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Forecasting Supply Chain Demand Approach Using Knowledge Management Processes and Supervised Learning Techniques

Forecasting Supply Chain Demand Approach Using Knowledge Management Processes and Supervised Learning Techniques
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Author(s): Menaouer Brahami (LABAB Laboratory, National Polytechnic School of Oran - M. Audin, Algeria), Abdeldjouad Fatma Zahra (National Polytechnic School of Oran, Algeria), Sabri Mohammed (National Polytechnic School of Oran, Algeria), Khalissa Semaoune (LREEM Laboratory, University of Oran 2, Algeria) and Nada Matta (TechCICO Laboratory, University of Technology of Troyes, France)
Copyright: 2022
Volume: 15
Issue: 1
Pages: 21
Source title: International Journal of Information Systems and Supply Chain Management (IJISSCM)
Editor(s)-in-Chief: John Wang (Montclair State University, USA)
DOI: 10.4018/IJISSCM.2022010103

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Abstract

In today’s context (competition and knowledge economy), ML and KM on the supply chain level have received increased attention aiming to determine long and short-term success of many companies. However, demand forecasting with maximum accuracy is absolutely critical to invest in various fields, which places the knowledge extract process in high demand. In this paper, we propose a hybrid approach of prediction into a demand forecasting process in supply chain based on the one hand, on the processes analysis for best professional knowledge for required competencies. And on the other hand, the use of different data sources by supervised learning to improve the process of acquiring explicit knowledge, maximizing the efficiency of the demand forecasting, and comparing the obtained efficiency results. Therefore, the results reveal that the practices of KM should be considered as the most important factors affecting the demand forecasting process in supply chain. The classifier performance is examined by using a confusion matrix based on their Accuracy and Kappa value.

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