Live Blogging #LlveWorx ’18, Day 2

Aiden Quilligan, Accenture Industry X.0, on AI:

  • Mindset and AI: must undo what Hollywood has done on this over years, pose it as human vs. machine.
  • We think it should be human PLUS machine.
  • he’s never seen anything move as fast as AI, especially in robotics
  • now, co-bots that work along side us
  • exoskeletons
  • what do we mean by AI?  Machine learning.  AI is range of technologies that can learn and then act. AI is the “new work colleague” we need to learn to get along with.
  • predictions: will generate #2.9 trillion in biz value and recover 6.2 billion hours of worker productivity in 2021.
  • myths:
    • 1) robots evil, coming for us: nothing inherently anti-human in them.
    • 2) will take our jobs. Element of truth in terms of repetitive, boring work that will be replaced. They will fill in for retiring workers. Some new industries created by them.  Believe there will be net creation of jobs.
    • 3) current approaches will still work.

6 steps to the Monetization of IoT, Terry Hughes:

  • Digital native companies (Uber) vs. digitally transforming companies
  • also companies such as Kodak that didn’t transform at all (vs. Fujifilm, which has transformed).
  • Forbes: 84% of companies have failed with at least one transformation program.  Each time you fail you lose 1/2 billion
  • steps:
    • 1) devices with potential
    • 2) cloud network communication
    • 3) software distribution
    • 4) partner and provider ecosystem
    • 5) create a marketplace.
    • 6) monetization of assets.
  • crazy example of software company that still ships packages rather than just download because of initial cost in new delivery system
  • 3 big software challenges for digitally transforming company
    • fragmented silos of software by product, business unit & software
    • messy and complex distribution channels
    • often no link between software and the hardware that it relates to
  • importance of an ecosystem
    • Blackberry example of one that didn’t have the ecosystem
  • 3rd parties will innovate and add value around a manufacturer’s core products
  • in IoT it’s a land grab for mindshare of 3rd-party innovators.
  • need strong developer program
  • tools for app development and integration
  • ease of building and publishing apps
  • path to discovery and revenue for developer
  • IDC: developer ecosystem allow enterprises to massively scale distribution
  • digitally native companies have totally different models (will get details later…)
  • hybrids:
    • GE Healthcare:  working with Gallus BioPharma
    • Heidelberg & Eig have digital biz model for folding carton printing. Pay per use
  • Ford is heading for mobility as a transformation

 


Bernard Marr: Why IoT, Combined With AI and Big Data, Fuels 4th Industrial Revolution

 

  • connecting everything in house to Internet
  • Spotify: their vision is they understand us better. Can correlate your activity on Apple Watch (such as spinning) & create a play list based on that)
  • FitBit: the photo will estimate your calorie content.
  • John Deere
  • ShotSpotter: the company that monitors gun shots
  • understanding customers & markets better than before:
    • Facebook: better at face recognition than we are. They can predict your IQ, your relationship status.
  • Lot of frightening, IMHO, examples of AI analyzing individuals and responding without consideration of ethics and privacy
  • 3) improving operations and efficiency:
    • self-driving boats
    • drones
    • medicine through Watson

panel on IoT:

  • Don’t be afraid of the cloud
  • Ryan Cahalane, Colfax: prepare for big, start small and move fast. They had remarkable growth with switch to IoT.  Not a digital strategy, but digital in everything they do. Have “connected welders,” for example.
  • Justin Hester, Hirotec: most importatnt strategic digital transformation decision your organization can make is the selection of a platform. The platform is the underlying digital thread that enables your team to meet  the unique and chanding needs of your organization and to scale those solutions rapidly. “Assisted reality” in ThingWorx
  • Shane O’Callahan, TSM (Ireland):  Make industrial automation equipment for manufacturing. Understanding your key value driver is where to start. Then start samll, scale fast and get a win!

Jeffrey Miller, PTC: Digital Transformation:

  • if you start with digital strategy you’re starting in wrong place Start with business strategy. 
  • Couple with innovation vision merged with digital strategy. Add business use cases.
  • Jobs: it’s not how much you spend on R & D, but “about the people you have, you you’re dled, and how much you get it”
  • create an environment for innovation
    • do we encourage experimentation?
    • is it ok to fail
  • identify digital technologies to provide the required operating capabilities:
    • have we conducted proofs of concept?
    • experimented, tested  and validated?
    • reviewed use cases & success studies?
    • delivered small, important, scalable successes?

