In anticipation of his upcoming Predictive Analytics World Manufacturing Chicago, June 19-22, 2017 conference presentation, Closing the Loop with Predictive Product Performance, we interviewed Richard Semmes, Senior Director, R&D at Siemens PLM. 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: The objective for predictive analytics in manufacturing is really to enable actionable business decisions that impact the way you design, build, or service your products. The most successful practitioners of predictive analytics in manufacturing use continuously updated data from many sources throughout their supply chain. The biggest challenges center on data ETL, aggregation, and continuous updates.
Q: In your work with predictive analytics, what behavior do your models predict?
A: Our models predict the performance of mechatronic products. We use predictive analytics to connect real world IoT data to the Digital Twin models of the virtual world. That allows manufacturers of physical goods to proactively manage their businesses by better understanding what is going to happen in their factories as well as their products in the field.
Q: How does predictive analytics deliver value at your organization? What is one specific way in which it actively drives decisions?
A: Predictive analytics serves to find issues with products we did not know existed. We use predictive models to understand the correlation between product features and product performance. We use that insight to proactively manage those products in the field as well as optimizing the product through design changes.
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: In one instance, we trained models using environmental data as well as IoT data from a very complex machine that produces other products. The trained model was able to show us the environmental and job characteristics that had the best correlation to job failure. That information can be used to warn the operator that there is increased risk of failure and it can be used to improve the machines to better handle those adverse situations.
Q: What surprising discovery have you unearthed in your data?
A: The extent to which environmental data should be taken into consideration when creating predictive models. While it is obvious that weather and other environmental state can influence product performance, the extent to which including environmental conditions helps discover product feature correlations is significant.
Q: Sneak preview: Please tell us a take-away that you will provide during your talk at Predictive Analytics World for Manufacturing.
A: You don’t need an army of data scientists to reap the benefits of predictive analytics in your business.
Don’t miss Richard’s conference presentation, Closing the Loop with Predictive Product Performance, at PAW Manufacturing, on June 20, 2017 from 1:30 to 2:15 pm. Click here to register for attendance.