Supply Chain Digital Twin

Supply Chains emerge as complex networks of interdependent companies. While companies know whom their immediate neighbours (buyers and suppliers) are, they often lack full supply chain visibility beyond these immediate connections. Yet somehow they must coordinate to deliver goods and services on time, whilst also reducing overall costs. This information asymmetry leads to well-documented emergent effects such as demand amplification, and systemic disruption risks. Information asymmetry results in wasted material, as each individual company in the network inflates inventory to act as a buffer against possible disruptions, with no access to the actual data. While methods such as Collaborative Planning, Forecasting and Replenishment have been proposed, they require companies to integrate individual ERP systems, or set up data patches, which require investment, time, and discipline. Cost becomes a barrier to the exchange of information, and investment becomes a risk as it perpetually ties companies together, preventing the formation of new alliances. A more fluid, flexible, and decentralised IT solution is needed to overcome information asymmetry. 

 

Researchers have hinted that IoT technology could be used to improve information asymmetry at the network level, to ensure accurate information is available to supply chain organisations in a secure manner. However, currently, there is no reference framework which can showcase such improvements and outline how they can be achieved. 

 

In this project, we aim to develop a reference framework for IoT facilitated improvement on information asymmetries at the supply network level. The reference framework will be based on the development of a “Supply Chain Digital Twin”, which include a data-driven analytics and simulation engine that replicates a real-life supply chain by using IoT data from goods that are flowing in the chain. The Digital Twin will be a platform that can collect and aggregate distributed data from SC partnering organisations, mitigating the need for integrating individual ERP systems. By doing so, network-level data can be used to perform both optimisations of coordination for the current state, and for what-if analysis for the future state. 

 

Funded by: Pitch In, Research England

Collaborators: Schlumberger, University of Oxford

Researchers: Edward Kosasih

 

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