Applications of Deep Learning in Supply Chain Management: A Systematic Literature Review Approach

Abstract

Deep learning (DL) has been widely used in the fields of image recognition medical diagnosis self-driving systems audio-visual design and so forth The provenance of DL is derived from deep (multi-layer) neural network packaged by commercial marketing Pursuant to the statistics from a prestigious database (Web of Science) it has prevailed over the past two years in the field of supply chain management (SCM) The amount of literature applying DL has increased significantly In fact some literature applying similar concepts has been neglected since they did not use the term "deep learning" as a topic or in content This research aims to systematically identify and screen out documents possessing the concepts of DL by applying systematic literature review (SLR) Discrepant from those reviews in SCM this research considers each stage of the supply chain as an independent task classified as micro processes and utilised macro processes to complete panorama Through clearly defining micro and macro processes this research locates major and corresponding applications of DL for each task The endeavour constructs guidance for enterprises to develop and improve their supply chain by appropriately applying DL Eventually the tendency of DL presents that a multitude of research has the potential of utilising multilayer neural network to cope with issues in SCM

Date of Award	2020
Original language	English
Supervisor	Chien-Hung Wei (Supervisor)