Industrial Engineering

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Investigating the Impact of Ergonomic Workplace Design on Employee Performance in the Healthcare Sector: A Case of Menelik II Referral Hospital
(Addis Ababa University, 2025-10) 29 Oct-29-2025 Kidist Fitsum Mechanical Eng/Industrial Eng ✓ 1; Kassu Jilcha (Assoc. Pro.)
Poor ergonomic workplace design in healthcare settings can lead to significant challenges for employees, including health problems and decreased performance. At Menelik II Referral Hospital, healthcare employees face various work-related challenges related to the facility's ergonomic workplace design, which negatively impacts their health and job performance. This study aims to investigate the impact of ergonomic workplace design on the performance of healthcare employees and propose an improvement approach for the hospital. The study focuses on how ergonomic workplace design factors affect the physical and mental well-being of healthcare employee and, as a result, their overall performance. The study employed a mixed-methods approach, combining qualitative and quantitative data through well-structured and pre-tested surveys, interviews, and direct observations by using purposive sampling technique. This methodology allowed for a thorough assessment of the current ergonomic conditions and their effects on employees performance. SPSS version 24 was used for statistical, Pearson correlation and multiple linear regressions data analysis. The findings show a significant relationship between poor ergonomic workplace design and a higher incidence of musculoskeletal issues, increased mental load, and reduced work performance among healthcare employees. The statistical analysis found a significant positive relationship between all three ergonomic domains and employee performance. Physical ergonomics showed the strongest correlation (r = 0.561, p < .001), followed by cognitive ergonomics (r = 0.442, p < .001) and organizational ergonomics (r = 0.436, p < .001). Multiple regression analysis revealed that these factors collectively explained 56.3% of the variance in performance, with physical ergonomics being the strongest predictor (β = 0.260, p < .001). Based on these results, this study proposes an improvement framework with specific recommendations and strategies for enhancing ergonomic conditions at the hospital. Implementing these strategies is expected to improve employee well-being and performance, in the end, contributing to better patient outcomes. This study provides actionable recommendations for Menelik II Referral Hospital and contributes to the existing body of knowledge on ergonomics in healthcare.
Improving Quality & Productivity through Lean Six Sigma DMAIC Approach implementation: A case of ERCO Textile and Garment PLC
(Addis Ababa University, 2025-09) Liyew Workie; Ermias Tesfaye(PhD)
The thesis addresses issues in the textile industry like high defect rates and inefficiency by investigating improvements in quality and productivity at ERCO Textile and Garment PLC using a modified DMAIC approach known as DMAVIIC. It points out important gaps in the literature, namely the lack of problem confirmation and the inability to integrate lean systems across departments. The study focuses on the knitting department and employs both qualitative and quantitative methodologies. According to preliminary assessments, there was a 17.96% defect rate, a sigma level of 1.36, and a cost of poor quality (COPQ) of 2,338,789.38 birr over three months. Both value-added and non-value-added operations were identified using value stream mapping, with cycle times of 1,320 and 918 minutes, respectively. After the suggested fixes were put into practice, the sigma level increased to 2.68, the COPQ dropped to 1,386,000.06 birr, and the defect rate dropped to 9.72%. Additionally, cycle periods for value-added operations were shortened to 861 minutes and for non-value-added activities to 250 minutes. Metrics of productivity increased, with worker productivity rising to 72.22% and machine efficiency reaching 76.3%. Time to market was shortened and customer satisfaction rose as a result of these enhancements. According to the study's findings, the DMAIC approach greatly increases the efficacy and efficiency of improvement projects by incorporating functional department collaboration and root cause verification.
Modeling Organizational Performance Through the Integration of Sustainable Quality Management Practices: The Case of Bishoftu Motor Vehicle Engineering Industry (BMVEI)
(Addis Ababa University, 2025-10) Meseret Bogale; Meseret Bogale (PhD)
This study rigorously investigates the modeling of organizational performance through the integration of Sustainable Quality Management Systems (SQMS) within the tank overhaul sector at Bishoftu Motor Vehicle Engineering Industry (BMVEI), Ethiopia. Amidst operational inefficiencies, including extended overhaul times, elevated defect rates, and suboptimal capacity utilization, BMVEI typifies challenges in resource-limited manufacturing environments. Employing an explanatory quantitative research design and structural equation modeling (SEM), the study examines key SQMS dimensions—top management support, customer focus, employee involvement, training and development, and continuous improvement—and their direct influence on organizational performance. Empirical findings reveal statistically significant positive relationships, with customer focus and training showing the highest impact, collectively explaining 62.9% of performance variance. This research contributes a robust, context-specific framework linking sustainability principles with quality management to advance operational efficiency and product quality. The results provide critical theoretical and managerial implications, advocating the adoption of integrative sustainable practices to promote resilience and competitiveness in manufacturing industries within developing economies.
