Process quality and capacity planning
Production planning is a function performed in isolation from process capability to estimate available capacity. Process capability is a systematic study performed to understand the process performance. After a complete review of the literature available on capacity and capability a gap was identified between them. This research is aimed at proposing a model for representing a relationship between machine capacity and performance capability. Also presented are the impact of capability on capacity utilization and capacity planning. A traditional machine capacity calculation model is replaced with a modified model, which incorporates the yield percentage. Where, capacity is estimated as a product of available time, productivity and yield percentage .The yield percentage is estimated based on the performance capability .A systematic methodology is provided for the manufacturer to arrive at identify the root cause of capacity related problems. The importance of quality in capacity planning is emphasized by explaining the effects of deviation to capacity plan that can occur due to variability in the process. A case study is carried out in an aircraft company on a single machine to estimate performance capability and capacity of the machine in comparison to the demand. The results from case study indicate that there exists a 32% deviation from the required capacity calculated considering the process performance. The manufacturer decision based on outcome of the proposed model, points out the need for improving both productivity and utilization of the machine. An alternative to the current decision was also presented to the manufacturer, to increase the available time of the machine that is to increase the machine operation time from 7.6 Hrs to 10 Hrs in order to meet customer demand. It is left to the discretion of the manufacturer to decide on a corrective action after giving due consideration for the costs involved in the solution to meet customer demand.
Thesis (M.S.) - Wichita State University, College of Engineering, Dept. of Industrial and Manufacturing Engineering