Patent ID: 9020625
Filing Date: 2015-04-28
Classification: G06Q

Abstract:
1. A method of process cost analysis, the method comprising: receiving one or more part specification limits for executing a process step; determining a per-unit cost function for executing a process step based on a per-unit manufacturing cost and a per-unit rework cost for each acceptable part manufactured by the process step, the per-unit rework cost applied to each part that violates at least one of the one or more part specification limits indicated as allowing rework after a first pass through the process step and undergoing rework so that the corresponding part is within the one or more part specification limits without scrapping the corresponding part; determining a percentage-of-acceptable-parts function for executing a process step based on a percentage of parts manufactured by the process step that are within the one or more part specification limits; receiving production data into non-transitory memory, the production data corresponding to a measured quality metric of the executed process step; determining a probability density function for the received production data; executing on a processor a correlation routine for cross-correlating the per-unit cost function with the probability density function of the production data to provide a first cross-correlation, the first cross-correlation corresponding to a weighted average per-unit cost at every set point of the process step; executing on the processor the correlation routine for cross-correlating the percentage-of-acceptable-parts function with the probability density function of the production data to provide a second cross-correlation, the second cross-correlation corresponding to an average percentage-of-acceptable-parts at every set point of the process step; determining an average effective per-unit cost at every set point of the processing step to produce a resultant of the process step by dividing the first cross-correlation by the second cross-correlation; determining an optimal process mean associated with a minimum average effective unit cost associated with a current data distribution; presenting a process offset based at least in part on a difference between the optimal process mean and a current process mean associated with the current data distribution, the process offset associated with a cost savings of the process step; iteratively determining the minimum average affective per-unit cost using different standard deviations for the probability density function of the received data corresponding to a theoretical reduction in process variability; and determining an optimal mean and minimum average effective per-unit cost at one or more standard deviations.