IoT-based “Regulation 3.0” Might Have Avoided Merrimack Valley Tragedies

Posted on 16th September 2018 in environmental, Essential Truths, government, Internet of Things, maintenance, open data, paradigm shift, real-time regulation

Pardon me: this is a very personal post.

For about an hour Thursday night we didn’t know whether my son’s home in Lawrence was one of those blown up by the gasline explosions (fortunately, he and his dear family were never at risk — they’re living in Bolivia for two years — but the house was right at Ground Zero). Fortunately, it is intact.

However, the scare took me back to an op-ed I wrote eight years ago in Federal Computer Week after the BP catastrophe in the Gulf, when I was working in disaster communications. I proposed what in fact was an IoT-based way to avoid similar disasters in the future: what I called “Regulation 3.0,” which would be a win-win solution for critical infrastructure companies (85% of the critical infrastructure in the US is in private hands) and the public interest by installing IoT-monitoring sensors and M2M control devices that would act automatically on that sensor data, rather than requiring human intervention:

  • in daily operations, it would let the companies dramatically increase their efficiency by giving real-time data on where the contents were and the condition of pipelines, wires, etc. so the operations could be optimized.
  • in a disaster, as we found out in Lawrence and Andover, where Columbia Gas evidently blew it on response management, government agencies (and, conceivably, even the general public, might have real-time data, to speed the response (that’s because of one of my IoT Essential Truths, “share data, don’t hoard it”).

We could never have that real-time data sharing in the past, so we were totally dependent on the responsible companies for data, which even they probably didn’t have because of the inability to monitor flow, etc.

Today, by contrast, we need to get beyond the old prescriptive regulations, which told companies what equipment to install (holding back progress when new, more efficient controls were created, and switch to performance-based regulation where the companies would instead be held to standards (i.e., in the not-too-distant future, when the IoT will be commonplace, collecting and sharing real-time data on their facilities), so they’d be free to adopt even better technology in the future.

However, Regulation 3.0 should become the norm, because it would be better all around:

  • helping the companies’ improve their daily operations.
  • cutting the cost of compliance (because data could be crunched and reported instantly, without requiring humans compiling and submitting it).
  • reducing the chance of incidents ever happening (When I wrote the op-ed I’d never heard of IoT-based “predictive maintenance,” which lets companies spot maintenance issues at the earliest point, so they can do repairs quickly and cheaper than if having to respond once they’re full-blown problems.).

I had a chance to discuss the concept yesterday with Rep. Joe Kennedy, who showed a real knowledge of the IoT and seemed open to the incident.

Eight years after I first broached the concept, PTC reports that the pipeline industry is now impementing IoT-based operations, with benefits including:

  • Situational awareness..
  • Situational intelligence..
  • and Predictive analytics.

Clearly, this is in the economic interests of the companies that control the infrastructure, and of the public interest.  The Time has come for IoT-based “Regulation 3.0.”

 

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Previewing “The Future Is Smart”: Siemens Leads Way In IoT Transformation

Posted on 26th July 2018 in 3-D printing, Augmented Reality, data, design, Essential Truths, Internet of Things, M2M, maintenance, management, manufacturing, paradigm shift, sensors, strategy, transportation

Huzzah!

On August 7th, HarperCollins’ new Leadership imprint (formerly Amacom) will publish The Future Is Smart, my guide to IoT strategy for businesses and the general public.  BTW: write me if you’d like to arrange a speaking engagement/book signing event!

As part of the build-up to the release, here’s another excerpt from the book, drawn from Chapter 5: “Siemens and GE:Old War Horses Leading the IoT Revolution.” It zeroes in on these two industrial companies from the 19th (!!) century that are arguably among the top IoT companies in the world (although, sadly, GE’s transformation, which I’ll detail in the next excerpt, has not resulted — so far — in a return to its former profitability). I highlighted these two companies in part to give comfort to old-line manufacturers that have been reluctant to embrace the IoT, and in part to shame them: if they can do it, why can’t you?

Siemens is a particularly exciting example, applying IoT thinking and technology to gain a competitive edge in the railroad business, which it has been involved in since the 19th century, and because its Amberg “Factory of the Future” is the epitome of the benefits of applying the IoT to manufacturing,  The excerpt is long, but I think the details on Siemens’ IoT transformation will make it worthwhile reading.

 

For all their (referring to Siemens and GE) own distinctive products and services, there are startling parallels between the two that are relevant to this book, particularly for readers whose companies have been unaware of the IoT or are modestly testing the waters. Both Siemens and GE have fully committed to the IoT and are radically reinventing themselves, their products, and their services. 

At the same time, they are not abandoning the physical for the digital: they still make products such as trains (NB: since this book went to press, GE announced it will quit to locomotive business as it struggles to regain momentum) and large medical diagnostic devices that remain necessary in the new economy, and those devices (as well as the new software lines) are used by many other companies in their own manufacturing. Both companies aren’t just testing the IoT: they are on the bleeding edge of innovation in terms of both IoT technology and services.

Siemens and GE embody most of the marks of the IoT company outlined in the first chapter:

  • Unprecedented assembly-line precision and product quality
  • Drastically lower maintenance costs and product failure
  • Increased customer delight and loyalty
  • Improved decision-making
  • Creating new business models and revenue streams

And, while they haven’t formally addressed the sixth IoT hallmark, the circular management organization, both companies exhibit management characteristics consistent with it.

Bottom-line: if these two relics of the early Industrial Age can make the IoT transformation, why can’t you?

(Siemens’) innovations in industrial automation are now associated with the concept of the digital factory. “Siemens set the course for the digital automation of entire production facilities as far back as 1996, when the launch of its Totally Integrated Automation (TIA) Portal enabled companies to coordinate elements of their production operations and to closely intermesh hardware with software.”

Siemens has benefited in recent years from the German government’s formal strategy for what it calls “Industrie 4.0,” to merge physical products with digital controls and communications. The initiative is supported by funding from the German Federal Ministry of Education and Research and the German Federal Ministry of Economic Affairs and Energy and emphasizes the merger of the digital and physical in manufacturing through cyber-physical control systems. Because the U.S. federal government doesn’t weigh in on specific economic plans to the same extent, the concept is more advanced in Europe, and the term has gathered cachet, especially as specific examples have proved profitable.

Factory of the Future:
The shining example of Industrie 4.0 is the previously mentioned Siemens plant in Amberg. It has increasingly computerized over the past 25 twenty-five years, and now is a laboratory for fusion of the physical and digital.

The plant’s 99.99885 percent quality rate would be astounding by any measure, but is even more incredible when you realize that it does not do daily repetitions of the same mass-production product run. Instead, Amberg is where the company makes the Simatic programmable logic controls (PLCs) .. that are the heart of its industrial output and which are used worldwide to allow Machine-to-Machine (M2M) automated assembly line self-regulation. They are made in more than a thousand variations for 60,000 customers worldwide, requiring frequent readjustments of the production line. In one of the ultimate examples of eating your own dog food, a thousand Simatic units are used to control the assembly line. Total output at the factory is 12 million yearly, or approximately one per second.

