Some groups have different meanings for the same words. Predictive maintenance and financial forecasting have similarity but are viewed very differently depending on whether you're talking to an Engineer or a Financial Analyst.
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Ben Parker:
Have you noticed how sometimes people assign different meaning to the same or similar words? It's like describing predictive maintenance to a stock analyst who only knows about financial forecast. They're similar, they have to do with data and the future, but they are very different.
Welcome to the Durability podcast. This is for those that are passionate about helping their organizations plan and prepare for the inevitable future. My name is Ben Parker, management consultant, chemist, creator, entrepreneur, co-founder, and member of the Durable Capital Group. I'm excited for you to join me as we discover what successful organizations do to build longterm durability. We explore the tactics and strategies that allow great organizations to combine the financial wisdom of asset management, the practical street smarts of the maintenance subculture with the real world paranoia of managing risks to ensure their organization's blast. We uncover what great leaders are doing from all types of capital intensive industries to make a decisions, build strong management teams and prepare to meet the future in whatever form it comes. This is the Durability podcast.
I'm back. It's exciting to be with you. Today, I want to share a little bit about how word associations sometimes get us in trouble. The reason sometimes word associations get us in trouble is because we assign different meaning to different words, and others may not have the same meaning to the same words that we do. When we hear something, when I hear something, I have a certain mental model that is framed around that. For example, when I hear the word predictive, I've got a certain understanding in my realm of reality that allows me to estimate what the context for something that's predictable and something that's not actually is. One thing that I find fascinating as I sit between the realm of technology, engineering and finance and operations is how same words are used across different industries and communities, but they have some misalignment in terms of what they actually mean.
A great example of this is conversations that I am sure many of you have had with your various family members, partners, different business relationships, where one person says one thing with a very specific outcome in mind and somebody else thinks another with a very different tolerance for that outcome. One fantastic example of this as all you dads out there would know when your wife asks you to watch the children, for example, that to me, and I'm not speaking for everyone and I'm obviously not the perfect parent, but I have a lot of fun parenting and I love my children.
But the thing is is that when my wife ask me to watch the children, in my mind I'm thinking, "Okay. If somebody needs to go to the hospital, I am there to take them, absolutely for sure, a hundred percent. If somebody is heading towards the road, I will keep them out of the street for sure, a hundred percent. If it comes down to giving them free rein to discover and make a mess, I'm not saying trash the house, but I'm saying a little more room to play. When I think of watching the children, I like to watch them discover some of those things. To my wife, who is a saint and is amazing, she doesn't have the same context for what watching the children means. To her, it really means don't let them trash the house, which I can respect and appreciate that, probably more so now after four kids later than I did earlier, but I understand that sometimes those words don't mean the same thing to the same people.
When you talk about business context where you're working with different disciplines, for example, finance and engineering or operations and accounting, various groups have different words for the things that they see and experience every day. When those words do not quite align with the different groups, sometimes you have a disconnect in the value that's placed on them. For example, predictive maintenance. In a true engineering sense of the word, predictive is a very fascinating model for managing physical assets because you are really heavily focused on condition-based management, collecting data in real time as a piece of equipment runs, as you track different activities and various ways to pull enough data to help make a model that then allows you to predict when something's going to fail, when the best time to perform maintenance, various things, and so the whole model there is based on your ability to collect information in real time, tie that information into some of your other work activities and be able to optimize the total outcome of your cost and the impact of that in your business.
It's interesting, and I've been in some fascinating conversations where you have the same model in your head of, "Okay, if I have the right amount of data and I have the right physical parameters as any good scientist and any good engineer would tell you with a very high degree of certainty, that I can predict a certain outcome and I can optimize for these given set of variables." Then you have a similar word conversation with somebody from a different industry say, "I use a stock broker or a financial analyst who only does financial forecasts and is looking at how to predict, say the market, or how much cash flow a certain business is going to need based on market forces and all the different dynamics, which you find is that their degree of uncertainty is actually much higher.
When people are talking about from a physical standpoint, their ability to predict an outcome at a certain range, I have found that it's interesting that they don't always put the same level of certainty into that model. Talking with financial forecasters and financial analysts, they're comfortable in a 30% to 60% accuracy range where you're dealing with some of the engineers on a predictive maintenance side, they're much more comfortable in a less than 10% range or whatever the numbers are. But it's fascinating to me, as you have conversations with these different groups, how one starts to put their mental model for what is accurate or how precise you can be in one instance onto another. It's been my experience, not in every case, but in certain circles where a financial forecast model is being assumed to replace the predictive maintenance model, which then makes it less reliable, and so there's not as much trust in that piece of data and in those discussions. It doesn't carry as much weight to the financial analyst as it does to the engineer who put together the model and vice versa, interestingly enough.
That's one thing that I have noticed, and I'm sure many of you have as well where unless you take a concerted effort to help each other understand the meaning and align your perspectives, then it's really hard to have an effective conversation and to plan effectively on what you need to do to build longterm durability into your organization, because the reality is it takes both, good analytics, good team decisions. That's all I've got for today, have a fantastic week. Join us next time on the Durability podcast.
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