Thesis Abstract

Abstract
This thesis explores the application of Artificial Intelligence (AI) and Machine Learning (ML) techniques for optimizing production processes in a manufacturing industry. The aim of this study is to enhance efficiency, productivity, and overall performance by leveraging advanced technologies to analyze and improve existing production systems. The research focuses on developing and implementing AI and ML algorithms to automate decision-making processes, identify patterns, and predict outcomes within the manufacturing environment. The introductory chapter provides an overview of the research, highlighting the background, problem statement, objectives, limitations, scope, significance, and structure of the thesis. The literature review chapter critically examines existing studies on AI, ML, and production optimization, presenting a comprehensive analysis of relevant theories, models, and methodologies. The research methodology chapter outlines the approach adopted in this study, including data collection methods, experimental design, software tools utilized, and the implementation process of AI and ML algorithms. It discusses the selection criteria for the case study in the manufacturing industry and explains how data was collected, processed, and analyzed to optimize production processes effectively. The findings chapter presents a detailed discussion of the results obtained from the application of AI and ML techniques in the manufacturing environment. It evaluates the performance improvements, cost reductions, and other benefits achieved through the optimization of production processes. The chapter also addresses challenges encountered during the implementation phase and provides insights into potential areas for future research. In conclusion, this thesis summarizes the key findings, implications, and contributions to the field of industrial and production engineering. It highlights the significance of leveraging AI and ML technologies for enhancing production efficiency and competitiveness in the manufacturing sector. The study underscores the importance of continuous innovation and adaptation of advanced technologies to meet the evolving demands of modern industrial settings. Overall, this research contributes to the growing body of knowledge on the integration of AI and ML techniques in optimizing production processes, offering valuable insights for practitioners, researchers, and policymakers seeking to drive sustainable growth and innovation in the manufacturing industry.

Thesis Overview