Operationalising Sustainability in Smart Manufacturing Systems: A Model-Based Approach

Abstract

This dissertation addresses the need for sustainability in manufacturing industries by leveraging digitalization and Model-Based Systems Engineering (MBSE). The research presents a comprehensive framework for operationalizing sustainability, covering key aspects such as sustainability assessment, Industry 4.0, and manufacturing representation. The dissertation explores Industry 4.0 technologies as potential solutions to address sustainability challenges, leading to research gaps in operationalizing sustainability assessment. A Conceptual model for smart manufacturing systems (SMS model) through a case study is introduced for a comprehensive representation of manufacturing processes and systems, guiding users to effectively model a manufacturing process and system. A real manufacturing system is represented using the SMS model, validating its practical applicability and effectiveness through a comparative Life Cycle Assessment (LCA) case study conducted in a shoe factory. Data is collected both with and without the SMS model, and the resulting environmental impact values are compared. Significant differences in impact values are observed, highlighting the SMS model's ability to enhance data collection for sustainability assessments. By connecting the SMS model to high-level Key Performance Indicators (KPIs), an integrated model for Sustainability KPI selection and assessment is proposed. Then a Unified Smart Factory model (USFM) is proposed, which unifies the SMS model, Data Process and KPI selection and assessment frameworks, providing a pipeline for effective measurement and improvement of sustainability KPIs. A case study involving a PCB assembly factory demonstrates the practical application of the USFM. Further, the dissertation delves into sustainability analytics using real-time manufacturing data, emphasizing continuous sustainability assessment's importance. Leveraging Industry 4.0, the study performs near real-time Life Cycle Assessment. The results showcase the impact of manufacturing process variations on sustainability assessments. The dissertation also explores simulation-based sustainability assessment using Plant simulation modelling. The study investigates the influence of systemic parameters in manufacturing systems on environmental impacts using Discrete Event Simulations (DES) based experiments. LCA models are developed for selected experiments, presenting valuable insights into sustainable manufacturing. Overall, the research offers a comprehensive framework for operationalizing sustainability in manufacturing, empowering industries to move towards smart and sustainable practices. This study lays the groundwork for future research in sustainable manufacturing, encouraging the exploration of advanced analytics for sustainability assessments in diverse industrial settings. The proposed frameworks present opportunities and a standardized pipeline of methods for adopting sustainability and promoting sustainability in manufacturing industry.