Industrial Engineering

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The Determinants and Business Impact of Additive Manufacturing Technology Adoption: A Case of Ethiopian Metal Manufacturing Industries
(Addis Ababa University, 2024-06) Amanuel Haile; Eshetie Berhan (PhD); Behailu Kemaw (Mr.) Co-Adviso
Additive Manufacturing (AM) technology, also called 3D-printing, is a disruptive technology providing opportunities and huge potential benefits through innovative way of manufacturing products. This study is concerned with identifying the determinants for adoption of Additive Manufacturing technology within Ethiopian metal manufacturing industries. In light of this objective, the study adopts both quantitative and qualitative approaches with purposive sampling. The quantitative approach used 7-likert scale questionnaire for empirical analysis of the data to determine the factors that influence the adoption of AM technology. For the qualitative approach, an interview is conducted to determine the business impact and the challenges of adopting AM technology. A sample of team leaders, engineers, metal researchers, technicians, IT professionals and quality controllers is taken to capture their opinions, views and reflections about the determinants for adopting AM technology in metal manufacturing industries. A self-administered questionnaire is used to rate the influence of technological, organizational, and environmental determinants on the decision to adopt the technology. Both SPSS and SmartPLS software tools are used for descriptive statistics analysis and structural equation modeling (SEM) of the collected data respectively. Results indicate that out of the determinants, the perceived value, perceived cost, top management support, organizational readiness, external pressure and perceived outside support affecting significantly a decision for adopting AM technology. The study further explores the benefits, potential business impact and the challenges of adopting AM technology in metal manufacturing industries. This study's contribution is to provide academics, business people and policymakers with insightful information about the determinants that influence AM technology adoption and its benefits enabling them to implement the necessary initiatives or policies that are essential for the technology to be adopted successfully. Lastly the study recommends further research in this field.
Developing Integrated Quality and Safety Management System Model in the Process Industry: A Case of Awash Wine
(Addis Ababa University, 2024-06) Henok Eshetu; Kassu Jilcha (PhD); Birhanu Tolasa (Mr.) Co-Advisor
Quality and safety management systems are essential in the beverage industry for ensuring the production of safe, high-quality beverages and increasing productivity and efficiency. Currently, Awash Wine faces significant challenges, such as employee safety problems, inefficiency, and the unproductive use of full employee working hours per day for wine-making processes. The primary objective of this research is to identify any existing gaps in the quality and safety management system of the case company. After assessing these gaps and their implications, the study aims to find effective solutions to address the identified issues, ultimately leading to improved productivity. The research methodology involves conducting a comprehensive literature review, collecting, and analyzing quantitative and qualitative data on the main factors that affect the productivity of the company regarding safety and quality. The collected data using both primary and secondary data collection methods was thoroughly analyzed using statistical software SPSS. The research methodology involves conducting a comprehensive literature review, collecting and analyzing quantitative and qualitative data on the main factors that affect the productivity of the company concerning safety and quality. The data was collected using both primary and secondary methods and thoroughly analyzed using the statistical software SPSS. The quantitative data was obtained from questionnaires administered to 86 employees, and their responses were analyzed using SPSS. The analysis revealed that continuous improvement, employee involvement, and safety evaluation and monitoring have a substantial positive correlation with organizational productivity, with results of r = 0.546, p = 0.000; r = 0.602, p = 0.000; and r = 0.572, p = 0.000, respectively. Additionally, the analysis showed that safety and quality leadership and customer focus also have a positive relationship with organizational productivity, albeit with a relatively lower result, with r = 0.489, p = 0.000 and r = 0.456, p = 0.000, respectively. The study's findings propose that a company's productivity is most impacted by two critical factors - the lack of monitoring and assessment of safety during regular operations, and the failure to prioritize continuous improvement. The researcher encountered constraints in terms of time, money, and access to important people and resources at Awash Wine, which could limit the scope and depth of the study. This indicates that the organization does not place a strong emphasis on enhancing its working culture in terms of continuous improvement. The study has the potential to be a useful tool for academics and industry consultants who are seeking to improve customer satisfaction and productivity in the process industry.
