Title: Artificial neural networks for demand forecasting of the Canadian forest products industry

Authors: Shashi K. Shahi; Peizhi Yan; Salimur Choudhury; Bharat Maheshwari

Addresses: Management Information Systems, Odette School of Business, University of Windsor, Windsor, ON, N9B 3P4, Canada ' Department of Computer Science, Faculty of Science and Environmental Studies, Lakehead University, Thunder Bay, ON, P7B 5E1, Canada ' Department of Computer Science, Faculty of Science and Environmental Studies, Lakehead University, Thunder Bay, ON, P7B 5E1, Canada ' Management Science, Odette School of Business, University of Windsor, Windsor, ON, N9B 3P4, Canada

Abstract: The supply chains of the Canadian forest products industry are largely dependent on accurate demand forecasts. The USA is the major export market for the Canadian forest products industry, although some Canadian provinces are also exporting forest products to other global markets. However, it is very difficult for each province to develop accurate demand forecasts, given the number of factors determining the demand of the forest products in the global markets. We develop multi-layer feed-forward artificial neural network (ANN) models for demand forecasting of the Canadian forest products industry. We find that the ANN models have lower prediction errors and higher threshold statistics as compared to that of the traditional models for predicting the demand of the Canadian forest products. Accurate future demand forecasts will not only help in improving the short-term profitability of the Canadian forest products industry, but also their long-term competitiveness in the global markets.

Keywords: artificial intelligence; artificial neural networks; ANNs; forest industry competitiveness; demand forecasting; uncertain demand and supply.

DOI: 10.1504/IJBIS.2024.142584

International Journal of Business Information Systems, 2024 Vol.47 No.3, pp.295 - 323

Received: 02 Dec 2020
Accepted: 20 May 2021
Published online: 11 Nov 2024 *

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