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March 21, 2013 /PRNewswire/ --
IBM) today announced new business consulting services and software that, together, help C-Suite decision makers predict and prevent damaging supply chain disruptions.
Through IBM's predictive analytics software and business consulting services, the new solution harnesses big data from instrumented assets and identifies irregularities in the manufacturing process, spots product irregularities, and forecasts a range of asset performance risks before a problem ever arises.
Operating, maintaining, and managing assets throughout their lifecycle is a massive built-in expense, made even more critical by the frequency of unpredicted, catastrophic machine failures. Asset downtime, especially, if unplanned, is a multi-million dollar issue for organizations; and the related unscheduled maintenance costs can range from three to 10 times the cost of scheduled maintenance.
"The world is entering a new era of smart - where decisions will be based on facts, data, and increasingly on the ability to apply analytics to massive data sets and extract very precise business insights," said
Fred Balboni, senior partner, Big Data Analytics, IBM Global Business Services. "Companies realize they have a new opportunity to capitalize on big data to address some of the intractable issues of the past, drive new levels of business efficiency, and create new levels of value for their customers. Our data shows us that businesses that are applying analytics to structured and unstructured data are outperforming their competitors in every industry."
Envision the myriad of components that combine to form the complex automobile manufacturing line. How can a decision maker know when it's time to replace any one of the thousands of machine parts, robots or sensors; and beyond that, how the line – or an oil rig, or a piece of heavy equipment -- can be taken off line for maintenance with minimal economic impact? IBM's new solution will uncover these data-driven insights, examining both static and streaming information, combined with analysis of asset sensors correlated with domains such as environmental and facilities monitoring systems.