Improving Service Delivery through Facility Layout Optimization: A Process Mapping Analysis in Teklehaimanot Health Center
(Addis Ababa University, 2024-07) Heran Bereket; Ameha Mulugeta (PhD); Birhanu Tolosa (Mr.) Co Advisor
Teklehaimanot Health Center plays a crucial role in providing healthcare service to the community in Addis Ababa, Ethiopia. However, providing service with short waiting and service time to the community has been presented as a challenge in the healthcare facility. Teklehaimanot Health Center still struggles to deliver quality service to patients due to suboptimal layout design of the health center facility. This paper aims to optimize the facility layout of the outpatient department to improve the service delivery of Teklehaimanot Health Center. The objective is to develop a health center facility layout in Teklehaimanot Health Center that will improve service delivery by exchanging the department room by considering patient path. The research methodology consists of qualitative and quantitative data collection related to the time for every operation that takes place under the outpatient department. The proposed research integrates process mapping analysis, time study, facility layout techniques, multi-criteria decision making, and simulation to address the issue presented. Service time and waiting time are the key measuring variables. From the existing facility, a redesigned layout is developed, and simulation is used to validate the new optimized layout. By utilizing process mapping analysis integrated with time study and facility layout improvement, the research provides data-driven recommendations for the health center facility layout with minimum unnecessary movement of patients and minimum travel distance. The impact is evaluated by measuring the variables of service and waiting time the improvements in waiting time and service time achieved through the facility layout changes are likely very significant and impactful for the Teklehaimanot Health Center. Based on the result, the optimized proposed facility layout minimizes the average waiting time from 2.5 hours to 2.01 hours, which is a 19.60% reduction, and minimizes the service time from 0.79 hours to 0.64 hours, which is an 18.98% reduction. This shows a positive impact on improving service delivery and patient satisfaction in the health center.
Developing AI-based Preventive Maintenance Model for BGI Ethiopia: Washing Head
(Addis Ababa University, 2024-07) Tsigereda Abebe; Gezahegn Tesfaye (PhD)
.for implementing real-time monitoring and continuous modelupdates to sustain and enhance the benefits improve production processes and reduce operational costs.The study concludes with recommendations value of AI-driven preventive maintenance in the breweryindustry, suggesting broader applications to kegs but also enhances the reliability and efficiency of theproduction line. The findings demonstrate the This predictive approach not only minimizesdowntime by preventing the processing of faulty performance, accurately forecasting potential kegrejections and enabling preemptive maintenance actions. including AdaBoost, to predict the readinessstate of Head-4. The AdaBoost model exhibited superior generating synthetic data to augment thedataset, the study trained several machine learning models, integrating real-time data from theComputerized Maintenance Management System (CMMS) and maintenance model to improveoperational efficiency and reduce these false rejections. By substantial downtime andeconomic losses. The objective of this research is to develop a predictive particularly at the washingHead-4. These rejections, caused by steam supply inconsistencies, result in linedue to the frequent rejection of kegs by the Slimline Monobloc 50 filler machine,
Dynamic Modeling of Transformation Capabilities towards Circular Economy for Automotive Manufacturing Company of Ethiopia (AMCE)
(Addis Ababa University, 2024-07) Yidersal Desale; Ameha Mulugeta (PhD)
Manufacturing industries face enormous hindering factors during the production process. One of the reasons is the scarcity of raw materials. Automotive Manufacturing Company of Ethiopia (AMCE) is one of the industries who faced the raw material scarcity. This scenario forced the industry to operate only for about 10.7% of its annual production capacity. Using these scarce raw materials wisely is crucial. Hence, this research aimed to visualize the capability of the industry to use this scarce raw material circularly through reducing, reusing, and recycling (3R)mechanisms. During data collection and analysis, both qualitative and quantitative data collection and analysis tools have been used. The data has been analyzed through system dynamics modeling of reducing, reusing, and recycling of raw material. The capability variables identified were technology, leadership, strategy, finance, human aspect, collaboration, culture and mindset. The strategic scenario has been developed through the change in the raw material input and automated work stations. As a result, the amount of waste accumulation decreases while reusing and recycling of scrap increases. As the automated work stations increase, the scraps recycling increase significantly. The company has the capability to transform to a circular economy system through reducing the defect, reusing and recycling of metallic scraps in order to enhance the raw material availability. Automated work station development will decrease the amount of waste accumulation by an average of 58%. Therefore, it is safe to conclude that circular economy can be applied for wise raw material utilization in the case company.
