Skip to main content

Magnificent Models | what are you predicting?


The next agenda topic in our executive meeting was IIoT and predictive service. Avid Andy had a feeling that this was going to be a controversial   topic as there was a lot of ambiguity surrounding the definition of data science-based modeling.  This was an opportunity to jump right in and start a brief discussion on the meaning and impact that data science-based learning models could have on the organization.  He began by defining a couple distinct types of models:



  1. The market today is filled with lots of misleading information, our competitors all say they can predict, we ourselves have manufacturers stating that they can also predict, and our customers have an expectation that we are predicting. My question is, predicting what? Aligned to which customer outcome? If we do not have a clear picture of all the elements combined with the customer outcome all we will get are unintelligent responses such as your automobiles "check engine soon" light.
  2. What level of confidence will you need from the predictive model? For instance, let's say that you were trying to predict when the air filters at any given jobsite are going to need to be replaced. Sounds relatively straightforward and the worst thing that would happen is that you may send somebody a bit too early or a bit too late. Another scenario might be when the battery needs to be changed in a pacemaker. The predictive algorithms and the confidence level that you are going to need to have in your outputs will need to be much higher on the replacement of the pacemaker battery.  This all boils down to the level of investment on what your sensing and how you're expected outcome when leveraging that data.



Once you understand the customer's objective (s) you need to combine that with your own business objectives. Is the reason that you are using predictive modeling is to give you an edge in the market? If that is the case than the models that you choose should be single models, possibly only looking at one element and having the right to claim, rightly so, that you are predicting an outcome. However, if you are trying to turn around or mitigate risk within your organization, you may need to look at multiple pieces of equipment and their predictive models to see how, when combined with one another, create different perspectives which may alter your course of business. These are very different approaches, they all need predictive models and data, certainly investment, but their level of sophistication is vastly different. Both are valuable, the bottom line will be how well aligned the model is to your business conditions and environment.



The final thing to remember, don't shy away from collecting data, even the data which you may not see a direct use for today. This data is commonly stored in an inexpensive format often referred to as a data lake. If your ambitions are to mature your predictive models and evolve them eventually into prescriptive models, you will need all the data you can get your hands on.  Andy was beginning to lose his team members in the executive meeting and decided to summarize using a few bullet points;

  • grab as much data as you possibly can
  • leverage other people's models to create a holistic model of your own which can align to customer outcomes
  • start small with laser focus and understand your market and the models value



Learning models, and the algorithms contained within, can drive incredible value to your business. It is key that you understand how the outputs of these data science-based objects influence action with in your current operating environments. Keep in mind that IIoT, data sciences, and even workforce sciences, are as much about the tools as they are about the cultural and market-based changes required to truly evolve your operation.



-----

Next post:  your face is our face

Thoughts?  feel free to leave replies or direct message

See all "last mile worker" posts here:  http://lastmileworkersolutions.com

-----

Labels

Labels:

Comments

Post a Comment

Popular posts from this blog

resilience, do you believe?

Seeking out the closest spot to the door of the office was not a common practice; however, at 07:00 on August 24th, 2022 the temperature was already 96' and 98% humidity.   The building would have a hell of a time keeping up today, rolling black-outs and brown-outs would be inevitable.   It likely won't be an outright equipment shut-down, instead we will have to endure the impending "brown-out" conditions, a slow, low voltage and high amperage stressing of our electrical infrastructure and connected systems. Our deliberate and continuous leverage of Industrial IoT (IIoT) technology and the companion AI prescriptive models have positioned us well. It had to be around the year 2018 when we invested in IIoT.   Heck, most were comparing our endeavors to the "Nest" thermostat, a pioneer (yet very fundamental) entrée into home-based IoT.   Naysayers were laughing that we would never see the benefits; however, our fragile National infrastructure and Mothe...
Post a Comment
Read more

is your "score", you?

Avid Andy begins to wonder if he may be schizophrenic.   Or could it be a sign of true genius when he can see a problem from all sides? Andy was reflecting on a service meeting that happened last week.   Conversation was around our ability to objectively measure things and make better decisions. At the center of almost any measurement environment are the fundamentals of math. To determine priority or influence, it seems logical when dealing with inanimate objects like sensors, assets, and sites in a portfolio to move towards "point-based systems". The real dilemma for Andy seemed to comes when we begin scoring humans and letting pre-determined algorithms establish an individuals path. Our service meeting quickly changed from tactical to philosophical discussions and one of the technicians, Pragmatic Paul, made mention of a black mirror program (Netflix) named "Nosedive".    In this futuristic episode, an individuals score dictates their behavior and those ...
Post a Comment
Read more

In$pired

As the steam from Avid Andy's coffee fogged his glasses on this crisp January morning, he reflected on last year and thought enthusiastically about the year ahead.   Sometimes the noise of business is deafening, we rarely take the time to contemplate our moves, instead are often thrown one direction or another.   Hey, face it, if you are reading posts to gain perspective you fall in the group of folks who pride themselves as obsequious hoop-jumpers.   We live to help others and expect that all of those around us feel the same way.   I just love Influential Irene.   Okay, it is out in the open, she is an inspiration for me and so many others.   Irene reminds folks every year, without fail, these three statements which she fondly refers to as "the punchline" (although this is no joke).   Businesses, of any size, will be successful if they remember that it is people that make a company.   Put this advice into practice, today: Sincerity |...
Post a Comment
Read more