2. Stephenson Strategies
Still Not Fully
Understood
• 12% — fully deployed at least
one project
• 14% — pilot or proof of concept
• 32% — planning or exploration
• 27% — no immediate plans
— 2019 survey
Writing this presentation reminded me that it’s important for those of us who spend our days immersed in the IIoT to step back and dispassionately examine the facts about its adoption — or lack thereof. The truth is, the IIoT is
not as far along as many of us assumed — or would like!
They show that — according to a survey discussed in a recent IndustryWeek webinar — that more than 50% of the companies surveyed either have no immediate plans regarding the IIoT or are only in the planning or exploration
phases. Only 12% have fully deployed at least one full-fledged product.
On one hand, that means that if you’re in that large group that either is ignoring the IIoT or only planning to do something with it, you’re in good company. On the other hand, for those of you who are fans of Clayton
Christiansen’s “disruptive technologies” concept, it means that you may soon find yourself at a competitive disadvantage in a totally transformed economy — if and when the IIoT does fully develop.
3. Stephenson Strategies
“Collective
Blindness”
Couldn’t:
• tell if machine was failing
• optimize assembly lines
• predict traffic for supply chain &
distribution
• spread timely information throughout
the company
• tell how customers used products
The reason why you may be lulled into a false sense of security and why the IIoT may be so disruptive of that current reality is that in the past we all — no exceptions! — suffered from what I call “Collective Blindness.” It was due
to the fact that there was simply no way for us to objectively gauge, in real time (real-time measurement, BTW, is a key element of the IoT) how things were working — or not.
As a result, with only fragmentary, historical evidence, which was hard to gather and equally hard to process and distribute, we were forced to build our companies, and the economy as well, on a jury-rigged combination of
hunches and work arounds. That left some staggering obstacles that reduced efficiency and precision:
• We couldn’t tell if a machine was failing in time to avoid shutdowns.
• We couldn’t optimize assembly lines, because various steps in the process were hard to coordinate
• We couldn’t optimize our supply chains and distribution plans because we had no idea what traffic problems, detours, etc., might affect deliveries
• Because it was so hard to gather data and equally hard to distribute it, it made sense for senior executives to parcel out information where and when they felt it was needed. By the time that information reached the end of the
enterprise — like the old “Telephone” parlor game, it might bear little or no resemblance to the original — and was ancient history.
• We couldn’t tell how our products were actually used once they were in the field. That meant, among other things, that we had to create “scheduled maintenance” routines that applied to all products, even though some
might need maintenance earlier and some, only late.
• Not being able to tell how products were actually used also meant we couldn’t tell whether our manuals and other documentation weren’t clear to users and led to errors, or whether some features that designers isolated in
the home office felt were important but users ignored, or something about the design that was a consistent problem for users, suggesting possible features for an upgrade. In fact, the difficulty of getting feedback could often
deceive us. For example, since customers found it so difficult to give feedback on what they liked or didn’t, those who either loved or hated the product were over-represented in comments: those in the middle who were
generally satisfied but had a suggestion to improve the product typically wouldn’t take the time and effort to contact you.
9. Stephenson Strategies
Predictive
Maintenance
• Risk Reduction
• Especially critical for remote drilling
platforms, pipelines, other remote
locations
• Problems spotted at earliest point
• Repairs quick, minimum cost
& inconvenience to customer
One aspect of the IIoT deserves special attention because it takes something that was previously a necessary evil — maintenance — and elevates it into a critical way to improve precision and even change your business model.
The change is predictive maintenance. The old make-shift model, preventive and scheduled maintenance, is replaced by a flexible model where maintenance and repairs are dictated by the real-time status of every individual
device.
One critical aspect is risk reduction. Think of extreme examples, such as off-shore drilling platforms, miles of pipeline or rail line in isolated areas, or jet turbines overhead. Because the IIoT allows early diagnosis of problems —
such as metal fatigue in a jet turbine — it’s now possible that a situation could be diagnosed while in the air and, due to the precision of the information, when the plane lands, far before there’s any real risk, the mechanic could
be ready with the exact replacement part, and know exactly what part of the engine the problem’s located in. The repair could be made quickly, at minimum cost and inconvenience to the customer.
But that’s not all predictive maintenance can do….
13. Stephenson Strategies
one more thing…..
“.. organizational issues.. now
center stage— no playbook. .. just
beginning .. process of rewriting the
organization chart …”
—Heppelman & Porter
Before I turn things over to Rich, I’d like to conclude by hopefully inspiring you with a vision of how the IoT can possibly do far more than produce all the wonderful improvements I’ve catalogued.
If I’m right, real-time data visualization will be at the heart of this change.
In the second of their articles on the IoT for the Harvard Business Review, PTC’s Jim Heppelmann and Prof. Michael Porter predicted that the changes for management with the IoT could be as dramatic as for manufacturing itself.
They speculated that “for companies grappling with the transition to the IoT, organizational issues are now center stage —and there is no playbook. We are just beginning the process of rewriting the organizational chart that has
been in place for decades.”
I believe that the new organizational chart will be one that would be impossible without the tools of the IoT, especially real-time data and data visualization. I call it…