Very well into the dawn of the fourth industrial revolution (industry 4.0), humankind can hardly distinguish between what is artificial and what is natural (e.g., man-made virus and natural virus). Thus, the level of discombobulation among people, companies, or countries is indeed unprecedented. The fact that industry 4.0 is explosively disrupting or retrofitting each and every industrial sector makes industry 4.0 the famous buzzword amongst researchers today. However, the insight of industry 4.0 disruption into the industrial sectors remains ill-defined in both academic and nonacademic literature. The present study aimed at identifying industry 4.0 neologisms, understanding the industry 4.0 disruption and illustrating the disruptive technology convergence in the major industrial sectors. A total of 99 neologisms of industry 4.0 were identified. Industry 4.0 disruption in the education industry (education 4.0), energy industry (energy 4.0), agriculture industry (agriculture 4.0), healthcare industry (healthcare 4.0), and logistics industry (logistics 4.0) was described. The convergence of 12 disruptive technologies including 3D printing, artificial intelligence, augmented reality, big data, blockchain, cloud computing, drones, Internet of Things, nanotechnology, robotics, simulation, and synthetic biology in agriculture, healthcare, and logistics industries was illustrated. The study divulged the need for extensive research to expand the application areas of the disruptive technologies in the industrial sectors.

Total Quality Management (TQM) projects, particularly those utilizing Six Sigma, are globally recognized for enhancing quality and productivity in the manufacturing industry. The study applies Six Sigma's DMAIC framework and SQC tools to improve defects in Messebo PP bag manufacturing's Convertex finishing section. The objective is to identify and eliminate defects, reduce rejection rates, and enhance sigma levels. in the define phase of DMAIC process, SIPOC tool, and customer questionnaires were used to map process flows and align improvement efforts with customer requirements (VOC). The Measure phase involved analyzed of existing data considering the performance of the Convertex section and process capability plots were generated to understand the current process performance against requirements. A key six most dominated defects (vital few) contributing to the majority of issues were identified through pareto analysis. The initial performance baseline revealed a Sigma level of 3.17, considered non-competitive, with a defect rate of 4.73%, a Defects Per Million Opportunities (DPMO) of 47,389, and process capability indices of 0.55 and 0.58 for processing quantity and defective units respectively. This data demonstrated the urgent need for improvement. The analyzing phase investigates the root cause of the vital few causes of defects considering team brainstormed and systematically categorized potential causes using Fishbone diagram, in which this structural analysis helps the team to understand past symptoms and underlying reason for the issues and develop effective solutions. Considering analysis phase, in the improvement phase specific solutions were developed and put for improvement, as a result the sigma level becomes 4.00 (industry average), defect rate 0.61%, DPMO 6174, and the process capability of produced quantity and defect units becomes 1.19 and 1.26 respectively, which is very capable and led to significant and immediate positive results in the overall quality and productivity of the Convertex section. Lastly, the control phase was conducted to sustain the results found in the improvement phase and to keep the changes moving forward. It has four procedures, standardization documentation, monitoring plan, and response plan, which control the newly implemented changes and developed procedures.

This paper deals with the service performance analysis and improvement using discrete event simulation has been used. The simulation of the heath care has been done by arena master development 14-version software. The performance measurement for this study are patients output, service rate, service efficiency and it is directly related to waiting time of patients in each service station, work in progress, resource utilization.Simulation model was building for Bahir Dar clinic and then, prepared the proposed model for the system. Based on the simulation model run result, the output of the existing healthcare service system is low due to presence of bottlenecks on the service system. Moreover, the station with the largest queue and high resource utilization are identified as a bottleneck. The bottlenecks, which have identified are reduced by using reassigning the existing resources and add new resources and merging the similar services, which has under low resource utilization (nurses). Finally, the researchers have proposed a developed model from different scenarios. Moreover, the best scenario is developed by combining scenario 2 and 3. And then, service efficiency of the healthcare has increased by 9.86 percent, the work in progress (WIP) are reduced by 3 patients from the system and the service capacity of the system is increased 34 to 40 patients per day due to the reduction of bottleneck stations.

Over the past 170 years clothing industries have changed from custom fitting and assembly of hand sewn garment to mass production and distribution of ready to wear products in the modern market. And most of the Ethiopian clothing industries in face a problem to competitive globally because of different problems when they produce their product. For example, most of the company doesn't optimize their resources, inadequate of raw materials, poor quality. Therefore, the optimal assignment of resource for the task and proper assignment of work stations is becoming the industries competitive. This research is addressed an optimization of assembly line of long pant product through discrete event simulation (DES) modeling. In this research, the simulation modeling and optimal line balancing of the long pant sewing line is carried out by using arena 14 version. Based on the simulation model run, results the operations that have high queue length and high resource utilization are facing serging, pocket positioning, size by size zip tack to body and front fly topstitch and a resources which have been low utilizations or below 50% utilizations are like BH machine, manual operator2, helper a2, and heat machine are idle operations in the production lines of the long pant model. And the operations that have high waiting time are bottlenecks for the production lines. identified bottlenecks are eliminated or minimized using sharing and adding of the existing resources (operators and machines) in order to optimize the resources as much as possible.Finally, a new model has developed by using opt quest model of arena with different types of scenarios by considering the constraints of the resources and controls and responses of the results. Therefore, the production system produces 498 long pants and by reducing one operator and one 4TOL machine. The researchers have identified the main bottle necks which have high work in process and low work in process, high queue length, low and high utilized resource specially the operators and the machines that allocated in the assembly lines.