http://ir.bdu.edu.et/handle/123456789/10160 Sat, 13 Jan 2001 07:33:43 GMT 2001-01-13T07:33:43Z http://ir.bdu.edu.et/handle/123456789/16589 PREDICTING THE CONDITION OF EQUIPMENT USING CBM-BASED RCM ANALYSIS (Case Study of Ries Engineering) Hemen, Zemenu In modern manufacturing, optimizing maintenance strategies is essential for cost reduction, competitive advantage, and product integrity. This study investigates the integration of Condition-Based Maintenance (CBM) and Reliability-Centered Maintenance (RCM) in vehicle manufacturing, focusing on Ries Engineering. The goal is to enhance system efficiency, minimize downtime, and improve reliability by constructing RCM-based CBM model for predicting equipment conditions. Following a Risk Priority Number (RPN) assessment, the study emphasizes the critical status of the EcoSport car's transmission system. It integrates oil analysis (viscosity, wear particles, contamination), vibration analysis (vibration levels, frequencies), and temperature analysis (thermal stress, cooling effectiveness). High RPN values identified critical failure modes: shift timing issues, fluid degradation, and solenoid or sensor failures. Each failure mode was analyzed to determine underlying causes and consequences, leading to targeted maintenance strategies. The results show that combining CBM with RCM will enhances maintenance efficiency, reduces costs, and improves reliability. Shift timing issues were managed through condition-based monitoring, fluid degradation was addressed with regular fluid changes and proactive oil analysis, and solenoid or sensor failures were mitigated with inspections and predictive maintenance. This structured approach significantly boosts operational reliability, extends component lifespan, and optimizes maintenance practices. Future research should validate these findings in real-world settings, implement continuous monitoring systems, provide targeted training, and conduct cost-benefit analyses. Refining the risk matrix and incorporating expert judgment are recommended to advance predictive maintenance capabilities. This study highlights the potential of integrating CBM and RCM to drive innovation in automotive maintenance and enhance overall operational excellence. Keyword: Condition-based maintenance; reliability-centered maintenance Sun, 01 Sep 2024 00:00:00 GMT http://ir.bdu.edu.et/handle/123456789/16589 2024-09-01T00:00:00Z http://ir.bdu.edu.et/handle/123456789/16588 Assessment of Productivity Loss and Forecast Addiction Levels of Khat, In Bahir Dar City. Gashaw, Asnakew Fentie Many companies promote the value of maintaining productivity to ensure the company's continuous financial success. A variety of factors could result in a loss in productivity. Khat chewing during work is a factor in productivity loss. Khat chewing is a daily activity that is extremely addictive and takes billions of hours of work time. The Markov chain model is a stochastic process and an excellent tool for evaluating the state of daily activities. It has led to the assessment of loss of productivity and forecasting of the addiction level of khat in a novel way using the Markov chain model to mitigate negative effects. The data were collected through pre-tested structured questionnaires and interviews. An SPSS version 21 was used for data analysis. The assessment of opportunity cost is an economic expense and productivity loss. The daily loss is 86,840.75 birrs, which is considered an economic burden and productivity loss khat users. The frequency prevalence of chewing the khat once a day was 60.7 %. This study evaluated addiction levels of khat were low, moderate, high, and chronic. In low addiction, 84% continued use in this period, and 16% increased to moderate addiction. At moderate addiction, 69% continued in this period, at high addiction, 93% continued during this period and 5% increased khat chewing by six or more hours. In chronic addiction level, 94% of respondents continued in this period and 5% decreased khat chewing use from four and six hours their khat chewing. POM/QM analysis determined khat addiction levels in the long-term behaviors were 0.5(low), 0.07(mod), 0.48(high), and 0.4(chronic) addiction levels. The long-term probability value of a high addiction level (0.48) is higher than the other addiction levels. To validate the developed model, the chi-square test used that it was greater than the critical value on the significance level (40.4 > 16.919). The results determined the independent variable that grams of khat used have an impact on the developed model of khat addiction levels. The chewing has been taking different chemicals with khat leaves and taking alcohol after chewing khat leaves. In this study, effectively assessed productivity loss and the Markov chain model have been successfully applied to evaluate and forecast future addiction levels of khat chewing. Create awareness about the economic burden and production loss related to khat. give knowledge of the addiction level of khat chewing to make policies and