AI-Based Defect Detection Model Development to Enable Zero Defect Manufacturing in Ethiopian Steel Industries
(Addis Ababa University, 2025) Mehret Getachew; Mehret Getachew (Assoc. Prof); Ameha Mulugeta (Asst. Prof.) Co-Supervisor
Product quality stands as the primary competing factor in manufacturing industries, especially in the rapidly evolving digital landscape of the current era. Zero Defect Manufacturing (ZDM) has emerged as a state-of-the-art quality enhancement framework in response to Industry 4.0 technologies. This data-centric approach encompasses four crucial strategies: detection, prediction, repair, and prevention. Detection, the cornerstone of ZDM, operates through two distinct methods. The physical method relies on direct product measurements to identify defects, while the virtual approach, known as virtual metrology, conducts quality inspections without physical contact. This novel technology is revolutionizing quality inspection practices in the steel industry on a global scale. Ethiopian steel industries are facing a persistent challenge of producing a significant volume of defective products due to their reliance on manual inspection methods, a practice that is both costly and time-intensive. The successful integration of virtual detection relies entirely on data accessibility (via robust data infrastructure) and advanced analytics. The pivotal components for achieving virtual defect detection in industries are organizational shifts toward digitalization and the adoption of standardized data analytics (DA) practices. Current literature on ZDM overlooks crucial factors related to industrial digital readiness and fails to analyze the interconnected impact of these factors on the digitization of manufacturing industries. While various digital maturity (DM) models are in use in industrial contexts, there is a lack of analysis regarding the intricate relationships among MM dimensions. Furthermore, the absence of a standardized approach to data analytics poses challenges. Although numerous data analytics frameworks exist beyond ZDM, customizing these methods for ZDM applications can complicate the development of effective AI models. Hence, the primary objective of this dissertation is to develop an AI-driven defect detection model. This will be achieved by initially addressing the challenges outlined in industrial digital readiness and standardized data analytics framework enabling the implementation of virtual defect detection. To achieve the primary goal, a multi-stage research methodology is employed, encompassing diverse analytical dimensions to tackle different aspects of the research problem. The work begins with an exploration of the theoretical underpinnings of zero-defect manufacturing, leading to the identification of challenges in effective industrial data analytics in ZDM and the proposition of a standardized DA framework. Subsequently, it proceeds to identify the crucial factors for digital readiness in ZDM implementation, utilizing structural equation modeling (SEM) to explore the connections among these factors using Statistical Package for the Social Sciences (SPSS) and Analysis of Moment Structures (AMOS) as a tool. A digital maturity assessment of industries is then conducted to devise a robust ZDM strategy. Causal relationship analysis of the digital readiness factors has been performed by collecting a data from a sample of 149 steel industries in Ethiopia, while a detailed assessment of digital maturity is carried out on a specific industry case, employing a well-established assessment framework and the six maturity levels identified in recent publications. Finally, an AI-based defect detection model is developed and validated using one year of time-series quality inspection data collected from the case industry, using R programming language with RSTUDIO as a tool and three ensemble learning algorithms. The finding from the relationship analysis of DM factors reveals that people and expertise play a crucial mediating role between the adoption of digital technology and digital maturity, underscoring the significance of human capital in digital transformation in manufacturing. The evaluation of digital readiness at the industry level indicates that the industry is presently positioned at level 2, representing the initial phase of connectivity (having attained computerization). The assessment exposes a notable digital maturity gap concerning people and expertise. Another significant empirical aspect of this dissertation is the development of a defect detection model, which exhibits enhanced accuracy in defect identification through the incorporation of comprehensive quality parameters. The modified Gradient Boosting model outperforms the other two ensemble learning models, which are Bagging and Random Forest in predicting the quality of the considered steel product (Rebar output quality parameters) based on the following R² values: 0.9538 for Yield Load, 0.9726 for Yield Stress, 0.9751 for Ultimate Load, 0.9718 for Tensile Strength, 0.9614 for Elongation after Fracture, 0.983 for Tensile strength to Yield strength ratio, and 0.964 for Bend Test. Comparative analysis with existing literature also demonstrates that this study achieves superior levels of prediction accuracy across most output variables. The DD model developed in this dissertation tackles the shortcomings of manual quality inspection techniques leveraging AI and machine learning on recorded QI data. Traditional methods (physical inspection), resulting in high costs, time inefficiencies, and inconsistencies, especially when detecting concealed defects. Unlike these approaches, the AI-based DD model autonomously learns and detects defects. Prior to delving into model development, the dissertation also explores key prerequisites for effective AI implementation and propose solutions to realize virtual defect detection: industrial digital maturity assessment and the adoption of standardized data analytics. These enable steel industries to shift systematically from manual quality inspections to a data-driven method and progress towards the implementation of each ZDM strategies. The research contributes to filling gap in the current literature of DM models by critically analyzing the relationships among the existing DM models dimensions. This enables industries to focus on the critical factors which highly contribute to DM. The proposed assessment guide provides practitioners with the tool to accurately determine their DM index and implement the appropriate ZDM strategy. The novel DA framework proposed also enables the development of AI model standardized (considering key elements of product quality control) which were fragmented in the ZDM literature. The developed AI-driven defect detection model addresses the current challenge for the steel industries quality inspection activities assisting operators in accurately estimating the occurrence of defects. Lastly, the proposed ZDM Continuous Improvement Cycle (CIC) provides a clear framework for continuous improvement, aligning the ZDM strategy with the industry's current digital maturity level. This approach enables industries to systematically progress through the four ZDM strategies, starting with detection as the baseline to prediction and prevention.
