RCA – Past, Present, and Future (a three-part series) Part 3

This is the final part of a three-part series.  If you haven't yet, read Part One and Part Two.

In parts 1 and 2 of this 3-part installment, I discussed the history of RCA up to the current state, based on my own experiences, observations, and discussions.  I broke up this history into different eras.  RCA 1.0 was the beginning of modern structured problem solving.  RCA 2.0 could be characterized as the “Guru Era” whereby smart early adopters innovated and improved upon existing RCA tools.  RCA 3.0 was marked by the introduction of RCA-specific software.  But what about RCA 4.0?

Reporting on the past is one thing, but predicting the future is dicey, to say the least.  Prediction is risky by default.  And when examining risk, I like to divide whatever system I’m looking at into variables and constants. Constants are, well, constant.  They are persistent, and relatively predictable.  Variables aren’t.  So, it makes sense to me to list out the primary constants in the world of RCA, and then make predictions based upon them.

Constant #1:  Time
Time marches forward – well, at least that’s how we perceive it.  We are all getting older.  I began my formal career in RCA at the age of 29.  Now I’m 47.  That happened in the blink of an eye.  The RCA 2.0 Gurus are a generation ahead of me, and while I’m sure they still possess sharp, curious minds, the field of RCA continues to advance beyond the valuable contributions they made.  This evolution in understanding will continue, of course, and for those that follow me, I’m sure they will continue to advance the understanding and application of RCA beyond what my generation has contributed.

Constant #2:  Innovation
Successors to the Gurus use these tools.  As such, through the crucible of experience, including both success and failure, they experience what works – and what doesn’t.  However, the key difference between a founder and a successor is that the identity of the successor is not symbiotic with whatever RCA method they are currently associated with.  That means that successors will be more likely to incorporate ideas that work and to discard those that don’t.  They will be less likely to mistake opportunities to innovate as a direct attack on their life’s work.  Let loose from gestational constraints, successors will tinker, and this will lead to innovation.

Constant #3:  Persistence of Demand
Change is inextricably linked to uncertainty, and uncertainty includes the possibility of failure.  We humans don’t accept failure, so there will continue to be demand for RCA.  But this constant has a twist.  The nature of demand in the RCA market is changing.  Many companies are generally less willing to invest in thoughtful introspection these days.  Yesterday’s problems are forgotten as today’s problems crowd in.  This doesn’t change demand for structured problem-solving.  It is, however, the source of pressure to deliver greater capability at a lower cost.

Constant #4:  Technological Acceleration
Technological improvement increases our ability to process data. Improvement is one thing – we all understand that this year’s iPhone is an improvement upon the last one.  But acceleration of improvement – this is a harder concept to visualize.  I fly in airplanes (a lot), and takeoff is always exciting.  This is due to acceleration.  But the rate of acceleration is relatively constant – the plane speeds up at about the same rate (call it .16g on average) until the rate of change flattens off.  You can tell by the force you feel as you are first pressed into your seat when the engines spool up.  However, once you reach takeoff speed, the force of acceleration is still there, it’s just stopped increasing.  At that point, it feels like weight.

But imagine if the plane continued to not only speed up, but speed up at an increasing rate.  As each second passed, you would feel increasingly heavier in your seat.  This phenomenon is happening right now to the rate of acceleration of technologic complexity, and it impacts everything, including the world of RCA.  As complexity increases, so too does the probability for things to go wrong.  Complexity leads to variation.  And variation leads to a wider range of outcomes, both good and bad.

These constants (time, innovation, demand, and technological acceleration) are true for all players in the world of root cause analysis.  The individuals in the system represent the variables.  My company Sologic is different than System Improvements, which is different than ThinkReliability.  But we are all subject to the constants.  I’m less concerned about the variables because they are simply harder to predict.  The constants, however, are much more predictable.  

RCA 4.0:
RCA 4.0 will be the era where companies utilize technology and data to not only solve existing problems, but also to predict future problems with a high degree of confidence.  Sound too far-fetched?  Consider the quantity of failure data currently residing in electronic media.  Almost every incident report is typed up into some electronic format.  These documents are currently siloed in individual instances, but any form of electronic data is available to being electronically digested, cataloged, and indexed.  Is it really so difficult to believe that one or more of the many AI efforts currently underway would be able to identify predictive common patterns based upon this homogenized data?  I believe it’s inevitable.  I can’t predict when, but I believe the world of RCA will fully embrace and incorporate AI (or vice versa) within the next decade.

Another RCA 4.0 prediction involves the currently fractionated RCA market.  RCA methods will converge and there will be much less differentiation.  RCA methods will become increasingly commoditized.  And effective RCA training will be available online, mostly free of charge.

If this does happen, RCA providers will be left with software as their primary differentiator.  Software is the next, and possibly final, frontier of structured problem-solving.  The company that successfully develops and markets the best software will prevail.  Market consolidation will inevitably realign the players.  Some will join forces willingly (or grudgingly), some will be bought up, and some (unfortunately) will not survive.

When will this all happen?  It is hard to know with certainty, but due to technological acceleration, it will be sooner than anyone thinks.

 

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