Process Cost Analysis System for Real-Time Manufacturing Optimization
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Solution Overview
Problem
Current statistical process control (SPC) systems lack an efficient method for real-time cost analysis and optimization, particularly in determining the average effective per-unit cost and optimal process mean, which are crucial for reducing waste and improving manufacturing efficiency.
Innovation Solution
A method involving the determination of per-unit cost functions and percentage-of-acceptable-parts functions, cross-correlated with probability density functions using a processor, to calculate the average effective per-unit cost and identify optimal process means, incorporating indirect and direct cost factors such as specification limit violations and rework costs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If traditional statistical process control methods are used to monitor and control process variation, then process quality and conformity can be improved, but real-time cost analysis and optimization capability is lost
Solution Approach 1:
The patent merges statistical process control methods with cost analysis functions into a unified system. The SPC module calculates control limits and process capability indices while simultaneously computing per-unit cost functions and average effective costs. This integration allows the system to monitor both quality metrics and cost metrics together, resolving the contradiction by combining what were previously separate functions into one comprehensive process control system that provides both quality assurance and cost optimization capabilities.
Solution Approach 2:
The process control system is designed with multi-functionality to perform both statistical process control and cost analysis. The system calculates traditional SPC metrics (control limits, capability indices) while simultaneously determining per-unit cost functions, percentage of acceptable parts, and average effective costs. This universal approach allows a single system to serve dual purposes: quality monitoring and cost optimization, eliminating the need for separate analysis systems.
2Productivity
If process monitoring and quality control are implemented, then conforming product production is improved, but real-time cost optimization is hindered
Solution Approach 1:
The patent segments the cost calculation into distinct functional components: per-unit cost function determination, percentage-of-acceptable-parts calculation, and average effective cost computation. Each component is calculated separately using specific formulas and inputs, then integrated to provide the final optimization metrics. This segmentation allows complex cost optimization to be broken down into manageable calculations that can be performed in real-time alongside traditional SPC monitoring.
Solution Approach 2:
The system implements feedback mechanisms that continuously monitor process data, calculate cost metrics in real-time, and provide recommendations for process optimization. The average effective cost calculation provides immediate feedback on the financial impact of process variation, enabling real-time adjustments to process parameters to optimize both quality and cost performance simultaneously.
3Loss of substance
If statistical process control tools are applied to detect and correct variations, then waste reduction is achieved, but real-time cost analysis is insufficient
Solution Approach 1:
The patent introduces new cost-related parameters to the traditional SPC framework. Instead of only monitoring quality parameters (mean, standard deviation, control limits), the system also calculates and monitors cost parameters including per-unit cost functions, percentage of acceptable parts, and average effective costs. These additional parameters enable real-time cost analysis while maintaining traditional quality control capabilities, providing comprehensive measurement of both quality and cost performance.
Data Source
AI summary
A method of process cost analysis that includes determining a per-unit cost function for executing a process step, determining a percentage-of-acceptable-parts function for executing a process step, and receiving production data into memory. The production data corresponds to a measured quality metric of the executed process step. The method further includes determining a probability density function for the received production data, executing on a processor a correlation routine for cross-correlating the cost function with the probability density function of the production data to provide a first cross-correlation, and 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 method includes determining an average effective per-unit cost to produce a resultant of the process step by dividing the first cross-correlation by the second cross-correlation.