This morning, IBM unveiled a new software and service package within its Predictive Asset Optimization product that is intended to predict and prevent equipment failure.
The idea: use big data, that nebulous concept, to identify irregularities (and calculate the potential risk of them) in instrumented assets so as to avoid slowdowns or failures in the manufacturing process.
Hardly a sexy topic, but the ramifications are massive. If there's a slowdown (or outright stoppage) in the supply chain, the losses pile up immediately. That's the downside to being extremely fast and efficient: that speed can come back to bite you when the operation goes off the rails.
(See: Crisis, Financial; 2008 edition.)
The potential damage, of course, depends on the product in question. You can imagine how awful a supply chain interruption would be to a company -- say, a large grocer -- that produces perishable items. One slowdown, and you've got pallets of food spoiling in your warehouse, and millions of dollars in potential revenue evaporating every minute.
IBM thinks it can improve uptime by deploying sensors on the assets of a production line and analyzing the liquid gold -- er, data -- that flows forth. By applying its analytical insight to the numbers, it says it can not just avoid catastrophic failure, but predict it, too -- important not only to avoid trouble but also avoid the additional costs; three to 10 times as much, IBM says -- that emergency maintenance carries.
The market opportunity for the company is real: roads, bridges, water supply systems, sewers, electrical grids and telecom networks are all in its sights. It's not just a run at factories, and spans some of the global economy's largest sectors, from automotive to electronics, transportation to telecom. For some, it's catastrophe avoidance; for others, it's customer support savings (e.g. product warranties).
The new capabilities will be offered through IBM's new Advanced Analytics Center in Columbus, Ohio.