Optimization and Re-Engineering of Manufacturing Business Processes Using Industry 4.0 Technologies

Abstract

The most important phenomenon of the 21st century, digital transformation, depends on emerging technologies driven by Industry 4.0 (I4.0). Every industry must adapt to the changes that this digital age has brought forth to offer faster and improved solutions to customers. Recognizing that technological advancements not only streamline existing processes but also introduce new products and services, this thesis contends that companies embracing I4.0 must undergo a re-engineering of their current business process models. Notably, existing Business Process Re-engineering (BPR) methodologies lack a crucial consideration the concept of I4.0 adoption. To fill this gap, the thesis introduces an I4.0-enabled BPR and optimization methodology. This novel approach not only serves as a foundational framework for planning digital transformations but also offers a seamless and efficient I4.0 adoption plan tailored for manufacturing companies. The thesis emphasizes a critical aspect in the development of I4.0-based Manufacturing Execution Systems (MES): Job Shop Scheduling (JSS). Throughout the requirements elicitation phase of MES, it became apparent that effectively managing machine breakdowns on the shop floor was a key concern. As a result, the thesis focuses on the integration of IoT-enabled Dynamic JSS techniques, offering the potential for real-time schedule updates and increased success rates for ongoing processes. A novel real-time dynamic scheduling model is introduced, addressing the challenges posed by the Flexible Job Shop Scheduling Problem (FJSSP) and accounting for the unpredictability introduced by random machine breakdowns. The thesis proposes a multi-strategy technique capable of regenerating an optimized dynamic schedule in response to unexpected machine interruptions. To validate the effectiveness of this methodology, an extensive computational study is conducted on eight benchmark problems alongside a real-world case study. The evaluation encompasses two performance objectives Robustness and Stability. Comparative analysis with three existing techniques from the literature consistently reveals superior results for the proposed methodology, indicating its potential for enhancing the performance of MES within the context of I4.0. This proposed technique has the potential to contribute to the continuous drive for the digital transformation of MES within the framework of I4.0. The efficacy of I4.0 relies heavily on the integration of effective MES, where a dynamic job shop scheduler serves as a central hub for connectivity and integration.