Enhancing circular economy in industrial machinery through data-driven RE-X processes and digital twin technologies

Abstract

This research explores the possibility, barriers, and integration process of digital twin technologies into Re-X processes within circular economy (CE) framework. It identifies challenges and opportunities in adopting CE principles to enhance resource efficiency, cost efficiency, waste reduction and sustainable practices. By monitoring predictive maintenance (PM) and lifecycle monitoring (LM) as key digital tool enablers supported by digital twins, this thesis aims to optimize reuse, recycling, refurbishment, and remanufacturing processes. As research methodology, a quantitative survey based method is employed to gather data from Finnish companies in various industries. Descriptive and inferential statistics such as ANOVA test and correlation analysis among variables, reveal significant relationships between digital technology adoption levels and the effectiveness of CE practices. The findings demonstrate the potential of digital twins to extend product lifecycles and emphasizes on resource consumption reduction to achieve sustainability goals. Moreover, this research delves into industry-specific factors to highlight strategies for overcoming challenges in particular areas in each industry such as workforce skill gaps and high implementation costs. A comprehensive framework is developed to guide stakeholders, industry partners, and involved sectors in the integration of digital technologies into CE ecosystems. This framework addresses technical and organizational barriers and underscores specific directions to enhance Re-X processes to certain CE outcomes. This thesis contributes to enhancing CE practices by aligning industrial performance techniques with environmental and economic objectives.