Vishal Gaur

We study how artificial intelligence is being adopted in three industrial sectors that recruit the largest proportion of MBA graduates-banking and financial services, management consulting, and technology, media and e-commerce-and examine the resulting implications for MBA hiring and skills requirements from MBA graduates. To conduct the study, we interviewed 34 executives at 30 companies across these sectors and in senior-, mid-, and early-career levels and supplemented the interviews with desktop research. Through this study, we develop guidelines for how the MBA curriculum needs to evolve to meet the evolving requirements of the industry. While the study is focused on MBA programs, its insights are broadly applicable to graduate and undergraduate business programs at universities.

Effective management of a firm’s operating cash flow is essential for supporting growth, servicing debt, and maintaining overall financial health. Mismanagement of cash flows can result in severe liquidity challenges and even business failure. However, managing operating cash flow is complex because of its intricate, endogenous relationships with operational variables, like sales, operating costs, inventory, payables, and the impact of exogenous macroeconomic factors on a firm. In this paper, we present a structural model of operating cash flow that untangles this endogeneity, allows us to estimate causal relationships among these variables, and provides a valuable tool for evaluating cash flow management policies. Applying our model to quarterly financial data from S&P’s Compustat database spanning from 1990 to 2020 along with macroeconomic indicators, we provide empirical evidence of the endogenous nature of cash flow with other operational variables. We then showcase the practical value of our model by (i) identifying the characteristics of structural shocks and the new equilibria they induce within the system; (ii) offering a tool for evaluating alternative managerial actions or policy decisions to counteract these shocks; (iii) predicting the impacts of macroeconomic events, such as global recessions and fluctuations in economic sentiment, on firm performance; and (iv) demonstrating superior forecasting performance compared with traditional univariate models. In summary, our structural model of operating cash flow enhances our understanding of its dynamics, enabling better-informed decision making and more effective cash flow management in firms. This paper was accepted by David Simchi Levi, operations management. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2021.03790 .

Companies that are investing in blockchain technology to enhance supply chain transparency face challenges in fostering collaborations with others and deciding what information to share. Transparency over the actions of supply chain partners can improve operational decisions, but sharing own data on the blockchain can put firms at a competitive disadvantage. In this paper, we investigate the resulting questions of when blockchain should be adopted in a supply chain and how it should be designed by analyzing two ways that it can enhance supply chain transparency: making the manufacturer’s sourcing cost transparent to the buyers (i.e., vertical cost transparency) and making the ordering status of buyers transparent to each other (i.e., horizontal order transparency). Given such transparency, firms can design a smart contract that automates transactions contingent on the revealed information and enables them to realize better equilibrium outcomes. We find that blockchain increases supply chain profit only when the manufacturer’s capacity is large and decreases supply chain profit otherwise. If the capacity is sufficiently large to eliminate the buyers’ competition, blockchain leads to a win–win–win and the incentives of all participants are naturally aligned. If the capacity is only moderately large, the manufacturer needs to compensate the buyers to facilitate a blockchain implementation. However, if the capacity is small, horizontal order transparency enabled by the blockchain mitigates the buyers’ overorder incentive to compete for the manufacturer’s capacity and increases double marginalization. For such cases, we show that a blockchain that only enables vertical cost transparency should (and can) still be adopted in a range of small capacity cases, and we propose an access control layer for the logistics data to implement such a blockchain. This paper was accepted by David Simchi-Levi, operations management. Funding: J. Liu was supported by the National Natural Science Foundation of China [Grant 72101110] and The MOE (Ministry of Education in China) Project of Humanities and Social Sciences [Grant 20YJC630084]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/mnsc.2023.4851 .

We investigate how different types of social information affect the demand characteristics of firms competing through service quality. We first generate behavioral hypotheses around both consumers’ learning behavior and firms’ corresponding demand characteristics: market share, demand uncertainty, and rate of convergence. We then conduct a controlled human-subject experiment in which a consumer chooses to visit one of two firms, each with unknown service quality, in a repeated interaction and is exposed to different information treatments from a social network: (1) no social information; (2) share-based social information, which details the percentage of people who visited each firm; (3) quality-based social information, which illustrates the percentage of people who received a satisfactory experience from each firm; or (4) full social information, which contains both share- and quality-based social information. A key insight from our study is that different types of social information have different effects on firms’ demand. First, promoting quality-based social information leads to a significantly higher market share, lower demand variability, and faster rate of convergence for a firm with significantly better service quality. Second, when the higher quality firm has only a marginal advantage over the other firm, promoting only share-based information leads to significantly higher market share and lower demand variability. A third important result is that providing only one type of social information can actually be more helpful to the higher quality firm than providing full social information. This paper was accepted by David Simchi-Levi, operations management.