Developing a Lean Service Quality Improvement Model to Enhance Medical Tourism in Healthcare Sector: A Case of Ethiopia
(Addis Ababa University, 2025-06) Berhanu Tolosa; Daniel Kitaw (Prof.); Kassu Jilcha (Assoc. Prof.) Co-Supervisors; Sisay Sirgu (Asst. Prof.) Co-Supervisors
Healthcare is a sector with unique features where defects and errors cannot be tolerated. Its service delivery is one of the principals and most complex systems on the globe due to rapidly growing pressure, waiting time, an aging population, increasing patients flow, limited resources and competing social needs to sustain life. Thus, the sector has turned its attention to a lean approach due to a growing influence in reducing waiting time to enhance service quality and increasing flexibility. The researcher begins the investigation by discussing the current healthcare challenges long waiting time (95 minutes in averages), long length of stay (15 days in average), lack of access to some services in the country, and driving factors that have contributed to the improvement of service quality, particularly at St. Paul’s Hospital Millennium Medical College. Hence, the study's primary goal is to develop lean quality service improvement model aimed at enhancing the patient satisfaction in the Ethiopian healthcare sector, specifically within the case healthcare, thereby increasing medical tourism. The study utilized various databases to conduct a comprehensive assessment of the literature and enhance service quality. A thorough review of the literature was conducted to investigate the concepts, practices, and challenges related to healthcare service quality improvement. The review process identified literature gaps, including the dimensions of healthcare service quality improvement, service quality and lean thinking integration, the absence of service design thinking in service quality improvement, and their impact on patient satisfaction and medical tourism, which prior work has not sufficiently addressed. The study adopted a mixed-methods approach, incorporating both primary and secondary data gathering methods. The Define, Measure, Analyse, Improve, and Control (DMAIC) technique was also used. To quantify service quality gaps, patient expectations and perceptions were gathered during the Define Phase. During the Measure Phase, the high weighted scores obtained from patient input were evaluated using Quality Function Deployment (QFD). In the Analyse Phase, a cause-and-effect diagram was used to determine the underlying reasons behind these high results. In the Improve Phase, inputs, resources, and methods were considered major causes, along with possible solutions. In the Control Phase, machine learning tools such as random forest, neural networks (NN), and support vector machines (SVM) were employed to predict healthcare patient waiting times, ensuring sustained service quality and patient satisfaction. Performance comparison metrics, including Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and coefficient of determination (R²), were assessed to identify the predictive model's accuracy. The study employs a dual approach of Structural Equation Modelling (SEM) and Artificial Neural Networks (ANN) to develop a model that identifies dimensions significantly impacting patient satisfaction and medical tourism. Additionally, sensitivity analysis was used to rank these dimensions, providing better insights and alternatives. A total of 225 patient data points were collected from respondents through a questionnaire to develop a lean service quality framework using SPSS, AMOS, and Artificial Neural Network (ANN). Among the three models for predicting waiting time, the support vector machine model demonstrated better prediction accuracy compared to the neural network and random forest models when assessing actual data. The support vector machine effectively mimics waiting time, significantly improving service quality. Based on the developed model, five components with significant factor loadings exceeding 0.50 have been identified. These components are service quality, lean thinking, lean service quality, patient satisfaction, and medical tourism considerations. The results were further analyzed to assess the model's fit. A decent model fit is implied by the RMSEA value of 0.05, which is below the permissible limit of 0.08. At 0.82, the Adjusted Goodness of Fit Index (AGFI) score is nearly at its suggested level. Tucker-Lewis Index, or TLI, is 0.96; the Normed-Fit Index (NFI) is 0.92; and the Comparative Fit Index (CFI) is 0.97. Since the NFI, CFI, TLI, and IFI values are higher than the suggested value of 0.9, they indicate strong model fits. The structural equation modelling is used as the input unit of the artificial neural network model to detect both nonlinear and linear relationships without robust speculative or theoretical bases. Artificial neural networks identify all linear, nonlinear, and non-compensatory relationships by avoiding assumptions in distribution and model development. The RMSE value of the artificial neural network model (0.88) indicates good predictive accuracy for the lean service quality improvement model. Regarding the dimension of healthcare lean service quality improvement, there is no weak effect in the verified model of the artificial neural network. The normalized importance levels for all dimensions are greater than 80%, which is very high. It showed a significant relationship with both structural equation modelling and artificial neural network model analysis. Hence, the dimensions in model 1, 2, and 3 indicated a significant relationship with each other.The study proposed the integration of lean thinking and service quality due to lack of available literature and the need for additional research on lean service quality adoption gaps, waiting time prediction, and improvement opportunities in the healthcare sector. The study is also original in its focus on developing a lean service quality model using a dual approach within the context of the healthcare sector. It contributes to healthcare lean service quality improvement based on quality function deployment, Six Sigma, and an artificial intelligence approach. It also ranked the importance levels of the lean service quality improvement dimensions through artificial neural network sensitivity analysis.
Lean-Six Sigma Approach for Enhanced Efficiency and Quality in Textile Manufacturing; a Case of Else Addis Industrial Development P.L.C., Ethiopia
(Addis Ababa University, 2025-03) Asefa Kebede; Ameha Mulugeta (PhD); Mehret Getachew (Mr.) Co-Advisor
The study focuses on addressing textile product quality and process inefficiency challenges, as well as how to enhance product quality and efficiency through the Lean Six Sigma principles, tools, and techniques in Ethiopia’s textile industry, specifically targeting the production of 40Ne yarn count. This particular yarn count was selected due to its recurring quality and efficiency issues, making it a critical area for improvement. The research employs both qualitative and quantitative methods to assess product quality and process efficiency, ensuring a comprehensive validation of the existing problems. During case company observation major defect were recorded with amount of defect rejected. The major defect identified were count variation, yarn hairiness, thin-thick place, winding fault, shape of the cone, bad piecing, and nep formation. From the major defect Pareto chart analysis revealed three major defect types contributing to 64% of quality issues. These are count variation (24%), yarn hairiness (22%), and thin/thick places (18%). Root cause analysis using fishbone diagrams identified key contributors including machine inconsistencies, operator skill gaps, material flow inefficiencies, and measurement errors. Also, from observed data, process efficiency evaluation showed big waste, with only 47.7% value-added time versus 52.3% non-value-added activities, primarily from motion waste (40.4%). The current sigma level of 3.25 indicated big process variability. The DMAIC framework was applied to address these challenges, integrating Lean tools like 5S and Value Stream Mapping (VSM) to reduce waste and Six Sigma methods to minimize defects DPMO and sigma levels was determined based production per months and defect rejection in production per month. Through the compressive analysis of the winding process using tools, process mapping, 5S, and DMAIC approach with tools, and based on the findings of the analysis, this study was developed and proposes an improvement strategy for the yarn manufacturing industry.
