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3 Things to Improve Reliability Knowledge Transfer

Are we going to lose valuable know-how due to generational handover?

Reliability engineering is a discipline that has been around for some time. In this paper “A Short History of Reliability” a ASQ fellow will go as far as to suggest that from 1816. In this blog post I ask myself, will this area of knowledge expand in the future or go down in popularity as a niche?

There will be a new generation of reliability professionals from the well known career paths, but will they be enough? I will argue that the career paths shall be widen to access a bigger pool of professionals.

  1. Paths to converge – Data Science
  2. Knowledge dissemination: more evangelist
  3. Open source as role model

Recently I listen the interview to David Oberhettinger (Chief Knowledge Officer of the NASA/Caltech Jet Propulsion Laboratory (JPL), at the podcast “Dare to Know”.  David highlighted that the professionals in the area are aging. In the 60s and 70s there was a big focus on this techniques and know-how, but unfortunately these days, seems that a lot of that know-how will be lost since there is not an effective transfer to the next generation of professionals. Is there something we can do about it?

1. Paths to converge: Data Science

First, I’d like to make the case introducing to the not-so-new Data Science discipline. In the recent years, the popularity of Data Science went through the roofs, being prayed as “the sexiest job of the 21st century” – and well paid professions. So yes, we are at the peak of the hype cycle, but most probably is here to stay. Is it possible that a new breed of professionals will come from this area? Seems to me that there is an overlap between the two disciplines.

Data science is the methodology or practices and Reliability Engineering is the application of some of those techniques – ‘domain specific knowledge’ or ‘substantive expertise’. Even though there are specifics from the RAMS world that don’t translate to other areas, I believe the underlying logic is similar.

For those of you interested in what I understand by Data Scientist and the different profiles within this role, there is this great white paper from O’Really, “Building Data Science Teams”. I would recommend to start on “What Makes a Data Scientist?”. As extra reading the DoD (Department of Defense) white paper “Data Science and the USAF ISR enterprise”, publish March, 2016,

Do you see this path as an entry for newcomers? Tesla seems to think so “Tesla is looking for an exceptional Data Analyst to impact the Reliability Engineering team”

2. Knowledge dissemination: more evangelist

Second, seems that David Oberhettinger has been pushing in NASA for knowledge management initiatives like “Knowledge Community Corner” and tools like “Accessing engineering ‘best practice’ information”, during the past years. I will like to add my take on the topic from a slightly different perspective. Instead of focus on the the processes to transfer knowledge to be retain, I will like to focus on the human and cultural aspect. The more professionals working and talking about this topic, the higher the chances to a successful transfer of know-how. Isn’t it?

I believe more knowledge dissemination might be needed. Some people call it technology evangelist, There are some established paths to become a reliability professional, but my thesis is that the education is not for the professionals in the area, but to the rest of the organization they operate in. In other words, we need to do a better job by disseminating knowledge in a corporate world.

3. Open source as role model

Finally, I believe the sector needs a shift of mindset. Let’s look at the open source movement / philosophy and take the lessons learned. Surprisingly not on the software side, as you might think, but on the best practices to share valuable information.

There are new business models based on the transparence nature of knowledge – for example GitHub. Everyone benefits from the open source nature of the service. In fact this business monetizes if you’d like to maintain private your information!

Specific examples at the reliability world that can translate to this business model are topics such as, failure modes
and mechanism, failure rates, environmental conditions, best practices, open standards, etc.

Last year I had the opportunity to attend a conference, where some representatives from the different standard institutions from Oil & Gas were present.They exposed their case and argue back and forth. One of my personal take aways is that each standard institutions push for their own agendas. And this might not be align between the institutions or the best interest of the sector mind. If this is something good or bad, remains to be seeing.

How will you improve it?

Is data science still too hyped? At the end of the day, what is a data scientist? Is the open source model a bit of a stretch for this sector? Is the role of the engineer to educate and disseminate content? I’m interested in your opinion.