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How to take the guess work out of expert judgment
Most humans can be quite technically minded. And sometimes we aren’t. Sometimes we expect every drug or medical procedure to be checked, approved, validated, tested on other people (et cetera) before we subject ourselves to whatever it is our doctor suggests. And other times we spend lots of money at casinos (note that I said ‘spend’ and not ‘invest’ or ‘win’). When it comes to reliability, we can sometimes be too technically minded. A reliability ‘number’ might not exist until our product has gone through exhaustive testing. Which can often be two years too late to do anything about if it turns out it doesn’t meet all our reliability dreams. So what can we do? Well … why spend money drying to generate information (through lengthy test) when you can just use the information stored in everyone’s brain? The most common answer (even if we don’t want to admit it) is that this sounds a lot like guess work. And guess work can sounds unprofessional. Or just wrong. But there are ways you can suck out information from a group of experts in a quantifiable and remarkably accurate way. Want to learn more? This webinar introduces you to some of the concepts that might interest you.

May 24, 2022 08:00 AM in Pacific Time (US and Canada)

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Speakers

Chris Jackson
Reliability Engineering and Management Consultant @Acuitas Ltd and Accendo Reliability
Chris Jackson can help you with a range of reliability engineering issues and problems. Chris has a PhD in Reliability Engineering and has been the Director of UCLA’s Center for Reliability and Resilience Engineering (CRRE) … so he can help you with the higher-level theory of reliability probability, modeling and statistics. He was an officer in the Australian Army where he (amongst other things) led hundreds of engineers and technicians to support multi-million dollar fleets of equipment … so he knows how the operational management side works. He has also consulted to many clients for many different issues … so he knows about organizational reliability management issues.