Enhancing Competitiveness through Technological Capability development of Metal and En-gineering Enterprises (Case: Small and Medium Enterprises in Addis Ababa, Arada Sub-City Administration)
(Addis Ababa University, 2024-06) Tamrnesh Tadesse; Ermias Tesfaye(PhD)
Technological capability plays an enormous role in firm improvement and getting competitive in the marketplace. The actual technological capacity of Ethiopia's metal industry is incredibly low. Be-cause of this shortcoming, the sector cannot import substitutes as intended, even though it cannot adopt, improve, reengineer, and disseminate them to other similar industries at the desired level for the nation. Therefore, the study's main objective is to enhance the competitiveness of small and me-dium metal and engineering enterprises (SMMEEs) in Arada sub-city by developing technological capabilities. We employ qualitative and quantitative study approach to achieve this. Both primary and secondary data collection methods are used to gather information through observation, focus group discussions, and different literature and reports. We next apply system dynamics to analyze the data obtained so that we can understand the causal elements affecting competitiveness through technological capability. Vensim software is used to carry out the simulation. The simulation period is 10 years, and we also use the descriptive analysis method. The analysis indicates that the competitiveness of small and medium metal and engineering enter-prises in the Arada sub-city is determined by technology transfer which affected by level of technol-ogy, their network and organizational learning capability, research and development which is influ-enced by their level of education, international market linkage which is affected by the market inno-vation ,demand condition and competitors’, organizational condition which is influenced by their existing knowledge financial capability ,investment and their commitment , innovation which affect-ed by product and technology innovation, obsolescence rate and r and d characteristics , and finan-cial capability which affected by inflation , investment and investment knowledge possibility. This study also revealed a lack of technology transfer practices, poor international market linkage, no use of research and development, and a lack of organizational vision for the development of technology. The competitiveness of the metal and engineering sectors in Arada Sub City is found to be unsatis-factory. Finally the system dynamic model shows the causal elements affecting competitiveness through technological development. And working on these elements can indeed foster the competitiveness.
A Heuristics and Discrete Event Simulation for Optimized Layout Design in Agricultural Machinery Maintenance - A Case Study of Wereta International Business Plc.
(Addis Ababa University, 2024-07) Fitsumberhan Hailemeskel; Ameha Mulugeta (PhD); Ayele Legesse ((Mr.) Co-Advisor
Agricultural machineries play a vital role in agricultural processes and help in the production of food and non-food items. The efficient layout of agricultural machinery maintenance facilities is crucial for minimizing process times and maximizing throughput. This paper deals with the development and optimization of an agricultural machinery maintenance facility using discrete event simulation considering Wereta International Business Plc.’s agricultural machinery maintenance facility layout as a case. The development of the facility layout is done in a way that introduces flexibility of departments and also that combines the different services given in the facility, maintenance, training (customer & staff) and pre-delivery inspection processes. The research tries to construct a new facility layout that decreases the travel distance, considers the relationship between departments using the CORELAP facility layout construction method. The newly constructed facility layout has been optimized using facility layout improvement technique, CRAFT (excel add-in) to further enhance the optimality of the facility layout by enhancing the material flow between departments to minimize the material handling cost. Finally, an improved facility layout, with 15 departments that include main and supporting facilities and also combines the services and training facilities is developed. The proposed facility layout reduces that has been constructed from scratch and then optimized decreases the distance traveled between departments by 2,015.11 meter (a reduction of 33.66%) and the total monthly material handling cost by 531,093.14 Birr per month (a reduction of 86.85%). The entity output of the existing system is 40 units and for the proposed layout, the entity output is 67, an increase by 67.50%. Also, the system output from the existing simulation model is 125.30 units and the proposed layout simulation modeling system output increased by 58.85% to 199.04 units.
