Abstract
Enterprises continuously seek decision support tools that can help automate and codify business decisions. This is particularly true in the business of consumer electronics manufacturing where components are often interchangeable and several manufacturers can supply the same component over the life of a product. In this kind of dynamic environment, businesses are faced with the choice of signing long-term (possibly quite risky) contracts or of waiting to procure necessary components on the spot market (where availability may be uncertain). Having analytical tools to analyze previous and forecast future market conditions is invaluable. We analyze a supply chain scenario from an economic perspective that involves both component procurement and sales uncertainties. The data we analyze comes from a multi-agent supply chain management simulation environment (TAC SCM) which simulates a one-year product life-cycle. The availability of simulation logs allows us access to a rich set of data which includes the requests and actions taken by all participants in the market. This rich informational access enables us to calculate supply and demand curves, examine market efficiency, and see how specific strategic behaviors of the competing agents are reflected in market dynamics.
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References
Barbosa, R.P., Belo, O.: Autonomous Forex Trading Agents. In: Proceedings of the 8th Industrial Conference on Advances in Data Mining: Medical Applications, E-Commerce, Marketing, and Theoretical Aspects, pp. 389–403
Benisch, M., Sardinha, A., Andrews, J., Sadeh, N.: CMieux: Adaptive Strategies for Competitive Supply Chain Trading. In: Proceedings of the 8th ACM International Conference on Electronic Commerce, pp. 47–58 (2006)
Cliff, D.: Explorations in evolutionary design of online auction market mechanisms. Electronic Commerce Research & Applications Journal 2(2), 162–175 (2003)
Collins, J., Arunachalam, R., Sadeh, N., Ericsson, J., Finne, N., Janson, S.: The Supply Chain Management Game for the 2006 Trading Agent Competition (Technical Report) CMU-ISRI-05-132, Carnegie Mellon University, Pittsburgh, PA (November 2005)
Contreras, J., Espinola, R., Nogales, F., Conejo, A.: ARIMA models to predict next-day electricity prices. IEEE Transactions on Power Systems 18(3), 1014–1020 (2003)
Etzioni, O., Tuchinda, R., Knoblock, C.A., Yates, A.: To buy or not to buy: mining airfare data to minimize ticket purchase price. In: Proceedings of the ACM Conference on Knowledge Discovery and Data Mining, pp. 119–128 (2003)
Ketter, W., Collins, J., Gini, M., Gupta, A., Schrater, P.: Detecting and Forecasting Economic Regimes in Multi-Agent Automated Exchanges. Decision Support Systems 47(4), 307–318 (2009)
Kohzadi, N., Boyd, M., Kermanshahi, B., Kaastra, I.: A comparison of artificial neural network and time series models for forecasting commodity prices. In: Neurocomputing (1996)
Kovalchuk, Y., Fasli, M.: Adaptive Strategies for Predicting Bidding Prices in Supply Chain Management. In: Proceedings of the International Conference on Electronic Commerce, pp. 6–12 (2008)
Nagali, V., Hwang, J., Sanghera, D., Gaskins, M., Pridgen, M., Thurston, T., Mackenroth, P., Branvold, D., Scholler, P., Shoemaker, G.: Procurement Risk Management at Hewlett-Packard. Interfaces 38(1), 51–60 (2008)
Nogales, F., Contreras, J., Conejo, A., Espinola, R.: Forecasting next-day electricity prices by time series models. IEEE Transactions on Power Systems 17(2), 342–348 (2002)
Raykhel, I., Ventura, D.: Real-time automatic price prediction for ebay online trading. In: Proceedings of the Innovative Applications of Artificial Intelligence Conference, pp. 135–140 (July 2009)
Wellman, M.P., Reeves, D.M., Lochner, K.M., Vorobeychik, Y.: Price prediction in a trading agent competition. Journal of Artificial Intelligence Research (21), 19–36 (2004)
Smith, T.F., Waterman, M.S.: Identification of Common Molecular Subsequences. J. Mol. Biol. 147, 195–197 (1981)
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Groves, W., Collins, J., Ketter, W., Gini, M. (2010). Analyzing Market Interactions in a Multi-agent Supply Chain Environment. In: Sharman, R., Rao, H.R., Raghu, T.S. (eds) Exploring the Grand Challenges for Next Generation E-Business. WEB 2009. Lecture Notes in Business Information Processing, vol 52. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17449-0_5
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DOI: https://doi.org/10.1007/978-3-642-17449-0_5
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