Many of our companies have institutional maintenance and reliability knowledge residing with an ageing workforce of engineers and trades. Without appropriate information-sharing strategies there is significant business risk that the knowledge leaves with them. Rooted in thoughtware developed by John Moubray, ‘the father of RCM’, Marius will share case studies and strategies that companies are using to save this vital institutional knowledge.
The struggle to find suitable experienced and talented workers is not new, it just took on another level following the Covid endemic. Firstly, more experienced and knowledgeable workers took early retirement than what industry experienced during the economic downturn in late the 2000s. The work methods change from in person meetings to remote media meetings, training courses are delivered on-line, very little interaction between coworkers since offices moved to home offices (companies have empty office buildings everywhere) and the informal meetings around coffee stations and offices almost disappeared. Operations and maintenance regimes changed from routines to “important and emergency” work only. The demographics in the workforce are constantly changing, experienced people leave and open jobs for younger less experienced workers with little or no transition of skills and knowledge. Workers have to learn on the job and errors and mistakes are more common.
Industry is starting to rely on AI to better operate and maintain plants and to start replacing human judgement. AI comes from two words Artificial and Intelligence and according to the Cambridge Dictionary, the words mean the following:
Artificial: made or produced by human beings rather than occurring naturally, especially as a copy of something natural.
Intelligence: the ability to learn, understand, and make judgments or have opinions that are based on reason
The first being artificial, should be concerning, even scary when made up or produced by inexperienced, and incorrect practices and methods. AI can only be as good as the elements producing the information and the intelligence to learn and make judgements need knowledge for correct interpretation.
Industry is in need for a systematic, robust, and repeatable method of retaining and sharing the knowledge to the new generation. This does not mean simply recording and transferring what the exiting workers did, or blindly follow our machine sensor and indicators but rather a methodology for capturing the information and knowledge, analyzing the data and presenting it in a way that is sensible and defensible.
To the best of our knowledge, RCM is still the best method to enable operations and maintenance to capture and retain the knowledge and information in a technically correct way (if performed correctly). RCM is a zero-base approach where all assumptions are unearthed, and decisions are made in a structured way. If applied correctly, RCM can be very useful to ensure that AI is meaningful (decisions based on facts and not just gut feel), that the knowledge of the retiring workforce is captured and retained and to ensure improper practices are not transferred to the new generation.