Knowledge that Transforms

Consumers increasingly want to know more about where and how the products they purchase are being made. To create transparency requires a company to both gain visibility into its supply chain and disclose information to consumers. In this paper, we focus on the dimension of visibility and investigate when companies can benefit from greater supply chain visibility. To do so, we design an incentivized human–subject experiment to study two key questions: (i) How does supply chain visibility impact consumers’ valuations of a company’s social responsibility (SR) practices in its upstream supply chain? (ii) What roles do indirect reciprocity and consumers’ prosociality play in affecting their valuations under different levels of visibility? In our design, greater visibility is represented by lower uncertainty in the outcomes of a company’s SR efforts. Our results show that consumers value greater visibility regarding a company’s SR practices in the upstream supply chain. This is especially true if consumers exhibit a self-serving bias and use uncertainty as an excuse not to pay for SR. We also observe that high prosocial consumers do not exhibit strong indirect reciprocity. Conversely, indirect reciprocity significantly increases low prosocial consumers’ valuations under High visibility. Our work adds to the experimental literature focusing on transparency and SR (which has primarily studied disclosure) by examining the equally important but understudied dimension of visibility. Furthermore, our results on consumer heterogeneity offer insights into what SR information resonates with a company’s target consumers. The online appendix is available at https://doi.org/10.1287/msom.2017.0685 .

Most consumers in rural areas of many developing countries cannot afford to purchase certain livelihood improvement products such as home appliances. To improve consumer welfare and manufacturer profit, many governments launch different types of subsidy programs that offer subsidies to consumers, manufacturers, or both. Motivated by a subsidy program developed by the Chinese government in 2007, we present a parsimonious model to determine the optimal subsidy program in different settings so as to gain a better understanding about the conditions under which it is optimal for the government to subsidize consumers only, manufacturers only, or both. Our analysis reveals that the structure of the optimal subsidy program depends on (a) whether there is a well-established market selling price for the products; and (b) the relative emphasis that the government places on consumer welfare versus manufacturer profit. Also, we find that governments can improve consumer welfare by developing subsidy programs that involve multiple (competing) manufacturers with different market sizes and adequate capacities. Our findings provide insights for developing effective government subsidy programs. The online appendix is available at https://doi.org/10.1287/msom.2017.0684 .

This paper examines how managerial short-termism can affect a firm’s inventory decision when external investors have only partial information about the firm’s demand uncertainty. We first study the scenario where the manager’s short-termism is exogenously given. We derive the full equilibrium spectrum ranging from stable separating to pooling equilibria, which yields insights for learning firms’ demand uncertainty from their inventory and sales information and for understanding the effect of managerial short-termism on firm performance. We then analyze the scenario where the manager’s short-termism is endogenous. We find that, unlike the scenario with exogenous short-termism, the first-best inventory decisions might be achieved in equilibrium on the basis of an alternative signal. This paper has been accepted for the Manufacturing & Service Operations Management Special Issue on Interface of Finance, Operations, and Risk Management.

Emissions regulation is a policy mechanism intended to address the threat of climate change. However, the stringency of emissions regulation varies across regions, raising concerns over carbon leakage—an outcome where stringent regulation in one region shifts production to regions with weaker regulation. It is believed that such leakage adversely increases global emissions. It is also believed that leakage can be eliminated by carbon tariffs, which are taxes imposed on imported goods so that they incur the same emissions cost that they would have if they had been produced in the regulated region. Results here contradict these beliefs. This paper demonstrates that carbon leakage can arise despite a carbon tariff but, when it does arise under a carbon tariff, it decreases emissions. Due in part to this clean leakage, results here indicate that a carbon tariff decreases global emissions. Domestic firm profits, on the other hand, can increase, decrease, or remain unchanged due to a carbon tariff, which suggests that carbon tariffs are not inherently protectionist as some argue. Rather, results here suggest that carbon tariffs improve the efficacy of emissions regulation, enabling it to reduce global emissions in many settings in which it would otherwise fail to do so.The online appendix is available at https://doi.org/10.1287/msom.2017.0674 .

Trade-in remanufacturing is a commonly adopted business practice under which firms collect used products for remanufacturing by allowing repeat customers to trade in used products for upgraded ones at a discount price. This paper studies how customer purchasing behavior and remanufacturing efficiency affect the economic and environmental values of such a business practice. We demonstrate a new benefit of trade-in remanufacturing: it helps exploit the forward-looking behavior of strategic customers, which could be much more significant than the widely recognized revenue-generating and environmental benefits of remanufacturing. High remanufacturing efficiency does not necessarily benefit a firm. With overly high remanufacturing efficiency, product durability is so high that repeat customers are reluctant to trade in and upgrade their used products. When customers are highly strategic, trade-in remanufacturing creates a tension between profitability and sustainability: on one hand, by exploiting the intensive forward-looking customer behavior, trade-in remanufacturing is quite valuable to the firm; on the other hand, with highly strategic customers, trade-in remanufacturing has a substantial negative impact on the environment and social welfare, since it may induce significantly higher production quantities without improving customer surplus. With nearly myopic customers, however, trade-in remanufacturing benefits both the firm and the environment. Therefore, understanding the interactions between customer purchasing behavior and trade-in remanufacturing is important to both firms and policy makers. Finally, to resolve the above tension, we study how a social planner (e.g., government) should design a public policy to maximize social welfare. The social optimum can be achieved by using a simple linear subsidy/tax scheme for both new production and remanufacturing. The proposed policy can also induce the firm to set the socially optimal remanufacturing efficiency. The online appendix is available at https://doi.org/10.1287/msom.2017.0696 .

