Browsing by Author "Alqoud, Abdulrahman"

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    Assessment Model for facilitating digital retrofitting in manufacturing: key factors and practical insights
    (Elsevier, 2024-10-15) Alqoud, Abdulrahman; Milisavljevic-Syed, Jelena; Salonitis, Konstantinos
    In an era where factories are required to evolve or face obsolescence, digital retrofitting offers a lifeline to manufacturing companies striving to stay competitive. Digital retrofitting, the process of integrating cutting-edge technologies such as IoT components into legacy manufacturing systems, is emerging as a crucial strategy. However, due to the diversity of these legacy systems, a one-size-fits-all solution is ineffective. Despite the transformative potential of digital retrofitting, there is a noticeable gap in the tools that enable decision-makers to accurately assess their readiness for this process. To bridge this gap, it is essential to identify and evaluate critical factors that determine a system’s preparedness for integration with advanced technologies. This study proposes 21 key factors that must be considered to ensure readiness for digitalisation. These factors are grouped under four critical dimensions: strategy and organisation, workforce enhancement, smart factory, and smart process. Engagement with 18 industry practitioners was undertaken to validate these factors, ensuring their practical relevance. The findings provide valuable insights into the critical factors that need to be evaluated before embarking on the journey of digitalisation, ensuring that companies are fully prepared to integrate new technologies and optimise their manufacturing processes.
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    Industry 4.0: a systematic review of legacy manufacturing system digital retrofitting
    (EDP Sciences, 2022-11-07) Alqoud, Abdulrahman; Schaefer, Dirk; Milisavljevic-Syed, Jelena
    Industry 4.0 technologies and digitalised processes are essential for implementing smart manufacturing within vertically and horizontally integrated production environments. These technologies offer new ways to generate revenue from data-driven services and enable predictive maintenance based on real-time data analytics. They also provide autonomous manufacturing scheduling and resource allocation facilitated by cloud computing technologies and the industrial Internet of Things (IoT). Although the fourth industrial revolution has been underway for more than a decade, the manufacturing sector is still grappling with the process of upgrading manufacturing systems and processes to Industry 4.0-conforming technologies and standards. Small and medium enterprises (SMEs) in particular, cannot always afford to replace their legacy systems with state-of-the-art machines but must look for financially viable alternatives. One such alternative is retrofitting, whereby old manufacturing systems are upgraded with sensors and IoT components to integrate them into a digital workflows across an enterprise. Unfortunately, to date, the scope and systematic process of legacy system retrofitting, and integration are not well understood and currently represent a large gap in the literature. In this article, the authors present an in-depth systematic review of case studies and available literature on legacy system retrofitting. A total of 32 papers met the selection criteria and were particularly relevant to the topic. Three digital retrofitting approaches are identified and compared. The results include insights common technologies used in retrofitting, hardware and software components typically required, and suitable communication protocols for establishing interoperability across the enterprise. These form an initial basis for a theoretical decision-making framework and associated retrofitting guide tool to be developed.
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    Multi-criteria decision making in evaluating digital retrofitting solutions: utilising AHP and TOPSIS
    (Elsevier, 2025) Alqoud, Abdulrahman; Milisavljevic-Syed, Jelena; Salonitis, Konstantinos
    In an era of digital transformation, evaluating effective strategies for upgrading manufacturing systems is crucial to maintaining competitiveness. Digital retrofitting has become a strategic approach integrating new digital technologies into legacy systems to share data and align with Industry 4.0 principles. However, various techniques and criteria exist for implementing digital retrofitting. Despite its importance, there is a notable lack of studies assessing these retrofitting approaches using multi-criteria decision making (MCDM) methodologies. This study addresses this gap by employing two MCDM techniques: the analytic hierarchy process (AHP) and the technique for order of preference by similarity to ideal solution (TOPSIS). It assesses three digital retrofitting alternatives, starter kit solutions, embedded gateway solutions, and IoT hardware-based solutions, against ten critical criteria. These criteria were weighted through pairwise comparison analysis based on a survey of twelve industry practitioners to reflect industry preferences. The aim is to determine the most effective digital retrofitting approach to aid manufacturers in transitioning to Industry 4.0. This study addresses the complexities of managing conflicting criteria in digital transformation. Moreover, the results contribute to decision-making methodologies by demonstrating their practical applications, thus guiding manufacturers through the intricate landscape of digital retrofitting.