Daniel Kitaw (Prof.)Eshetie Berhan (PhD)Daniel Ashagrie2024-03-122024-03-122023-05https://etd.aau.edu.et/handle/123456789/2457This dissertation explores the application of Statistical Process Control (SPC) techniques in the manufacturing sector. Industries demand multivariate process monitoring technique capable of identifying cause of variation, and conducting fast and accurate fault detection analysis. However, the existing techniques fall short of satisfying this demand. Hence, the research question is devised as follows: how to design a multivariate process control procedure that can effectively monitor and control the production system, identify the root causes of variation, and provide solutions for improvement. The literature review conducted in this study revealed that while SPC techniques have been extensively studied and applied in various industries, the multivariate analysis of identifying cause of variation is relatively limited. Their practical implementation and adaptation to the industry have not been thoroughly explored. The main objective of this research is to design a procedure that can bridge the theoretical gap that exist in the manufacturing sector. By addressing this gap, it is anticipated that the productivity, quality, and overall performance of the production system can be improved. To address these limitations, a novel approach called the GANNT chart is introduced in this research. The GANNT chart incorporates three key theories: Graph theory (G), Artificial Neural Networks (ANN), and Hotelling T2 (T). By combining these theories, the proposed approach aims to enhance the process control technique used in the production system. The GANNT chart mimics human decision-making processes and serves as a decision support system for both process engineers and operators.The GANNT chart methodology offers several advantages. Firstly, it analyzes the correlation effects between variables using Hotelling T2, allowing for a more comprehensive understanding of process variation. Secondly, it leverages graph theories to retain and utilize knowledge from previous successful operations, facilitating continuous improvement. Lastly, the system is trained using Artificial Neural Networks, enabling it to provide solutions to future challenges based on learned patterns from past operations. The proposed model is validated in the cement industry to assess its effectiveness and practicality. The results demonstrate that the GANNT chart effectively addresses the identified gaps in the application of SPC techniques to the cement production process. The model's ability to accurately detect process deviations and provide insights into the causes of variation contributes to improved productivity, quality, and overall performance. As a future research direction, this study highlights two suggestions. The first one is examining and extending the assumptions to design this model in such a way that it considers different scenarios not covered by this research. The second direction is extend the implementation of GANNT chart to various industries, including service giving industries, and study and explore its applicability. In conclusion, bridging the gap between theory and practice, this research aims to contribute to the advancement of multivariate process control to the industry, ultimately leading to enhanced operational efficiency and product quality.en-USNeural NetworkGraph theoryHotelling T2GANNT chartVARIMA modelcement process controlThesis