ThingWorx Analytics Video: microcosm of why IoT is so transformative!

I’ll speak at PTC’s LiveWorx lollapalooza later this month (ooh: act quickly and I can get you a $300 registration discount: use code EDUCATE300) on my IoT-based Circular Company meme, so I’ve been devouring everything I can about ThingWorx to prepare.

Came across a nifty 6:09 vid about one component of ThingWorx, its Analytics feature. It seems to me this video sez it all about both how you can both launch an incremental IoT strategy (a recent focus of mine, given my webinar with Mendix) that will begin to pay immediate benefits and can serve as the basis for more ambitious transformation later, especially because you’ll already have the analytical tools such as ThingWorx Analytics already installed.

What caught my eye was that Flowserve, the pump giant involved in this case, could retrofit existing pumps with retrofit sensors from National Instruments — crucial for two reasons:

  • you may have major investments in existing, durable machinery: hard to justify scrapping it just to take advantage of the IoT
  • relatively few high-end, high-cost machinery and devices have been redesigned from the ground up to incorporate IoT monitoring and operations.

Note the screen grab: each of these sensors takes 30,000 readings per second. How’s that for real-time data?  PTC refers to this as part of the “volume, velocity and variety challenge of data” with the IoT.

As a microcosm of the IoT’s benefits, this example shows how easy it is to use those massive amounts of data and how they can be used to improve understanding and performance.

There are three major components:

  • ThingWatcher:
    This is the most critical component, because it sifts through the incredible amount of data from the edge, learns what constitutes normal performance for that sensor (creating “pop-up learning flags”), and then monitors it future performance for anomalies and, as the sample video shows, delivers real-time alerts to users (without requiring human monitoring) so they can make adjustments and/or order repairs.  Finds anomalies from edge devices in real-time. Automatically observes and learns the normal state pattern for every device or sensor. It then monitors each for anomalies and delivers re- al-time alerts to end users.
  • ThingPredictor:
    For the all-important new function of predictive maintenance, two different types of ThingPredictor indicators pop up when if anomalies are detected, predicting how long it may be until failure, allowing plenty of time for less-costly, anticipatory repairs. Because the specific deviation is identified in advance, repair crews will have the needed part with them when needed, rather than having to make an additional trip back to pick up parts.

    If you ask for a standard predictive scoring you don’t specify which performance features to include and get a simple predictive score. However, you can specify several key features to evaluate and get a more detailed (and probably more helpful) answer. For example,  “if you indicate an important feature count of three, the causal scoring output will include the three most influential features for each record and the percentage weights of each feature’s influence on the score.”

  • ThingOptimizer:
    Finally, you can use “ThingOptimizer” to do some what-if calculations to decide which possible “levers,” as ThingWorx calls the key variables, could change the projections to either maximize a positive factor or minimize the negatives. “Prescriptive scoring results include both an original score (the score before any lever attributes are changed) and an optimized score (the score after optimal values are applied to the lever attributes). In addition, for each attribute identified in your data as a lever, original and optimal values are included in the prescriptive scoring results.” It sort of reminds me how the introduction of VisiCalc allowed users, for the first time, to play around with variables to see which would have the best results.
Best of all, as the video illustrates, ThingWorx Analytics would facilitate the kind of “Circular Company” I’ll address in my speech, because the exact same real-time data could simultaneously be used by operating personnel to fine tune operations and catch a problem in time for predictive maintenance, and by senior management to get an instant overview of how operations are going at all the installations. Same data, many uses.
Bottom line: a robust IoT platform could be the key to an incremental strategy to begin by improving daily operations and reducing maintenance problems, and also be the underpinning for more radical transformation as your IoT strategy becomes more advanced!  See you at LiveWorx!

Game-changer! AR Enables IoT merging of physical and digital

Several months ago I wrote about an analogy to the world of business prior to the Internet of Things,  in which a metaphorical illness called “Collective Blindness” affected every human for all time, so that we were unable to peer inside things. We just accepted that as an inevitable limitation, creating all sorts of work-arounds to try to be able to cope in the absence of real-time information about things of all sorts.

I then said that the Internet of Things would allow us to end Collective Blindness, getting — and sharing — the real-time data we’d need to make better decisions and work more precisely.

Now I’ve seen the tool that allows us to end that Collective Blindness: PTC’s Augmented Reality (AR), tool, Vuforia.

At last week’s PTC Liveworx conference, there was a mind-blowing demo of Vuforia by Terri Lewis, director of solutions and tech at Caterpillar, as it applied to the company’s XQ Gen Set, a portable power generator for job sites and special events.  As PTC CEO James Heppelmann reiterated several times, the software is creating

a single new reality that’s physical and digital at the same time….. and democratizing AR.”
(my emphasis)

Used as a sales tool, Vuforia Studio Enterprise lets the customer look inside the product, as contrasted with a static brochure.  That’s neat, but what’s really incredible is how it lets maintenance people peer inside the device, and do so in a way (as Heppelmann said, “humans prefer to use sight an sound simultaneously”) that is much more effective in terms of zeroing in not only on what’s wrong, but also these specifics (such as replacement part numbers, etc.) to quickly repair them.  Incidentally Heppelmann and Harvard Prof. and biz guru Michael Porter are collaborating on another article, this one on how to apply AR in a business setting (turns out that Porter is a member of the PTC board, and in the past few years he’s been using it as a lab to evaluate business use of the IoT).

