A Digital Twin to Forecast and Optimize Cash/product Flows of a Flexible Job Shop

Nicolas Leblanc (), Marc-André Ménard (), Jonathan Gaudreault (), Stéphane Agnard () and Yves Proteau ()
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Nicolas Leblanc: Université Laval, Lab-Usine – Joint Research Unit for Advanced manufacturing
Marc-André Ménard: Université Laval, Lab-Usine – Joint Research Unit for Advanced manufacturing
Jonathan Gaudreault: Université Laval, Lab-Usine – Joint Research Unit for Advanced manufacturing
Stéphane Agnard: APN-SCHIVO
Yves Proteau: APN-SCHIVO

A chapter in Technology Management for Intelligent, Open and Responsible Organizations and Ecosystems, 2026, pp 267-274 from Springer

Abstract: Abstract Cash flow optimization and forecasting is important for any manufacturing company. Forecasting cash flow requires a good overview of the end date of each task needed to fulfil a customer order. We study a flexible job shop where each job is composed of a sequence of tasks and each task must pass through different work centers. To forecast the revenue generated, we developed a digital twin that uses a discrete simulation model that considers the company's business rules to forecast when each job will end and the time revenue will be generated. We compare the actual policy of the company (First-in/First-out) to another version where the company would use a constraint programming optimization model for scheduling with the objective of optimizing cash flow. We show that the constraint programming model improves revenue and is more robust to stochastic event. In addition, the digital twin allows several questions to be answered (“what-if”), enabling the company to assess the profitability of different courses of actions.

Keywords: Digital Twin; Simulation; Optimization; Flexible Job Shop Problem; Constraint Programming Scheduling (search for similar items in EconPapers)
Date: 2026
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Persistent link: https://EconPapers.repec.org/RePEc:spr:prbchp:978-3-032-23124-6_33

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DOI: 10.1007/978-3-032-23124-6_33

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