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Inspectioneering Journal

The Reliability Data Revolution

By Ryan Myers, Product Manager at Pinnacle, Andrew Waters, Principal Data Scientist at Pinnacle, and Rachel Salaiz, Data Engineer at Pinnacle. This article appears in the November/December 2020 issue of Inspectioneering Journal.
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Clive Humby famously said: “Data is the new oil.” As a society, we spend inordinate amounts of time and resources creating, collecting, and organizing data. When harnessed effectively, data can transform entire industries as evidenced by companies such as Amazon, Netflix, Spotify, and many others. These companies have integrated data analytics into their products and entire business models, which has enabled them to provide unparalleled levels of value to their customers. In the reliability industry, we also significantly invest in the creation and collection of data. But, are we really using our data to drive smarter reliability decisions?

We are in the midst of the largest digital transformation era the world has ever seen. Recent innovations have led many companies to invest in a variety of initiatives such as machine learning and digital twin technologies. While these tools can provide a multitude of benefits, they can easily become expensive investments that fail to provide the desired business value. After all, when you look at reliability performance over the past several decades, the pace of improvement has stagnated despite these new innovations and significant capital investments.

Why is this the case? We typically see two schools of thought when it comes to reliability management and performance improvement. One approach focuses on the application of first principles accompanied by subject matter expert (SME) based analysis and decision-making. The other focuses on leveraging large volumes of data (dubbed “big data”) and sophisticated algorithms that conduct much of the analysis and decision-making processes. In other words, segments of the reliability industry are pitted against each other in a battle of humans versus machines, or more specifically, a battle of intuition versus data. However, this is flawed thinking on both sides – while machines have time and time again throughout history proven their ability to outperform humans at certain tasks, we need to recognize that people are still better than machines in many areas, especially where we lack data or the right algorithms to process the data effectively.

Facilities that can successfully leverage both their data and expertise while effectively integrating this unified model into their core business processes can improve performance, eliminate non-value-added activities, and ultimately, gain a competitive edge over their peers. The next step-change in reliability performance will come from humans working effectively with machines, rather than at odds with one another.

The Evolution of Data in Reliability

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