Demand Forecasting for Improved Production Planning; A Time Series Analysis and ARIMA Modeling in case of NEHE Purified Water Bottling Company, Ethiopia
(Addis Ababa University, 2018-09) Tola Kebede; Ameha Mulugeta (PhD)
Improving production planning and operational efficiency in the beverage business requires accurate demand forecasts. In order to increase the accuracy of demand forecasting at NEHE Beverage Complex PLC, this study examines the use of time series analysis, namely the ARIMA (AutoRegressive Integrated Moving Average) model. By creating a trustworthy forecasting model using past sales data, the study seeks to address the issues of demand volatility, inventory imbalances, and production inefficiency. The results show that ARIMA modeling greatly increases demand prediction accuracy, offering a solid foundation for improved inventory control, resource allocation, and production scheduling. This lowers waste and operating expenses by allowing NEHE Beverage Complex PLC to better match its production procedures with consumer demand.
Optimizing Production Waste and Enhancing Operational Efficiency through Integrated Lean Management and Reliability Analysis: A Case Study of National Tobacco Enterprise
(Addis Ababa University, 2025-10) Fasil Asegdew; Kassu Jilcha (Assoc. prof.)
Global manufacturing consistently grapples with significant production waste and operational inefficiencies, detrimentally affecting profitability, competitiveness, and sustainability. This thesis, from an industrial engineering standpoint, critically examines these pervasive challenges within the National Tobacco Enterprise (NTE), focusing specifically on its Make-Pack department. Quantitative analysis of 30-month operational data revealed chronic underperformance: cigarette reject rates averaged 5.02% (exceeding a 3% target), Non-Tobacco Material (NTM) waste reached 3.38% (above a 1% target), and Overall Equipment Effectiveness (OEE) consistently fell below target at 35.86%. These metrics underscore a critical need for integrated improvement strategies, as isolated Lean Management or Reliability-Centered Maintenance (RCM) interventions have proven insufficient. To address this, a comprehensive mixed-methods research approach was employed. This involved rigorous statistical analysis of production KPIs, Value Stream Mapping (VSM) to identify process bottlenecks, and in-depth equipment reliability assessments using Failure Mode and Effects Analysis (FMEA) and Root Cause Analysis (RCA). Qualitative data from interviews further contextualized findings, revealing reactive maintenance cultures and skill gaps. A key finding established a quantifiable link: equipment unreliability, reflected in low OEE, accounted for 50.8% of reject rate variability and 78.1% of NTM yield variability. Root causes spanned inadequate preventive maintenance, sensor control deficiencies, and insufficient operator training. This study proposes and empirically validates an integrated Lean-RCM framework, offering a synergistic solution for sustainable waste optimization and enhanced operational efficiency within complex manufacturing environments like NTE, setting a benchmark for the industry.
Enhancing Motor Insurance Claims Processing Efficiency through Simulation Modeling Approach: A Case Study of Tsehay Insurance S.C.
(Addis Ababa University, 2025-09) Natnael Baynesagn; Kasu Jilcha (Assoc.Prof.)
This study investigates the enhancement of motor insurance claims processing efficiency at Tsehay Insurance S.C. in Ethiopia through a simulation modeling approach. The research systematically integrates primary qualitative data gathered from stakeholder interviews with comprehensive secondary data sourced from historical claims records. This dual approach aims to identify and analyze critical bottlenecks that adversely affect processing times and customer satisfaction. Through meticulous analysis, the research reveals significant variations in processing durations, with some claims extending over a staggering 853 days to resolve. Such delays not only undermine customer trust but also erode the competitive edge of the insurance provider. The simulation modeling conducted in this study demonstrates that implementing automation technologies could potentially reduce overall claims handling processes times by approximately 30%, effectively addressing these inefficiencies and enhancing service delivery. The study provides actionable recommendations, emphasizing the need for standardized procedures, improved communication among stakeholders, and continuous training for staff to adapt new technologies. This study uniquely contributes empirical evidence regarding the impact of automation on claims processing efficiency and demonstrates how simulation modeling can effectively identify and mitigate workflow bottlenecks. Moreover the quantifiable benefits of automation, the research emphasize actionable recommendations aimed at refining claims processing practices. These include the establishment of standardized procedures that can enhance consistency, fostering improved communication channels among stakeholders and improving continuous training programs for staff. Furthermore, this study advocates for a customer-centric approach in the redesign of claims processes, emphasizing the importance of actively incorporating customer feedback to inform decision-making. By harnessing simulation modeling techniques, the study aims to promote data-driven strategies that enhance operational efficiency and elevate customer satisfaction within the insurance industry. The implication of these findings are profound for insurers aiming to optimize their claims processes in an increasingly competitive market, landscape, as they pave the way for future research endeavors focused on operational improvements and technological advancements in insurance practices.