Matt,  PTC: Bringing Business Value to AR:

  • augmented service guidance
  • remote expert guidance
  • manufacturing: machine setup and turnover, assembly and process
  • example of Bell & Howell towers to store online sales in WalMart stores for customer pickup: very expensive to send one to a store for salesperson to use in sales — now just use AR app to give realistic demo without expense.
  • service: poor documentation organization, wants accurate, relevant, onsite info for technician. Want to remove return visits because the repair wasn’t done 1st time, or there’s a new technician. Manuals in binders, etc. Instead, with AR, requirements are quick access to current info. Finally, a demo.

Suchitra Bose, Accenture: Manufacturing IIoT, Driving the Speed of Digital Manufacturing:

  • convergence of IT and OT
  • expanding digital footprint across your entire factory
  • PTC has wide range of case studies (“use cases” in biz speak…) on aspects of IoT & manufacturing.

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

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!

Wahoo! Liveblogging #Liveworx ’18!

Always my fav event, I’ll be liveblogging #LiveWorx ’18.  Stay tuned!

Keynote: Jim Heppelmann:

  • “from a place to a pace” — how fast are we moving?
  • no longer OK to think of a future destination, builds inertia (“your main competitor”). Disruption may have already happened. Hard to sustain advantage due to pace of change. Must “embrace a pace of change”
  • Um, this sounds like argument for my circular company paradigm shift!!!
  • Customer Experience Center will occupy top floor of new building.
  • combo of  physical, human and digital — transforming all at once speeds change:
    • physical: been constrained by subtractive manufacturing, while nature improves via cell division (i.e., additive). “Adopt Mother Nature’s mindset.” — new additive aspects of Creo. Example of Triumph cycle sing-arm using additive. CREO uses AI to optimize performance: non-symmetrical design. Still need to use simulation tests: new intermittent, continuous style: they are doing new partnership with ANSYS (product simulation software), unified modeling and simulation with no gaps. Historically, simulation only used at end of design cycle, now can use it throughout the process: “pervasive simulation.”
      • ANSYS “Discovery Live”: optimizes for real-time. Integrates with Creo — instant feedback on new designs. “simulation critical to innovation.”
    • digital: working with Microsoft Azure (Rodney Clark, Microsoft IoT VP). Microsoft investing $5b in IoT.  1st collaboration is an industrial welder: IoT data optimizes productivity.  BAE can train new employees 30-40% quicker.
    • finally, human: “Mother Nature designed ups to interface with the physical. How do we integrate with the digital? — Siri, Alexa, Cortna still too slow.  Sight is our best bet. “Need direct pipeline to reality ” — that’s AR. “Smart, connected humans.” Sysmex: for medical lab analysis. Hospitals need real-time access to blood cell analysis. They have real-time calibration of analysis equipment. Also improving knowledge of the support techs, using AR and digital twins when repairs are needed.
      • Will help 2.5 billion workers become more productive
      • AR can project how a process is being programmed (gotta see this one. will try to get video).
      • All of their human/digital interface initiatives united under Vuforia. Already have 10,000 enterprises using it.
    • Factories are a new focus of PTC. 200 companies now using it in 800 factories. Examples from Woodward & Colfax.  Big savings on new employee training.

Keynote: Prof. Linda Hill, HBS, “Collective Genius”:

  • Innovation= novel + useful
  • Example of Pixar: collective genius “filmmaking is a team sport.”
  • 3 characteristics of creative organizations they looked at:
    • “creative abrasion” — diversity and debate
    • “creative agility” — quickly test the idea & get feedback. Experiment rather than run pilots, which often include politics
    • “creative resolution” — ability to make integrative decisions. Don’t necessarily defer to the experts.
    • sense of community and shared purpose.
  • values: bold ambition, collaboration, responsibility, learning.
  • rules of engagement: respect, trust, influence, see the whole, question everything, be data-driven.

Ray Miciek, Aquitas Solutions. Getting Started on IoT-based Maintenance:

  • his company specializes in asset maintenance.
  • “produce products with assets that never fail”
  • 82% of all asset failures are random, because they are more IT-related now
  • find someplace in org. where you could gain info to avoid failure.
  • Can start small, then quickly expand.

 

Holy Clayton Christensen! Is Local Motors prototype for future of manufacturing?

In the latter stages of writing The Future Is Smart, I came across Local Motors, an amazing company that is not only an IoT innovator but also might pr0vide a model to revolutionize American manufacturing in general.

I’d read an article years ago about the company when it was locally-based, but since it was focused entirely on off-road & fast cars at the time (both of which leave me cold) I didn’t follow up.

