Saudi Cultural Missions Theses & Dissertations
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Item Restricted Evaluating the Effectiveness of Lean Manufacturing Practices in Enhancing Supply Chain Resilience with AI Applications in the Manufacturing Industry in Saudi Arabia(University of Gloucestershire, 2024-08) Alshubayki, Khalid Abdullah; Plummer, DavidThis study investigates the effectiveness of integrating lean manufacturing practices with artificial intelligence (AI) to enhance supply chain resilience (SCR) within Saudi Arabia's manufacturing sector. Prompted by the disruptions of the Covid-19 pandemic, the research addresses the critical need for resilient supply chains. Using a quantitative research approach with a survey design, data was collected from professionals across Saudi manufacturing companies. The study explores key lean practices, AI technologies, current adoption levels, and readiness for AI integration. Descriptive statistics, Pearson correlation, regression analysis, and independent sample t-tests were employed to analyse the data. Findings reveal a proactive interest in AI and lean practices, with 34.9% in the planning phase and 29.2% considering AI implementation. However, only 2.8% have fully integrated AI, highlighting execution gaps. A significant skills gap was identified, with 56.6% lacking specific training in AI and lean methodologies, and 44.4% perceiving insufficient workforce skills. Additionally, 62.2% of companies lack a clear AI strategy, and 48.2% perceive insufficient managerial support. Based on these insights, recommendations include developing comprehensive AI integration strategies, investing in workforce training, and promoting strong managerial support for AI initiatives. The study highlights the importance of strategic clarity, skill development, and leadership commitment in leveraging AI and lean practices to enhance SCR. By addressing these gaps, Saudi manufacturing companies can better anticipate and respond to supply chain disruptions, ensuring operational continuity and competitive advantage. The findings contribute valuable insights for both academia and industry, guiding future efforts to bolster supply chain resilience in the face of global uncertainties.8 0Item Restricted Evaluating Supply Chain Resilience within Fast Moving Consumable Goods Distribution Companies: A Case Study Approach(University of Dundee, 2024-01-29) Aljohani, Malak; Johnston, KeithThe aim of this thesis is to investigate the supply chain resilience of Fast-Moving Consumable Goods FMCG distribution companies in KSA. With the increased complexity of the business, companies showed interest in adopting proactive risk management strategies such as Supply Chain Resilience. This thesis begins by presenting the methodology of selecting the most suitable resilience assessment tool for the FMCG distribution company supply chain by using the Analytic Hierarchy Process (AHP) decision-making methodology. Based on criteria such as Usability, Adaptability, and Completeness, the result reveals that one optimal choice. The chosen assessment tool was applied to a case study of an FMCG distribution company in Saudi Arabia. The Findings indicate that the tool effectively offers a preliminary evaluation of supply chain resilience for the company. The factors used to assess resilience were Supply chain design, supplier-related factors, relational competencies, physical capital resources, and human capability resources. with some limitations and need for improvement such as the lack of standard metrics for parameters. The case study result concluded that the supply chain resilience of FMCG distribution company has a limited capability to enhance the resilience in the supplier-related factors. According to the used assessment tool in this thesis, an FMCG distribution company can reach 82 % overall resilience at maximum. While rooted in a specific case study, the finding offers insights applicable to the broader FMCG distribution industry. Additionally, this thesis contributes to the existing literature by spotlighting supply chain resilience assessment tools. It recognizes limitations in factors' weighting assumptions and interdependency considerations, which could influence assessment accuracy.29 0