One downside of the Amberg system’s efficiency is that automation has nearly eliminated assembly line jobs: the only time humans touch one of the products is to put the initial circuit board on the assembly line. The 1,100-person workforce deals almost entirely with computer issues and overall supervision of the assembly line. Nevertheless, Siemens doesn’t visualize a totally automated, workerless factory in the future:

“We’re not planning to create a workerless factory,” says [Plant Manager Professor Karl-Heinz] Büttner. After all, the machines themselves might be efficient, but they don’t come up with ideas for improving the system. Büttner adds that the employees’ suggested improvements account for 40 percent of annual productivity increases. The remaining 60 percent is a result of infrastructure investments, such as the purchase of new assembly lines and the innovative improvement of logistics equipment. The basic idea here, says Büttner, is that “employees are much better than management at determining what works or doesn’t work in daily operation and how processes can be optimized.” In 2013 the [plant] adopted 13,000 of these ideas and rewarded employees with payments totaling around €1 million.

As Siemens develops new IIoT software, it is deployed at the Amberg factory to control the Simatic control units, which generate more than 50 million data points daily for analysis. Among other programs, the factory runs the NX and Teamcenter project lifecycle management software, allowing the staff to share realtime insights on the assembly line and fine-tune its operation.

Siemens’s strategy of merging the physical and digital has meant that its software offerings constantly expand, and they facilitate the kind of real and virtual collaborative workstyles that will be discussed at length in Chapter 8. Among others, they include offerings that specifically address key aspects of the IoT:

  • Product Lifecycle Management software programs, which let engineers both model new products and extensively test them virtually, without having to build and test physical models. This both cuts costs and allows more experimentation with “what if” variations on a design, because the risk of creating alternatives is so low. As we will see later, products designed with PLM can reach the market 50 percent faster. One particularly interesting part of the PLM offerings is one specifically for additive manufacturing (i.e., 3-D printing), to capitalize on this emerging option. Siemens has brought all of these programs together under the Teamcenter label, emphasizing that it provides an “open framework for interoperability,” a critical example of the “share the data” Essential Truth discussed in Chapter 2, allowing anyone who needs it companywide to access critical realtime data.
  • Digital Twins used in coordination with PLM, discussed earlier (Chapter 4) as the highest manifestation of the digital/physical synthesis, allow rigorous testing of products before they are launched.
  • Perhaps the most important of these software offerings for full realization of the Industrie 4.0 vision is the new combination of Siemens XHQ Operations Intelligence Software with the open-systems Siemens MindSphere cloud that adds advanced analytics and machine learning. Also, because it is cloud-based, the XHQ data can be ported to other cloud-based applications. If your company is considering an IoT initiative, the cloud-based alternative not only can save money compared to self-storage, but also opens the opportunity for using cloud-based Software as a Service (SaaS).

 

Railigent

Fittingly, some of the most dramatic examples of Siemens’s IoT thinking in action have centered on one of its oldest lines of business: those electric trains invented in the nineteenth century.  The company’s Railigent system (which connects to its IoT Mindsphere platform) can:

  • cut rail systems’ operating costs by up to 10%
  • deliver eye-popping on-time performance (only 1 of 2,300 trains was late!)
  • and assure 99% availability through predictive maintenance.

Its new Mobility Services have taken over maintenance for more than fifty rail and transit programs.

Again, the company’s years of experience building and operating trains pays off in the cyberworld. Dr. Sebastian Schoning, ceo of Siemens’s client Gehring Technologies, which manufactures precision honing tools, told me that it was easier to sell Siemens’s digital services to his own client base because so much of the products they already own include Siemens devices, giving his customers confidence in the new offerings.

The key to Siemens’s Mobility Services is Sinalytics, its platform architecture for data analysis not just for rail, but also for industries ranging from medical equipment to windfarms. More than 300,000 devices currently feed realtime data to the platform. Sinalytics capitalizes on the data for multiple uses, including connectivity, data integration, analytics, and the all-important cyber security. They call the result not Big Data, but Smart Data. The platform also allows merging the data with data from sources such as weather forecasts which, in combination, can let clients optimize operating efficiency on a real-time M2M basis.

Elements of an IoT system on the trains that can be adapted to other physical products include:

  • Sensing. There are sensors on the engines and gearboxes. Vibration sensors on microphones measure noises from bearings in commuter trains. They can even measure how engine oil is aging, so it can be changed when really needed, rather than on an arbitrary schedule, a key predictive maintenance advantage.
  • Algorithms: These make sense of the data and act on it. They read out patterns, record deviations, and compare them with train control systems or with vehicles of the same type.
  • Predictive Maintenance: This replaces scheduled maintenance, dramatically reducing downtime and catastrophic failure. For example: “There’s a warning in one of the windows (of the control center display): engine temperature unusual. ‘We need to analyze the situation in greater depth to know what to do next—we call it root cause analysis,’ (says) Vice-President for Customer Support Herbert Padinger. ‘We look at its history and draw on comparative data from the fleet as a whole.’ Clicking on the message opens a chart showing changes in temperature during the past three months. The increased heat is gradually traced to a signal assembly. The Siemens experts talk with the customer to establish how urgent the need for action is, and then take the most appropriate steps.”8 Padinger says that temperature and vibration analyses from the critical gearboxes gives Siemens at least three days advance notice of a breakdown—plenty of time for maintenance or replacement. Predictive maintenance is now the norm for 70 to 80 percent of Siemens’s repairs.
  • Security: This is especially important given all of the miles of track and large crowds on station platforms. It includes video-based train dispatch and platform surveillance using Siemens’s SITRAIL D system, as well as cameras in the trains. The protections have to run the gamut from physical attacks to cyber-attacks. For security, the data is shared by digital radio, not networks that are also shared by consumers.

When operations of physical objects are digitized, it allows seamlessly integrating emerging digital technologies into the services—making these huge engines showcases for the newest technologies. For example, Siemens Digital Services also included augmented reality (so repair personnel can see manuals on heads-up displays), social collaboration platforms, and—perhaps most important—3-D printing-based additive manufacturing, so that replacement parts can be delivered with unprecedented speed. 3-D printing also allows a dramatic reduction in parts inventories, It allows for replacement of parts that may no longer be available through conventional parts depots. It may even improve on the original part’s function and durability, based on practical experience gained from observing the parts in use. For example, it’s often possible with 3-D printed replacement parts to consolidate three or four separate components into a single one, strengthening and simplifying it. Siemens has used 3-D printing for the past last three years, and it lets them assure customers that they will have replacement parts for the locomotive’s entire lifespan, which can exceed thirty years.

