Article: A multiple agent-based system for the intelligent demand planning of new products Journal: European Journal of Industrial Engineering (EJIE) 2024 Vol.18 No.3 pp.410 - 432 Abstract: New product development (NPD) is pivotal in the firm's innovation and organic growth. Despite its strategic importance to the firm's success, it poses many managerial challenges for the effective launch of new products due to the inherent difficulty in new product demand planning. Such difficulty stems from an absence of historical sales data, shortened product life cycles, and a rapid shift in today's consumer behaviours. To deal with those demand planning challenges, this paper aims to propose a multiple agent-based system (ABS) that can overcome the shortcomings of traditional demand forecasting tools and improve forecasting accuracy significantly through the inclusion of meaningful information available from both internal and external data sources. The proposed ABS incorporates causal information obtained from four different types of agents: the coordination agent, the task agent, the data collection agent, and the interface agent. Through a series of simulation experiments, we found that the ABS improved forecasting accuracy over the traditional forecasting methods in demand planning situations where only a limited amount of historical data is available in the early introductory stages of NPD. [Received: 11 August 2022; Accepted: 24 March 2023] Inderscience Publishers - linking academia, business and industry through research

Title: A multiple agent-based system for the intelligent demand planning of new products

Authors: Hokey Min; Wen-Bin Yu

Addresses: Maurer Centre 312, Allen and Carol Schmidthorst College of Business, Bowling Green State University, Bowling Green, Ohio 43403, USA ' Department of Business and Information Technology, Missouri University of Science and Technology, Fulton 106C, 301 W, 14th Street, Rolla, MO 65409, USA

Abstract: New product development (NPD) is pivotal in the firm's innovation and organic growth. Despite its strategic importance to the firm's success, it poses many managerial challenges for the effective launch of new products due to the inherent difficulty in new product demand planning. Such difficulty stems from an absence of historical sales data, shortened product life cycles, and a rapid shift in today's consumer behaviours. To deal with those demand planning challenges, this paper aims to propose a multiple agent-based system (ABS) that can overcome the shortcomings of traditional demand forecasting tools and improve forecasting accuracy significantly through the inclusion of meaningful information available from both internal and external data sources. The proposed ABS incorporates causal information obtained from four different types of agents: the coordination agent, the task agent, the data collection agent, and the interface agent. Through a series of simulation experiments, we found that the ABS improved forecasting accuracy over the traditional forecasting methods in demand planning situations where only a limited amount of historical data is available in the early introductory stages of NPD. [Received: 11 August 2022; Accepted: 24 March 2023]

Keywords: new product development; NPD; demand planning; agent-based system; ABS; simulation; business intelligence; predictive analytics.

DOI: 10.1504/EJIE.2024.138206

European Journal of Industrial Engineering, 2024 Vol.18 No.3, pp.410 - 432

Received: 11 Aug 2022
Accepted: 24 Mar 2023
Published online: 30 Apr 2024 *

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Title: A multiple agent-based system for the intelligent demand planning of new products

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