A Multiple-Agent based System for Forecasting the Ice Cream Demand using Climatic Information

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.

Meeting Name

1st International Symposium on Management Intelligent Systems, IS-MiS 2012 (2012: Jul. 11-13, Salamanca, Spain)

Department(s)

Business and Information Technology

Keywords and Phrases

Forecasting; Intelligent systems; Agent-based systems; Amount of information; Autonomous computers; Behavioral patterns; Changing environment; Climatic informations; Decision makers; Demand forecasting; Demand planning; Forecasting techniques; Heterogeneous sources; Ice creams; Operational efficiencies; Seasonality; Autonomous agents

International Standard Book Number (ISBN)

978-3-642-30863-5

International Standard Serial Number (ISSN)

2194-5357

Document Type

Article - Conference proceedings

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2012 Springer Verlag, All rights reserved.

Publication Date

01 Jan 2012

Link to Full Text

Share

 
COinS