Abstract
A multiple agent-based system is intended to capture complex behavioral patterns by utilizing a collection of autonomous computer systems (called agents) that can interact with decision makers and then learn, perform, and delegate tasks on their behalf. With its ability to handle a large amount of information from heterogeneous sources in dynamically changing environments, a multiple agent-based system can significantly improve the company’s business intelligence and operational efficiency. Though rarely used in demand planning, this paper proposes a multiple agent-based system for demand forecasting of ice cream which poses unique challenges due to volatility and seasonality of ice cream consumption. To validate the usefulness of the proposed system for demand planning, the forecasting outcomes of the proposed system was compared to those of traditional forecasting techniques. Our experiments showed that the proposed multiple agent-based system outperformed its traditional forecasting counterparts in terms of its accuracy and consistency.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Bradbury, B.: The weather factor: Does a 100(degree) day beat a TPR? Frozen Food Age 48(8), 8–9 (2000)
Cawthorn, C.: Weather as a strategic element in demand chain planning. The Journal of Business Forecasting Methods and Systems 17(3), 18–21 (1998)
de Menezes, L.M., Bunn, D.W., Taylor, J.W.: Review of guidelines for the use of combined forecasts. European Journal of Operational Research 120(1), 190–204 (2000)
Granger, C.W., Ramanathan, R.: Improved methods of combining forecasts. Journal of Forecasting 3, 197–204 (1984)
International Dairy Food Association. Ice cream sales & trends (2012), http://www.idfa.org/newsviews/media-kits/ice-cream/ice-cream-sales-and-trends/ (retrieved on February 16, 2012)
Makridakis, S.: Why combining works? International Journal of Forecasting 5(4), 601–603 (1989)
Liang, W., Huang, C.: Agent-based demand forecast in multiechelon supply chain. Decision Support Systems 42, 390–407 (2006)
Min, H.: Artificial intelligence in supply chain management. International Journal of Logistics: Research and Applications 13(1), 13–39 (2010)
Newell, A.: Putting it all together. In: Klahr, D., Kotovsky, K. (eds.) Complex Information Processing: The Impact of Herbert A. Simon. Lawrence Erlbaum Associates, Hillsdale (1989)
Reis, B.Y.: A multi-agent system for on-line modeling, parsing and prediction of discrete time series data. In: Mohammadian, M. (ed.) Computational Intelligence for Modeling, Control & Automation, pp. 164–169. IOS Press (1999)
Yu, W., Graham, J.H., Min, H.: Dynamic pattern matching using temporal data mining for demand forecasting. In: Proceedings of the 2nd International Conference on Electronic Business, Taipei, Taiwan, December 10-13, pp. 400–402 (2002)
Yu, W., Graham, J.H.: A multiple agent architecture for demand forecasting in electronic commerce supply chain systems. In: Proceedings of the 17th ISCA (The International Society for Computers and their Applications, Inc.) International Conference on Computers and their Applications, San Francisco, CA, April 4-6, pp. 462–466 (2002)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Yu, WB., Min, H., Lea, BR. (2012). A Multiple-Agent Based System for Forecasting the Ice Cream Demand Using Climatic Information. In: Casillas, J., Martínez-López, F., Corchado Rodríguez, J. (eds) Management Intelligent Systems. Advances in Intelligent Systems and Computing, vol 171. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-30864-2_22
Download citation
DOI: https://doi.org/10.1007/978-3-642-30864-2_22
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-30863-5
Online ISBN: 978-3-642-30864-2
eBook Packages: EngineeringEngineering (R0)