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Deciphering Big Data for ACM Success

The RELIABILITY Conference -
by Tyler Pietri, Program Engineer, Azima DLI

The advent of concepts like Big Data and IIoT promise a brave new world: enterprise level transparency with total situational awareness of industrial asset performance everywhere all the time. It will take time before the full implications of big data are understood, but “total situational awareness” is in our midst, at least as it relates to asset health and performance. Visibility will extend to a variety of day-to-day operating activities, from production and supply chain management to maintenance and reliability practices.

Particularly in the case of large or multi-facility enterprises, measuring performance across layers of management and between silos of specialists - and people-to-people (P2P) -can result in imperfections. Complex communication channels can be the culprit behind stifling inertia and opaque decision-making where issues get lost in bureaucratic translation.

Having the right tools to identify key performance and maintenance trends enables companies to predict which facilities might need the most attention in the coming months or years to ensure they stay reliable. Shifting maintenance resources to the facilities in need can be an effective production risk management strategy. Using predictive technologies as a basis ensures that a company’s risk management strategy is proactive and not reactive.

In this session, Tyler Pietri will discuss how enterprises of all sizes and types can ensure consistency within their predictive maintenance programs leveraging Big Data. They will offer case examples of how plants can benefit from the aforementioned data comparisons. They further will detail how machine health data, when tracked and leveraged effectively, can not only help to compare machine performance across plants and over time, but specifically illustrate ROI of maintenance programs to date and pinpoint critical compliance areas.

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