A combination of product mix and production volume of the drive production line at Rockwell Automation is analyzed using a simulation model aiming to improve the performance and optimal designing of the production line. The performance of the production line under different production scenarios in Arena simulation environment is developed to represent the production line and find the optimal combination product mix to meet future customer demands. The main purpose of this research is to improve the system's performance, make re-configurable line by adding flexibility in the assembly line and show how simulation modeling can be used to evaluate alternative designs for flexible assembly line in a dynamic uncertain manufacturing environment. The best satisfaction of the production requirements under dynamic production for the power drive assembly is validated with real application. The research is done in Rockwell Automation's power drive assembly process to improve their existing problems in the assembly line and identify the capacity of the assembly line through the discrete event simulation program Arena under a combination of product mix and product volume. The goal is to analyze the current assembly process and determine how to achieve future production goals. Some of the ways to achieve the goal are by improving the efficiency of the assembly line, determining the maximum operational capacity of the line, and assisting in developing an accurate and intelligent simulation model. The power drive assembly process is studied under different scenarios. The simulation model for power drive assembly system is developed for thirteen categories of the product. To make the assembly line flexible, the workstations are allowed to mirror each other. The purpose of making the assembly line flexible is to meet current demand. If more frame drives are ordered, it may be necessary to simply produce more of that drive on that given day. By building this flexibility into the assembly line, it would be able to handle fluctuations in the product demand. This flexibility is more of a short-term solution and will work best if the assembly line can handle the predicted production volumes. To add flexibility in the assembly line, the following two scenarios are added in the model by adding the capability to run different frame drives at different workstations. An additional workstation to the assembly line will create a more substantial impact on the product cycle time. The two-station operation time is distributed with logical break into three stations. The easiest and most effective way to reduce the time in the operation is to remove some of the steps that the operator must go through. While we cannot simplify the product and remove steps from the assembly process, we can move some of the steps to an additional workstation. The goal is to come close to that by dividing the operations into three parts instead of the two that they are currently in. Based on the TAKT times, this will be necessary to meet future production. There are four large parts to the plan that will increase the production, improve the efficiency of the operation and provide flexibility for changes in demand. These four parts all compliment each other when brought together. By improving the layout of the material, the operator will spend more time building drives and less time walking to retrieve the parts he/se needs to build a drive. Eliminating the need for that same operator to search for components to replenish his stock by bringing in a person whose entire job is spent making sure that everyone has all of the components that need also greatly improve operator efficiency. Creating carts that the parts runner will replenish will improve the parts runner's efficiency as well as give the assembly line an added degree of flexibility. Finally, adding an additional workstation will remove the bottleneck workstations. Together all of these things will play a huge role in the ability of the assembly line achieving their projected production goals. Validation of the power drive model is done by comparing the actual throughput with the simulated one as well as the cycle time spent in system. The data from the simulation model is gathered from the consecutive replications of 8 hours daily (single shift), 16 hours (double shift), 40 hours (weekly single shift), 250 hours (yearly, single shift). Finally, the replications will be identified, which are required to obtain a satisfactory confidence interval for power drive cases. The comparison is done using queue, WIP, productivity and utilization statistics, etc Moreover the detailed interpretation of the above results is done to find learning curve as well as flexibility of the systems in future. Future research direction, a fuzzy knowledge based system will be developed to incorporate a prototype system design in power drive assembly systems and is capable for recognition static and dynamic features of assembly for power drive. As knowledge acquisition and representation are important steps in the modeling process, this knowledge-based simulation makes the system easy to acquire the knowledge for better representation of the manufacturing scenario in the model. This fuzzy system is also considered to capture dynamic behaviors, which represents a more realistic scenario of the resources in the internal supply chains. This tool can help the management system for decision making for controlling production to meet future customer demands.
|Number of pages||1|
|Publication status||Published - 2004|
|Event||IIE Annual Conference and Exhibition 2004 - Houston, TX, United States|
Duration: May 15 2004 → May 19 2004
|Other||IIE Annual Conference and Exhibition 2004|
|Period||5/15/04 → 5/19/04|
ASJC Scopus subject areas