(612d) A Transactional Digital Twin for Optimizing Supply Chain Business Processes
AIChE Annual Meeting
2021
2021 Annual Meeting
Computing and Systems Technology Division
Planning, Scheduling, Supply Chain and Logistics
Thursday, November 11, 2021 - 1:27pm to 1:46pm
The proposed framework is developed in the Julia programming language, which brings the advantages of using the powerful libraries available within the Julia environment. These include graph structures via MetaGraphs, discrete event simulation via SimJulia, and mathematical optimization via JuMP. As an integrated platform, the framework does not require the use of APIs to link the simulation and optimization software packages. The platform also streamlines the building and maintenance of digital twins since the discrete event simulation and MILP models can be generated automatically from the process graph. Thus, any modification to the metadata or graph structure, is automatically mapped to the digital twin models. An order-to-cash case study is presented where the digital twin reactively optimizes the priorities of customer orders in a multi-stage transactional network to improve profitability and on-time delivery. A sensitivity study is also shown to assess debottlenecking strategies. Both the case study and sensitivity study showcase the added value of using a digital twin for supply chain business process optimization.
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