Improving Spare Parts Inventory Management System to Enhance Customer Satisfaction in My Wish Enterprise PLC
(Addis Ababa University, 2025-06) Eyerusalem Mulugeta; Gulelat Gatew (PhD)
The primary objective of this study was to assess and improve the spare parts inventory management system to enhance customer satisfaction at My Wish Enterprise Plc. The research addressed persistent challenges in aligning inventory performance with customer expectations, particularly in demand forecasting, stock level monitoring, supply chain coordination, and inventory accuracy. Inefficient inventory systems often result in service delays, stock-outs, and reduced customer satisfaction—issues observed within the company. A quantitative design employed a structured questionnaire, administered to 20 employees via purposive sampling for primary data collection. Data analysis utilized SPSS for descriptive statistics, correlation, and multiple linear regression techniques to examine relationships between variables. Complementary time series analysis of historical spare parts sales in STATA was also conducted to specifically assess the operational aspects and challenges of demand forecasting accuracy for these items. Findings from the employee survey revealed that all four inventory practices positively and statistically significantly affect customer satisfaction (p<0.05). The regression model demonstrated strong explanatory power, with an R² of 0.798, indicating that nearly 80% of the variation in customer satisfaction is explained by the four predictor variables. However, the ARIMA (0, 1, and 1) time series analysis on spare parts sales highlighted inherent challenges in forecasting intermittent demand, evidenced by a Mean Absolute Error (MAE) of 64,112.96 and a Root Mean Squared Error (RMSE) of 179,628.26. This indicated the model's struggle to accurately predict large, sporadic sales spikes, underscoring the operational complexity of achieving high forecasting accuracy for such items. The study concludes that continuous improvements in demand forecasting, supply chain coordination, and inventory accuracy are essential for achieving higher customer satisfaction. Recommendations include adopting more advanced and data-driven forecasting models specifically suited for intermittent demand (e.g., specialized time series and AI/ML approaches), enhancing real-time supply chain coordination, and investing in digital inventory tracking systems. These measures are expected to streamline inventory operations, boost service responsiveness, and meet customer expectations more effectively.
Impact Of Lean Safety For Oil And Gas Industry: Case of North- Sea Upstream Sector
(Addis Ababa University, 2024-06) Desalegn Yeshitila; Daniel Kitaw (Prof.)
The oil and gas industry upstream exploration and production is one of the most hazardous businesses by the nature of the frontiers materials handled, the contents of the process, and the nature of the working environment and conditions. Moreover, drained profit margin due to low crude oil price arguably compromised safety with a primary focus on project delivery within cost and time budget. With all the odds and challenges, the sector's stakeholders are applying stringent safety standards, regulations, industry guidelines, and best practices to minimize and avoid workplace injuries to employees. Unfortunately, oil and gas upstream personal injuries are an everyday phenomenon that needs innovative personal safety systems in the sector to bring a paradigm shift in a personal safety system based on every individual involvement, personal engagement, top management leadership, and active participation. A change in mindset from Safety Compliance to Safety continuous improvement is required. A cultural shift from a top-down management structure to employee engagement and involvement with making safety every individual responsibility must be established to enable safety cultural change in the oil and gas industry. A workplace with everyone taking responsibility for safety measures results in a safer, more efficient, more productive work environment. It enhances employees’ morale, helping a sense of pride in their safety culture and ownership of the safe working environment. The most popular process continuous improvement methodology and operational excellence methodologies will be used in this dissertation to address the gaps in oil and gas upstream drilling and exploration personal safety systems and personal injury prevention and continuous safety system improvement. Problem-solving and learning from safe practices, mistakes, incidents, and accidents are important to any continuous improvement process. Lean thinking can turn every incident into a safety improvement opportunity. Learning from losses should never be a blame game but a process review and an improvement initiative. The conventional incident investigation mainly focuses on adverse incidents without learning from the positive developments. The oil and gas industry needs radical and systematic reform more than ever. As usual, sticking to the old way of working has unsustainable social, economic, environmental, health and safety consequences to the least and detrimental effects to the worst. The industry needs to learn from the experience of other industries, such as manufacturing and healthcare, to be able to ‘do more with less’ by process optimization, value streaming, doing things ‘right the first time,’ doing it safely, and integrating safety in every process, and focusing on safety as a value, etc. The oil and gas industry is one of the conservative industries with capital-intensive investment and a complex supply chain. The era of ‘easy’ oil and gas access is over. The upstream oil and gas exploration trend has become the most remote place where logistics and transportation are becoming a challenge. Because of thin reserves, wells drilled become long-reach horizontal wells, Deep-Sea, and hostile offshore environments. In these Satellite marginal fields, it is difficult to tie up to existing installation, depleted reservoir with geological, reservoir and other technical characteristics, uncharted environment with attached high safety risk challenges and hazardous conditions and acts where the industry performs exploration activities. By its nature, the oil and gas industry has a high environmental footprint in terms of carbon emission, uncontrolled spills, and waste disposal. Due to this effect, governing (regulatory) bodies are applying maximum pressure on the industry so that it should minimize the environmental impact due to its operation and apply various innovative techniques such as carbon capturing, carbon trading, and stringent HSE standards and on top of these, focus on clean energy. For any industry, human capital is one of the important inputs of product and service production. The oil and gas industry is at an immense challenge, as the most experienced and skilled workforce the industry depends on will retire within the coming five to seven years www.forbes.com; Satish Tyagi et al., (2015). Thus, knowledge transfer and successive planning could be a demanding task for an industry that has already faced daunting challenges. Especially experience related to workplace safety practice The oil and gas industry has been experiencing the longest and toughest downturn by any standard Lópeza, (2015); this is the right time for the industry to ‘change for the better; a strategic change is mandatory, not an option. The sector needs an integrated strategic change that could be expressed in innovating process improvement with personal safety embedded in every process. The focus of this study is to explore how oil and gas safety systems could be continuously improved beyond the basic safety compliance through employee engagement, employee involvement, and innovative safety system that could be used for oil and gas upstream sector injury prevention, problem-solving, building safe working environment and value addition in the context of oil and gas industry, with particular focus in North-Sea upstream sector. As any business organizations focus on productivity improvement, product and service quality, and customer services, not least safety is also one of the important business processes that need the involvement of top and frontline employees, with everyone’s responsibility, avoiding the common mistake of leaving safety for safety department and safety officers. In line with this, innovative safety system methodology, tools, and HSE standards are reviewed in the oil and gas industry context, specifically from the point of personal injuries prevention. Innovative safety system implications on the personal safety, safe working environment, value innovation, and environmental contribution of the industry will be assessed, and a conceptual and analytical continuous safety improvement system model will be developed. The conceptual and descriptive research methodology will be applied to evaluate, assess, benchmark, and develop a continuous safety system improvement model that would fit the upstream oil and gas industry context. In this dissertation, the concept of continuous safety system improvement development from employee engagement and involvement in continuous safety process improvement and learning is considered to positively improve the working environment, safety culture, and workforce morale. In this dissertation, the Norwegian oil and gas offshore exploration and production sector has been considered close to explore the sectors safety practice from the point of ‘respect for people,’ continuous improvement, employee involvement, and daily safety practice beyond compliance in general and people-based safety approach in particular through employee engagement and continuous learning.
