UPDATED 10:01 EDT / MARCH 21 2013

New IBM Big Data Predictive Analytics and Consulting Service Focuses on Taking Infrastructure Maintenance out of the Break/Fix Era

The largest hidden problem in business, government, and society in general today is infrastructure maintenance and repair. Every machine and structure ages every day, every part gets a little weaker and more worn, everything that businesses and societies depend on moves a little closer to its failure date.

Most of this wear and normal aging is hidden, however, and often it is very hard to know the optimal time to invest in intervention. As a result, most companies and governments rely basically on a break/fix strategy — don’t fix it if it ain’t broke. The big problem with that is failures create unnecessary emergencies and hugely magnifies the costs. The failure of a machine in an airplane assembly plant can shut down an assembly line, costing the company millions of dollars. When the I-35W Mississippi River bridge in Minneapolis failed during evening rush hour on August 1, 2007, 13 people died and 145 were injured. When BP’s Macando Well in the Caribbean blew out in part due to a concrete seal failure, 11 workers on the Deepwater Horizon rig  were killed and an entire region devastated. The costs of that failure are still being paid.

Clearly break/fix is a poor strategy. The problem is knowing when something needs to be fixed before it breaks.

Today IBM has announced a new service combining Big Data predictive analytics and business consulting, designed to answer that vital question for a production line, a business, a city, even structures as large as the U.S. Interstate Highway System. The Predictive Asset Optimization solution helps organizations recognize significant but small indicators of growing problems and identify the optimal strategy for interceding to prevent a failure with minimum interruption, risk, and expense.

The new software and services are part of IBM’s ongoing leadership in analytics and Big Data, where the company is helping clients move away from making decisions on gut instinct to reliance on accurate analysis of past and present data. IBM CEO Ginni Rometty discussed this issue during a recent Council on Foreign Relations speech where she described the idea of using analytics to interpret the footprints in the sand that can help an organization make more fact-based decisions.

The problem is huge. Nearly 2.5 quintillion bytes of  raw data are created every day. According to engineering estimates, the U.S. will need to spend $2.2 trillion over the next five years just to bring national infrastructures up to date. This new service is intended to help clients in the automotive, electronics, aerospace, defense, manufacturing, mining, transportation, telecommunications, energy, and utilities industries as well as governments extract the information from that huge flow of Big Data to act in time. For instance:

  • IBM is working with the City of Cambridge, Ontario, to move from break/fix to a proactive, best practices approach to avoiding emergencies that are sapping the resources of its Transportation and Public Works department.

  • A global auto manufacturer is working with IBM analytics to use real-time data to monitor production quality and identify and resolve issues early, reducing the defect rate by 50 percent in 16 weeks of production.

  • A global manufacturer is using IBM analytics to save $130M in warranty costs per year.

  • A regional utility company is using IBM predictive analytics to detect potential problems in an aging infrastructure and intervene before they failure, resulting in 20 percent productivity gains for service trucks and 20 percent reduction in fuel costs due to the reduction in truck rolls.

“Analytics technology gives us valuable insight into trends and what we can expect in the future,” said Michael Hausser, Director of Asset Management and Supporting Services, Transportation and Public Works Department, City of Cambridge. “We were heading toward a point where reliability of service would be reduced, and we’d be beyond our resource capacity to reactively issues in a timely matter. We are in a transition to be more proactive and gain efficiencies in day-to-day maintenance management activities.”


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