Statistica® Process Optimization

Table of Contents

• Root Cause Analysis 
• Response Optimization 
• General Optimization
• Predictive Quality Control Charts
• References

Using years' worth of past data collected for a manufacturing process, Statistica Process Optimization can find trends that reoccur over time. These trends are then used to predict means, minimums, maximums, and ranges for samples not yet created. Understanding the sample trends and forecasting future samples proves invaluable to the manufacturing process.

In a process with hundreds or thousands of process inputs, all potentially affecting the final product, Statistica will determine a subset of those predictors with the most influence. This allows us to focus on a hand full of important parameters of the complex process while at the same time, giving greater ability to influence the final product.

Root Cause Analysis 

Response Optimization 

General Optimization

Predictive Quality Control Charts

References

Hill, T., Eames, R., Lahoti, S. (2008). Finding direction in chaos: Data mining methods make sense out of millions of seemingly random data points. Quality Digest, December, 20-23.

Hill, T., EPRI/StatSoft Project 44771: Statistical Use of Existing DCS Data for Process Optimization, EPRI, Palo Alto, CA, 2008). (Note: This paper is available to EPRI members. It can also be purchased. Search for paper title on http://epri.com)

Grichnik, T., Hill, T., & Seskin, M. (2006). Predicting quality outcomes through data mining. Quality Digest, September, 42-47.

Lewicki, P; Hill, T; Qazaz, C. (2007). Multivariate Quality Control. Quality Magazine, April, 38-45.