Proceedings of ICLT 2017
ARTIFICIAL NEURAL NETWORKS-BASED TECHNIQUES IN SUPPLY CHAIN MANAGEMENT
Siravat Teerasoponpong; Apichat Sopadang; Poti Chao; Aicha Sekhari; Yacine Ouzrout
Graduate School, Chiang Mai University, Chiang Mai, Thailand; Graduate School, Chiang Mai University, Chiang Mai, Thailand; Supply Chain and Engineering Management Research Unit, Faculty of Engineering, Chiang Mai University, Chiang Mai, Thailand; Graduate School, Chiang Mai University, Chiang Mai, Thailand; Graduate School, Chiang Mai University, Chiang Mai, Thailand
International Conference on Logistics & Transport 2017, Bangkok, Thailand, pp. 74-83
Download PDF | View interactive page
Abstract
Purpose: This paper aims to investigate applications of Artificial Neural Networks based techniques within the field of supply chain management under key components of supply chain process. Design/methodology/approach: The literature review of ANNs-based techniques was conducted. The key activities of SCM based on Lambert et al (1998) and Banomyong & Supatn (2011) was established as a foundation and scope of the review. This paper set out to review recent research works conducted during the past years, from 2005 – 2017. The classification of the papers was also constituted under 8 basic categories: simulation, experimental, classification, case study, analytical, conceptual, surveys, and comparative. The results were then discussed to outline the future direction of ANNs applications in SCM. Findings: The review indicated the tendency of ANNs-based techniques for problem-solving and modelling among the field of SCM. ANNs-based techniques were found to be effective among the problematic domains of SCM regarding a pattern recognition which mostly found in the issues regarding forecasting and simulation. Research limitations/implications: Limitation is related to the availability of research papers in some of SCM domain which ANNs is required to be established. From academic point of view, this implicates the gap which can be fulfilled by future research works. Practical implications: The review of ANNs-based applications might provide practitioners with guidance in selecting an applicable ANNs-based technique to deal with problematic issues in supply chains. Originality/value: This paper contributes to knowledge of ANNs-based applications which extend toward domain of SCM activities as well as identifies further research direction.
Keywords
Supply chain management; artificial neural networks; ANNs applications
Citation
Siravat Teerasoponpong; Apichat Sopadang; Poti Chao; Aicha Sekhari; Yacine Ouzrout (2017). ARTIFICIAL NEURAL NETWORKS-BASED TECHNIQUES IN SUPPLY CHAIN MANAGEMENT. Proceedings of the International Conference on Logistics & Transport (ICLT 2017), Bangkok, Thailand, pp. 74-83.