Adaptive Manufacturing for SMEs: Digital retrofitting, predictive maintenance, and servitisation strategies

Small and medium sized enterprises (SMEs) face growing pressure to modernise in response to volatile market demand, increasing product complexity, and rapid technological change. At the same time, they are expected to improve sustainability performance, strengthen data transparency, and respond more quickly to operational disruption. Although Industry 4.0 has introduced significant opportunities through cyber-physical integration, connectivity, and data-driven decision-making, many SMEs continue to struggle with implementation. Financial constraints, legacy equipment, limited internal capabilities, and the risks associated with poorly sequenced adoption often prevent transformation from progressing beyond isolated initiatives. Flexible and responsive manufacturing systems (FRMS) represent the performance objective of this transition, but for SMEs the central challenge is not simply adopting advanced technologies; it is doing so through a structured and feasible pathway. This book positions that pathway as an incremental and decision-supported transformation process tailored to the operational realities of SMEs.

To address this challenge, the book proposes a unified and practical transformation framework built around three interconnected strategies. The first is digital retrofitting, which enables SMEs to incrementally upgrade legacy systems through Industry 4.0 technologies without requiring full-scale replacement. The second is maintenance strategy selection, which applies structured multi-criteria decision-making methods to improve operational reliability, responsiveness, and asset resilience within dynamic manufacturing environments. The third is digital servitisation, which supports the transition from product-based offerings to service-oriented business models capable of generating new value from digitally enabled operations. Taken together, these strategies form a coherent transformation logic in which retrofitting generates data, maintenance strategy converts that data into resilience, and servitisation leverages that resilience for strategic growth. The book also addresses broader themes associated with industry 5.0, including sustainability, organisational readiness, cybersecurity, and human-centred industrial transformation.

The book will be key reading for graduate students, educators, and academic researchers in industrial engineering, manufacturing systems and digital transformation.

Jelena Milisavljevic-Syed is a senior lecturer in manufacturing systems at the Sustainable Manufacturing Systems Centre, Cranfield University, UK. Jelena is a thought leader in designing evolving cyber-physical-social systems and AI-supported decision-making for sustainable manufacturing and services. Her work bridges mechanical, control, and industrial engineering to advance Industry 4.0/5.0 through adaptable, concurrent, and human-centric design. She is a member of ASME, ASEE, BFWG, MIET, IEEE and the Design Society.

Abdulrahman Alqoud is head of the Research and Studies Department at a Confidential Government entity in Riyadh, Saudi Arabia. Dr Alqoud focuses on driving strategic insights and research initiatives to enhance decision-making. Leveraging a PhD in Engineering and certifications such as PMP, C-SBP and C-KPI, he emphasises delivering actionable solutions aligned with organisational goals. With over a decade of experience across academia and industry, his expertise spans Industry 4.0, decision-making and the modernisation of manufacturing systems.

Jiahong Li is a postdoctoral researcher at the Binjiang Institute of Hangzhou Dianzi University, China. He obtained his PhD from Cranfield University, with research focusing on predictive maintenance technologies, decision-making, intelligent enterprise transformation roadmaps, and sustainable reverse supply chains. His current research explores the capability boundaries of AI agents in constrained industrial scenarios and the development of interpretable geological hazard prediction models. Dr. Li's work aims to advance trustworthy and practical AI applications for industrial intelligence and risk prediction.

Mohammed Khan is a PhD researcher at Cranfield University, UK. He helps organizations transition from traditional product-led models to digitally-enabled, service-based growth (servitization). He specializes in bridging the gap between rigorous data-driven analysis and practical, board-ready implementation roadmaps. He has a background spanning blue-chip industrial operations and advanced academic research. His research focuses on the critical intersection of leadership, service design, and performance systems, ensuring transformation isn't just a plan, but a repeatable capability.