We formulate the problem of designing a personalized recommendation system for an online business-to-business (B2B) marketplace, propose a method to solve it, and evaluate results using a controlled pilot test. Our research is conducted in collaboration with IndiaMart, the dominant online B2B platform in India serving approximately 60 million buyer firms and 5 million seller firms in more than 50 million products and services. In this problem, buyers place requests for quotation (RFQs) to the platform, and the objective of the platform is to match the RFQs with suitable sellers with the highest likelihood of acceptance. Our problem entails two major challenges: (1) high-dimensional and sparse data regarding product category and spatial engagement, and (2) class imbalance because the volume of "accepted" records in historical clickstream data is significantly larger than that of "declined" records. We propose new variables motivated by the choice estimation literature to address high-dimensionality, and evaluate alternative approaches including the Synthetic Minority Over-sampling Technique (SMOTE) and a new resampling approach, which we call Panel Data Augmentation Technique (PDATE), to counter class imbalance. Our method yields a significant improvement in out-of-sample predictive accuracy. A controlled pilot test conducted at IndiaMart shows that our method provides a consistent and significant improvement in the quality of recommendations sustained over time.

We develop a database of all empirical research related to operations management in the journals Management Science, Manufacturing and Service Operations Management (M&SOM), and Production and Operations Management (POM) from the beginning of 1999 to the end of 2016. This database includes 236 empirical papers. We analyze the set of empirical papers to look for longitudinal trends and other bibliometric patterns. In particular, we show that (a) empirical research as a whole is gaining in popularity as measured by the publication rates in these three journals, (b) empirical papers in M&SOM are more likely to get citations than nonempirical papers, and (c) researchers are now more commonly using instrumental variables and are more likely to consider endogeneity challenges in their research design. Using our database, we propose three dimensions on which empirical operations management papers can be compared, including their main objective, their data sources, and their identification strategy.

Field research has shown that companies are investing in blockchain technology for their supply chains in order to benefit from enhanced visibility, but face challenges in creating partnerships with other firms in their supply chain, some of whom could be competitors, under uncertainty about where the cost and benefit of blockchain enabled network visibility will fall. On the one hand, visibility over actions of supply chain partners can improve operational decisions, but on the other hand, sharing own data on a blockchain creates opposing forces. In this paper, we investigate these questions theoretically by studying the impact of network visibility in a specific two-tier supply chain setting that consists of an upstream supplier and two competing downstream manufacturers. The supplier’s capacity is limited so that the manufacturers compete on both the supply side and the demand side. Network visibility enables the manufacturers to know each other’s existence. By comparing the cases with and without network visibility, we study how the network visibility affects the supplier, the manufacturers, and the entire supply chain. We find that the manufacturers can benefit from network visibility when the supplier’s capacity is sufficiently small or sufficiently large, but the underlying reasons are different. When the supplier is highly capacitated, network visibility creates value to the manufacturers by mitigating their over-order incentive. When the supplier’s capacity is sufficient, network visibility creates value to the manufacturers by mitigating their under-order incentive. Moreover, network visibility can help improve the supplier’s profit when her capacity is not too small because in this case, the supplier can sell more with network visibility. Finally, from the perspective of the entire supply chain, we find that network visibility can help alleviate double marginalization and improve the total supply chain profit if and only if the supplier’s capacity is sufficiently large. Furthermore, as long as network visibility is beneficial to the entire supply chain, it is also beneficial to both the supplier and the manufacturers at the same time, hence blockchain can be naturally initiated by any firm in the supply chain network. Our model particularly addresses the question whether competing firms would join a shared blockchain.

Product expiration is an important problem in the consumer packaged goods (CPG) industry costing 1%–2% of gross retail sales and eroding industry profits substantially. It can be caused by several factors related to store operations, supply chain practices, and product characteristics. Existing methods used in the industry are inadequate to identify the causes of expiration, leading to inadequate efforts to reduce expiration. Using retail data for 768 SKUs and 10,000 stores (745,638 store-SKU–level observations), as well as upstream supply chain data from a CPG manufacturer, we show the extent to which expiration of products in retail stores is driven by case size, inventory aging in the supply chain, minimum order rules, manufacturers’ incentive programs for the sales force, replenishment workload, and many control variables. A counterfactual analysis based on the model shows that our subject manufacturer can reduce expiration by up to $38.82 million per year by implementing four selected initiatives inv...