Firms are exposed to a variety of low-probability, high-impact risks that can disrupt their operations and supply chains. These risks are difficult to predict and quantify; therefore, they are difficult to manage. As a result, managers may suboptimally deploy countermeasures, leaving their firms exposed to some risks while wasting resources to mitigate other risks that would not cause significant damage. In a three-year research engagement with Ford Motor Company, we addressed this practical need by developing a novel risk-exposure model that assesses the impact of a disruption originating anywhere in a firm's supply chain. Our approach defers the need for a company to estimate the probability associated with any specific disruption risk until after it has learned the effect such a disruption will have on its operations. As a result, the company can make more informed decisions about where to focus its limited risk-management resources. We demonstrate how Ford applied this model to identify previously unrecognized risk exposures, evaluate predisruption riskmitigation actions, and develop optimal postdisruption contingency plans, including circumstances in which the duration of the disruption is unknown.

We offer a new network perspective on one of the central topics in operations management—the bullwhip effect (BWE). The topic has both practical and scholarly implications. We start with an observation: the variability of orders placed to suppliers is larger than the variability of sales to customers for most firms, yet the aggregate demand variability felt by suppliers upstream does not amplify commensurably. We hypothesize that changes to the supplier’s customer base can smooth out its aggregate demand. We test the hypothesis with a data set that tracks the evolution of supply relationships over time. We show that the effect of customer base management extends beyond the statistical benefits of aggregation. In particular, both the formation and the dissolution of customer-supplier relationships are associated with the smoothing of the aggregate demand experienced by suppliers. This provides fresh insight into how firms may leverage their customer-supplier relationships to mitigate the impact of the BWE. This paper was accepted by Jay Swaminathan, operations management.

We analyze a signaling game between the manager of a firm and an investor in the firm. The manager has private information about the firm's demand and cares about the short-term stock price assigned by the investor. Previous research has shown that under continuous decision choices and the Intuitive Criterion refinement, the least-cost separating equilibrium will result, in which a low-quality firm chooses its optimal capacity and a high-quality firm over-invests in order to signal its quality to investors. We build on this research by showing the existence of pooling outcomes in which low-quality firms over-invest and high-quality firms under-invest so as to provide identical signals to investors. The pooling equilibrium is practically appealing because it yields a Pareto improvement compared to the least-cost separating equilibrium. Distinguishing features of our analysis are that: (i) we allow the capacity decision to have either discrete or continuous support, and (ii) we allow beliefs to be refined based on either the Undefeated refinement or the Intuitive Criterion refinement. We find that the newsvendor model parameters impact the likelihood of a pooling outcome, and this impact changes in both sign and magnitude depending on which refinement is used. Disciplines Business | Finance and Financial Management This journal article is available at ScholarlyCommons Abstract We investigate a phenomenon in which firms may attempt to influence their market valuation by choosing an inventory stocking quantity which does not optimize expected profits. We employ the newsvendor model within a signaling game to examine a relatively common scenario in which the firm's equity holder has incomplete information concerning the demand for the firm's product. We apply a perfect Bayesian equilibrium solution and identify ranges of model parameters where the firm's stocking quantity decision does not maximize expected profits. This includes instances in which a firm facing high demand chooses a lower stocking quantity than that which would optimize expected profits and a firm facing low demand chooses a higher stocking quantity than that which would optimize expected profits. This result contrasts with prior research, which has shown that when equity holders have incomplete information about the quality of the firm's opportunities, high quality firms will consistently overinvest and low quality firms will invest to optimize expected profits. We show an extreme example of this behavior in which a high demand firm chooses that stocking quantity which would have been optimal under complete information for a low demand firm.

We analyze a signaling game between the manager of a firm and an investor in the firm. The manager has private information about the firm's demand and cares about the short-term stock price assigned by the investor. Previous research has shown that under continuous decision choices and the Intuitive Criterion refinement, the least-cost separating equilibrium will result, in which a low-quality firm chooses its optimal capacity and a high-quality firm over-invests in order to signal its quality to investors. We build on this research by showing the existence of pooling outcomes in which low-quality firms over-invest and highquality firms under-invest so as to provide identical signals to investors. The pooling equilibrium is practically appealing because it yields a Pareto improvement compared to the least-cost separating equilibrium. Distinguishing features of our analysis are that: (i) we allow the capacity decision to have either discrete or continuous support, and (ii) we allow beliefs to be refined based on either the Undefeated refinement or the Intuitive Criterion refinement. We find that the newsvendor model parameters impact the likelihood of a pooling outcome, and this impact changes in both sign and magnitude depending on which refinement is used. Disciplines Business | Finance and Financial ManagementThis journal article is available at ScholarlyCommons: http://repository.upenn.edu/fnce_papers/63 AbstractWe investigate a phenomenon in which firms may attempt to influence their market valuation by choosing an inventory stocking quantity which does not optimize expected profits. We employ the newsvendor model within a signaling game to examine a relatively common scenario in which the firm's equity holder has incomplete information concerning the demand for the firm's product. We apply a perfect Bayesian equilibrium solution and identify ranges of model parameters where the firm's stocking quantity decision does not maximize expected profits. This includes instances in which a firm facing high demand chooses a lower stocking quantity than that which would optimize expected profits and a firm facing low demand chooses a higher stocking quantity than that which would optimize expected profits. This result contrasts with prior research, which has shown that when equity holders have incomplete information about the quality of the firm's opportunities, high quality firms will consistently overinvest and low quality firms will invest to optimize expected profits. We show an extreme example of this behavior in which a high demand firm chooses that stocking quantity which would have been optimal under complete information for a low demand firm.

