Predict Failure versus Predictive Maintenance

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In a recent post by ARC Advisory Group, Peter Reynolds notes that 80% of assets fail randomly despite being supported by programs designed for asset maintenance and reliability. Only 3-5% of maintenance performed is predictive. The vast majority of maintenance is either break-fix or executed based on the OEM’s asset maintenance schedule – needed or not.

A broad set of factors drive asset performance, including variabilities in process conditions/flow outside the asset itself, which previously may not have been considered relevant to determining asset condition. With advanced analytics, the compute power is available to combine asset health, asset condition, and process variables to determine the asset’s true risk of failure.

More importantly, machine learning will provide a means to see beyond a conventionally-understood state leading to asset failure. These machine learning models require an understanding of the operating and failure mode states of these assets. As Reynolds points out, this probably means working with operating personnel, not maintenance personnel, to develop the models. This marks a change from condition-based maintenance and less sophisticated predictive models.

Using sophisticated machine learning models, asset managers can know that a given asset will continue through a rough spot, not fail as might have been predicted by condition monitoring or prognostic models, and will in fact go on to a longer operation. This suggests that the P-F curve in ARC’s post could look more like a sine wave than a gradual drop off. The key is to have confidence in the algorithm’s prediction that failure is actually not imminent. Only the right set of machine learning analytics can predict into the future without a loss of confidence.

Predictive and prescriptive analytics will indeed drive the next wave of improvements in asset performance. But only the right algorithms will provide the highest return on investment for those seeking lasting improvements in asset performance.

 

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How To Not Be Stubborn In 2020

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I pulled the following two predictions about analytics and decision making from a recent list of 100 predictions by Gartner analysts (subscription required):

  • By 2018, decision optimization will no longer be a niche discipline; it will become a best practice in leading organizations to address a wide range of complex business decisions.
  • Through 2020, over 95% of business leaders will continue to make decisions using intuition, instead of probability distributions, and will significantly underestimate risks as a result.

Apparently most of us will refuse to get the message about optimizing decisions, even after years of tools and best practices being in place. In Gartner’s 2020, we’re all still stubborn foot-draggers.

In my experience, predictions like these often require a grain of salt. Generalizations such as “over 95% of business leaders” at “leading organizations” who “significantly underestimate risk” lack the mathematical precision necessary to inspire confidence and change behavior.

Predictions like this often contain a grain of truth, as well. We frequently prefer our personal comfort zone, resist change, suffer from confirmation bias, and respect the confines of our organization’s formal and informal cultural.

Keep in mind that being stubborn can quickly lead to being history. Accenture CEO Pierre Nanterme notes that half of the companies in the Fortune 500 disappeared between 2000 and 2015. Why? New business models based on digital technologies, including decision optimization. The rapid pace of change and disruption will continue, and increase.

So, how do you avoid becoming a historical footnote by 2020?

  • Start with the end in mind. Decision optimization starts with the BI dashboards that (I hope) you are using today, and extends to advanced analytics that include prediction, simulation, and prescription. Knowing where you’re heading helps you plan a route and schedule for reaching your destination.
  • Start small. You won’t get to optimal decisions immediately. Identifying what decisions you can automate helps you pinpoint feasible projects with measureable ROI. Chances are, regardless of how digital your industry is now, there is low-hanging fruit to be picked.
  • Start now. Start this quarter or this month or this week, or even today. With hosted and cloud solutions, you don’t need to complete a big IT project before you can start improving decision making through analytics. In fact, you don’t have time for the typical enterprise project that requires years.

The year 2020 may seem like a long way off.  In truth, it’s 12 calendar quarters away. That’s not long. Start now and you’ll be 12 quarters ahead of some other stubborn dog.

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