The new Mobility Services approach’s results are dramatic:

  • None of the Velaro trains that Siemens maintains for several operators have broken down since implementing Sinalytics. Among those in Spain only one has left more than fifteen minutes behind time in 2,300 trips: a 0.0004 percent lateness rate.
  • Reliability for London’s West Coast Mainline is 99.7 percent.
  • Perhaps most impressive because of the extreme cold conditions it must endure, the reliability rate for the Velaro service in Russia is 99.9 percent.11

Siemens’s ultimate goal is higher: what the company calls (pardon the pun) 100 percent Railability.

When it does reach those previously inconceivable quality benchmarks, Siemens predicts that, as the software and sensors evolve, the next stage will be new business models in which billing will be determined by guaranteeing customers availability and performance. The manufacturing industry is now at the stage where the automation of complete workflows is the only way to ensure a long-term, defendable, competitive position.

Siemens emphasizes that it’s not enough to simply digitize the design process. Everything from design through supply chain, manufacturing, distribution, and service must be linked in a continuous digital web, with “complete digital representation of the entire physical value chain is the ultimate goal.”

 

The fact that Siemens doesn’t just sell these IoT services but makes their own manufacturing the laboratory to develop and test them is an incredible testimonial to the IoT’s transformative potential in every aspect of companies’ operations. So, as I asked above, why are you holding back? Like to think that The Future Is Smart will give you the manual you need to make the transition (why wait for August  7, when you can preorder today?).

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I have seen the future, and it’s written in Chalk (PTC’s Vuforia Chalk, that is!)

Posted on 18th June 2018 in aging, Augmented Reality, automotive, collaboration, communication, Internet of Things, maintenance, manufacturing, paradigm shift

I just had to take time out from my live blogging of PTC’s LiveWorx ’18 to focus on one of the topics Jim Heppelmann mentioned in passing in his keynote: the new variation on the company’s Vuforia AR app: Chalk.

Significant in its own right, I suspect Chalk will have an additional, critical impact: democratizing AR.

It is an app aimed at, and accessible to, both corporate audiences AND the general public.  Downloadable for both iPhone & iPads & Android devices, I suspect that it will quickly become popular both to support remote repair staff for companies and just plain folks who are trying, for example to help a family member far away to deal with a car or plumbing repair. Not to mention the fact (mandatory disclaimer: while I work part-time for Apple, I’m not privy to any corporate internal strategy) that the spiffy new $329 6th-generation iPad really facilitates AR, and Chalk was developed in conjunction with the Apple ARKit technology so it should really become popular.

Chalk has two components:

  • real-time video and voice sharing of the same view
  • Chalk Marks, simple handswipes that allow one of the participants to highlight the part that is the subject of the question.  The “Marks” appear to be anchored to the subjects they’re “drawn” on.

Real-world uses vary from a remote super-expert helping a field technician to identify and deal with a rare problem to your millennial helping Mom master her personal technology. I saw an amazing demo this morning with one mechanic in Germany (ok, he was actually 2′ away…) directing the mechanic working on a Mercedes how to add coolant.  As the press release announcing the app said:

“Today, remote assistance can be frustrating and cumbersome. People struggle for words to describe things that are unfamiliar, whether it be a new appliance or the back of a cable box. And when the problem can’t be described clearly, it becomes almost impossible for someone else to solve. Vuforia Chalk provides a simple and intuitive solution where people can now use Chalk Marks to get a common understanding of a problem, and the steps required to solve it.”

I’ve written before that I suspected many companies got into e-commerce in the 9o’s because a CEO’s kids got him to order a book from Amazon during the holidays & he came back raving about this new device.  I can’t help thinking that this will be just the kind of low-cost (heck, in this case, no-cost) introduction to AR And the IoT that will break down some companies’ skepticism, pay off with immediate bottom line benefits in cost savings and efficiency in service operations, and get them interested in most expensive AR such as PTC’s digital twins and predictive maintenance.  Or, as ABI analyst Eric Abbruzzes said:

“Mainstream augmented reality is at the beginning of a strong positive inflection point, and Vuforia Chalk is a great example of how AR can transition from enterprise-only to use in everyday life,” said Eric Abbruzzese, ABI Research. “We see Vuforia Chalk as a fundamentally disruptive form of remote communication that will be well received across multiple sectors and for multiple use cases.”

Now to get my granddaughter to download the app so we can collaborate on the 3D-printer that I got her for her 12th- birthday!

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OtoSense: the next level in sound-based IoT

Posted on 3rd August 2017 in automotive, data, Internet of Things, maintenance, mobile, sensors, transportation

It sounds (pardon the pun) as if the IoT may really be taking off as an important diagnostic repair tool.

I wrote a while ago about the Auguscope, which represents a great way to begin an incremental approach to the IoT because it’s a hand-held device to monitor equipment’s sounds and diagnose possible problems based on abnormalities.

Now NPR reports on a local (Cambridge) firm, OtoSense, that is expanding on this concept on the software end. Its tagline is “First software platform turning real-time machine sounds and vibrations into actionable meaning at the edge.”

Love the platform’s origins: it grows out of founder Sebastien Christian’s research on deafness (as I wrote in my earlier post, I view suddenly being able to interpret things’ sounds as a variation on how the IoT eliminates the “Collective Blindness”  that I’ve used to describe our past inability to monitor things before the IoT’s advent):

“[Christian} … is a quantum physicist and neuroscientist who spent much of his career studying deaf children. He modeled how human hearing works. And then he realized, hey, I could use this model to help other deaf things, like, say, almost all machines.”

(aside: I see this as another important application of my favorite IoT question: learning to automatically ask “who else can use this data?” How does that apply to YOUR work? But I digress).

According to Technology Review, the company is concentrating primarily on analyzing car sounds from IoT detectors on the vehicle at this point (working with a number of car manufacturers) although they believe the concept can be applied to a wide range of sound-emitting machinery:

“… OtoSense is working with major automakers on software that could give cars their own sense of hearing to diagnose themselves before any problem gets too expensive. The technology could also help human-driven and automated vehicles stay safe, for example by listening for emergency sirens or sounds indicating road surface quality.

OtoSense has developed machine-learning software that can be trained to identify specific noises, including subtle changes in an engine or a vehicle’s brakes. French automaker PSA Group, owner of brands including Citroen and Peugeot, is testing a version of the software trained using thousands of sounds from its different vehicle models.

Under a project dubbed AudioHound, OtoSense has developed a prototype tablet app that a technician or even car owner could use to record audio for automated diagnosis, says Guillaume Catusseau, who works on vehicle noise in PSA’s R&D department.”

According to NPR, the company is working to apply the same approach to a wide range of other types of machines, from assembly lines to DIY drills. As always with IoT data, handling massive amounts of data will be a challenge, so they will emphasize edge processing.

OtoSense has a “design factory” on the site, where potential customers answer a variety of questions about the sounds they must monitor (such as whether the software will be used indoors or out, whether it is to detect anomalies, etc. that will allow the company to choose the appropriate version of the program.

