Deploying hybrid modelling to support the development of a digital twin for supply chain master planning under disruptions

Ehsan Badakhshan*, Peter David Ball

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


Supply chains operate in a highly distuptive environment where a SC master plan should be updated in line with disruptions to ensure that a high service level is provided to customers while total cost is minimised. There is an absence of knowledge of how a SC master plan should be updated to cope with disruptions using hybrid modelling. To fill this gap, we present a hybrid modelling framework to update a SC master plan in presence of disruptions. The proposed framework, which is a precursor to a SC digital twin, integrates simulation, machine learning, and optimisation to identify the production, storage, and distribution values that maximise SC service level while minimising total cost under disruptions. This approach proves effective in a SC disrupted by demand increase and lead time extension. Results show that employing hybrid modelling leads to a noticeable improvement in service level and total cost. The outcome of the new knowledge on using hybrid modelling for managing disruptions provides essential learning for the extension of modelling through a digital twin for SC master planning. We observe that in the presence of disruptions it is more economical to keep higher inventory at downstream SC members than the upstream SC members.
Original languageEnglish
Number of pages32
JournalInternational Journal of Production Research
Early online date10 Aug 2023
Publication statusE-pub ahead of print - 10 Aug 2023

Bibliographical note

© 2023 The Author(s)


  • Hybrid modelling; digital twins; supply chain disruptions; simulation; machine learning

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