ABSTRACT
In the manufacturing industry, a newly developed product would go from a stage of research and development to preliminary design, detailed design and prototyping in order to reach a final goal of mass production. In general, during the designing and prototyping phase, the product is produced in a small amount only for testing and verification. Once the design is approved and all manufacturing processes are verified, only then will the product be produced in a large quantity to deliver to customers. For this reason, a production line for prototyping and mass manufacturing is normally set up differently with regard to the number of parts produced. Product development is the process of either designing, creating and marketing a newly designed product or improving an existing one to satisfy the customer requirements. In modern industries, manufacturers have to compete in an extremely demanding market and new products would be developed consistently to satisfy the needs of customers. Different techniques are used in order to analyze and optimize production capacity such as the use of design of experiments. However, with a complex production system, using the design of experiments alone can be costly and time-consuming. Thus, many engineers are turning towards system modeling and simulation software in order to find an optimal scenario suitable for their production requirements. Advantages of using process simulation software are (Pisuchpen 2010): 1) an ability to evaluate many potential alternatives and determine the best approach to the problem with
less time and cost than the conventional method; 2) an ability to evaluate various system performance indices such as cost, production time and resource utilization simultaneously; 3) an ability to run a “What if” scenario to evaluate proposed changes; and, finally, 4) an ability to reduce the risk of inappropriate expenditure by running vigorous system simulations to verify all important managerial aspects before spending the company’s capital. Many engineers successfully used simulation modeling to solve their production problems. Abed (2008) used a simulation study to increase the production capacity of a rusk production line. He thoroughly studied and analyzed several bottlenecks in his production system. After that, simulation experiments with seven different scenarios were developed in order to find the best approach. He found that by adding two new machines, replacing three old machines, modifying two other machines and decreasing the time in one process, the system performance could be improved by 50% in production with a decrease in total production time of 11.4%. Hasgul (Hasgul & Buyuksunetci 2006) used simulation models to evaluate the bottlenecks in a mixed-model production line in a refrigerator company. He found that by rearranging the vacuum station and changing AGV’s cell selection rule, the cycle time of the system was reduced and the bottleneck problem at the vacuum station was solved. Ramis et al. (Ramis & Palma et al. 2001) used simulation modeling to evaluate different alternatives for process improvement of an ambulatory surgery center. They used statistical data taken from a clinical hospital where patients would enter and leave within the same day. From
their study, it was found that by using two beds for patient preparation and five beds for postoperation recovery, the maximum throughput of 10 surgeries per day could be achieved. Line balancing is a method of leveling the workload across all processes in a value stream in order to remove the bottleneck or excess capacity and streamline the process for maximum output. Reducing production lead time, minimizing Work In Process (WIP) and maximizing resource utilization are the cornerstones of modern manufacturing strategies such as Lean, Quick Response and Just In Time (JIT) (Benjaafar 2002). Pisuchpen and Chansangar (2014) used line balancing to modify production line in order to increase the productivity of a vision lens factory. In their study, the productivity was improved by 8% and Work In Process (WIP) was decreased by 12%. In the present study, a possible bottleneck was found at S2: CNC Lathe. Therefore, a simulation model using ARENA simulation software was developed in this study to analyze and increase the production capacity of a rocket parts manufacturing plant in order to enhance the production line from prototyping to mass production using the line balancing principle.