TechCrunch did a great article on the concept, which underscores really making sound detection precise will take a lot of time and refinement, in part because of the fact that — guess what — sounds from a variety of sources are often mingled, so the relevant ones must be determined and isolated:

“We have loads of audio data, but lack critical labels. In the case of deep learning models, ‘black box’ problems make it hard to determine why an acoustical anomaly was flagged in the first place. We are still working the kinks out of real-time machine learning at the edge. And sounds often come packaged with more noise than signal, limiting the features that can be extracted from audio data.”

In part, as with other forms of pattern recognition such as voice, this is because it will require accumulating huge data files:

“Behind many of the greatest breakthroughs in machine learning lies a painstakingly assembled dataset.ImageNet for object recognition and things like the Linguistic Data Consortium and GOOG-411 in the case of speech recognition. But finding an adequate dataset to juxtapose the sound of a car-door shutting and a bedroom-door shutting is quite challenging.

“’Deep learning can do a lot if you build the model correctly, you just need a lot of machine data,’ says Scott Stephenson, CEO of Deepgram, a startup helping companies search through their audio data. ‘Speech recognition 15 years ago wasn’t that great without datasets.’

“Crowdsourced labeling of dogs and cats on Amazon Mechanical Turk is one thing. Collecting 100,000 sounds of ball bearings and labeling the loose ones is something entirely different.

“And while these problems plague even single-purpose acoustical classifiers, the holy grail of the space is a generalizable tool for identifying all sounds, not simply building a model to differentiate the sounds of those doors.

…”A lack of source separation can further complicate matters. This is one that even humans struggle with. If you’ve ever tried to pick out a single table conversation at a loud restaurant, you have an appreciation for how difficult it can be to make sense of overlapping sounds.

Bottom line: there’s still a lot of theoretical and product-specific testing that must be done before IoT-based sound detection will be an infallible diagnostic tool for predictive maintenance, but clearly there’s precedent for the concept, and the potential payoff are great!

 

LOL: as the NPR story pointed out, this science may owe its origins to two MIT grads of an earlier era, “Click” and “Clack” of Car Talk, who frequently got listeners to contribute their own hilarious descriptions of the sounds they heard from their malfunctioning cars.   BRTTTTphssssBRTTTT…..

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Sound’s emerging IoT role

Posted on 7th March 2017 in automotive, data, Internet of Things, maintenance, manufacturing, sensors

Could sound be a critical IoT tool?

I’d fixated in the past on a metaphor I called “Collective Blindness,” as a way to explain how difficult it used to be to get accurate, real-time data about how a whole range of things, from tractors to your body, were actually working (or not) because we had no way to penetrate the surface of these objects as they were used. As a result, we created some not-so-great work-arounds to cope with this lack of information.

Then along came the IoT, and no more collective blindness!

Now I’m belatedly learning about some exciting efforts to use another sense, sound, for the IoT.  Most prominent, of course, is Amazon’s Alexa and her buddies (BTW, when I ask Siri if she knows Alexa, her response was an elusive “this is about you, not me,” LOL), but I’ve found a variety of start-ups pursuing quite different aspects of sound. They nicely illustrate the variety of ways sound might be used.

technician using Auguscope to detect   sound irregularities in machinery

First is Augury.

What I particularly love about their device and accompanying smartphone app it is that they are just about the lowest-cost, easiest-to-use, rapid payback industrial IoT devices I can think of.

That makes them a great choice to begin an incremental approach to the IoT, testing the waters by some measures that can be implemented quickly, pay rapid bottom-line benefits and therefore may lure skeptical senior management who might then be willing to then try bolder measures   (this incremental approach was what I outlined in my Managing the Internet of Things Revolution e-guide for SAP, and I’ll be doing a webinar on the approach in April with Mendix, which makes a nifty no-code, low-code tool).

Instead of requiring built-in sensors, an Auguscope is a hand-held device that plant personnel can carry anywhere in the building it’s needed to analyze how the HVAC system is working. A magnetic sensor temporarily attaches to the machine and the data flows from the Auguscope to the cloud where it is analyzed to see if the sound is deviating from pre-recorded normal sounds, indicating maintenance is needed. Consistent with other IoT products that are marketed as services instead of sold, it uses a “Diagnostics as a Service” model, so there are no up-front costs and customers pay as they go. The company hopes that the technology will eventually be built into household appliances such as washers and dryers.

Presenso is the second company using sound to enable predictive maintenance.  It is sophisticated cloud-based software that takes data from a wide range of already-installed sensors and interprets any kind of data: sound, temperature, voltage, etc.  It builds a model of the machine’s normal operating data and then creates visualizations when the data varies from the norm. Presenso’s power comes from combining artificial intelligence and big data.

Finally, and most creative is Chirp (hmm: Chrome wouldn’t let me enter their site, which it said was insecure. Here’s the URL:www.chirp.io/ — try at your own risk…) , a UK company that transmits data using audio clips that really sound like chirps. It’s amazing!  Check out this video of an app in India that uses sound to pay fares on the country’s version of Uber:


Another Chirp app is a godsend to all who forget Wi-Fi passwords: your phone “chirps” a secure access code, allowing you to join the network automatically.   The company has released iOS and Android versions.  As VentureBeat reported:

“Each chirp lasts a couple of seconds, and the receiving device “listens” for a handful of notes played quickly in a certain order, in a certain range, and at a certain speed. While there are other easy ways of sharing files and data in real-time, such as Bluetooth, Chirp doesn’t require devices to pair in advance, there is no need to set up an account, and it’s ultimately a much quicker way of sharing files.

“That said, with Chirp, the file itself isn’t sent peer-to-peer, and the data doesn’t actually travel directly via audio. Chirp merely decodes and encodes the file, with the associated sound serving as the delivery mechanism. A link is generated for the recipient(s) to access it on Chirp’s servers, but the process from sending to receiving is seamless and near-instant.”

In terms of IoT applications, it could also connect with physical objects (hmm: retailing uses??). The Chirp platform is so cool that I suspect it will be a global hit (the company says it’s already used in 90 countries).

So, I’ve had my senses opened: from now on, I’ll add voice and sound in general to the list of cool IoT attributes.  Because voice and sound are so ubiquitous, they really meet the late Mark Weiser’s test:  “the most profound technologies are those that disappear. They weave themselves into the fabric of everyday life until they are indistinguishable from it.” What could be more woven into the fabric of everyday life than sound — and, potentially, more valuable?

BTW: let me put in a plug for another IoT voice product. During the past two months, I recorded 7 hours of my voice speaking a very strange mishmash of sentences drawn from, among others, Little Women, Jack London’s Call of the Wild, The Wizard of Oz, and The Velveteen Rabbit (I worried about the she-wolf sneaking up on Meg, LOL….). Using the algorithms developed for Alexa, the Vocal ID team will slice and dice my voice and create a natural sounding one for someone who cannot speak due to a birth defect or disease.  I hope you’ll join me in volunteering for this wonderful program.

