Crucial Decision with Ryan Sitton
Welcome Ryan Sitton to the podcast. Ryan is the founder and CEO of Pinnacle Engineering and has spent a lot of time around reliability and production engineering. Notably, he is the author of “Crucial Decisions”; a book that we will discuss in the episode and how it applies to maintenance and reliability.
More about Ryan:
He has a mechanical engineering background and has worked at a gas company called Oxy and also worked as an Energy Regulator for the State of Texas. He has a PhD in Data Science and is the founder of Pinnacle Engineering.
Data Science has become an inevitable necessity in that; data plays a crucial role in what we tend to accomplish in our daily activities.
In this episode we covered:
- What is a crucial decision? What makes it different from other ordinary decisions in a facility?
- What types of crucial decisions brought about the issues we saw in Texas Big Freeze?
- How does risk assessment fit into decision making? Do we underestimate or overestimate risk?
What is a crucial decision? What makes it different from other ordinary decisions in a facility?
Crucial decisions are those with a lasting impact. For instance; The Big Freeze in the Texas power industry elicited a number of issues regarding past crucial decisions.
In reliability, it is about understanding the decisions that people and computers make that have lasting impacts.
Often decisions are made based on subjective opinion rather than their potential impacts.
What types of crucial decisions brought about the issues we saw in Texas Big Freeze?
The Electrical Reliability Council of Texas made power sustainability decisions based on reliability by redundancy. Meaning; there were back up facilities in case one goes down.
The strategy works well if;
- One facility doesn’t affect another.
- There are no events that can affect large pieces of the system.
In the Freeze, however, some pieces of a facility’s system affected other plants.
The crucial decision would have been to ensure that we have enough redundancy to overcome the challenges.
How does risk assessment fit into decision making? Do we underestimate or overestimate risk?
We have to look at the probability of certain outcomes. For instance; look at major events in the recent past and the money being diverted to avert their impacts. One would say we are overestimating the risks.
Case in point; the reaction to shut down the economy based on the dangers of COVID-19 to a small population segment.
In large chemical refineries, reliability leads might invest a lot of money to protect against risks. The quantitative analysis however, may reveal that the risks are too low to warrant such investments.
Does data limit our ability to make decisions? Can we rely on our subjectivity? How does it all fit together?
Despite the inherent resistance to change, there is a need to remove some subjectivity from decision making. To do this;
- Define the intended objective to ease quantitative analysis; do we need to have zero failures or all the failures we face have to be controllable?
- Use subjective analysis to determine the known (available good data) and the unknown.
What do we do in cases where data is incomplete or invalid?
The traditional process would be to gather experts for subjective and collective opinions. Data flow process demands that the data dictates the thresholds where we decide on specific issues i.e. at what ROI, profit points etc.
If data is incomplete, data flow decision making calls for further analysis or activities to reduce uncertainties.
How do we overcome the challenge of transitioning between the two methods of decision making?
- Decide on the ultimate objective.
- Decide on the criteria for decision making towards that objective
- Get everyone on board in the laid down process.
What are key steps in making the shift?
- Define the objective
- Determine the criteria that feed into that objective.
Does one need a software to facilitate the decision making process?
Software is not always needed but decision making might require too many variables that cannot be considered at once by the mind alone.
The software tools are available to make the process more efficient, repeatable and valid.
We need some sort of PDCA loop that can evaluate the decisions.
Where can people find out more about quantitative analysis?
“Crucial Decisions” by Ryan Sitton
“The Failure of Risk Assessment” by Douglas Hubbord.
Where do people get started in making better decisions?
- It is essential to understand the objective
- Understand the flow of data ie. Data gathering, organizing, analysis to determine strategy and the recycle loop.
- Separate the assumptions from what is truly known (data-informed)
Data exists in 3 buckets;
- Existing and usable data.
- Not enough data but the user has enough knowledge to create data.
What makes the biggest difference?
The key point is understanding the objective especially in large facilities. Integrate the data analysis with expert opinion.
There is a transition in decision making and industry leads have to participate and learn.
Where can people find you?
“Superforecasting”- Philip Tetlock
“Thinking Fast and Slow”- Daniel Kehneman
Ryan Sitton Links:
- Pinnacle Linkedin
- Ryan Sitton Linkedin
- Crucial Decisions by Ryan Sitton
- The Failure of Risk Management by Douglas Hubbard
- Super Forecasting by Philip Tetlock & Dan Gardner
- Thinking Fast and Slow by Daniel Kahneman
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