Many companies favor offering a large product variety to their customers with the intention of increasing the customer base. For example, one may see twenty or more models of a digital equipment at a retailer, each model competing with others in features and price. Similarly, online travel websites offer a variety of travel products consolidated from different suppliers. Choosing the right variety of products to offer to the customers turns out to be a difficult problem. Offering a low-margin product increases the customer base, but it may also cause the current customers to switch to the low-margin product, damaging revenues. Large product variety increases the customer base, but it may also increase the operational costs. As the inventories are consumed or as the company learns more about the customer choices, it may be necessary to adjust the offered assortment. Despite the ubiquitous nature of choice, the interface between the operational decisions and the customer choices are not well-understood. The primary goal of this research is to develop a framework for integrating customer choice into operations management decisions.
This research will focus on both static and dynamic optimization problems with customer choice behavior. In the static setting, the parameters of the underlying choice model are known and the objective is to decide which assortment of products to offer. In the dynamic setting, the parameters of the underlying choice model are uncertain. One must estimate the parameter values based on data and adapt the decisions as the knowledge of the parameter values evolves. Besides methodological contributions, this research will impact the relevant course offerings in inventory control, supply chain management and revenue management. Simulation games to validate the findings will be developed and used as an instruction platform. Students at all levels and from diverse backgrounds will have opportunities for research experience.
