Raghu Arunachalam, Norman M. SadehAcknowledgements. The research reported in this 2. LITERATURE REVIEW3. TAC-SCM: MOTIVATIONS AND DESIGN OBJECTIVESThe Trading Agent Competition (TAC) has been an annual event since its inception in 2000 [28]. TAC provides a forum for researchers studying trading agents and focuses their energies on a common problem. Between 2000 and 2002, TAC revolved around a TraIn Summer 2002, encouraged by the success of their competition, TAC organizers solicited proposals for the addition of a new game that would introduce new challenges to the trading agent community and be representative of a somewhat more strategic segmenMore specifically, a good candidate game needed to address the following issues:Strategizing. The game scenario had to leave room for supply chain trading agents to strategize about their own choices and those of the competition.Uncertainty and incomplete information. Supply chain management is in great part about managing uncertainty and risks and working with incomplete information. Uncertainty comes in many shapes and forms and includes dealing with changes in both upstream aRealism. The applied nature of supply chain management dictated that any effort in this area would need to be made with an eye on actual practice. If the lessons learned from designing successful agents were to find wider acceptance, scenarios depicted iGenerality. The challenges introduced in the game needed to be representative of a broad class of supply chain situations.Simplicity. To be successful and encourage participation, it was critical that the game be simple enough to enable a good number of competitors to submit entries. This, of course, to a degree, runs counter to the aforementioned realism requirement. Getti4. TAC-SCM: GAME OVERVIEW4.2. Bidding on Customer Orders6. THE 2003 TAC-SCM TOURNAMENTTAC-o-matic7. AGENT DESIGNS IN THE 2003 TAC-SCM TOURNAMENTAcknowledgements. The research reported in thisTHE SUPPLY CHAIN TRADING AGENT COMPETITION Raghu Arunachalam, Norman M. Sadeh CMU-CS-04-164 September 27, 2004 School of Computer Science Carnegie Mellon University Pittsburgh, PA 15213-3891 Also appears as Institute for Software Research International Technical Report CMU-ISRI-04-133 and To Appear in “Electronic Commerce Research & Applications Journal” Acknowledgements. The research reported in this paper has been funded by the National Science Foundation under ITR Grant 0205435. TAC-SCM is the product of a collaboration between Carnegie Mellon University’s e-Supply Chain Management Laboratory and the Swedish Institute of Computer Science. In particular, the authors would like to acknowledge the contributions of Joakim Eriksson, Niclas Finne and Sverker Janson. They also wish to thank all members of the new TAC-SCM community for their feedback on the design of the game and their participation. The authors also thank the RedAgent Team for sharing Figure 10 and 11 with them.Keywords. Agent-based systems, market game, multi-agent simulation, procurement, intelligent agents, supply chain management, trading agents. Page 1Abstract Supply chain management deals with the planning and coordination of bidding, production, sourcing and procurement activities associated with one or more products. It is central to today’s global economy, leading to trillions of dollars in annual transactions worldwide. With the emergence of electronic marketplaces, it is only natural to seek automated solutions that are capable of rapidly evaluating a large number of bidding, sourcing and procurement options. In this paper, we detail a game we have designed to promote the research and evaluation of such solutions under realistic conditions. The game requires agents to manage the assembly of PCs, while competing with one another both for customer orders and for key components. We discuss how the game captures the complexity, stochasticity and competitive nature inherent to supply chain environments. A Web-based multi-agent simulation platform developed for the game was implemented in 2003 and validated in the context of the first Supply Chain Management Trading Agent Competition (TAC-SCM). A total of 20 teams from around the world competed with one another. We review agent strategies developed by different teams and discuss the merits of competition-based research over more traditional research methodologies in this area. Page 21. INTRODUCTION Supply chain management is concerned with planning and coordinating bidding, production, sourcing and procurement activities across the multiple organizations involved in the delivery of one or more products. It is central to today’s global economy, leading to trillions of dollars in annual transactions worldwide. Supply chains are highly dynamic environments that are subject to: • market fluctuations, such as surges in customer demand or drops in supply availability; • operational contingencies, such as delays in supply delivery, losses of capacity, or quality problems; and, • changes in strategies employed by competitors, customers or suppliers Accordingly, supply chain performance can significantly benefit from decision making processes that constantly monitor changing conditions and dynamically evaluate available trading and operational options in light of these conditions. With the emergence of electronic marketplaces, automated programs or “intelligent agents” offer the promise of significantly increasing the number of options one can consider and of substantially improving supply chain performance. Simple versions of such programs have been demonstrated in other domains, though the prospect of delegating routine supply chain decisions to software agents still makes many managers nervous. How will the agents react under changing conditions? Could competitors develop strategies that exploit some of their potential weaknesses? While routine planning and control decisions in static supply chains are now relatively well understood, this is not the case of dynamic supply chain trading environments, where companies more openly compete for customer orders and components. Evaluating the benefits and possible limitations of intelligent agent functionality in these more challenging environments can not be convincingly Page 3done by just relying on traditional methodologies, where a given technique is evaluated under a set of predefined conditions. Instead, supply chain trading environments