Capacity utilization improvement for auxiliary machineries: In case of BGI Ethiopia
(Addis Ababa University, 2023-10) Ermiyas Girma; Gulelat Gatew (PhD)
This study aims to improve the capacity utilization of auxiliary machines in brewery factories, focusing on the CO2 plant, boiler plant, cooling plant, and air plant at BGI Ethiopia. These machines are vital for supporting the brewing process, and any breakdown or inefficiency can significantly impact the overall brewery operation. The study identified underutilization of the capacity of auxiliary machines at BGI Ethiopia. Despite its potential to produce 3200 hectoliters per day, the factory is currently operating at 75% capacity, producing only 2400 hectoliters per day. The challenges contributing to this underutilization include auxiliary machine failures, high downtime, breakdowns, and inadequate application of monitoring and control systems. To address these challenges, the study adopts a research methodology that involves collecting quantitative and qualitative data. Quantitative measures such as breakdown and stoppage time, production output, shifts, and workers are used to assess the current state of auxiliary machine capacity utilization. Workplace site observations and expert opinions are also considered, along with secondary data from literature, reports, publications, logbooks, machine displays, and articles related to capacity utilization. The collected data is analyzed using statistical techniques, including mathematical analysis for quantitative data and thematic analysis for qualitative data. This analysis aims to identify root causes and trends to gain insights into the current state of auxiliary machine capacity utilization. The findings are presented through tables, charts, and graphs to support the analysis. Factors that positively or negatively impact auxiliary machine capacity utilization in brewery plants are highlighted. The discussion focuses on the main root causes of breakdowns and inefficiencies, the impacts of downtime on capacity utilization, potential solutions, and best practices to prevent breakdowns. Based on the research findings, recommendations are provided to improve auxiliary machine capacity utilization. These recommendations address factors such as equipment upkeep, operator training, scheduling, and system integration. Applying these recommendations can enhance capacity utilization, improve production efficiency, and increase revenue for BGI Ethiopia.
Cluster Development for Metal Work Industries: Case Study on Addis Ababa Micro and Small-Scale Metal Work Industries
(Addis Ababa University, 2011-11) Tariku Tamiru; Daniel Kitaw (PhD); Gulelat Gatew (Mr.)
The contribution of micro, small and medium Scale industries towards employment generation, alleviation of poverty and inequalities and development of backward areas is recognized worldwide. Although, these industries have great role in economic development of our country, they doesn‟t benefit as their potential due to their problems related to their size. Most of the problems are lack of: market, skilled manpower, working capitals, information and low level of competitiveness. In addition there is lack of cooperation among themselves, which limits sharing of resources between them. Micro and small industries clustering is a group of these industries, producing similar products, located in a geographical proximity, sharing resources (like knowledge, technology etc) among themselves in order to overcome their internal and/or external challenges, and that compete each other but also cooperate with one another. The study is concerned on cluster development of micro and small metal work industries in Addis Ababa city. It covers 32 micro and small scale metal work industries selected from all sub cities with the objectives of developing micro and small metal work industry cluster to create a conducive environment for the development of inter-firm cooperation, improve the competitiveness of the industries, to solve marketing problems of the industries, and to facilitate metal work technology transfer to the country. For the accomplishment of the objectives of the study the selected industries were assessed through questionnaires, interviews and observation. The results obtained from the survey shows that Addis Ababa micro and small metal work industries faced with the following problems. These were:- lack of: market, product displaying area (marketing area), working capital, technical training, trust among the metal working industries; and flexibility of price and quality of raw materials. In order to solve the observed problems with the support of published and unpublished literatures the study proposed the development of micro and small metal work industry cluster and identifies suitable cluster location in the city. Taking into consideration of other countries experiences, for realization of the benefits of developed metal work industry cluster it should be suggested that to properly cooperate all collaborators of the cluster.