Improving Printing Process Efficiency by Identifying and Mitigating Production Delays through System Modelling and Simulation at Berhanena Selam Printing Enterprise
(Addis Ababa University, 2025-06) Getaw Yirga; Gezahegn Tesfaye (PhD)
In the competitive printing industry, operational efficiency is crucial to staying ahead. Efficient operations minimize downtime, reduce waste, and improve resource utilization, which directly impacts profitability and customer satisfaction. Operational efficiency also enables companies to be more agile and responsive to market changes, ultimately giving them a competitive edge in an industry where margins can be tight and customer expectations are high. The Overall Equipment Effectiveness of Berhanena Selam Printing Enterprise for the 2023/24 period, standing at 14.84%, indicates room for significant improvement in operational efficiency. In light of this evidence, it is crucial to propose a research agenda focused on identifying and mitigating production delays through system modeling and simulation approaches to improving operational efficiency. This study adopts a quantitative approach to enhance operational efficiency by analyzing job order processing data through system modeling and simulation. The study investigates the operational efficiency of the wave-offset and offset machines in a printing process by identifying key factors contributing to production delays: operating speed, machine failures, setup time, and make-ready time. Using ARENA software for simulation and analysis, various improvement scenarios were evaluated. Increasing machine capacity to 90% of design speed, reducing machine failure and repair times by half, and cutting setup and make-ready times by half were proposed as strategies to enhance efficiency. These actions resulted in improved Overall Equipment Effectiveness by 4.62% for the wave-offset machine and 28.73% for the offset machine, respectively.
Model development for Industry Extension Services in Small and Medium Enterprises to Promote Innovation and Technology Utilization: A Case study of Gulele Sub City Wereda 04 and 07.
(Addis Ababa University, 2025-06) Cherent Tsegaye; Merertu Wakuma (PhD); Micheal Getachew (MSc.)
Industry Extension Services (IES) refer to support programs designed to enhance the productivity and competitiveness of businesses and also plays a crucial role in supporting small and mediumsized enterprises (SMEs) by providing essential support mechanisms by collaboration with TVET trainers. Industry extension service elements consist of entrepreneurship, kaizen, technological and technical support. This studies concern on technology support ones only due to the wider concept of to address the others industry extension packages. Small and medium enterprises (SMEs) faces different challenges such as poor infrastructure, lack of appropriate marketing options, lack of integrated technology to increase their productivity and lack of financial options are the main challenges SMEs faces currently. The primary objective of this research is to identify and analyze any existing gaps in the technology support system of the case enterprise and develop a model for industry extension services on the promoting innovation and technology utilization within SMEs. It looks at how much these services help SMEs embrace and use technology in better way and techniques to improve their overall performance, productivity, and competitiveness. The research methodology involves conducting a comprehensive literature review, collecting, and analyzing quantitative and qualitative data on the main factors that affect the Industry Extension Services (IES) of the SMEs regarding technology support system. The collected data using both primary and secondary data collection methods was thoroughly analyzed using statistical software SPSS Version 26. The quantitative data was obtained from questionnaires administered to 102 respondents are participated that are 36 Trainer,4 deans ,42 SMEs workers,8 woreda experts and 12 SMEs owners are participated and their responses were analyzed using SPSS. The study's findings propose that Industry Extension Services (IES) for SMEs can solve different critical factors such as poor infrastructure, lack of market relations, and skill of SMEs workers to handle the technology effectively and efficiently and fostering innovation and technology utilization.so This study highly attempts to find possible weaknesses and opportunities for improvement in the current support systems provided to SMEs by critically evaluating the efficiency of industrial extension services due to promoting innovation and technology. The results of this study will aid in the development of more specialized and successful methods to assist SMEs in fostering innovation and the use of technology by policymakers, business professionals, and development organizations.
Predicting and Analyzing the Impact of Occupational Safety& Health on Labor Productivity: A Case of Ethiopia Plastic Industry
(Addis Ababa University, 2024-10) Feleke Workie; Kassu Jilcha (Assoc. Prof.); Ayele Legesse (Mr.) Co-Advisor
Back ground: the Ethiopian plastic industry occupational health and safety risks are negatively impact labours motivations and output. While the information of Ethiopian plastic industry occupational health and safety, such as a review of situational analysis and needs assessment and the impact of energy use on labour productivity in the industry, specially address how to predict or look into the relationship between worker productivity and OHS in Ethiopia plastic industry out of 396 employees of 70 target labours are take all. Objective: In order to create mitigation plans for preventive and corrective measures, the objective is to forecast and analyze the impact of OHS on labour productivity in EPI. Methods: the following techniques to be used to predict and assess how OHS would affect worker productivity in Ethiopia plastic industry. review of relevant literature: analyse the corpus of work on occupational health and safety in multinational industrial enterprises, with a focus on the plastic industry. This to give insight on the possibility, challenges, and state of the industry’s OHS protocols. Gatherings of data: gathering pertinent information on worker productivity, OHS metrics, and other Ethiopian plastic industry component. This data to be gathered through unstructured interview, primary and secondary data sources, and surveys, and then analysed the integration of all that are regression analysis and correlation analysis. Purpose: The aim of this study was to determine how worker productivity in Ethiopia plastic industries was affected by occupational safety and health. Results and discussion: According to the study's findings, the majority of workers are aware of how workplace safety and health affect labours productivity. Furthermore, the survey discovered that even though workers are aware of the risks to their health and safety at work, they often forget to wear personal protective equipment because they think it's too hot. The study comes to the conclusion that worker productivity is strongly impacted by occupational health and safety. In order to reduce workplace accidents and injuries, this study advises management to protect employees and supply them with personal protective equipment. In order to reduce workplace accidents and increase productivity, the study also suggests that management provide routine training and education on occupational health and safety issue.
