A Data-Intelligence-Driven Digital Twin Framework for Improving Sustainability in Logistics

Abstract

As supply chains evolve toward the adoption of the Industrial Internet of Things (IIoT), vast amounts of data are collected by different systems across the manufacturing, logistics and transportation value chain. John G Russell (Transport) is a UK-based company involved in multiple lines of business in the supply chain. As the company adopts the utilization of data intelligence as a way to collect, process and utilize data for insights, this presents an opportunity for applying artificial intelligence (AI) approaches such as reinforcement learning (RL), to identify trends, and offer recommendations for improving the sustainability and efficiency of its logistics. Preliminary results show that we can achieve up to a 20–30% reduction in carbon emissions from the fleet of a segment of the transport business lines of the Russell Group. This paper presents a holistic framework for achieving sustainable supply chains, reducing costs as well as achieving operational efficiency using a supply chain digital twin.

Keywords

• Internet of Things (IoT)
• digital twins
• machine learning (ML)
• reinforcement learning (RL)
• sustainability
• data integration