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IoT Intangibles: Increased Customer Loyalty

Posted on 9th January 2017 in 3-D printing, automotive, circular company, collaboration, data, design, home automation, Internet of Things, logistics, maintenance, marketing, paradigm shift, retail, sensors, services, strategy, supply chain

There are so many direct, quantifiable benefits of the IoT, such as increased quality (that 99.9988% quality rate at Siemens’s Amberg plant!) and precision, that we may forget there are also potential intangible benefits.

Most important of those is customer loyalty, brought about by dramatic shifts both in product designs and how they are marketed.

Much of this results from the IoT lifting the veil of Collective Blindness to which I’ve referred before: in particular, our prior inability to document how products were actually used once they left the loading dock. As I’ve speculated, that probably meant that manufacturers got deceptive information about how customers actually used products and their degree of satisfaction. The difficulty of getting feedback logically meant that those who most liked and most hated a product were over-represented: those who kinda liked it weren’t sufficiently motivated to take the extra steps to be heard.

Now, by contrast, product designers, marketers, and maintenance staffs can share (that critical verb from my Circular Company vision!) real-time data about how a product is actually operating in the field, often from a “digital twin” they can access right at their desks.

Why’s that important?

It can give them easy insights (especially if those different departments do access and discuss the data at the same time, each offering its own unique perspectives, on issues that will build customer loyalty:

  • what new features can we add that will keep them happy?
  • can we offer upgrades such as new operating software (such as the Tesla software that was automatically installed in every single car and avoided a recall) that will provide better customer experiences and keep the product fresh?
  • what possible maintenance problems can we spot in their earliest stages, so we can put “predictive maintenance” services into play at minimal cost and bother to the customer?

I got interested in this issue of product design and customer loyalty while consulting for IBM in the 9o’s, when it introduced the IBM PS 2E (for Energy & Environmental), a CES best-of-show winner in part because of its snap-together modular design. While today’s thin-profile-at-all-costs PC and laptop designs have made user-friendly upgrades a distant memory, one of the things that appealed to me about this design was the realization that if you could keep users satisfied that they were on top of  new developments by incremental substitution of new modules, they’d be more loyal and less likely to explore other providers.

In the same vein, as GE has found, the rapid feedback can dramatically speed upgrades and new features. That’s important for loyalty: if you maintain a continuing interaction with the customer and anticipate their demands for new features, they’ll have less reason to go on the open market and evaluate all of your competitors’ products when they do want to move up.

 

Equally important for customer loyalty is the new marketing options that the continuous flow of real-time operating data offer you. For a growing number of companies, that means they’re no longer selling products, but leasing them, with the price based on actual customer usage: if it ain’t bein’ used, it ain’t costing them anything and it ain’t bringing you any revenue!

Examples include:

  • jet turbines which, because of the real-time data flow, can be marketed on the basis of thrust generated: if it’s sitting on the ground, the leasee doesn’t pay.  The same real-time data flow allows the manufacturer to schedule predictive maintenance at the earliest sign of a problem, reducing both its cost and the impact on the customer.
  • Siemens’s Mobility Services, which add in features such as 3-D manufactured spare parts that speed maintenance and reduced costs, keeping the trains running.
  • Philips’s lighting services, which are billed on the basis of use, not sold.
  • SAP’s prototype smart vending machine, which (if you opt in) may offer you a special discount based on your past purchasing habits.

At its most extreme is Caterpillar’s Reman process, where the company takes back and remanufactures old products, giving them a new life — and creating new revenues — when competitors’ products are in the landfill.

Loyalty can also be a benefit of IoT strategies for manufacturers’ own operations as well. Remember that the technological obstacles to instant sharing of real-time data have been eliminted for the supply chain as well. If you choose to share it, your resupply programs can also be automatically triggered on a M2M basis, giving an inherent advantage to the domestic supplier who can get the needed part there in a few hours, versua the low-cost supplier abroad who may take weeks to reach your loading dock.

It may be harder to quantify than quality improvements or streamlined production through the IoT, but that doesn’t mean that dependable revenue streams from loyal customers aren’t an important potential benefit as well.

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Siemens’s Mobility Services: Trains Become IoT Labs on Wheels

Posted on 18th November 2016 in 3-D printing, circular company, data, Internet of Things, M2M, maintenance, mobile, paradigm shift, sensors, transportation
George Stephenson's Killingworth locomotive Source: Project Gutenberg

George Stephenson’s Killingworth locomotive
Source: Project Gutenberg

As those of you who know rail history understand, with Stephenson as your last name, you’re bound to have a strong interest in railroads! Add in the fact that I was associate producer of an award-winning documentary on the subject back in the early 70’s, and it’s no wonder I was hooked when I got a chance to meet with some of Siemens’s top rail executives on my trip to Barcelona last week (Disclaimer: Siemens paid my expenses, but didn’t dictate what I covered, nor did they have editorial review of this piece).

What really excites me about railroads and the IoT is that they neatly encapsulate the dramatic transformation from the traditional industrial economy to the IoT: on one hand, the railroad was perhaps THE most critical invention making possible 19th century industry, and yet it still exists, in recognizable but radically-evolved form, in 2016. As you’ll see below, trains have essentially become laboratories on wheels!

I dwelt on the example of the Union Pacific in my e-book introduction to the IoT, SmartStuff, because to CIO Lynden Tennison was an early adopter, with his efforts focused largely on reducing the number of costly and dangerous derailments, through measures such as putting infrared sensors every twenty miles along the rail bed to spot “hotboxes,” overheating bearings. That allowed an early version of what we now know as predictive maintenance, pulling cars off at the next convenient yard so the bearings could be replaced before a serious problem. Even though the technology even five years ago was primitive compared to today, the UP cut bearing-related derailments by 75%.

Fast-forward to 2016, and Siemens’s application of the IoT to trains through its Mobility Services is yielding amazing benefits: increasing reliability, cutting costs, and even leading to possible new business models. They’ve taken over maintenance for more than 50 rail and transit programs.

While I love IoT startups with a radical new vision and no history to encumber them, Siemens is a beacon to those companies firmly rooted in manufacturing which may wonder whether to incorporate the IoT in their services and strategy. I suspect that its software products are inherently more valuable than competitors from pure-play software firms at commercial launch because the company eats its own dogfood and applies the new technology first to the products it manufactures and maintains — closing the loop.

Several of its executives emphasized that one of the advantages Siemens feels they enjoy is that their software engineers in Munich work in a corner of an old locomotive factory that Siemens still operates, so they can interact with those actually building and maintaining the engines on a daily basis. When it comes to security issues, their experience as a manufacturer means they understand the role of each component of the signaling system. Dr. Sebastian Schoning, ceo of Siemens client Gehring Technologies, which manufactures precision honing tools, told me that it was easier to sell these digital services to its own client base because so much of their current products include Siemens devices, giving them confidence in the new offerings. GE enjoys the same advantages of combining manufacturing and digital services with its Evolution Series locomotives.