Mitigating Fashion Obsolescence Risk through Transit Time Reduction: An Agent Based Modeling Study of Apparel Supply Chain
(Addis Ababa University, 2025-06) Mulugeta Fekade; Ameha Mulugeta (PhD)
The global fashion industry is experiencing rapid trend acceleration, with styles shifting every three to four months due to social media influence and fast fashion dynamics. This short lifecycle increase the risk of fashion obsolescence, especially for manufacturers in developing countries like Ethiopia, where delayed deliveries lead to missed market windows and financial loss.in the bole lemi special economic zone (BLSEZ), a major garment manufacturing hub, long transit times caused by inefficient logistics infrastructure, slow customs procedures, and underperforming logistics agents reduce the competitiveness of local manufacturers and threaten the zone’s ability to retain global investment. This study aims to mitigate the risk of fashion obsolescence by improving logistics responsiveness and reducing supply chain transit time within BLSEZ. A mixed methods approach was employed, including surveys, interviews, document review, and direct observation to assess the current logistics performance and stakeholder capacity. Based on this empirical data, an agent modeling technique was used to simulate and evaluate three improvement scenarios: introducing new logistics agents, improving the performance of existing agents by 10%, and combining both strategies. Each scenario was analyzed to determine its effectiveness in enhancing delivery speed and supply chain reliability. The results showed meaningful improvements across all scenarios, with scenario 1 achieving a 40.52% transit time reduction, scenario 2 yielding a 34.45% improvement, and scenario 3 producing the most significant result with a 58.74% reduction. A phased implementation is proposed: short term (within 1 year) incentives to attract competitive logistics firms, followed by medium and long term improvements in customs clearance infrastructure, technology use, and production speed reduce 58.74% obsolescence risk. These findings highlight the critical of integrated logistics strategies in aligning Ethiopia’s garment industry with the time sensitive demands of the global fashion market. The study provides evidence based insights for policymakers, and manufacturers seeking to enhance competitiveness through responsive supply chain systems.
Investigating the Influence of supply chain management practices on the sustainability of plastic industries: A Case Study of ENPLAST P.L.C.
(Addis Ababa University, 2024-10) Abera Aragie; Gezahegn Tesfay (PhD)
This study seeks to investigate the Influence of supply chain management practice sustainability of Plastic Industries through Recycling In the case of ENPLAST P.L.C. This research studied SCM in the area of supply chain integration, information sharing, customer relationship management, and internal lean practices. Explanatory survey design was used while a questionnaire was used to gather primary data. The study covered census of 66 employees of ENPLAST P.L.C. The study used questionnaire as primary data collection tool. The data collected was analyzed with the aid of descriptive statistical techniques such as frequencies, percentages and mean score. More so, correlation and multiple linear regressions were used to establish the relationship between study variables using Statistical Package of Social Sciences Version 22. The findings of the study revealed that the combined effect of various SCM practices influenced organizational performance positively. The correlation result shows that there is positive and significant relationship between all SCM practices (supply chain integration, information sharing, customer relationship management, and internal lean practices) and organizational performance. The result of regression also revealed that all predictor variables (supply chain integration, information sharing, customer relationship management, and internal lean practices) have statistically significant contribution on organizational performance. The adjusted R² of 0.502 indicates 67.2% of the variance in organizational performance can be predicted by SCM practices of the company. Thus, it can be concluded that improved SCM practices are significantly influencing organizational performance. Therefore, the management of ENPLAST P.L.C. Share Company should influence its supply chain integration, information sharing, customer relationship management, and internal lean practices as a way of improving the company performance.
Application of Green Lean Six Sigma (GLSS) through DMAIC Approach: A Case of Walia Steel Industry (WSI)
(Addis Ababa University, 2025-06) Seble Negash (PhD); Yitagesu Yilma (PhD); Birhanu Tolosa (Mr.) Co-Advisor
In today’s manufacturing world, producing high-quality products while reducing waste and environmental harm is increasingly critical. This study explores the application of Green Lean Six Sigma (GLSS) an integrated framework combining Lean principles, Six Sigma methodologies, and sustainability practices within the Walia Steel Industry (WSI), a major steel profile manufacturer in Ethiopia. The research applies the DMAIC (Define, Measure, Analyze, Improve, Control) approach to assess and address rising product defects, which increased from 2.34% in 2022/23 to 3.06% in 2023/24, alongside growing material waste and emissions. A mixed-method case study was employed, drawing on production records, interviews, and observational data. Analytical tools such as Pareto charts, Material Flow Analysis (MFA), simplified Life Cycle Assessment (LCA), fishbone diagrams, and the 5 Whys method were used to identify and analyze key defects in CHS, RHS, SHS, and LTZ products. Root causes were traced to poor machine calibration, substandard raw materials, and inadequate storage conditions. The process capability assessment revealed a DPMO of approximately 10,213, corresponding to a Sigma level of 2.6 for without 1.5σ shift and 3.8 for with 1.5σ shift. MFA projected 149.83 tons/year of material waste, while LCA estimated this waste contributes approximately 45 tons of CO₂e emissions annually. Additionally, Lean wastes particularly Defects, Waiting, Motion, and Overprocessing were conceptually mapped using the TIMWOOD framework, based on qualitative observations. As a result, the study proposes a tailored GLSS-based conceptual framework designed to reduce defects by an estimated 46.7%, potentially lowering CO₂e emissions by 21 tons/year. The framework integrates Lean waste reduction, Six Sigma quality control, and Green sustainability tools across all DMAIC phases. This research fills a critical gap in the literature by demonstrating how GLSS can be applied in developing country contexts like Ethiopia to drive operational efficiency and environmental performance. The findings offer a scalable model for sustainable manufacturing applicable to similar industrial settings.ment.
