Optimal Design of the Online Auction Channel: Analytical, Empirical, and Computational Insights

The advent of electronic commerce over an open-source, ubiquitous Internet Protocol (IP) based network has led to the creation of new channels such as business-to-consumer online auctions, the focus of our study. They typify a new generation of mercantile processes that facilitate the flow of goods and services, connecting producers, suppliers, intermediaries and end-consumers. The vast body of economics literature on auctions notwithstanding; the lack of appropriate mechanism design considerations that factor the intricacies of the online environment can result in significant losses of revenue. This work presents an analytical model that characterizes the revenue generation process for a popular kind of supply chain oriented online auction, namely Yankee auctions. Such auctions sell multiple identical units of a good to multiple buyers using an ascending and open auction mechanism, which has its roots in the English auction, yet is significantly different. We put forward a portfolio of tools, varying in their level of abstraction and information intensity requirements, which help the auctioneers maximize their revenues. A critical component in supply chain management is the understanding of consumer demand. This study sheds new light on how online auctions can be used to construct empirical demand curves for the auctioned goods. The methodologies used to validate the analytical model range from empirical analysis to simulation. A key contribution is the design of a partitioning scheme of the discrete valuation space of the bidders such that equilibrium points with higher revenue structures become feasible. Our analysis indicates that the auctioneers are, most of the time, far away from the optimal choice of key control factors such as the bid increment, resulting in substantial losses in a market with already tight margins.