The key to Siemens’s Mobility Services is Sinalytics, its platform architecture for data analysis not just for rail, but also for industries ranging from medical equipment to wind farms. More than 300,000 devices currently feed real-time data to the platform,   Consistent with my IoT-centric “Circular Company” vision, Sinalytics capitalizes on the data for multiple uses, including connectivity, data integration, analytics, and the all-important cyber security — they call the result not Big Data, but Smart Data. As with data services from jet turbine manufacturers such as Rolls Royce and GE, the platform also allows merging the data with data from sources such as weather forecasts which, in combination, can let clients optimize operating efficiency on a real-time M2M basis.  

With the new approach, trains become IoT laboratories on wheels, combining all of the key elements of an IoT system:

  • Sensing: there are sensors on the engines and gearboxes, plus vibration sensors on  microphones measure noises from bearings in commuter trains. They can even measure how engine oil is aging, so it can be changed when really needed, rather than on an arbitrary schedule.
  • Algorithms to make sense of the data and act on it. They read out patterns, record deviations & compare them with train control systems or vehicles of the same type.
  • Predictive maintenance replaces scheduled maintenance, dramatically reducing down-time and catastrophic failure.For example: “There’s a warning in one of the windows (of the control center display): engine temperature unusual. ‘We need to analyze the situation in greater depth to know what to do next  — we call it  ‘root cause analysis,” (say) Vice-President for Customer Support Herbert Padinger. ‘We look at its history and draw on comparative data from the fleet as a whole.’ Clicking on the message opens a chart showing changes in temperature during the past three months. The increased heat is gradually traced to a signal assembly. The Siemens experts talk with the customer to establish how urgent the need for action is, and then takes the most appropriate steps.”  He says that temperature and vibration analyses from the critical gearboxes gives Siemens at least three days advance notice of a breakdown — plenty of time for maintenance or replacement.  Predictive maintenance is now the norm for 70-80% of Siemens’s repairs.
  • Security (especially important given all of the miles of track and large crowds on station platforms): it includes video-based train-dispatch and platform surveillance using its SITRAIL D system, as well as cameras in the trains. The protections have to run the gamut from physical attacks to cyber attacks.  For security, the data is shared by digital radio, not networks also shared by consumers.

When operations are digitized, it allows seamlessly integrating emerging digital technologies into the services. Siemens Digital Services also included augmented reality (so repair personnel can see manuals on heads-up displays), social collaboration platforms, and — perhaps most important — 3-D printing-based additive manufacturing, so that replacement parts can be delivered with unprecedented speed. 3-D printing also allows dramatic reduction in parts inventories and allows replacement of obsolete parts that may no longer be available through conventional parts depots or even — get this — to improve on the original part’s function and/or durability, based on practical experience gained from observing the parts in use.  Siemens has used 3-D printing for the past last 3 years, and it lets them assure that they will have replacements for the locomotive’s entire lifespan, which can exceed 30 years.

The results of the new approach are dramatic.

  • None of the Velaro trains that Siemens maintains for several operators have broken down since Sinalytics was implemented. Among those in Spain only 1 has left more than 15 min. behind time in 2,300 trips: .0004%!

  • Reliability for London’s West Coast Mainline is 99.7%

  • Perhaps most impressive, because of the extreme cold conditions it must endure, the reliability rate for the Velaro service in Russia is 99.9%!

Their ultimate goal is a little higher: what Siemens calls (pardon the pun) 100% Railability (TM).

And, consistent with what other companies find when they fully implement not only IoT technology, but also what I like to call “IoT Thinking,” when it does reach those previously inconceivable quality benchmarks, the company predicts that, as the software and sensors evolve, the next stage will be new business models in which billing will be determined by guaranteeing customers availability and performance.

PS: I’ll be posting more about my interviews with Siemens officials and the Gartner event in coming days.

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Distributed Manufacturing by 3D Printing Revolution for IoT Comes of Age!

Posted on 12th July 2016 in 3-D printing, circular company, circular economy, collaboration, Internet of Things, manufacturing, paradigm shift

Two major developments in the 3-D printing world, from Fictiv and (who woulda thunk it!) UPS, make me think the time has come for “distributing manufacturing” and getting away from the old massive, manufacturing mentality exemplified by Ford’s River Rouge plant.

OK, first a confession and a little history. Being short & named David, I’ve always had a fascination with David & Goliath, and you can bet who I’d root for. I also was deeply touched by two visionaries in my past:

  • Steve Clay-Young, who used to run the workshop at the old Boston Architectural Center & turned me on to a neat, nearly-forgotten bit of WWII history: either Popular Science or Popular Mechanix (can’t remember which), organized a network of hobbyists with metal lathes, who played a major role in the war effort. The magazine published plans for turning metal for munitions, and these guys each worked in their workshops to make them.
  • Eric Drexler, the nano-tech guru, spoke at the Eco-Tech conference in the ’90s about his vision of a bread-box-size gizmo on your kitchen counter that would churn out all sorts of customized products for you.

Now, it’s all taking place, and I suspect 3D printing will be a crucial element in the IoT-based transformation of the economy.

 

                                   Fictiv distributed manufacturing model

Fictiv is a startup founded to “democratize manufacturing,” which just went public with its new “distributed manufacturing” service using a nationwide network of 3D high quality printers and CNC machines:

 

“We route parts to machine with open capacity so you don’t wait 5 days for a part that takes 5 hours…. We aggregate orders so every customer receives the benefits of large purchasing power….”

Perhaps coolest, “Parts are produced as close to customers as possible to reduce inefficiencies in logistics and shipping lead-time”  so that (for an extra charge) they’re fabricated and delivered in 24 hours, and otherwise delivered in two days.  I suspect that, just as having sensors on their products that results in real-time feedback allowing GE to compress the design cycle, especially upgrades, that this proximity and quick turn-around will allow designers to radically alter the design process by “failing rapidly,” just the way early spread-sheet software allowed business managers to do “what-if” hypotheticals for the first time.

By bundling orders, they give startups the bargaining power of large companies.As co-founder Dave Evans, an experienced product design pro, says, distributed, local manufacturing can even the playing field for smaller companies, especially startups just designing their first products:

“When ordering from a large manufacturing company, parts need to navigate through their complex system and then be shipped from the machine warehouse direct to the customer, increasing lead times.

From an engineer’s perspective, when you’re in the prototyping and ideation stages, time is everything and even a 1-2 day loss from a 3PL (third-party logistics player) matters significantly.

