Saudi Cultural Missions Theses & Dissertations
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Item Restricted Is there an effect of COVID-19 on mergers and acquisitions of companies implementing industry 4.0 through the use of IoTs in Saudi Arabia?(Saudi Digital Library, 2023-02-03) Al Deen, Hytham Jamal; Den Besten, MatthijsThe COVID-19 pandemic had a great effect not only on daily life but also caused a significant catalyst in the global transition to digitization. Although countries had various measures and lockdown procedures, working remotely became the new norm while at the same time the world was battling with the pandemic from a healthcare perspective. This caused significant growth in the technology and healthcare sectors leading to many mergers and acquisitions (M&As) within these sectors. The role of industrial policy across nations was variable and has been greatly dependent on the country’s priorities and market patterns. Simultaneously and even before the pandemic, the world’s transition into industry 4.0 showed a broad industrialization into smart technology which was only catalyzed by the pandemic itself. Because technology was such a focus during this time period, companies already transitioning into industry 4.0 through the use of IoTs in some sectors tremendously benefitted from the pandemic and this was evident in the frequency and size of the mergers and acquisitions occurring in this sector. Methods This qualitative research design is based on a grounded theory approach because the focus of the study is to understand what happened to mergers and acquisitions in Saudi Arabia and what changes occurred. The focus of the interviews is to get a better understanding of how firms were affected by COVID-19, how they saw the acquisition landscape going forward and how their approach to managing acquisitions has changed. Results Data was collected from 10 employees of M&A practitioners (including executives and consultants) different companies within Saudi Arabia and also outside Saudi Arabia for comparison purposes. Initially 13 codes were identified as the key elements that were clear drivers to M&A adoption, then grouped into 4 second order themes: Demand for technological advancement, regulator / regional research, increased digitalization from pandemic, and local and international environmental laws and regulations. Conclusion Based on the results of this study, it cannot be said whether COVID-19 has affected frequency and size of mergers and acquisitions for companies using IoT both in Saudi Arabia and globally. These changes in M&A trends seem to be dependent on the sector as tech and healthcare companies flourished dramatically while other sectors seemed to struggle. This shows that regardless of the resources Saudi Arabia has and its willingness to make such a strong reputation, the lack of cross-border M&As gives a strong indication that this may be due to a lack of industrial policies and focus on ESGs.6 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.19 0Item Restricted Production Planning in the Context of Industry 4.0 with Focus on Efficient Job Allocation & Workers’ Real-Time Status(Saudi Digital Library, 2023-08-15) Albassam, Abdullah Mohammed; Niknam, Seyed AIndustry 4.0 (I4.0) has emerged a distinct impact on industrial workforce and created demand for diverse set of workforce skills and domain knowledge. Accordingly, I4.0 production systems are in need for developing and utilizing an appropriate workforce planning that considers workers with different type of skills to cope with the production requirements and keep up an efficient production. The I4.0 philosophy advocates the usage of advanced wearable technologies. Such wearable devices are able to monitor workers’ status and record vital signs and physiological data. It is well known in literature that workers’ performance in production systems is linked to their job satisfaction level as well as psychological well-being. There is much active research in the area of advanced physiology measurement technologies and incorporating the workers’ health data into industrial applications in real time. In essence, it is expected that smart wearable health devices provide the ability to boost job satisfaction, reduce human errors, and affect performance by helping managers for more efficient task matching and scheduling. This research is focused on developing job assignment models in the context of I4.0 and has considered both workers’ physiological status and the skills required to achieve the production goals. The ultimate goal of the proposed models is to maximize productivity by matching operations tasks to workers with different required skills and various skill levels. This study also considers workers' performance indicator which is predicted by machine learning models using workers’ physiology measurement. The assignment model could provide promising results in moving toward real-time application of workers’ physiological status in order to better assign production tasks and maximize production value.13 0Item Restricted Applications Of Artificial Intelligence In Supply Chain Management In The Era Of Industry 4.0(2023) Ali, Arishi; Krishna, KrishnanNowadays, an emerging trend in Supply Chain Management (SCM) is a focus shift from classical Supply Chain (SC) to digital SC. However, decisions in the digital SC context require new tools and methodologies that consider the digitalization environment. Artificial Intelligence (AI) methodologies can provide learning, predictive, and automated decision-making capabilities in the digital environment. Among a wide range of problems in the SCM field, risk management, logistics, and transportation have received less attention from an AI perspective. The work presented in this dissertation proposes three AI-based approaches to help SCs manage their operations more effectively using creative risk monitoring and logistics/transportation solutions in the era of Industry 4.0. In the first study, a Digital Twin (DT) framework for analyzing and predicting the impact of COVID-19 disruptions on the manufacturing SC is developed to support the decision-making process in disrupted SC. The proposed Digital SC Twin (DSCT) model is aimed to work as an online controlling tower to monitor the behavior of physical SC in the digital environment and guide SCM managers to make the necessary adjustments to minimize risks and maintain SC stability during disruptions. In the second study, a contactless truck-drone delivery model for last-mile problems in the SC is introduced to support logistics and transportation operations during pandemics. A hybrid AI approach is developed to provide quality real-time solutions for the introduced truck-drone delivery system. In the third study, a collaborative Multi-Agent Deep Reinforcement Learning (MADRL) approach for vehicle routing in the SCM is designed to facilitate collaboration and communication among multiple vehicles in the SC distribution networks. Overall, the methods and models presented in this dissertation can enable SCs to transform their traditional practices, provide cost savings, support real-time decision-making, and enable self-optimization and self-healing capabilities in the age of Industry 4.056 0