SAN JOSE, Calif. and QUEENSLAND, Australia, Feb. 20, 2014 /PRNewswire/ -- IBM (NYSE: IBM) today announced a new collaboration with Thiess, one of the world's largest contract miners, to use Big Data to improve machine availability and operational productivity utilizing predictive analytics and modeling technologies. This initial collaboration focuses on Thiess' Mining haul trucks and excavators, and will help unify asset management and business operations.
Unlike traditionally data-intensive industries such as banking or telecommunications, which rely on advanced information technology (IT) to drive operational performance, asset-intensive industries such as mining, have typically not invested as much operationally in IT systems.
Today, many organizations in natural resource industries rely heavily on either a 'fix-it when it breaks' approach or time-based scheduled maintenance techniques. These methods often result in unnecessary downtime, premature component replacements, extra expense and lost production. They also do not explicitly factor in an individual piece of equipment's actual condition and performance capability.However, this trend is changing, as companies realize how IT can help them extract liquid, rocks and insights out of the ground. The increased deployment of machine and environmental sensors combined with new data collection methods is enabling the development of predictive machine maintenance analytics which can help increase equipment availability, lower production costs and provide greater operational flexibility. The IBM Research and Thiess collaboration has been integrating current and historical machine sensor data, along with maintenance and repair, operational, and environmental data to use as a basis for data-driven operational optimization. Factors such as repair and inspection history, payload size, sensor-based component alerts, operator variability, weather, and ground conditions are being used to construct models which assess and predict the life of discrete components and the overall health of a piece of equipment. This information will enable decision makers to co-optimize maintenance and production decisions, resulting in better operational performance. "Analytics and modeling can offer great opportunities to improve our business, but we need to integrate them with our current processes in order to have a real bottom-line impact. Working with IBM to build a platform that feeds the models with the data we collect and then presents decision support information to our team in the field will allow us to increase machine reliability, lower energy costs and emissions, and improve the overall efficiency and effectiveness of our business," said Michael Wright, Executive General Manager Australian Mining, Thiess. Early detection of even minor anomaly and malfunction patterns can be used to predict the likelihood of component failures and other areas of risk. This will dramatically increase the uptime of the equipment and improve Thiess' ability to manage the full life of discrete components, overall machine health and the deployment of limited maintenance resources.
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