What’s important to consider here is that in manufacturing, things can and will go wrong. So when remote manufacturers inevitably have to manage errors, there’s a lot of complexity to deal with …. This is very evident in overseas mass manufacturing, which is why companies put engineers as close to the source as possible. It’s amazing how few companies consider the same principles during the early prototyping stages of a product when time is everything.

The beauty in working with smaller, local manufacturers on the other hand, is that parts can be picked up as soon as they’re ready or delivered via same-day courier, saving you the 1-2 days of shipping. In addition, if things go wrong (they always do), smaller shops have more agility, fewer organizational layers, and in general can respond more quickly compared with their larger counterparts.”

              3D printing at The UPS Store

Equally important is the continuing stream of 3D services being offered by UPS. which recently announced a nationwide on-demand 3D printing network.  The network will combine 3D printers at more than 60 The UPS Stores® and Fast Radius’ On Demand Production Platform™ and 3D printing factory in Louisville, KY. My friends at SAP will marry its SAP’s extended supply chain solutions will be integrated with the UPS 3D network and — most important — its global logistics network “to simplify the industrial manufacturing process from digitization, certification, order-to-manufacturing and delivery.”

If I’m correct, the UPS network will concentrate on prototyping at this point, but it’s easy to see that it could soon have a dramatic impact on the replacement parts industry. Why should the manufacturer warehouse a large supply of spare parts, just because they might be needed, when they could instead simply transmit the part’s digital file to the nearest UPS 3D printer, generate the part, and use UPS to deliver it in a fraction of the time.

Combine that with the predictive maintenance possible with feedback from sensors on products, and you truly have a revolution in product design and maintenance as well as manufacturing. It would also foster the IoT-based circular company vision that I’ve been pushing, because supply chain, manufacturing, distribution, and maintenance would all be linked in a great circle.

Sweet!

 

 

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The IoT Can Improve Safety and Profitability of Inherently Dangerous Job Sites

Posted on 9th July 2015 in collaboration, communication, data, energy, Essential Truths, Internet of Things, M2M, maintenance, management, mobile, open data, paradigm shift, sensors, strategy, supply chain

You may remember I wrote several months ago about a collaboration between SAP and SK Solutions in Dubai (interesting factoid: Dubai is home to almost 25% of the world’s cranes [assume most of the rest nest at Sand Hill, LOL], and they are increasingly huge, and that makes them difficult to choreograph.

I’m returning to the subject today, with a slightly broader emphasis on how the IoT might manage a range of dangerous job sites, such as mining and off-shore oil rigs, allowing us to do now that we couldn’t do before, one of my IoT Essential Truths.

I’m driven in part by home-town preoccupation with Boston’s bid for the 2024 Olympics, and the inevitable questions that raises on the part of those still smarting from our totally-botched handling of the last big construction project in these parts, the infamous “Big Dig” tunnel and highway project.

I’m one of those incurable optimists who think that part of ensuring that the Olympics would have a positive “legacy” (another big pre-occupation in these parts) would be to transform the city and state into the leading example of large-scale Internet of Things implementation.

There are a couple of lessons from SAP and SK Solutions’ collaboration in Dubai that would be relevant here:

    • The system is real-time: the only way the Boston Olympic sites could be finished in time would be through maximizing efficiency every day. Think how hard that is with a major construction project: as with “for want of a nail the kingdom was lost,” the sensitive interdependence between every truck and subcontractor on the site — many of which might be too small to invest in automation themselves — is critical. If information about one sub being late isn’t shared, in real-time, with all the other players, the delays — and potential collisions — will only pile up. The system includes an auto-pilot that makes immediate adjustments to eliminate operator errors. By contrast, historical data that’s only analyzed after the fact won’t be helpful, because there’s no do-overs, no 2025 Olympics!
    • The data is shared: that’s another key IoT Essential Truth.  “Decision-makers using SK Solutions on a daily basis span the entire organization. Besides health and safety officers, people responsible for logistics, human resources, operations and maintenance are among the typical users.”  The more former information silos share the data, the more likely they are to find synergistic solutions.
    • The system is inclusive, both in terms of data collection and benefits: SK Solutions’ Founder and Inventor Séverin Kezeu, came up with his collision-avoidance software pre-IoT, but when the IoT became practical he partnered with SAP, Cisco, and Honeywell to integrate and slice and dice the data yielded by the sensors they installed on cranes and vehicles and other sources.  For example, the height of these cranes makes them vulnerable to sudden weather changes, so weather data such as wind speed and direction must be factored in, as well as the “machinery’s position, movement, weight, and inertia…. The information is delivered on dashboards and mobile devices, visualized with live 3-D images with customizable views. It’s also incredibly precise.”As a result, by using SAP’s HANA platform, a system developed to reduce construction accidents also makes predictive maintenance of the cranes and other equipment, and lets the construction companies monitor Key Performance Indicators (KPIs) such as asset saturation, usage rates, and collisions avoided.  McKinsey reports that construction site efficiency could improve dramatically due to better coordination: “One study found that buffers built into construction project schedules allowed for unexpected delays resulting in 70 to 80 percent idle time at the worksite.Visibility alone can allow for shorter buffers to be built into the construction process.”

Several other great IoT solutions come to mind at the same time, both relating to dangerous industries. Off-shore oil rigs and mining were treated at length in the recent McKinsey omnibus IoT forecast, “The Internet of Things: Mapping the Value Beyond the Hype:”

  • off-shore rigs: “Much of the data collected by these sensors [30,000 on some rigs] today is used to monitor discrete machines or systems. Individual equipment manufacturers collect performance data from their own machines and the data can be used to schedule maintenance. Interoperability would significantly improve performance by combining sensor data from different machines and systems to provide decision makers with an integrated view of performance across an entire factory or oil rig. Our research shows that more than half of the potential issues that can be identified by predictive analysis in such environments require data from multiple IoT systems. Oil and gas experts interviewed for this research estimate that interoperability could improve the effectiveness of equipment maintenance in their industry by 100 to 200 percent.” (my emphasis). 
  • mining: “In one mining case study, using automated equipment in an underground mine increased productivity by 25 percent. A breakdown of underground mining activity indicates that teleremote hauling can increase active production time in mines by as much as nine hours every day by eliminating the need for shift changes of car operators and reducing the downtime for the blasting process. Another source of operating efficiency is the use of real-time data to manage IoT systems across different worksites, an example of the need for interoperability. In the most advanced implementations, dashboards optimized for smartphones are used to present output from sophisticated algorithms that perform complex, real-time optimizations. In one case study from the Canadian tar sands, advanced analytics raised daily production by 5 to 8 percent, by allowing managers to schedule and allocate staff and equipment more effectively. In another example, when Rio Tinto’s (one mine) crews are preparing a new site for blasting, they are collecting information on the geological formation where they are working. Operations managers can provide blasting crews with detailed information to calibrate their use of explosives better, allowing them to adjust for the characteristics of the ore in different parts of the pit.”
 In all of these cases, the safety and productivity problems — and solutions are intertwined.  As McKinsey puts it:
“Downtime, whether from repairs, breakdowns, or maintenance, can keep machinery out of use 40 percent of the time or more. The unique requirements of each job make it difficult to streamline work with simple, repeatable steps, which is how processes are optimized in other industries. Finally, worksite operations involve complex supply chains, which in mining and oil and gas often extend to remote and harsh locations.”
Could it be that the IoT will finally tame these most extreme work situations, and bring order, safety, and increased profitability?  I’m betting on it.
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GE & Accenture provide detailed picture of current IoT strategy & deployment

