is not permitted. We develop an efficient procedure to compute the optimal decisions in the general case. We illustrate the benefits of blending and using a multi-period model, employing data obtained from a cooperative: the net profit of the cooperative could improve by 100-900% depending upon the model parameters, which translates to Indian National Rupees 1.94-17.46 million per annum.

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.

Problem definition: As related party transactions (RPTs) increase in global supply chains, understanding the impact of corporate governance on such transactions becomes crucial for businesses. RPTs often lead to operational diversion due to power disparities between parent and its subsidiaries. In this study, we explore how operational diversion in RPTs within multinational firms is affected by the roles of foreign subsidiaries and corporate governance mechanisms. Methodology/results: Using a unique data set on RPTs of Korean multinational firms from 2006 to 2013, we compare the performance of multinational firms engaging in RPTs with two types of foreign subsidiaries: vertical and horizontal. We conduct our empirical analysis based on the adoption of International Financial Reporting Standards (IFRS) in Korea in 2011, which acts as a policy shock affecting corporate governance and deterring operational diversion. Our results show that the improvement in operational performance of a multinational firm following the IFRS adoption is more significant when the parent firm engages in transactions with vertical subsidiaries compared with horizontal ones. We further show that strong corporate governance mechanisms, such as internal governance, institutional ownership, and large shareholders, play a crucial role in restraining operational diversion in RPTs involving vertical subsidiaries. Managerial implications: The implications of our study extend to shareholders and auditors, highlighting the importance of prioritizing monitoring efforts concerning a parent firm’s RPTs with vertical subsidiaries, especially when corporate governance mechanisms are weak. In contrast, RPTs with horizontal subsidiaries are relatively robust against operational diversion, making them a natural deterrent to such malpractices. Funding: This work was supported by the Institute of Management Research at Seoul National University, the Hankuk University of Foreign Studies Research Fund [2023], and the Research Fellowship Fund of the Sangnam Institute of Management, Yonsei University [2020-22-0007]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2022.0372 .

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 NikolopoulosThe 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.

Since the beginning of 2020, many companies’ supply chains have been affected due to the disruptions caused by the pandemic. From production stoppages in facilities to shortages of supplies, logistical problems, and shortages of labor, the problems persist even now. Many companies made adjustments to their supply chains to cope with the impacts of supply chain disruptions. These adjustments include changes in product offerings and production schedules, changes in sourcing and distribution strategies, and changes in scheduling work and outsourcing. In this research, we report on a survey of the effects of the pandemic on supply chains and the consequent adjustments made by companies to cope with these effects. The survey includes long-term and short-term adjustments in different industries such as automotive, semiconductor, pharmaceutical, food and beverage, transportation, and retail. We also present lessons learned from such disruptions and identify the factors for companies and industries to consider in making their supply chains resilient to similar disruptions.

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.

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.

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.

Our paper is motivated by a manufacturer that sells a seasonal product through multiple retailers competing on an online marketplace, such as the Amazon marketplace. Demand and selling price uncertainty are key features of the online marketplace. Sourcing choices are differentiated by cost and available lead times—delaying shortens the lead time which is more expensive but yields more accurate information about future selling price and demand. Thus, ahead of the season, each retailer faces a continuous‐time decision problem about when to place an order with the manufacturer and in what quantity. The manufacturer is interested in knowing the ordering pattern of the retailers in order to plan production. We consider two sourcing strategies varying in the flexibility of order timing: an optimal precommitted ordering time strategy and an optimal time‐flexible ordering strategy. We prove that the former is optimal when the selling price is constant and the latter when the selling price is uncertain. We show that time‐flexible ordering can be mutually beneficial for the retailer and the manufacturer in a wide range of scenarios and that the manufacturer can favorably influence order timing by adjusting its wholesale price trajectory. The predictions of our model are consistent with the experience of a large U.S. manufacturer that motivated our study.

