• Jim Gavigan

Why is IIoT Adoption so slow?

I saw a statistic the other day that blew my mind, It came from a study on IIoT use cases (well worth the read) put on by LNS Research and I will paste the graphic that hit me below:

So, what stands out to you? Look again....

26% of respondents said "We do not expect to invest in IoT technologies in the foreseeable future." An additional 21% did not expect to invest in the IoT in the next 12 months. So, 47% of respondents say "Meh" to the IIoT.

Here are a few graphics showing benefits that people are looking for from the IIoT (the first from the LNS study and the second from a blog from GE Automation):

So, looking at the potential use cases for IIoT technology, why are 47% of those respondents (over 1,000 of them) said that they weren't looking to invest in the coming year and over half of those said not in the foreseeable future?

Here was a telling quote from the article that speaks to why:

This is true and is always a challenge when I talk with my potential clients about IIoT projects. So, how do we handle this? We go after business use cases that have a significant dollar impact, but where a minimal effort in some new tools could have a significant impact on the use case.

Some that I personally have worked on:

  • a 25% gap in production (running at 75% capacity and didn't know why). The customer had 20 years of data, but it wasn't in a format they could deal with to improve production

  • $500k/year issue with not sending full orders to a customer (why were the batches so inaccurate?)

  • Excess chemical usage to avoid other downtime events from plugging in the process

  • 8%-10% capacity deficit on a major asset

In each case, the impact was at least several hundred thousand dollars and the effort required to make a significant dent was under $60k in each case. Did we completely solve every issue with the initial spend? No, but we got enough benefit that the customer kept spending until we got "good enough" and the hurdle rate was always in the client's favor. Even if you spend $125k on a $500k problem and make a 50% dent in the $500k problem for a $250k benefit, that is a hurdle rate any financial person would sign up for.

In the last bullet item above (8-10% capacity deficit one), I did some data analysis on about 2 1/2 years' worth of data and found that the customer made some decisions about a year and a half ago that led to the drop in production on the asset. Did the changes they make benefit another asset? Were they trying to meet some other demand by making changes to an upstream process that unknowingly had detrimental affects on one of their major assets? Time and the customer will tell me, but just the fact that I was able to take several years' worth of data and very quickly put it in a context to make this discovery was huge and very well may help them close their production gap, which is worth millions of dollars to them. Better yet, their investment was quite small for a gain like this.

So, how do we get the 47% of people who aren't looking to leverage IIoT technology to understand all of the benefits of the IIoT? Yes, leveraging the IIoT eventually needs to be an ongoing program, but to get started, start small, attack A problem and keep the scope small enough where it doesn't get out of hand.

As I said in my last article, it is CRITICAL that business stakeholders are involved, engaged, and have a significant say in the final solution. Maybe you have to "just do something" to show the stakeholders the possibilities, and let them come back and tell you what a final solution would look like, rather than ask on the front end "What do you want?" I often find as in the quote above that people don't know what they don't know. Sometimes, building a prototype is the only way to engage the stakeholders.

As Henry Ford said many years ago, if he had asked his customers what they wanted, they would have asked for bigger and faster horses. Mr. Ford had the forethought to build a car for them. Mr. Ford always had the needs and requirements of his customer base in mind, yet had the vision to look beyond what was currently possible in his customers' minds and build something that would suit their needs better than they could have imagined. That is our challenge at Industrial Insight. We cannot ignore our customers' needs and requirements, but must look beyond what they believe is possible and we must solve real problems.

#IIoT #IndustrialInternetofThings #ManufacturingIntelligence

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