Enhancing Competitiveness Through Value Chain Analysis in Case of Abyssinia Integrated Steel plc.
(Addis Ababa University, 2025-06) Nardos Mekoya; Ameha Mulugeta
The thesis investigates the significant challenges of rework waste and productivity inefficiencies at Abyssinia Integrated Steel Manufacturing, a prominent Ethiopian producer of rebar. Over the period from 2013 to 2015, the company experienced an average rework waste of 6,500 kg annually, underscoring the critical need for systematic improvements within its production processes. The primary objectives of this research are twofold: first, to conduct a comprehensive Value Chain Analysis (VCA) to identify key inefficiencies across the production stages; and second, to develop strategic interventions aimed at enhancing competitiveness and operational performance. The study employs a mixed-methods approach, integrating qualitative insights from interviews with production and quality control staff, alongside quantitative data analyzed using PROCAST simulation software. This software facilitates detailed modeling of thermal stress and solidification behaviors during the continuous casting process. The findings reveal that the improper control of casting parameters, particularly temperature and speed, is a major contributor to defect rates. Specifically, the research identifies an optimal casting temperature range of 1650°C to 1700°C and a casting speed of 1.5 m/min as critical conditions for minimizing turbulence and ensuring effective solidification. These parameters significantly reduce the incidence of defects, such as longitudinal and transverse cracks, which have plagued the production process. In conclusion, this study underscores the importance of integrating VCA with predictive simulation tools to create a robust framework for quality control and operational decision-making. By systematically identifying and addressing inefficiencies, Abyssinia Integrated Steel can enhance resource utilization, improve worker safety, and bolster its competitiveness in both domestic and international markets. The implications of this research extend beyond the case study, providing a valuable framework for other manufacturers in developing countries aiming to align production efficiency with quality standards through data-driven and strategic interventions. This approach not only contributes to operational excellence but also supports sustainable manufacturing practices in the steel industry.
Enhancing a Vehicle Competency Assurance Service in Case of Addis Ababa City Administration Driver & Vehicle License & Control Authority (DVLCA).
(Addis Ababa University, 2024-06) Berhe Tesfakiros; Gezahegne Tesfaye (PhD); Sharmarke A. (Mr.) Co-Adviser
The Addis Ababa City Administration Driver & Vehicle License & Control Authority (AA-DVLCA) has a lot of problems and inefficiencies when it comes to providing services for vehicle competency assurance. The efficacy and efficiency of vehicle inspections are significantly impacted by problems including antiquated equipment, a shortage of skilled workers, and ineffective processes. Longer inspection periods and lower service quality are caused by these issues, which increase the risk to public safety and erode customer happiness. The study uses both qualitative and quantitative research approaches to address these problems. Site visits, semi-structured interviews, and document analysis are some of the techniques used to gather data with the goal of comprehending existing issues and seeing areas for change. In addition, a discrete event simulation model is created to assess the functionality of the current system and suggest improvements. In order to simulate numerous scenarios and evaluate their effects on system performance and service quality, the model includes a variety of factors and variables. The results of the study show that by modernizing inspection methods and maximizing resource utilization, considerable improvements in inspection times and service standards can be attained. Purchasing state-of-the-art inspection tools, improving staff development initiatives, and putting in place thorough quality control protocols are among the main suggestions. The report also emphasizes how crucial it is to build ongoing feedback systems and include stakeholders in order to guarantee long-lasting advancements in vehicle competency assurance services. It is anticipated that these actions will greatly improve the effectiveness, dependability, and general caliber of services rendered by the AA-DVLCA.
Developing a Decision Support System for Spare Parts Inventory Management to Reduce Repair Cost at Sunshine Construction.
(Addis Ababa University, 2024) Biruk Gebremedhine; Ameha Mulugeta (PhD); Ayele Legesse (Mr.) Co-Advisor
The research aims to develop a decision support system (DSS) for spare parts inventory management in order to reduce maintenance costs at Sunshine Construction Company. The study employs a well-designed approach, incorporating data analysis techniques and leveraging DSS systems to gain insights for optimizing inventory management and reducing repair costs. The literature review examines inventory management in the construction industry, focusing on areas like optimizing stock levels, lead-time, decision support systems, maintenance, and repair cost control. It explores strategies such as (s, S) and (q, r) policies, modern inventory management technologies, and the importance of collaboration among stakeholders. A critical issue identified in the review is the need to address the challenge of inaccurate demand forecasting, which can significantly impact inventory optimization efforts. The data collection and analysis used both quantitative data from a case company concerned department recording data like inventory levels, repair costs, maintenance costs) and qualitative data (through interviews). Tools like Excel, FMEA, and the EOQ model are used to analyze and optimize factors like stock levels, lead times, reorder quantities, and safety stock. The expected findings and recommendations aim to improve inventory performance, reduce costs, and promote business success in spare parts management at Sunshine Construction Company. The study aims to contribute innovative solutions related to demand forecasting, inventory policies, maintenance cost control, and technology integration

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