strategies for controlling and preventing khat chewing in the city. Keywords: Addiction level, Evaluate, Forecast, Khat, Markov chain, Prevalence. Productivity loss. Fri, 01 Nov 2024 00:00:00 GMT http://ir.bdu.edu.et/handle/123456789/16588 2024-11-01T00:00:00Z http://ir.bdu.edu.et/handle/123456789/16587 Enhancing Manufacturing System Productivity Using System Dynamics (Case of Yetmen gypsum and gypsum products manufacturing & sales Plc.) Eyakem, Tadesse Kebede Manufacturing system productivity of Yetmen Gypsum and Gypsum Products Manufacturing & Sales Plc is low. Average plant capacity utilization rate of the firm within last four consecutive years is 44.43%. This research aims to enhance manufacturing system productivity of YGMS Plc using system dynamics by the application of Vensim Software to identify inefficiencies, simulate potential improvements, and implement data driven strategies for continuous operational enhancements. Findings of this study showed that major factors significantly affecting manufacturing system productivity of YGMS Plc are resource related downtime, human error, maintenance related downtime, and management related issue. Experimentation of simulation model considering time dynamics of modeled manufacturing system indicated that policy intervention based on the findings of this research improved the company’s productivity from 0.119616Kg/Birr to 0.371381Kg/Birr. Also model validation test defined the real problem of the manufacturing system. When plant downtime hours due to human error related factors, resource related factors, and maintenance related factors of the SD model are separately decreased by 20%, the multifactor productivity of the firm are increased by 5.84%, 12.98% and 2.69% respectively. Hence, the scenario analysis indicated that all factors have significant effect in the multifactor productivity of the firm. However, resource related factors have the highest effect in the improvement of multifactor productivity of YGMS Plc followed by human error related factors and maintenance related factors respectively. Therefore, policy intervention of YGMS Plc for its productivity enhancement shall focus to improve resource related factors, human error, and maintenance related factors affecting the firm’s productivity. Key Words: Productivity improvement, System dynamics, Quantitative model, Conceptual Model, Factor analysis, Sensitivity analysis. Tue, 01 Oct 2024 00:00:00 GMT http://ir.bdu.edu.et/handle/123456789/16587 2024-10-01T00:00:00Z http://ir.bdu.edu.et/handle/123456789/16586 MODELING OF LEAN SIX-SIGMA FOR FOOD PROCESSING INDUSTRIES TO REDUCING WASTE THROUGH PROCESS CONTROL (IN CASE OF SENSELET FOOD PROCESSING PLC.) BELETE, GETACHEW The food processing industry plays a vital role in meeting the global demand for safe, nutritious, and affordable food products. However, the industry faces ongoing challenges in maintaining operational efficiency, reducing costs, and upholding product quality standards. The lean six sigma methodology has emerged with integrated 5S and DMAIC techniques as an improvement approach to address these challenges and drive continuous improvement within the food processing industries and assuring sustainability. This research study aims to model lean six sigma framework in the food processing industry by controlling process variations, with a focus on identifying the critical success factors and potential barriers to its adoption. The study employed a mixed-methods research design, combining qualitative interviews with industry experts and a quantitative survey of food processing industries in case of Senselet Food Processing PLC, to gain a comprehensive understanding of the lean six sigma implementation. Using purposive sampling, the study involved 18 respondents. The five-why analysis identified the top five root causes of waste in the potato processing industry: poor inventory management, waiting times, unnecessary motion, operator issues, and product defects, with average rankings of 4.90, 4.89, 4.82, 4.77, and 4.73, respectively. Following the implementation of suggested Lean Six Sigma measures, the sigma value of the potato processing line improved from 4.00 to 5.00, reflecting a 20% enhancement in sigma level. This indicates significant progress in reducing waste and improving overall efficiency in the processing operations. This result suggests that the lean six sigma manufacturing technique is an effective method for identifying the root causes of waste in a manufacturing or processing industry, thereby enhancing the company's productivity. The study employed Arena and Visio software’s to model the Lean Six Sigma framework in the food processing industry, highlighting its potential to enhance operational efficiency, eliminate non-value-added activities, reduce process variations, lower costs, and improve product quality. Key Words: lean, Six Sigma, DMAIC, arena software, Visio software, software, Sustainability, Tue, 01 Oct 2024 00:00:00 GMT http://ir.bdu.edu.et/handle/123456789/16586 2024-10-01T00:00:00Z