Richard Goodwin, Pinar Keskinocak, Sesh Murthy, Frederick Wu, and Rama Akkiraju
Much of the attention in artificial intelligence (AI) for Business has focused on business to consumer transactions. Shopping bots, systems to recommend movies and books based on similar opinions by other users and news filtering agents, are just some examples. However, we feel that AI can have a larger impact on the supply chain that delivers goods and services to the end consumer. Reductions in costs and the pervasiveness of the Interact have encouraged companies to move towards using e-commerce for transactions with their business partners. Companies are willing to invest resource because of the reduced product cycle times and the lower transaction costs that they expect. A result of this movement is that companies can afford to interact with a larger number of trading partners and form project and customer specific partnerships that would have been too costly in the past. To manage a larger and more dynamic set of partnerships and to be able to take advantage of transient opportunities, business users will need decisionsupport systems to identify and analyze the opportunities in terms of their business objectives. In this paper, we describe our agent-based decision-support framework for creating systems to support trading partners in the e-supply chain. In particular, we will focus on the issues that need to be addressed in order to create a viable and useful decisionsupport system.