Archive for May, 2017

May 26th 2017

Wise Practitioner – Manufacturing Predictive Analytics Interview Series: Richard Semmes at Siemens PLM

By: Bala Deshpande, Conference Co-Chair, Predictive Analytics World for Manufacturing 2016

In anticipation of his upcoming Predictive Analytics World Manufacturing Chicago, June 19-22, 2017 conferenceRichard Semmes 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.​

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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. 

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

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May 2nd 2017

Wise Practitioner – Predictive Analytics Interview Series: Edward Shihadeh at Auspice Analytics, LLC

By: Eric Siegel, Founder, Predictive Analytics World

In anticipation of his upcoming conference presentation, How to Revolutionize Your Model Optimization, at Predictive Analytics World for Business Chicago, June 19-22, 2017, we asked Edward Shihadeh, Chief Data Officer at Auspice Analytics, LLC, a few questions about his work in predictive analytics.

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

A: Although we predict a variety of different outcomes, we specialize in predicting retention, calculating the probability that individuals in a university, business, or program will stay or leave. Because the costs of acquiring a customer far exceed the costs of retaining one, this focus allows us to bring great value to our clients.

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

A: We employ our analytics not only in businesses and universities but also in the public policy arena. Working with a major city whose homicide rate was recently 2nd in the country, we used our methodology—which entails not just specialized modeling but also sophisticated appends and measurement of both individual and contextual data—to provide metropolitan police with lists of individuals at risk of committing murder, so that the police could intervene. The result: murder in this city has been driven down by 50% since 2012, translating to 100 lives saved from a tragic end and 100 potential offenders whose lives were turned around.

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

A: We work with a major university for whom the leading predictive analytics solution in the university retention market had failed—leaving the university at risk of losing millions of dollars. By applying our methodology and including contextual (e.g., supra-individual) and behavioral data and measures, we created a model that was more than 99% accurate in predicting the overall percentage of students who would return the next year and 96% accurate in predicting which specific students were at risk of leaving the university. This resulted in millions in savings for this university.

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

A: The example above, in which the leading predictive analytics solution in the university retention market failed to deliver acceptable results, documents two important findings. First, this industry can serve our clients far better by offering custom solutions than by applying off-the-shelf models. Second, it demonstrates the enormous increase in predictive power that we can gain from including contextual and behavioral data guided by behavioral and social science.

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

A: Whether we work in business, health care, financial services, our models predict human behavior; As our successes illustrate, big leaps in the power of predictive analytics come not just from model optimization but also from a sophisticated understanding of the role carefully-selected individual and contextual data, and carefully-crafted measures, will play in predicting outcomes. These gains come not from mindlessly appending data, or blindly applying the latest statistical technique, but from drawing on behavioral and social science to identify data and measures that truly increase predictive power. In other words, it's about the carpenter, not the hammer. 

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Don't miss Edward’s conference presentation, How to Revolutionize Your Model Optimization on Monday, June 21, 2017 from 3:30 to 4:15 pm at Predictive Analytics World Chicago. Click here to register to attend

By: Eric Siegel, Founder, Predictive Analytics World

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