Another example of Vuforia’s work in maintenance demonstrated at the conference was by Flowserve, the world’s largest flow control company. Vuforia helps them manage devices in real-time (the person at the pump can see what is actually happening), cutting the number of repair trips from three to one, because they are able to diagnose the problem at the beginning, and bring the replacement parts with them. Then they can do do real-time simulations to see if the problem has been solved. The company believes they saved $2 billion in excess repart costs in 2015 alone.

 Vuforia Studio AR lets users set up augmented reality simulations in minutes without writing code, and can also be used in product design review.

I had a chance to try the XQ Gen Set visualization with an AR headset myself, and it was as powerful as promised.

I must admit the first time I tried on an AR headset — and almost jumped on one of the other users because I was jumping back to avoid falling several hundred feet off a sharp cliff into the ocean — I was amazed by the realism, but didn’t really think much about its serious business uses.  PTC’s Vuforia Studio AR made me a believer: it’s helping us cure Collective Blindness, and AR will be yet another tool to bring about unprecedented precision and efficiency in every aspect of manufacturing and product maintenance!

Failure to inspect oil rigs another argument for “real-time regulation”

The news that the Bureau of Land Management has failed to inspect thousands of fracking and other oil wells considered at high risk for contaminating water is Exhibit A for my argument we need Intnet of Things-based “real-time regulation” for a variety of risky regulated businesses.

According to a new GAO report obtained by AP:

“Investigators said weak control by the Interior Department’s Bureau of Land Management resulted from policies based on outdated science and from incomplete monitoring data….

“The audit also said the BLM did not coordinate effectively with state regulators in New Mexico, North Dakota, Oklahoma and Utah.”

Let’s face it: a regulatory scheme based on after-the-fact self-reporting by the companies themselves backed up by infrequent site visits by an inadequate number of inspectors will never adequately protect the public and the environment.  In this case, the GAO said that “…. the BLM had failed to conduct inspections on more than 2,100 of the 3,702 wells that it had specified as ‘high priority’ and drilled from 2009 through 2012. The agency considers a well ‘high priority’ based on a greater need to protect against possible water contamination and other environmental safety issues.”

By contrast, requiring that oil rigs and a range of other technology-based products, from jet engines to oil pipelines, have sensors attached (or, over time, built in) that would send real-time data to the companies should allow them to spot incipient problems at their earliest stages, in time to schedule early maintenance that would both reduce maintenance costs and reduce or even eliminate catastrophic failures. As I said before, this should be a win-win solution.

If problems still persisted after the companies had access to this real-time data, then more draconian steps could be required, such as also giving state and federal regulators real-time access to the same data — something that would be easy to do with IoT-based systems. There would have to be tight restrictions on access to the data that would protect proprietary corporate information, but companies that are chronic offenders would forfeit some of those protections to protect the public interest.


Automated factories: that’s not the IoT’s potential!

It’s easy to see why some people make the assumption that one of the results of the Internet of Things will be fully-automated factories.

After all, if automatic, real-time machine-2-machine data sharing would allow self-starting and self-regulating machinery, wouldn’t that allow us a utopian vision of completely autonomous manufacturing?

Instead, I think Bosch’s Volkmar Denner nailed it with this blog entry. He says that rather than complete automation:

“Instead, it’s about finding ways to increase agility. Putting that into figures, optimizing resource allocation within a more flexible production process can result in a jump in productivity of as much as 30 percent. Our goal is to be able to customize even the smallest unit volumes while retaining optimum productivity, and ultimately leading to achieve optimized multi-variant series production.”

I agree totally that what’s going to happen is an end to centralized management and top-down control of information (see my last post, on “Buckyball Management”!, with decentralized, self-management emerging that could threaten old industry leaders who don’t get it (see my posts about how GE does get it!) :

“… And I’m convinced that this shift will provide opportunities for established companies to offer new business models. But they too need to watch out: the IoTS is shaking up what until now has been very much a closed market, opening it up for entirely new players such as IT companies. Here, the IoTS is not just about connecting objects, machines, and systems. On the contrary, it’s also about how to use the data that this connectivity generates. And instead of using this information only within the plant itself, now everyone along the manufacturing chain can be given access to the data over the internet. Once again, the knowledge gained from these data can be applied to generate new business models.”

Denner says that one of the #IoT services that Bosch — the leading supplier of automotive sensors and one of the leaders in industrial sensors — is developing is predictive maintenance, which innovators such as GE (with its jet turbines) and the railroads (I’ve never traced my ancestry on my father’s side, but I harbor the possibility that I’m descended from the Stephensons, pere et fils, who invented the locomotive, so I have a warm spot in my heart for that industry…) are already doing.  As Denner says, “Having such a solution in place allows organizations to offer their customers new and improved levels of service, including a guarantee of reduced downtimes.”

So don’t count out the human element in manufacturing once the IoT is commonplace: in fact, it will be more important, and more valuable, than ever!