Posted on 10th October 2014 in collaboration, data, health, Internet of Things, M2M, maintenance, management, manufacturing, paradigm shift, strategy

I’ll admit it: until I began writing the “Managing the Internet of Things Revolution” guide to Internet of Things strategy for SAP, I was pre-occupied with the IoT’s gee-wiz potential for radical transformation: self-driving cars, medical care in which patients would be full partners with their doctors, products that customers would be able to customize after purchase.

GE_Accenture_IoT_reportThen I came to realize that this potential for revolution might be encouraging executives to hold off until the IoT was fully-developed, and, in the process, ignoring low-hanging fruit: a wide range of ways that the IoT could dramatically increase the efficiency of current operations, giving them a chance to experiment with limited, less-expensive IoT projects that would pay off rapidly and give them the confidence and understanding necessary to launch more dramatic IoT projects in the near future.

This is crucially important for IoT strategies: instead waiting for a radical transformation (which can be scary), view it instead as a continuum, beginning with small, relatively-low cost steps which will feed back into more dramatic steps for the future.

Now, there’s a great new study, “Industrial Internet Insights Report for 2015,” from GE and Accenture, that documents many companies are in the early stages of implementing such an incremental approach, with special emphasis on the necessary first step, launching Big Data analytics — and that they are already realizing tangible benefits. It is drawn from a survey of companies in the US, China, India, France, Germany, the UK, and South Africa.

The report is important, so I’ll review it at length.

Understandably, it was skewed toward the industries where GE applies its flavor of the IoT (the “Industrial Internet”): aviation, health care, transportation, power generation, manufacturing, and mining, but I suspect the findings also apply to other segments of the economy.

The summary underscores a “sense of urgency” to launch IoT initiatives:

“The vast majority (of respondents) believe that Big Data analytics has the power to dramatically alter the competitive landscape of industries just within the next year, and are investing accordingly…” (my emphasis).

84% said Big Data analytics “has the power to shift the competitive landscape for my industry” within just the next year, and 93% said they feared new competitors will enter the field to leverage data.  Wow: talk about short-term priorities!

It’s clear the authors believe the transformation will begin with Big Data initiatives, which, IMHO, companies should be starting anyways to better analyze the growing volume of data from conventional sources. 73% of the companies already are investing more than 20% of their overall tech budget on Big Data analytics — and some spend more than 30%! 80 to 90% said Big Data analytics was either the company’s top priority or at least in the top 3.

One eye-opening finding was that 53% of respondents said their board of directors was pushing the IoT initiatives. Probably makes sense, in that boards are expected to provide necessary perspective on the company’s long-term health.

GE and Accenture present a  4-step process to capitalize on the IoT:

  1. Start with the exponential growth in data volumes
  2. Add the additional data volume from the IoT
  3. Add growing analytics capability
  4. and, to add urgency, factor in “the context of industries where equipment itself or patient outcomes are at the heart of the business” where the ability to monitor equipment or monitor patient services can have significant economic impact and in some cases literally save lives [nothing like throwing the fear of God into the mix to motivate skeptics!].
For many companies, after implementing Big Data software, the next step toward realizing immediate IoT benefits is by installing sensors to monitor the status of operating assets and be able to implement “predictive maintenance,” which cuts downtime and reduces maintenance costs (the report cites some impressive statistics: ” .. saving up to 12 percent over scheduled repairs, reducing overall maintenance costs up to 30 percent, and eliminating breakdowns up to 70 percent.” What company, no matter what their stance on the IoT, wouldn’t want to enjoy those benefits?). The report cites companies in health care, energy and transportation that are already realizing benefits in this area.
Music to my ears was the emphasis on breaking down data-sharing barriers between departments, the first time I’ve seen substantiation of my IoT “Essential Truth” that, instead of hoarding data — whether between the company and supply-chain partners or within the company itself — that the IoT requires asking “who else can use this data?” It said that: “System barriers between departments prevent collection and correlation of data for maximum impact.” (my emphasis). The report went on to say:

“All in all, only about one-third of companies (36 percent) have adopted Big Data analytics across the enterprise. More prevalent are initiatives in a single operations area (16 percent) or in multiple but disparate areas (47 percent)…. The lack of an enterprise-wide analytics vision and operating model often results in pockets of unconnected analytics capabilities, redundant initiatives and, perhaps most important, limited returns on analytics investments.”

Most of the companies surveyed are moving toward centralization of data management to break down the silos. 49% plan to appoint a chief analytics officer to run the operation, and most will hire skilled data analysts or partner with outside experts (insert Accenture here, LOL…).

The GE/Accenture report also stressed that companies hoping to profit from the IoT also must create end-to-end security. Do do that, it recommended a strategy including:
  1. assess risks and consequences
  2. develop objectives and goals
  3. enforce security throughout the supply chain.
  4. use mitigation devices specifically designed for Industrial Control Systems
  5. establish strong corporate buy-in and governance.

For the longer term, the report also mentioned a consistent theme of mine, that companies must begin to think about dramatic new business models, such as substituting value-added services instead of traditional sales of products such as jet engines.  This is a big emphasis with GE.  It also emphasizes another issue I’ve stressed in the “Essential Truths,” i.e. partnering, as the mighty GE has done with startups Quirky and Electric Imp:

“Think of the partnering taking place among farm equipment, fertilizer, and seed companies and weather services, and the suppliers needed to provide IT, telecom, sensors, analytics and other products and services. Ask: ‘Which companies are also trying to reach my customers and my customers’ customers? What other products and services will talk to mine, and who will make, operate and service them? What capabilities and information does my company have that they need? How can we use this ecosystem to extend the reach and scope of our products and services through the Industrial Internet?'”

While the GE/Accenture report dwelt only on large corporations, I suspect that many of the same findings would apply to small-to-medium businesses as well, and that the falling prices of sensors and IoT platforms will mean more smart companies in this category will begin to launch incremental IoT strategies to first optimize their current operations and then make more radical changes.

Read it, or be left in the dust!

PS: as an added bonus, the report includes a link to the GE “Industrial Internet Evaluator,” a neat tool I hadn’t seen before. It invites readers to “see how others in your field are leveraging Big Data analytics for connecting assets, monitoring, analyzing, predicting and optimizing for business success.” Check it out!

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