Reverse Logistics Network Development for E-Waste Management in Ethiopia
(Addis Ababa University, 2023-10) Zeritu Agegnehu; Ameha Mulugeta (PhD)
This research is aiming to solve problem of managing ever increasing e-wastes particularly mobile phone wastes in Ethiopia generated in assembling companies from their production process and end users caused due to its technology advancement, shortest life span compared to other EEE and tendency to use latest model by identifying the amount of mobile phone waste in the country and factors affect implementation of reverse logistics and related issues. Due to its opposite pole economic effect, the negative in its nature of hazardous materials content that damage the environment and human and positive effect due to the presence of valuable material in it, proper management through designing circular economy driven reverse logistics networks. The objective of designing optimum reverse logistics network and selecting appropriate business model for the enabling of the RLN in circular economy point of view, in reverse logistics a take-back practice being no longer makes the industries sustainable. For the achievement of companies for its competitiveness and countries sustainable development, implementation of circular economy effectively that is with closed concern on the two building blocks known as network design and business model is critically. Using qualitative and quantitative data collecting through observation and interview and using questionnaire format too together with secondary document revision valuable data is collected. Literatures written in general on reverse logistics designing and factors affecting its implementation, circular economy and circular business model are reviewed selecting as much latest as possible majorly using electronic data bases. Using the design and analysis tool together with data organizing, the result indicates that the network with 5 CC located in 5 cities with a total capacity of collecting 8,681,600 end-of-life MP is optimum but needs intervention and cooperation of government and all other stakeholders for its effectiveness on protecting human and environment in addition to solving the assembling industries problem of recycling and disposal.
Adoption of Additive Manufacturing for Auto Parts Production: Case of Bishoftu Automotive and Manufacturing Industry
(Addis Ababa University, 2023-10) Sultan Asefa; Ameha Mulugeta (PhD)
This study investigates the adoption of additive manufacturing in the Bishoftu Automotive Industry in Ethiopia. The study applies an integrated framework of Diffusion of Innovation (DOI) and Technology-Organization-Environment (TOE). This study also used combined data from primary and secondary sources using quantitative and qualitative methodologies to allow for the exploration of the factors and constraints influencing the decision to adopt additive manufacturing. Additionally, the research undertakes a thorough literature analysis of Adoption theories such as DOI, TOE, and factors affecting additive manufacturing adoption. Five point Likert scale was the method used to collect the useful information for this study. The questionnaire was distributed to managers, engineers, and technicians in the Bishoftu Automotive and Manufacturing Industry. Subsequently, the collected data was subjected to analysis through the application of descriptive statistics and partial least squares structural equation modeling (PLS-SEM) utilizing SPSS version 27 and SmartPLS version 4.0.9.6 software. The study outcomes revealed that several critical determinants significantly impact the adoption of additive manufacturing (AM) in the automotive sector. These determinants encompass relative advantage, compatibility, complexity, trialability, observability, technology-related factors, and organizational as well as environmental factors The results of this study enhance our understanding of the adoption of additive manufacturing and provide valuable practical guidance for decision-makers within the Bishoftu Automotive Industry. Drawing from these findings, recommendations have been formulated to facilitate the effective integration of AM in the automotive sector. Additionally, this study identifies potential areas for future research in this field.
Designing a Multivariate Process Control Procedures for Production System Case of Ethio Cement PLC
(Addis Ababa University, 2023-05) Daniel Ashagrie; Daniel Kitaw (Prof.); Eshetie Berhan (PhD)
This 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.
Improving Workplace safety and Employee Working Behavior to Enhance the Productivity of Elevator Installation Process: A case study of SINTEC ETHIOPIA PLC.
(Addis Ababa University, 2023-10) Ephrem Gezahegn; Kassu Jilcha (PhD)
This paper explores the relationship between workplace safety, employee behavior and elevator installation enhancement. The objective is to understand the factors that contribute to enhanced elevator installation process by focusing on safety practices and promoting employee behavior. The study conducted a comprehensive literature review to identify the strength, weakness, and gaps in existing research. The review literature tells that workplace safety measures and employee working behavioral issues, such as the implementation of safety practices, including personal protective equipment(PPE) and adherence to regulatory guidelines, significantly impact the overall productivity of elevator installations during the process. However, gaps in the existing literature are identified. Limited research has been conducted on the specific challenges and limitations faced in implementing workplace safety practices during elevator installations. This study aims to fill these challenges by conducting primary research involving interviews, site observation and surveys with professionals in the elevator installation industry together with the secondary data. The data collected will provide insights into the practical experiences, perceptions, and recommendations of industry experts regarding the enhancement of elevator installation process through improvement of workplace safety and employee behavior. To analyze the data collected from surveyed questionnaire, the study implemented SPSS Software (Statistical package for Social Science) and Fish Bone diagram tool and Process Failure Mode Effect Analysis (PFMEA) techniques used to analyze root causes of the collected data from the professional’s. So that, The findings of this research will contribute to the development of strategies and interventions aimed at improving workplace safety practices, raising positive employee behavior, and ultimately enhancing elevator installation process.
