A Software Industry Veteran’s Take on Predictive Analytics

I’m about 4 months into the job here at Predikto as VP, Sales.  The predictive analytics market is an exciting new market with predictably (pun intended) its share of hype.  Nevertheless, this is key niche of the Industrial Internet of Things sector. I’d like to share some observations on what I’ve learned thus far.

We focus on asset-intensive industries, helping organizations leverage the terabytes of data they have accumulated to anticipate the likelihood of an adverse event, whether that is a battery on a transit bus about to fail, or indications that a fuel injector on a locomotive diesel engine, while still operating, is doing so at a less than desired level of performance.   We predict these events in a time horizon that allows the customer to take action to rectify the issue before it creates a problem, in a way that minimizes disruptions to operations.  Our technology is cutting edge Open Source, leveraging Spark, Python and Elastic Search hosted by AWS.

The use cases we’re being asked to solve are fascinating and diverse.   Some companies are contacting us as part of an initiative to transform their business model from selling capital assets to selling a service, an approach popularized by Rolls Royce with their jet engines, the “power by the hour” approach and similar to the software industry’s transition from selling perpetual licenses with maintenance contracts, to selling Software as a Service (SaaS).  In order to sell capital assets like construction equipment and industrial printing equipment this way, our customers will offer service level agreements, with Predikto in place to allow them to proactively deal with issues likely to degrade their service commitment.  So while our tactical focus has been on helping clients maximize product “uptime”, the strategic driver is helping them transition to a new way of generating revenue while getting closer to the customers.  It’s been gratifying to realize the impactful role our offering is playing in facilitating these transitions.

Other organizations are complex, asset-intensive businesses, where an equipment failure can have a cascading effect on revenues and customer service.  For example in the work we are doing with railroads we’ve learned there are a multitude of areas where sub-optimal performance of equipment or outright failure, can have significant impact.  The North American railroad network in 2014 set new records for revenue-ton-miles, a key efficiency metric; this was accomplished over a rail network which is highly congested.   In this environment, a delay has huge ripple effects.  Any number of factors can lead to a delay, ranging from a rockslide blocking a section of track to a locomotive breaking down, to a wheel failure on a rail car, which can cause a derailment.   On top of this, in order to operate safely and comply with government regulations, railroads have invested heavily in signaling and equipment monitoring assets, as well as machinery to maintain the track and roadbeds, which must work reliably.  Our abilities to implement in weeks and generate actionable predictions regarding locomotive and rail car health, as well as monitoring other equipment and even the condition of the rails, are making a major difference in helping to facilitate efficient, safe rail operations.

 

Having a blast…more to come.

Kevin Baesler, VP of Sales