By: Bala Deshpande, Conference Co-Chair, Predictive Analytics World for Manufacturing 2015

In anticipation of his upcoming Predictive Analytics World for Manufacturing conference co-presentation, Utilizing On Board Technologies To Improve Maintenance Practices in Airlines, we interviewed Kumar Satyam, Manager, Advisory at PricewaterhouseCoopers, LLP. View the Q-and-A below to see how Kumar Satyam has incorporated predictive analytics into manufacturing at PricewaterhouseCoopers, LLP. Also, glimpse what’s in store for the PAW Manufacturing conference.

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

A: PwC Analytics group works on a wide range of advanced analytics projects. Some of the examples of types of predictive models developed are customer choice, market response, demand forecasting, lifetime value analysis, sentiment analysis, failure modeling, supply chain optimization and propensity modeling

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

A: Through predictive analytics, PwC enables its clients to rapidly discover, quantify and deliver value from data with intelligent analytics and scalable end-to-end business solutions.  By enabling our clients to quantify the potential benefits from analytics engagements, we help them prove the value of predictive analytics to their respective organizations.

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: Recently PwC Analytics team and Travel and Transportation team worked together to develop a delay/cancellation prediction model for predictive maintenance for a large US carrier. Through a couple of months of  pilot test run, we were able to demonstrate that the developed model can help the carrier save up to 25-30% of the currently occurring maintenance related delays/cancellations for the modeled fleets and components. This effectively translates to millions of dollars’ worth of savings per year if expanded across fleets.

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

A: One of the surprising discoveries from our analysis was that the maintenance logs of airlines contain lot of relevant information about a potential delay/cancellation that can occur in the future once the unstructured test data is analyzed.

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

A: Key Takeaway – Current computing power and analytics capabilities enable us to combine sensor data from aircrafts with unstructured maintenance log data to predict a significant percentage of delay/cancellations before they occur. This can enable airlines to perform predictive maintenance resulting in improvement of on-time performance and customer satisfaction with reduction in delay associated costs.

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Don't miss Kumar Satyam’s conference co-presentation, Utilizing On Board Technologies To Improve Maintenance Practices in Airlines, at PAW Manufacturing, on Wednesday, June 10, 2015, from 3:30-4:15 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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