Lithium-Ion Battery Value Chain Analysis: Assessing Ethiopia's Competitiveness and Strategic Framework
(Addis Ababa University, 2023-06) Seyidu Wohabrebi; Ermias Tesfaye (PhD)
The Lithium-Ion Battery (LIB) industry has gained significant importance as a key enabler for electric vehicles. The Federal Democratic Republic of Ethiopia has ambitious plans to decarbonize its transportation sector through the introduction of a sizable number of electric buses and small cars. However, these cars will eventually require new batteries after a while. This study uses a value chain analysis of the LIB industry, with a focus on assessing Ethiopia's competitiveness in the LIB value chain. It examines the entire value chain of LIBs, starting with the upstream stage involving the extraction and processing of raw materials, moving on to the midstream stage involving the manufacture of battery cells and component assembly, and finishing with the downstream stage involving battery integration and end-use applications. The study assesses Ethiopia's current position and potential for competitive advantage in each stage of the value chain using a combination of SWOT and Porter's five-force frameworks. It examines important elements such as the availability of raw materials, technological capabilities, and infrastructure development. The results highlight that Ethiopia has an estimated total of 3 million tons of Lithium brines linked to the potash horizons in Danakil, Afar region, with a 50% recovery rate. The nation also possesses abundant reserves of other important raw materials like nickel and graphite. Lithium, a key component of LIBs, holds significant value and is abundantly available in Ethiopia. Compared to graphite and nickel, lithium extraction requires less complex infrastructure and lower capital investments. Experts suggest that Ethiopia should prioritize lithium extraction to maximize its potential in the LIB value chain. However, substantial investments in infrastructure, technology, and knowledge are still required for the extraction and processing of these materials. Based on the analysis, the study provides a strategic framework to enhance Ethiopia's competitiveness in the LIB value chain. It can also serve as a valuable resource for policymakers, industry stakeholders, and investors interested in understanding Ethiopia's competitiveness in the LIB sector.
Enhancement of Overall Equipment Effectiveness (OEE) through the use of Industry 4.0: In the case of Hilina Energy-Enriched Foods Manufacturing Industry
(Addis Ababa University, 2023-06) Solomon Muhabaw; Ermias Tesfaye (Ass. Prof.)
Overall equipment effectiveness (OEE) is a metric used to measure machinery effectiveness while Industry 4.0 (I4.0) is a revolution of digital transformation which used to change convectional manufacturing industry to smart manufacturing industry, through its evolution always open for improvement of productivity, equipment effectiveness and efficiency of machinery, as well as create comfort zone for human and other habitats. This study aims to identify equipment effectiveness metrics and possible solutions for enhancing OEE in the manufacturing industry, specifically in the case of the Hilina energy-enriched foods manufacturing industry. By utilizing a mixed-methods explanatory research approach that involves qualitative and quantitative data collection and analysis with the aid of advanced analytics techniques and recognize patterns trained with data, big data is used. The data analysis identifies the biggest losses occurring at auxiliary grand machines, especially the filler and packaging machines, resulting in a current actual OEE of only 33.46 percent. However, the remaining auxiliary machines have an OEE of 79.64 percent, giving an overall OEE of 68.34 percent. Consequently, the study advocates using I 4.0 technologies such as the Internet of Things (IoT), smart devices with sensors, machine learning, and vision to mitigate downtime, speed, and quality losses of auxiliary grand machines and provide solutions to these issues. Moreover, through the analysis of big data, the study provides maintenance strategies and machine setups that can help reduce unexpected failures, the largest losses in organizations. Thus, the use of I 4.0 technologies can enhance OEE in the manufacturing industry by providing possible solutions for each problem. It is noted that the OEE improved from 68.34% to 73.36%. This study concludes that by using the industry 4.0 in the organizations can effectively enhance the effectiveness of their equipment and achieve maximum effectiveness.
