Extending Discrete-Event Simulation Capabilities through Python Integration for Industry 4.0 Technologies

Paper i proceeding, 2026

Extending the capabilities and potential of existing manufacturing technologies for problem-solving, cost-cutting and inclusiveness is a hot topic within the field of advanced manufacturing. There is a solid need to make new and existing technologies interoperable for solving manufacturing problems. This paper puts forward a practical framework that extends the commercial discrete-event simulation (DES) into a data-driven plant twin by integrating FlexSim, commercial simulation software, with Python through an application programming interface (API) called FlexSimPy. Methodologically, the FlexSim holds the physics and flow, while Python supplies external data, orchestration, API integrations, automation, and evaluation. Using a section of the pump manufacturing process as a case study, the integrated solution dynamically evaluates CO2 emission by ingesting time-varying external signals from the grid and production data for its evaluation under two scenarios of process reorganization and shift scheduling. The outcomes of the framework are an API-based DES-Python pipeline that executes new scenarios without rebuilding the core model, reproducible results, and a direct support carbon-aware operational decision. It also provided a reusable orchestration pattern for automated scenario execution and statistically defensible comparisons. It was a scope-2 bounded study, single-day evaluation and triangular power approximation. For future work, the study has demonstrated the capabilities of DES as an adaptive digital twin environment for Industry 4.0 technologies.