This dissertation introduces the Binary Customer Satisfaction Model for addressing logistics issues. In typical logistics problems, the arrival of customers through a demand process is considered external to the management decisions. In practice, it is typically the case that customers will respond to changes is service policy by changing their behavior. The Binary Customer Satisfaction Model provides a simple customer behavior model that directly interacts with the service policy and provides for analysis of managerial insights.

The Binary Customer Satisfaction Model assigns customers to one of two satisfaction states. Satisfied customers have one demand rate, while unsatisfied customers have a different demand rate. The Binary Customer Satisfaction Model accommodates situations where satisfied customers demand more, as well as those when satisfied customers demand less, a possibility undertreated in existing literature. Satisfaction changes for each customer when the customer demands service. Satisfaction occurs when the customer receives service, while unsatisfaction occurs when the customer attempts to receive service but is unable to.

The Binary Customer Satisfaction Model is generally applicable to a wide range of logistics problems. In this dissertation, we consider the application of the model to the newsvendor inventory problem as well as an M/M/s queueing model without a buffer. We also briefly consider extensions to these models and how the Binary Customer Satisfaction Model can inform management of these extended cases.

Key to these insights is a well-defined concept of myopic management policies. This dissertation defines myopic policies in such a way to allow explicit comparison between optimal and myopic policies, and quantitatively present the value of considering the effect of service policy on future customer behavior.

In both the inventory and queueing contexts, we find that the Binary Customer Satisfaction Model gives two major insights. The first confirms the intuitive result that, if satisfied customers are more likely to arrive, then it is worthwhile for a manager to provide a level of service that would appear too high to a myopic manager, as future increases in customer demand will offset the additional cost. Similarly, if satisfied customers are less likely to arrive, the manager should prepare a lower level of service.

The second main insight is that it is not enough to simply observe the demand to find an optimal policy when customer behavior depends on service policies. Even with full knowledge of the demand, and beginning with the optimal demand level, a myopic manager will choose a suboptimal service policy, which will in turn create a suboptimal policy, which will cause the myopic manager to move further away from optimality. This spiraling effect makes clear the importance of not blindly making policies based on empirical observation of demand.