C ompanies that experience a disruption in their supply chain often face a difficult decision-either accept the information that they have regarding the duration of the disruption, or invest in collecting better information. This choice is not clear since better information may not be attainable, and if it is attainable, it may not improve operational decision-making. In light of this dilemma, we collaborate with a multinational division of a Fortune 500 manufacturing firm to develop stochastic linear programming models that quantify the value of disruption duration information. Our models allow us to examine characteristics of the disrupted part that may be associated with the value of better information. We focus on characteristics that are knowable at the outset of the disruption, as those can help the firm decide whether to invest in collecting better information. Using our research partner's supply chain and production data, we find that the value of information can vary materially-from < 1% to over 99% of the cost of the disruption, underscoring the value of identifying disruptions that are sensitive to information quality. To address this, we use the company's data to identify several part-related characteristics that influence the value of disruption duration information. These findings can help managers to identify parts in their own supply chains whose impact in a disruption is sensitive to different levels of duration information, and allow them to make informed decisions on whether or not to gather better information when a disruption strikes.

This research investigates how information asymmetry between the firm and its investors can influence supply chain disruptions. In such settings, these actors may be induced to take steps which exacerbate rather than ameliorate both the likelihood and impact of disruptions. By better understanding these mechanisms, managers and investors alike are better armed to avoid the costly consequences.

Operational decisions under information asymmetry can signal a firm’s prospects to less informed parties, such as investors, customers, competitors, and regulators. Consequently, managers in these settings often face a trade-off between making an optimal decision and sending a favorable signal. We provide experimental evidence on the choices made by decision makers in such settings. Equilibrium assumptions that are commonly applied to analyze these situations yield the least cost separating outcome as the unique equilibrium. In this equilibrium, the more informed party undertakes a costly signal to resolve the information asymmetry that exists. We provide evidence, however, that participants are much more likely to pursue a pooling outcome when such an outcome is available. This result is important for research and practice because pooling and separating outcomes can yield dramatically different results and have divergent implications. We find evidence that the choice to pool is influenced by changes in the underlying newsvendor model parameters in our setting. In robustness tests, we show that choosing a pooling outcome is especially pronounced among participants who report a high level of understanding of the setting and that participants who pool are rewarded by the less informed party with higher payoffs. Finally, we demonstrate through a reexamination of two previous studies how pooling outcomes can substantively extend the implications of other extant signaling game models in the operations management literature. Data, as supplemental material, are available at http://dx.doi.org/10.1287/mnsc.2015.2407 . This paper was accepted by Serguei Netessine, operations management.

Problem definition: Operational disruptions can impact a firm’s risk, which manifests in a host of operational issues, including a higher holding cost for inventory, a higher financing cost for capacity expansion, and a higher perception of the firm’s risk among its supply chain partners. Academic/practical relevance: Although disruptions have been studied extensively in the operations management literature, the emphasis has been on mitigating the deleterious impact on the firm’s production potential rather than its risk. We examine whether implementing and credibly attesting to having effective internal control systems will meaningfully influence the impact of operational disruptions on the firm’s risk and market valuation. Methodology: We exploit a 2004 regulatory change that required certain firms to attest to having control systems in place. Our triple-difference regression specification takes advantage of a regulatory anomaly that excluded a well-defined set of firms from complying with the control system requirement. Results: We find that firms that were obligated to comply with this regulatory requirement experienced a materially smaller increase in their risk and a smaller decrease in their market value in the aftermath of an operational disruption. Firms that were not obligated to comply with this requirement did not experience such benefits in the aftermath of a disruption and instead, experienced a larger increase in their risk. Managerial implications: Fostering a better understanding of whether credible control systems can reduce the impact of disruptions on the firm’s risk and value is important as it identifies a broader set of mitigation strategies available to operations managers. This can help managers achieve a better fit between their risk mitigation initiatives and their objectives and budget constraints.