We live in turbulent times.  Transformational times.  But what are the possible implications for the production industries and for process plant reliability engineering?

In 2020, Deloitte (Deloitte Touche Tohmatsu Limited) attempted to address the first part of this question by examining four (4) possible scenarios for the future of Europe’s chemical process industry.  The results of their study (refer Figure 1) are relevant for many industries and suggest that “proactive transformation” will be required for a profitable and sustainable future (i.e., Scenario 1).

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The established tools for process plant reliability engineering are:

• Asset Criticality Analysis (ACA)
• Reliability-Centered Maintenance (RCM)
• Failure Modes and Effects Analysis (FMEA)
• Risk-Based Inspection (RBI)

QUESTION: Are you truly impressed by the measurable and significant performance increase that these tools have delivered?

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Data is good.  Quality data is better.  “Big data” is even fashionable.  But it won’t help you solve a “small data” problem.

Let me guess.  The quality of your CMMS data is not great, but if it was, you could really do something with it.  Since decades, in fact.  Just need to re-tweak those failure codes!

And recently, you were very tempted by a consultant’s new “data-driven” approach that promised to deliver staggering results.  And why not?

Because it’s not a “big data” problem.

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“Technical” reliability engineering is “quantitative”.  This implies that the benefits of improvement measures (e.g.: design changes or alternative maintenance strategies) can be quantified in terms of system performance parameters.  Refer, for example, Figure 1.

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The term “reliability improvement journey” is well-established in the chemical process industry. The decade-long, tortuous journey of one company is shown in terms of operational availability (i.e., production) and relative maintenance cost at Figure 1.

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