Supply Chain Disruptions and the Role of Information Asymmetry

Abstract

My research examines how firm operational decisions influence and are influenced by firm value. In particular, I focus on these relationships in the context of low probability, high impact disruptions. Over the last several years, companies have faced rising levels of risk and volatility that affect their operations and supply chains. Some recent examples include the unrest in the Middle East, global financial shocks, volcano-related transportation disruptions in Europe, oil price volatility, and natural disasters. As a result, supply chain executives increasingly have a dual mission -- to systematically address extreme risks such as hurricanes, epidemics, earthquakes or port closings, and to manage conventional risks, such as forecast errors, sourcing problems, and transportation breakdowns. In an environment where extreme risks are difficult to predict and have a variable impact on the firm, there is no panacea that will fully insulate the company and its operations. With my research I intend to provide firms with meaningful insights on how to manage this uncertainty by measuring and mitigating the level of risk in their operations. My dissertation focuses on one important aspect of this issue -- how information asymmetry between the firm and its investors may lead managers within the firm to take actions which increase rather than decrease the firm's exposure to low probability, high impact disruptions.

In the first chapter, I examine the role of information asymmetry in inducing managerial decisions that contribute to supply chain disruptions. I use signaling game theory to develop a stylized model of a capacity investment decision by the firm's management. I integrate the Newsvendor Model, a canonical capacity planning tool, into the signaling game in order to tie the results directly to common operations management decision settings. In the model, the manager has private information about the firm's operations and may take a suboptimal capacity decision in order to signal her private information to an uninformed investor, and thereby influence the short-term stock price of the firm. Distinguishing features of the analysis are that: (i) I allow the capacity decision to be either in discrete increments or continuous, and (ii) I allow beliefs to be refined based on either the Undefeated refinement or the Intuitive Criterion refinement. Previous research has shown that under continuous decision choices and the Intuitive Criterion refinement, information asymmetry gives rise to the least cost separating equilibrium, 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. I 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. Such an outcome makes clear, however, that managers may knowingly under-invest in capacity.

If management engages in such myopic decision-making, then some portion of supply chain disruptions may be self-inflicted. This has direct implications for how to effectively mitigate disruptions. For instance, proper consideration should be given to the development of managerial incentive schemes to ensure they aren't inducing such undesirable outcomes. To gain some insight on when such myopic decision making can be expected, I run a numerical analysis consisting of approximately 1.5 million scenarios based on the inputs in our model. Feeding the results of this numerical analysis into an empirical model, I show that the parameters of the Newsvendor Model have a significant influence on the likelihood of myopic decision making, and that the magnitude and direction of this influence is highly sensitive to which assumptions are relaxed. Finally, I provide evidence from executive interviews that support the results of our model.

This analysis is important because it provides a tractable model to analyze myopic behavior in a common operations management setting. It is relevant to my research because it shows that supply chain disruptions can be traced to management's purposeful actions, and the circumstances under which such behavior should be expected. It is also surprising because it reveals that the outcomes from the model are highly sensitive to two assumptions which have been widely employed in the literature -- capacity choices with continuous support and the application of the Intuitive Criterion refinement.

In the second chapter, I present the results of a controlled experiment that analyzes whether the Intuitive Criterion refinement or the Undefeated refinement is a better predictor of decisions made under information asymmetry. Recall that chapter 1 considers the implications of both discrete capacity decisions and refining the participants' beliefs using the Undefeated refinement as opposed to the Intuitive Criterion refinement. While using discrete support for capacity choices is well established in the operations literature, the use of the Undefeated refinement has received less attention. Deciding which refinement to employ is central in analyses involving better informed decision makers that are called upon to make choices which may provide a costly yet informative signal to less informed parties. A challenge in such settings is how to handle the plethora of equilibrium outcomes that are often produced from the analysis. Researchers address this issue by using belief refinements, which pare the set of equilibrium outcomes by making assumptions on how the players in the game form their beliefs.

Both the Undefeated and Intuitive Criterion refinements are theoretically sound, and researchers are justified in adopting either approach on those grounds. Our experiment, however, is the first direct empirical evidence of whether individuals make decisions which are consistent with the Undefeated refinement compared to the Intuitive Criterion refinement. I examine this issue in a setting central to operations management -- a capacity investment decision. I find that the Undefeated refinement is a much better predictor of individual choices and that these results stand up when greater complexity is added to the game. The proportion of subjects making choices consistent with the Intuitive Criterion, however, is relatively low and reduces further as the complexity of the game increases.

A common criticism of complex experiments is that the subjects may not understand the game, and this lack of understanding governs their behavior. I address this by running practice rounds to acclimate the subjects to the game, having subjects change roles during the game, and requiring subjects to define their strategies before playing each round in the game. I also ask subjects to rate their understanding of the game before they are paid. I show that individuals making decisions which are consistent with the Undefeated refinement report a higher understanding of the game and earn more money from the game.

These results provide strong support that decisions are made consistent with the Undefeated refinement rather than the Intuitive Criterion refinement. This is surprising because the Undefeated refinement has not been applied in our field, and yet it is more predictive of actual decision making. It is also important because, as I show in both chapters 1 and 2, the results generated by the Undefeated refinement can often be materially different compared to those generated by the Intuitive Criterion refinement. For instance, the Undefeated refinement is far more likely to predict a pooling equilibrium such that managers at superior firms commit to lower capacity levels while managers at inferior firms commit to higher capacity levels. This ties to the theme of my research because it demonstrates that superior firms can expose themselves to potential disruption by building out less than the optimal level of capacity.

In the final chapter, I examine whether managers exercise significant discretion in disclosing supply chain disruptions to investors. A major challenge in empirical research on supply chain disruptions is the possibility that selection issues prevent the identification of material, disruptive events. It is not clear whether managerial disclosure of such events is influenced by the expected impact of the event on the firm's share price, nor is it clear whether this impact would differ if managers were more forthcoming. I empirically examine these issues using a sample of over 500 disruption announcements collected from company press releases. I take advantage of an exogenous regulatory shock, the enforcement date of new corporate disclosure rules, to identify whether managers were previously exercising significant discretion in deciding whether or not to reveal material disruptions affecting the firm. I find that after the regulatory change, managers disclosed far more material disruptive events, indicating that they had previously been suppressing their release. I also find that there is a significant amelioration in the average impact of disruptions on firm value after managers improve their disclosure practices. Finally, I show that disruptions attributed to the firm's internal operations are far more damaging to firm value than those attributed to environmental factors, and this difference persists after disclosure is improved.

The impact of disruptions on firm value can vary widely. My findings are important for managers and investors alike because they help identify the types of disruptions and the firm characteristics that contribute disproportionately to the most damaging announcements. Countermeasures to mitigate the risk of disruptions have a cost, and insights into the types of disruptions that represent the greatest risk to company value will help managers assess whether the company is investing appropriately to mitigate the most material risks.