In this study, we develop a flexible optimization framework for real-time ambulance dispatching and relocation. In addition to ambulance redeployment, we consider a general dispatching and relocation strategy by which the decision maker has the option to (i) select any available ambulance to dispatch to a call or to queue the call and (ii) send an idle ambulance to cover the location of an ambulance just dispatched to a call. We formulate the problem as a stochastic dynamic program, and, because the state space is unbounded, an approximate dynamic programming (ADP) framework is developed to generate high-quality solutions. We assess the quality of our solutions by developing a lower bound on the expected response time and computing a lower bound on the expected fraction of late calls of any relocation policy. We test the performance of our policies and available benchmarks on an emergency medical services system in Mecklenburg County, North Carolina. The results show that our policies are near optimal and significantly outperform available benchmarks. In particular, our ADP policy reduces the expected response time and fraction of high-priority late calls by 12% and 30.6%, respectively, over the best available static benchmarks in the case study. Moreover, the results provide insights on the contribution of each dispatching, redeployment, and reallocation strategy. The online appendix is available at https://doi.org/10.1287/msom.2017.0649 .

Using a dyadic panel data set that links U.S. suppliers with their major buyers, we study how trade credit responds to various types of competition in supply chains, as well as the impact of trade credit on firm performance. We find that suppliers with smaller market share are associated with more trade credit, confirming that suppliers with weak market power use trade credit as a competitive tool. Next, we find that buyers with larger market share are associated with more trade credit, whereas suppliers selling to a concentrated buyer base are associated with less trade credit; by differentiating competition in buyers’ markets from competition between supply chain partners, we show that the two types of competition have different impacts on trade credit. In studying the impact of trade credit on firm performance, we make the distinction between an industry-average trade credit and an individual supply chain’s deviation from such an industry average. When suppliers offer trade credit at their industry-average level, this action facilitates trade and, thus, is positively associated with both parties’ performance; conversely, when suppliers are more aggressive in their trade credit strategy than the industry average, then the excess trade credit is negatively associated with buyer performance. One managerial message from our research is that buyers should be cautious about trade credit far exceeding the industry-average level. The online appendix is available at https://doi.org/10.1287/msom.2017.0640 . This paper has been accepted for the Manufacturing & Service Operations Management Special Issue on Interface of Finance, Operations, and Risk Management.

Under current regulations, red blood cell (RBC) units can be transfused to patients up to 42 days after donation. However, recent studies suggest an association between the age of transfused RBCs and adverse clinical outcomes for their recipients. Therefore, there is an interest in inventory management policies that could reduce the age of transfused RBCs without compromising their availability. In this work, we study the performance of a practical family of threshold-based allocation policies, designed to trade off the age of RBC transfusions with their availability at hospitals. To this end, we consider a stylized model of a hospital blood bank that procures its required blood from local donations. For this model, we develop a new method to exactly evaluate the performance of the threshold policy in terms of the distribution of the age of allocated units and the proportion of outdates and lost demand. Through numerical and structural results, we obtain new insights on the performance of the threshold policy and in particular on how it compares with shortening the shelf life of RBCs (e.g., from 42 to 28 days). We verify and discuss the robustness of these results to the model assumptions in a simulation study calibrated using data from a Canadian hospital blood bank. The online appendix is available at https://doi.org/10.1287/msom.2017.0650 .

Rapidly evolving infectious disease epidemics, such as the 2014 West African Ebola outbreak, pose significant health threats and present challenges to the global health community because of their heterogeneous geographic spread. Policy makers must allocate limited intervention resources quickly, in anticipation of where the outbreak is moving next. We develop a two-stage model for optimizing when and where to assign Ebola treatment units across geographic regions during the outbreak’s early phases. The first stage employs a novel dynamic transmission model to forecast the occurrence of new cases at the region level, capturing connectivity among regions. We introduce an empirically estimated coefficient for behavioral adaptation to changing epidemic conditions. The second stage compares four approaches to allocate units across affected regions: (i) a heuristic based on observed cases, (ii) a greedy policy that prioritizes regions based on the reproductive number, (iii) a myopic linear program that allocates resources in the next period based on an iterative estimation–optimization approach coupled with the underlying epidemic model, and (iv) an approximate dynamic programming algorithm that optimizes over all future periods. After testing the allocation schemes under different budgets and time periods, we find that the myopic policy performs best, even when limited data are available. Our methodology could be generalized to other disease outbreaks, including the Zika virus, and other interventions.The online appendix is available at https://doi.org/10.1287/msom.2017.0681 .