Warehouse Management Improvement Model via Lean Warehousing for the Case of National Oil Ethiopia PLC (NOC)
(Addis Ababa University, 2023-06) Abraham Negash; Gulelat Gatew (PhD)
This research has provided a specific model framework of ways to solve and address the warehousing problems using lean management techniques. The problems identified at the case company were synthesized to reflect on which performance indicator they affect by utilizing the studies of various researchers. Furthermore, 24 lean tools associated with lean warehousing presented and validated by different researchers were identified and they were characterized into seven based on their management techniques. The wastes in warehousing of the case company were assessed seven principle of wastes which directly apply to a distribution warehouse. Interview with employees, first hand observation and collection of responses from questionnaire a sample size of 16 people was instrumental to analyze the wastes at each stages of the warehousing activities. The root cause of these wastes was analyzed by using combination of the 5W 1H history analysis method and 5 Whys root cause analysis technique and then Pareto analysis was utilized to recognize significant causes of warehousing problems. Consequently, twenty two cases were identified and these problems were further clustered to seven based on the notion they represent. Afterward, it was necessary to identify which warehousing issues correspond to the problems stated in the problem statement. To analyze that, a questionnaire was distributed amongst 73 sample population in which the analysis was undertaken by using Relative Importance Method. With that regard, Storage System, Standardization, Existing Human Resources, Warehouse Design and Training were found to be attesting to the problem statement. The next step taken after the identification of causes of major warehousing wastes was to identify which lean management tools can have an impact in solving these root causes. The sample size for questionnaire distribution and analysis for this step is similar to that of the previous step. With that regard, Continuous Improvement Management, Human Resource Management Lean Tools and Product and Process Management Lean Tools were found to have an impact in solving the warehousing issues at the case company. The findings of this research indicate that the warehousing problems at the case company can be solved by utilizing the above mentioned lean management techniques and the tools that they are comprised of.
Identifying Root Causes of Delay and Modeling Metal Industry Projects from Owners’ Perspective: A Case of Selected Metal Industry Projects
(Addis Ababa University, 2023-10) Andinet Haile; Kassu Jilcha (Assoc. Prof.)
Being behind schedule is a familiar case in the manufacturing sector that challenges the overall progress of projects resulting in poor performance and an inability to keep up with the dynamics of the business environment. Project delivery in the metals industry depends not only on its schedule but also on the performance of many stakeholders and the external environment that positively or negatively affects them. In this study, critical causes of delay were identified and feedback loop diagram is used to indicate effect of major causes of delay variable Interaction. Therefore, the study uses a causal loop diagram to show how the major causes of metal industry project delay factor interacts with in and with other system variable. Primary data was collected through questionnaires, group discussion and observations by the researcher. The questionnaires consist of 32 causes of delays which were distributed to project owners, the project managers, and Governmental staff researchers at the Manufacturing Technology and Engineering Industry Research and Development Center. Whereas; the secondary data were obtained from the literature reviewed and reported documents. The study aims at identifying the critical cause of project delay, then further models to understand the effect of factors interaction related to the causes of delay by the System dynamics model. accordingly, the critical factors that were identified in this research are; Effect of Inflation, Inadequate foreign currency, Bureaucracy, Unavailability of an infrastructure facility (interruption of electric power), Inadequate raw material, Difficulty in financing the Project, Existence of corruption, Unacceptable level of change orders by clients, Late material delivery, Unavailability of supply chain management, Inaccuracy submitted bid document and Late procurement of items. These causes are used to model the causal loop diagram. The study contributed to the metals industry projects by determining the critical factors of delay and the effects of system variable interaction on project delay system. and finally, devise mitigation strategies that potentially reduce project delays.