The Centers for Disease Control and Prevention promoted the Test-to-Stay (TTS) program to facilitate in-person instruction in K-12 schools during COVID-19. This program delineates guidelines for schools to regularly test students and staff to minimize risks of infection transmission. TTS enrollment can be implemented via two different consent models: opt-in, in which students do not test regularly by default, and the opposite, opt-out model. We study the impacts of the two enrollment approaches on testing and positivity rates with data from 259 schools in Illinois. Our results indicate that after controlling for other covariates, schools following the opt-out model are associated with 84% higher testing rate and 30% lower positivity rate. If all schools adopted the opt-out model, 20% of the total lost school days could have been saved. The lower positivity rate among the opt-out group is largely explained by the higher testing rate in these schools, a manifestation of status quo bias.

We study the problem of managing an inventory system with two supply sources which differ in their lead times and unit prices. We consider a simple class of policies called Tailored Base-Surge (TBS) policies for managing this system. These policies order a constant amount in every period from the slow supplier and order up to a target level in every period from the faster and more expensive supplier. We prove an asymptotic convergence result on the cost of an optimal TBS policy as the unit inventory holding cost goes to zero while keeping the service level constant and simultaneously increasing the unit cost of procurement from the faster supplier. The asymptotic value of cost also serves as an upper bound on the optimal cost of all dual sourcing systems with the same service level and product of unit procurement and holding cost. This bound is better than the previously best known upper bound on the cost of an optimal dual sourcing policy in the same regime.

The sudden spread of COVID-19 infections in a region can catch its healthcare system by surprise. Can one anticipate such a spread and allow healthcare administrators to prepare for a surge a priori? We posit that the answer lies in distinguishing between two types of waves in epidemic dynamics. The first kind resembles a spatio-temporal diffusion pattern. Its gradual spread allows administrators to marshal resources to combat the epidemic. The second kind is caused by super-spreader events, which provide shocks to the disease propagation dynamics. Such shocks simultaneously affect a large geographical region and leave little time for the healthcare system to respond. We use time-series analysis and epidemiological model estimation to detect and react to such simultaneous waves using COVID-19 data from the time when the B.1.617.2 (Delta) variant of the SARS-CoV-2 virus dominated the spread. We first analyze India's second wave from April to May 2021 that overwhelmed the Indian healthcare system. Then, we analyze data of COVID-19 infections in the United States (US) and countries with a high and low Indian diaspora. We identify the Kumbh Mela festival as the likely super-spreader event, the exogenous shock, behind India's second wave. We show that a multi-area compartmental epidemiological model does not fit such shock-induced disease dynamics well, in contrast to its performance with diffusion-type spread. The insufficient fit to infection data can be detected in the early stages of a shock-wave propagation and can be used as an early warning sign, providing valuable time for a planned healthcare response. Our analysis of COVID-19 infections in the US reveals that simultaneous waves due to super-spreader events in one country (India) can lead to simultaneous waves in other places. The US wave in the summer of 2021 does not fit a diffusion pattern either. We postulate that international travels from India may have caused this wave. To support that hypothesis, we demonstrate that countries with a high Indian diaspora exhibit infection growth soon after India's second wave, compared to countries with a low Indian diaspora. Based on our data analysis, we provide concrete policy recommendations at various stages of a simultaneous wave, including how to avoid it, how to detect it quickly after a potential super-spreader event occurs, and how to proactively contain its spread.

