Productivity optimization of manufacturing system using computer simulation, design of experiment and response surface methodology

Abstract

Productivity plays a significant role for most companies in order to measure the efficiency. In reality there is an essential need to evaluate the different factors which increasing productivity and achieving the high level of quality, a high production rate and machine utilization. Therefore, in the manufacturing industry, managers and engineers are seeking to find methods in order to eliminate the common problems in manufacturing systems such as bottlenecks and waiting times. This is because that all of these kinds of problems impose extra cost to the companies. In addition, manufacturing companies are striving to sustain their competitiveness by improving productivity, efficiency and quality of manufacturing industry for instance high throughput and high resource utilization. So it can be acquired by finding ways to deal with various industrial problems which have affected the productivity of manufacturing systems such as high lead time and work-in-progress (WIP) and etc. This thesis concentrates on the application of design of experiment (DOE) and computer simulation to recognize and to weight the significance of different factors in the production line as well as to achieve the optimum productivity. Arena 13.9 software was selected as the simulation software. A case study of color industry was chosen to implement and evaluate the result of applying DOE and response surface methodology (RSM) by using Minitab 16 and Expert-Design 6.5. Data required for developing the simulation model were gathered from the factory documents and stop-watch method. The results reveal that applying computer simulation and design of experiment in the manufacturing systems is very useful and helpful where the weakness of the system will be found after doing the experimentation. Based on the main objectives of this thesis the effect of the factors which have the most significant influence on the productivity was assessed and determined. Finally the best factor setting which result in the maximum productivity was determined.