Transformer Failure Root Cause Analysis and Developing Mitigation Strategies: A Case of Addis Ababa City
(Addis Ababa University, 2023-06) Esubalew Birhan; Kassu Jilcha (PhD)
The distribution transformer is a critical component of the distribution system that must be very reliable in order to provide consumers with uninterrupted power supply. The failure of a distribution transformer has an impact on the supply system's reliability and power quality. This study aimed at failure root cause analysis and mitigation strategy development for distribution transformer in Addis Ababa. The study addresses 3,825 number of distribution transformers from the year 2020/21 to 2022/23 and the failure rate obtained is 13.4%. The collected data were recorded failure documents, interview and group discussion from Kotebi maintenance centers, Addis Ababa City Administration Electric Utility (AACAEU) and four electric utility districts in Addis Ababa. In the first part, the results of failure analysis are statistically summarized. In second part, the failure modes, effect analysis (FMEA) is used to analyze distribution transformer failure modes, causes, local and end effects of failure. To identify the most critical parts of the transformer, a risk priority number (RPN) is calculated based on the severity labelling, probability of occurrence, and probability of detection. Thus, from FMEA analysis, the most vulnerable parts of distribution transformers that have highest RPN value of 576 is insulation failure due to human related errors as a primary cause and with electrical mode of failure. Other component failure due to operational error and with thermal mode of failure is 512 RPN followed by winding failure, bushing failure and tap changer failure. FMEA leads to the identification of future preventative measures to be implemented to decrease the risk of the transformer by eliminating the causes of failure, thus minimizing the severity and probability of occurrence. The direct and indirect relationship between failure causes and effects of FMEA components are analyzed using causal loop diagram(CLD) and could clearly recognize unseen failure factors and their consequences. The root cause of transformer failure that covers 75% is human related factors, which is the primary source of operational failure causes like overloading, short circuit, internal problem and others. Solving failure of distribution transformers in Addis Ababa, the developed failure mitigation strategy is an asset management system of especially focused on predictive type supervisory control and data acquisition (SCADA) system. This study could fill the identified gaps, which means it has its own contribution to the body of knowledge of failure analysis. Finally, the outcome of this study has been thesis document, strategic solutions to minimize transformer failure and input source for further research.
Service Quality Improvement of Healthcare Through Complex Systems Perspective: A Case of Yekatit-12 Hospital
(Addis Ababa University, 2023-10) Gezahegn Gebeyehu; Ameha Mulugeta (PhD)
Service quality of healthcare is usually expressed as the extent of service providers’ attempt to meet or even exceed patients’ expectations during the service on the one hand and the level of patients’ satisfaction with their perceived quality of service on the other hand. On top of that, healthcare service sectors are an integral component of all types of communities, regardless of where they are located, since they are geared toward satisfaction for patients while offering the proper services in the proper manners and at the proper times. Nevertheless, it’s an obvious phenomenon that the existing healthcare system has been regarded as a linear hierarchic interaction although recent developments point out healthcare systems as complex entities that exhibit multifaceted patterns of non-linear interactions in their very nature. Thus, this research aims to enhance the quality of current healthcare services through a complex systems perspective. To do so, data was collected from the case hospital (Yekatit-12 Hospital) through direct observation, interview questions, and SERVQUAL dimensions-based questionnaires. Having corroborated with data interpretations from the Statistical Package for the Social Sciences (SPSS), the level of patient satisfaction was analyzed based on the five-point Likert scale and then complex causality noticed in the system was depicted using the system dynamics (SD) modeling. The result showed the three most problematic SERVQUAL dimensions in the hospital’s healthcare service. i.e., longer waiting time (reliability-related), complaints managing problems (empathy-related), and lack of a trend of intentionally asking about patients’ needs accompanied by swift action (courtesy-related) issues in decreasing order of their severity. Having applied SD modeling, all three problems listed above have been addressed and consequently, healthcare professionals and other academics in similar niches are encouraged for the furtherance of what is done here.
Investigating Workplace Risks & Ergonomic Interventions for Agricultural Harvest Workers: A Case of Agri-flower Strawberry Harvesting Workers of Holeta
(Addis Ababa University, 2023-10) Hana Girma; Kasu Jilcha (Assoc.Prof.)
strawberry harvesting workers in Holeta, as there is limited research conducted on this specific domain. In conclusion, this research contributes to the existing body of knowledge by providing a detailed investigation of workplace risks and proposing practical ergonomic interventions for Agri-Flower strawberry harvesting workers. The study's findings and proposed interventions improve worker safety, reduce musculoskeletal disorders, and enhance productivity in the agricultural sector.

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