Problem definition: We investigate variations of the bullwhip effect across foreign subsidiaries and explore how it is affected by various features of foreign subsidiaries. Academic/practical relevance: During the era of global supply chain restructuring, researchers and executives of multinational firms should understand variations of the foreign subsidiary's bullwhip effect, given that its reduction is a key strategy for coordinating supply chains. Methodology: Our work is based on a balanced panel data set of Korean owned subsidiaries that provides distinguishable information on subsidiary purchases. It enables us to estimate an alternative measure for the bullwhip effect by the ratio of purchase volatility to demand volatility and compare it with the traditional measure of the ratio of production volatility to demand volatility. Results: Our alternative measure differs signifi-cantly from the traditional measure and better reflects the prevalence of the bullwhip effect across foreign subsidiaries. Using this new measure, we find that the bullwhip effect is strongly affected by country-specific factors and foreign subsidiary-specific factors. These findings support not only previous analytical findings on the bullwhip effect but also provide new remedies to reduce the bullwhip effect. Managerial implications: Our study advises managers of multinational firms. First, although locating foreign subsidiaries in developing countries tends to reduce production costs, it could instead increase the bullwhip effect, especially for politically unstable countries or those with lower import penetration. Second, we provide empirical evidence that relocating a subsidiary to a country close to its main suppliers or to one that incurs lower transportation costs will reduce the bullwhip effect. Finally, deploying expatriate managers and wholly owning the subsidiary are recommended strategies to reduce the bullwhip effect of foreign subsidiaries.

The reservation of goods to be produced in the micro, small, and medium enterprises (MSME) sector, in the early years after India's independence, addressed the dual needs of development of the industrial sector and production of goods. However, these industrial policies created an incentive for firms to remain small so that they can continue to avail of the benefits provided by the Government. On the positive side, the MSMEs typically employ more labor intensive production processes and consequently contribute significantly to the provision of employment opportunities, generation of income, and poverty reduction. But, on the negative side, the policies have also partly facilitated the creation of a divide in terms of productivity between the MSMEs and large sized firms. In particular the policy raises important questions for a firm auctioning supply contracts among suppliers with a significant cost differential. In this paper we propose an idea to allocate supply contracts wherein a manufacturing firm partitions the stochastic demand into mutually exclusive portions and awards each portion to a different supplier. We characterize such an optimal procurement mechanism when there are two types of suppliers and an arbitrary number of demand portions. We show that the optimal procurement may require the manufacturer to intentionally withhold some demand portion, and this arises when one type of supplier is considerably inefficient in serving a demand portion. We extend our analysis to the cases with multiple types with two suppliers and two types with multiple suppliers. The optimal partition is composed of at most six contiguous demand portions, and it may include a detrimental demand portion that only generates a negative expected payoff to both supplier types. Our demand partitioning mechanism leads to a strictly higher manufacturer's expected payoff than the conventional winner‐take‐all case unless one supplier type completely dominates the other. We present numerical experiments that indicate when such a mechanism holds the greatest advantage for the buyer.

Production and Operations ManagementEarly View Original Article Introduction to the Focused Issue on the POM-Finance Interface in Commodity and Energy Markets Nicola Secomandi, Corresponding Author Nicola Secomandi ns7@andrew.cmu.edu Tepper School of Business, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, Pennsylvania, 15213-3890 USA Corresponding author: Tepper School of Business, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, Pennsylvania 15213-3890, USA. Email: ns7@andrew.cmu.eduSearch for more papers by this authorSridhar Seshadri, Sridhar Seshadri sridhar@illinois.edu Gies College of Business, University of Illinois Urbana-Champaign, 1296 S. Sixth, Champaign, Illinois, 61820 USASearch for more papers by this author Nicola Secomandi, Corresponding Author Nicola Secomandi ns7@andrew.cmu.edu Tepper School of Business, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, Pennsylvania, 15213-3890 USA Corresponding author: Tepper School of Business, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, Pennsylvania 15213-3890, USA. Email: ns7@andrew.cmu.eduSearch for more papers by this authorSridhar Seshadri, Sridhar Seshadri sridhar@illinois.edu Gies College of Business, University of Illinois Urbana-Champaign, 1296 S. Sixth, Champaign, Illinois, 61820 USASearch for more papers by this author First published: 17 July 2021 https://doi.org/10.1111/poms.13540 by Subodha Kumar, after 1 revision. Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat No abstract is available for this article. Early ViewOnline Version of Record before inclusion in an issue RelatedInformation