An Intelligent-internet of Things (IoT) outbound logistics knowledge management system for handling temperature sensitive products

Abstract

A comprehensive outbound logistics strategy of environmentally-sensitive products is essential to facilitate effective resource allocation, reliable quality control, and a high customer satisfaction in a supply chain. In this article, an intelligent knowledge management system, namely the Internet-of-Things (IoT) Outbound Logistics Knowledge Management System (IOLMS) is designed to monitor environmentally-sensitive products, and to predict the quality of goods. The system integrates IoT sensors, case-based reasoning (CBR) and fuzzy logic for real-time environmental and product monitoring, outbound logistics strategy formulation and quality change prediction, respectively. By studying the relationship between environmental factors and the quality of goods, different adjustments or strategies of outbound logistics can be developed in order to maintain high quality of goods. Through a pilot study in a high-quality headset manufacturing company, the results show that the IOLMS helps to increase operation efficiency, reduce the planning time, and enhance customer satisfaction.