Part 2: Predictive Analytics in the Industrial World – Driving Business Value

Author: Beena Ammanath
Part 2: Predictive Analytics in the Industrial World – Driving Business Value

Following the last Q&A blog with with Beena Ammanath, Executive Director, Data Science Products and Delivery, GE Digital, this week we explore the application and impact of predictive analytics for the industrial company.

Are there distinctions between how predictive analytics is used in the industrial world vs. consumer applications?

In both cases it boils down to creating algorithms that can help drive and improve value to the business or consumer. It is the degree to which those analytics are used for mission critical applications where there are differences.

In the case of consumer applications, predictive analytics has primarily been algorithms to improve experience from social media engagement to transactional activities like shopping.  A retailer knowing in advance when the shopping e-cart app may crash and avoiding such an incident can result in strengthening consumer confidence and loyalty.  This is an important business outcome, but is not mission critical. If some piece of an industrial application in the Aviation industry fails, it could potentially cause flight delays with an engine that must be taken offline for maintenance. In the Healthcare space, an MRI machine that has faulty performance can impact important patient results. Providing predictive insights to avoid unexpected downtime, performance issues or even prescribing alternative actions, such as optimum time to service an engine is essential for industrial companies.

Also, security, durability and reliability of the analytics platforms are critical factors in Industrial Internet.  In the consumer world, software updates and changes can be rolled out in minutes, but that is not possible in the industrial domain where there is more liability for performance. Updates can involve hundreds, or even thousands of machines and sensors in operation for one single application, e.g. flight management. Because GE is putting our software on machines that have lifespan of 20+ years, there is a lot of testing that needs to be done to ensure it is reliable too.

As a GE Digital executive, would you share examples of the impact of predictive analytics?

The aviation industry, for example, estimates that during any given flight, the travel time, fuel use, and flight path are 18% to 22% inefficient. Potential costs savings are substantial: a 1% reduction in jet fuel use alone could save the industry $30 billion over 15 years.
In our transportation business, our Trip Optimizer application uses analytics to deliver an optimized plan for fuel efficiency. It has saved customers more than 56 million gallons of fuel so far. That’s one piece of a much larger system of advanced algorithms designed to optimize the entire rail network, and that will lead to billions of dollars of savings across the industry.

With all this data analytics and insights, what ways are best practices being shared?

GE built Predix to be the operating system for the Industrial Internet – it captures best practices for other industrial companies to leverage too. A healthy ecosystem is crucial to the success of the Industrial Internet, which is why we put so much emphasis in cultivating a large network of partners and developers on the platform. In creating Predix we are helping accelerate the time it takes to deliver outcomes for our customers, and helping other industrial companies transform themselves in the same way GE has transformed into a digital industrial company. They pass those benefits onto their customers in the form of increased productivity and efficiency.



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