Digital twin modelling for optimizing the material consumption: A case study on sustainability improvement of thermoforming process

•Sensor and PLC data enabled digital twin applications stand as a remedy to minimize material consumption, maximize product performance and prevent rework through process quality improvements in production lines by providing insights to process parameters, enabling preventive actions, and hence optimize production.•Digital twin applications require in-depth simulation expertise and an advanced understanding of both the process and the product. A digital twin can be modeled and validated after a detailed analysis of the process characteristics, process parameters, and process challenges with material modelling.•Finite element simulations and several data analytic tools can be combined to obtain the digital twin. Neo-Hookean (NH) Model is helpful while estimating temperature-dependent material definitions through WLF approximation, as it provides adequately accurate results for the simulation of the system.•The digital twin's implementation into the production line has significantly improved production KPIs and quality by decreasing the scrap ratio by 50% and the raw material consumption by 10%, resulting in a significant annual cost savings.•Digital twins can be used as enablers of production optimization with an improved sustainability performance. Collaboration and production data availability are the enablers of success. Reducing material consumption is a rising issue for manufacturers as the UN’s Sustainable Development Goals and the European Union’s Green New Deal minimize carbon footprints. Sensors and PLC data-enabled digital twin applications stand as a remedy to minimize material consumption, maximize product performance and prevent rework through process quality improvements. They also provide insights to process parameters, enabling preventive actions and hence optimize production. However, digital twin applications require in-depth simulation expertise and an advanced understanding of the process and the product. This paper aims to present a digital twin modeling application for improving process and product quality. Hence, the sustainable production performance in a refrigerator production line of a large Turkish durable goods manufacturer, Arçelik, collaborates with a Digital Twin Startup, Simularge. The digital twin modeling used sensors and PLC data. The development team validated the digital twin after a detailed analysis of the process characteristics, process parameters, and process challenges with material modeling. Finite element simula