Building resilient engineer to order systems

Zhou, Yuxuan 2024. Building resilient engineer to order systems. PhD Thesis, Cardiff University. Item availability restricted.

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Abstract

Engineer-to-order (ETO) supply chains, common in industries such as shipbuilding and capital goods manufacturing, face unique challenges due to customization and project-based designs, leading to high uncertainty, cost overruns, and delays. This highlights the need for resilience improvement. While system dynamics (SD) archetypes are well-established for make-to-stock, make-to-order, and assemble-to-order systems, an ETO-SD model is lacking. The thesis aims to create a resilient ETO systems archetype to improve its performance under various rework scenarios. It finds that a holistic order book controller can maintain desired lead times despite rework and disturbances. Critical sable conditions across archetypes with different rework ratios show that longer lead times negatively impact system stability. Additionally, the study quantifies the ‘Think Slow Act Fast’ theory, demonstrating that it can reduce overall lead times and production subsystem costs, with a slight increase in design subsystem costs. The research also examines how order book parameters affect system resilience, recommending optimal order book controller settings for different rework ratios, which form a 'bathtub-like' curve as rework ratios increase. This thesis significantly advances the field by developing a comprehensive suite of ETO-SD archetypes, filling a critical gap in existing literature. It provides a deeper understanding of dynamic behaviours in ETO systems, particularly how they respond to disturbances and rework, and extends the application of SD models to ETO environments. iii Practically, this research offers actionable solutions for resilience improvement in ETO industries, including effective capacity planning through system parameter selection, strategies to mitigate rework effects, and methods to minimize system dynamics variance, thereby enhancing both efficiency and effectiveness. These contributions offer practical tools and strategies directly applicable to real-world ETO environments, bridging the gap between theory and practice.

Item Type:	Thesis (PhD)
Date Type:	Completion
Status:	Unpublished
Schools:	Business (Including Economics)
Date of First Compliant Deposit:	30 January 2025
Date of Acceptance:	28 January 2025
Last Modified:	30 Jan 2025 09:53
URI:	https://orca.cardiff.ac.uk/id/eprint/175657

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