SACM - United Kingdom
Permanent URI for this collectionhttps://drepo.sdl.edu.sa/handle/20.500.14154/9667
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Item Restricted THE IMPACT OF INDUSTRY 4.0 TECHNOLOGIES ON SUPPLY CHAIN RESILIENCE IN SAUDI ARABIA(Saudi Digital Library, 2025) Alanazi, Jawaher; Greasley, AndrewThis dissertation examines the impact of Industry 4.0 technologies on supply chain resilience in Saudi Arabia, where initiatives such as Internet of Things and the Global Supply Chain Resilience Initiative (GSCRI) are central to industrial transformation. A systematic literature review (SLR) guided by the PRISMA framework was conducted, covering academic and grey literature published between 2020 and 2025. The search initially yielded 315 records; after applying inclusion and exclusion criteria, 26 studies were selected for detailed analysis. The findings indicate thatInternet of Things (AI), blockchain, the Internet of Things (IoT), and digital twins are the leading technologies supporting resilience capabilities, particularly in risk mitigation, operational continuity, agility, and sustainability. However, barriers such as high implementation costs, limited absorptive capacity, institutional inertia, and fragmented infrastructure constrain their full potential. This research contributes by integrating insights from policy initiatives, industry practices, and recent empirical studies to show how Industry 4.0 adoption strengthens resilience in supply chains within an emerging economy context. It underscores the strategic role of digital transformation in enabling continuity, flexibility, and sustainability. The study concludes with practical recommendations for policymakers and practitioners in Saudi Arabia, emphasizing the need to strengthen digital infrastructure, enhance workforce skills, and foster cross-sector collaboration. It also highlights avenues for future research, including empirical studies on digital maturity and comparative analyses across sectors and regions.22 0Item Restricted CYBERSECURITY OF CRITICAL INFRASTRUCTURE’S MANUFACTURING SYSTEMS A NOVEL FRAMEWORK AND APPROACH FOR PREDICTING CYBERATTACKS BASED ON ATTACKER MOTIVATIONS(Saudi Digital Library, 2025) Alqudhaibi, Adel; Sandeep, JagtapIndustry 4.0 signifies a transformative shift in industrial operations, powered by the integration of automation, connectivity, and digital technologies. This shift enhances diagnostics, autonomous decision-making, automation, and data analysis by machinery and networking equipment, revolutionizing the manufacturing and critical infrastructure sectors. However, the increased reliance on such technologies raises significant cybersecurity concerns. These vulnerabilities are particularly acute in Industrial Control Systems (ICS) , which are commonly used in critical infrastructure (CI) for operational and supervisory control. Industry 4.0 manufacturing systems face increasing cybersecurity threats due to the lack of predictive threat detection, inadequate security frameworks, and growing system complexity. Existing approaches are reactive, failing to incorporate attacker motivations and proactive risk mitigation. As a result, manufacturing systems are exposed to numerous cyber-attacks that can have catastrophic concerns for critical infrastructure sectors such as energy, transportation, and water. Addressing these challenges requires a comprehensive and systematic approach to cybersecurity that is specifically tailored to the nature of these systems. This research introduces a novel cybersecurity approach that predicts potential cyberattacks by considering attacker motivations and the specific characteristics of CI systems. Machine learning (ML) models are employed to predict potential attack methods, offering a proactive solution to prevent cyber threats before they occur. This approach demonstrates a substantial improvement in predictive accuracy, as confirmed by initial evaluation results. Cybersecurity in CI manufacturing systems remains reactive, relying on post-attack mitigation rather than proactive threat prevention. This research addresses the gap by developing a predictive cybersecurity approach Predicting Cyberattacks in Critical Infrastructures (PCCI) which anticipates cyber threats based on attacker motivations and CI system vulnerabilities. Using machine learning (ML) models, this approach enhances attack method prediction, significantly reducing false positives and improving detection accuracy. The proposed framework shifts cybersecurity from a reactive to a proactive stance, contributing to enhanced resilience in Industry 4.0 environments. Initial tests demonstrate notable improvements in prediction accuracy, validating its potential for real-world application. Beyond the implementation of predictive cybersecurity models, this research presents a comprehensive cybersecurity framework that emphasises sustainability within the manufacturing sector. The framework is structured to protect critical resources by ensuring the confidentiality, integrity, and availability of data, while simultaneously enhancing operational resilience. It incorporates proactive strategies for anticipating cyber threats and underscores the importance of comprehensive employee education at all organisational levels. This framework seeks not only to mitigate immediate