In anticipation of his upcoming Predictive Analytics World for Manufacturing conference presentation, Improved Statistical Process Control of Mature Manufacturing Processes Using Multiple Available Data Streams, we interviewed Peter Frankwicz, Senior Process Engineer at Elmet Peter Frankwicz imageTechnologies. View the Q-and-A below for a glimpse of what’s in store at the PAW Manufacturing conference.

Q: What are the challenges in translating the lessons of predictive analytics from other verticals into manufacturing?

A: Most small business manufacturing companies are focused on relatively simple statistical process control (SPC) or end-of-line quality control.  The next step to predictive statistical process control is a major undertaking in both collection of relevant manufacturing process data and product & yield metrics.  The data-based “return on investment” in higher yield has to overcome management angst of higher risk of product scrap at the end of the manufacturing line.

Q: In your work with predictive analytics, what behavior do your models predict?

A: Predictive statistical process control analytics are in use to optimize powder metallurgical properties, such as tap density, and sintered ingot properties for thermomechanical processing to sheet and rod products.

Q: How does predictive analytics deliver value at your organization? What is one specific way in which it actively drives decisions?

A: Many small business manufacturing companies have a “we have always made it this way” mentality.  Predictive analytics and statistical process control methods allows process engineering to deliver data-based and statistically significant understanding of manufacturing processes to management.  Predictive analytics drives several specialized [Elmet Technologies refractory metal product – manufacturing process] combinations to optimize yield and quality.

Q: Can you describe a successful result, such as the predictive lift (or accuracy) of your model or the ROI of an analytics initiative?

A: Predictive models were developed to understand refractory metal powder properties.  Use of these models to direct manufacturing production resulted in an over $20,000 monthly reduction in scrap product in the “downstream” sheet rolling department.

Q: What surprising discovery have you unearthed in your data?

A: Data mining and statistical analysis of available process data revealed surprising manufacturing process sensitives.  Many of these process sensitives were only known at the level of tribal knowledge on the manufacturing floor.

Q: Sneak preview: Please tell us a take-away that you will provide during your talk at Predictive Analytics World for Manufacturing.

A: There is a high need in small business manufacturing for simple and robust predictive analytical methods.  Even starting the path with data mining and statistical analysis of available data streams can discover surprising and valuable manufacturing process insights and yield optimization strategies.


Don’t miss Peter’s conference presentation, Improved Statistical Process Control of Mature Manufacturing Processes Using Multiple Available Data Streams , at PAW Manufacturing, on Tuesday, June 21, 2016, from 2:40 to 3:25 pm. Click here to register for attendance.

By: Bala Deshpande, Founder, Simafore and Conference Co-Chair of Predictive Analytics World for Manufacturing.