Today, the strips are outdated, no longer outlandish. Outages are, in fact, predictable… with the right technology in place, that is.
That technology is predictive asset maintenance analytics, which is being put to work in a number of industries. Automotive is one.
Two years ago, we learned that Volvo launched predictive asset maintenance for its products based on SAS Warranty Analytics and embedded analytics in its vehicles. The year before that, Toyota teamed up with Salesforce.com to introduce Toyota Friend, a Salesforce Chatter-based asset management solution that interconnects you, your car, your dealership, and Toyota itself. Both Volvo and Toyota's solutions help give them, and their customers, a heads-up when something might be about to go wrong with one of their vehicles.
The oil and gas industry also is deriving benefits from predictive asset maintenance on the ocean floor, as we learned here on All Analytics. (See Shell Taps Big-Data From Way Down Deep.)
Most of what goes into predicting and diagnosing equipment failure and other asset management analytics is simply putting information already on hand to good use, suggests Anuj Marfatia, an IBM program director who gave a presentation on predictive asset maintenance analytics (and shared the Dilbert cartoons) at a recent conference. Still, many organizations ignore their data -- commonly deleting otherwise useful data after six to eight months, he said.
Part of the problem is that a pervasive culture of big data is overwhelming the enterprise mindset. The solution, therefore, is to break the big data down into "manageable data." "Where is your biggest problem?" Marfatia asked rhetorically. "What are the processes associated with it? …Who are the people associated with it?"
He wants specifics. He wants enterprises to understand their assets. He wants managers to be able to accurately measure the size of their issues. And he wants them to work it all out on a whiteboard.
"It's really being able to take bite-sized pieces. Managing all [of the data] at once has not been successful."
Marfatia related a case study of an automaker that had all of the data it needed to fix a serious problem in its manufacturing process -- but didn't analyze it. Once the company did so, it was able to predict when failure would occur. The data revealed that these failures were consistently happening during a certain time of day and that the equipment was at its hottest during that time.
With this analysis, a manager familiar with the factory layout instantly diagnosed the problem: sunlight. During the time of day of equipment failures, "light came in [through the windows] and heated up the engine blocks."
By accurately diagnosing what Marfatia called an otherwise "undetectable" problem and acting to move its essential processes appropriately, the automaker realized an 80% savings within a single year.
Indeed, big money is at stake when it comes to predicting "unplanned outages." Navigant Research recently reported that in the utility sector alone, asset management and anomaly prediction analytics are a $2 billion-plus industry; that figure will more than triple by 2023 as smart grids get smarter.
It's easy to see why. Israel Electric Corporation, for instance, once had but 30 minutes of notice before a system or equipment failure; since deploying predictive maintenance analytics, the company can determine failure 30 hours in advance.
The difference that predictive asset maintenance solutions can make is huge -- and the ramifications are obvious. "If something is failing," Marfatia said, "you need to fix that as soon as possible."
After all, it's not 1997 anymore.