security risks but also to integrate long-term resilience into cybersecurity strategies, thereby promoting the sustainability of manufacturing operations. A key finding of this research is the significant gap in robust security standards and proactive measures within the manufacturing sector concerning cybersecurity. Despite the growing adoption of Industry 4.0 technologies, many systems remain vulnerable to cyberattacks due to the absence of sufficient security protocols during the early stages of implementation. The absence of standardized guidelines contributes to insufficient employee knowledge and preparedness, leaving them vulnerable to cybersecurity risks. Addressing these gaps is essential for the manufacturing sector to fully capitalize on Industry 4.0 advancements while ensuring the protection of critical systems from emerging cyber threats. The study concludes by recommending a redirection of security resources and procedures to the manufacturing industry. It emphasises the need for increased investment in employee awareness, training programs, and more robust cybersecurity protocols specifically tailored to the needs of industrial systems. By implementing these recommendations, organisations can better mitigate risks, enhance their cybersecurity posture, and ensure the continuity of critical manufacturing and infrastructure operations in the face of progressing cyberattacks.20 0Item Restricted THE DEVELOPMENT OF A FRAMEWORK FOR THE IMPLEMENTATION OF INDUSTRY 4.0 FOR MANUFACTURING IN A DEVELOPING COUNTRY: A CASE STUDY OF SAUDI ARABIA(ScienceDirect, 2022-07-01) Rajab, Sulaiman; Afy-Shararah, Mohamed; Salonitis, KonstantinosObjective of the Research: The purpose of this research is to investigate Industry 4.0 implementation barriers in Small and Medium-Sized enterprises within the manufacturing sector in developing countries. One of the objectives driving this research is the exploration of associations between Industry 4.0 enablers and lean manufacturing. Further, the research aims to construct a framework guiding stakeholders in implementing Industry 4.0 by overcoming common barriers cited by experts in the field. Research Problems: The main problem driving this research is the dearth of information on Industry 4.0 implementation in developing countries. Simultaneously, little research has been conducted to identify the barriers of industry 4.0 in SMEs within manufacturing realms in emerging economies. Inadequate research investigated the associations between lean manufacturing and industry 4.0 implementation. Methodology: This is a mixed methods research study. On the qualitative side, focus groups are used to collect open-ended responses to questions related to barriers facing the adoption of Industry 4.0. Quantitatively, Interpretive Structural Modelling is used to construct the framework driving stakeholders’ decisions to adopt and implement industry 4.0. Further, survey research is used to validate experts' opinions on the utility of the ISM based model. In sum, three distinct data collection techniques were used: (a) focus groups, (b) descriptive data for interpretive structural modeling, and (c) survey responses. Based on the quantitative and qualitative methods of data collection, a series of data analysis strategies were followed. Key Findings: The current study reported strong associations linking Industry 4.0 enablers and lean manufacturing outcomes. On the one hand, the use of Industrial Internet of Things (IIoT) improved customers’ connectivity and engagement in each stage of the sustainable manufacturing process. Thereby, improving customer satisfaction, a key element in measuring lean manufacturing. Additionally, the deployment of cyber security as well as cloud computing technology facilitates the transfer and storage of information minimizing the wasteful utilization of physical and technical infrastructure, and manifestation of lean manufacturing practices. By the same token, the increasing use of simulation and analytics technology minimizes the reliance on manning thereby reducing further waste, the purpose of lean manufacturing. Implications of the Research: Results of the ISM model were validated by using a questionnaire showing the reliability and validity of the ISM constructed model that has been adopted as the accepted framework guiding Industry 4.0 implementation. The proposed framework in this study departs from existing models in significant ways. First, it does not prescribe sequential steps since Industry 4.0 implementation is a complex process requiring simultaneous work from various divisions across the organization. Second, the framework is scalable and flexible, allowing it to fit many applications regardless of the size or nature of the industry. Third, the model originated from contexts in developing countries, making it appropriate for implementation in markets like Saudi Arabia. Conclusion of the Research: This research concluded that the implementation of Industry 4.0 is neither straightforward nor linear. Experts voiced concern regarding the technical and management infrastructures facing developing countries' manufacturing sectors. The research suggested that the adoption of Industry 4.0 is a multi-step simultaneous process involving more than a single practice overcoming several barriers at the same time.31 0