SFlex-WMS: a novel multi-expert system for flexible logistics and warehouse operation in the context of Industry 4.0

Abstract

Production lines form the backbone of a manufacturing plant while the warehouse is the heart that pumps the supplies through logistics veins. However, logistics issues among manufacturing industries are well known for causing downstream production problems. Non-transparent warehouse operation and inevitable human error in logistics activities seriously jeopardise the entire downstream manufacturing processes. Existing warehouse management solutions require many sensors spanning the warehouse for tracking logistics activities which are cost-ineffective and inflexible. One aspect of intelligent WMS that has not been explored is the integration of computer vision modeling with Artificial Intelligence (AI) to create a more flexible, transparent, and autonomous warehouse management system (WMS). This study aims to devise a Smart and Flexible WMS (SFlex-WMS) to improve logistics operations in terms of operation costs, process time, and space utilisation. The highlight of the proposed framework is the two major work packages (WP) which focus on flexible, autonomous sensing mechanisms for inventory, logistics tracking, and space mapping, as well as reconstructing the warehouse environment model that reflects all physical changes in the warehouse. SFlex-WMS intends to realize real-time transparent monitoring of warehouse operations. By exploiting the outputs from both WPs, SFlex-WMS is expected to achieve more effective and flexible warehouse operations.