Now it’s diversifying into a cute small urban shuttle device, the Olli, which is being produced at Local Motion’s Knoxville microfactory, taps IBM’s Watson, and which they label “the world’s first self-driving cognitive vehicle.” Very cool.

co-creation

The first of Local Motor’s revolutionary aspects is its design process, which it labels “co-creation” (AKA crowdsourcing — in fact founder/visionary John B. (Jay) Rogers, Jr. says he was inspired by the Jeff Howe book of the same name). It uses a SaaS platform, where the company posts design challenges, and then community members (some experts, some just enthusiasts) offer their ideas. Eventually, the community votes on which designs to actually produce:

“An active process where brands and their customers work together with solvers, designers, and engineers to accelerate product and technology development. We call this group our Community and proudly work to empower genius ideas and brilliant solutions from Community members across the globe.”

The participatory aspect even extends to the shop floor: buyers can opt to personally take part in the final assembly process (and designs are also easily customized after the sale as well).

The company has also provided consulting services on co-creation for organizations ranging from the US Army to Airbus. 

This is not unlike my “share data, don’t hoard it” IoT Essential Truth, which is also at the heart of my Circular Company vision: when you involve and empower a wide range of people, you can unleash creativity that even the most talented person can’t.

direct digital manufacturing

The second Local Motors innovation is use of creative technologies, especially 3D printing, in manufacturing, what they call “direct digital manufacturing (DDM).”  The process mimics what Siemens does at its “Factory of the Future,”  where complete digitalization gives them quality, precision, and the opportunity for mass customization:

“DDM creates significant unfair advantages: the ability to produce parts directly from a CAD file; elimination of investments in tooling; reduction in time lag between design and production and, best of all, elimination of penalties for redesigns — unlocking mass customization that was previously unobtainable.”

According to Chief Strategy Officer Justin Fishkin, the economies possible with the DDH approach means the Rally Fighter model was profitable after only the 60th one was built.

microfactories

I’ve written before about Ford’s River Rouge plant, the ne plus ultra of the first Industrial Age: iron ore went in one end of the 1 x 1.6 mile factory and Model Ts came out the other.

By contrast, Local Motors is building several supermarket-sized “microfactories” around the globe at a cost 1/100th of that for conventional car plants, which “..will also act as points of sale, or what Fishkin calls ‘experiential dealerships.’”

 


The jury’s still out on Local Motors (Rogers, for example, has come in for some scathing tell-all comments by former employees), but even if it isn’t a roaring success, it will have a lasting legacy for challenging such long-held assumptions about the entire design/build process. and for exploiting the full benefits of digitization.  It’s the essence of Christensen’s disruptive innovation.

We’ll be watching

 

OtoSense: the next level in sound-based IoT

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…..

Servitization With IoT: Weird Biz-Speak, But Sound Strategy

I love it when manufacturers stop selling things — and their revenues soar!

That’s one of the things I’ll cover on May 2nd  in”Define Your Breakout IoT” strategy, (sign-up) a webinar I’m doing with Mendix. I’ll outline an incremental approach to the IoT in which you can make some early, tentative steps (such as implementing Augury’s hand-held vibration sensor as a way to start predictive maintenance) and then, as you gain experience and increase savings and efficiency, plow the savings back into more dramatic transformation.

One example of the latter that I’ll detail in the webinar is one of my four “Essential Truths” of the IoT: rethink products. By that I meant not only reinventing products to be smart (especially by building in sensors so they can report their real-time status 24/7), but, having done that, exploring new ways to market them.  Or, as one graphic I’ll use in the presentation puts it, in mangled biz-speak, “servitization.”

              Hortilux bulbs

Most of the examples I’ve written about in that regard have been from major businesses, such as GE and Rolls-Royce jet turbines, that are now leased as services (with the price determined by thrust generated), but Mendix has a smaller, niche client that also successfully made the conversion: Hortilux, a manufacturer of grow lights for greenhouses.

The Hortilux decided to differentiate itself in an increasingly competitive grow light market by evolving from simply selling bulbs to instead providing a comprehensive continuing service that helps its customers optimize availability and lifetime of grow light systems, while cut energy cost.     

Using Mendix tools, they created Hortisensehttp://www.hortidaily.com/article/31774/Hortilux-launches-Hortisense-software-suite, a digital platform that monitors and safeguards various grow light processes in the greenhouse using sensors and PLCs. Software applications interpret the data and present valuable information to the grower anytime, anywhere, and on any device.

With Mendix, Hortilux created an application to collect sensor data on light, temperature, soil, weather and more. Now users can optimize plants’ photosynthesis, energy consumption, and greenhouse maintenance. Most ambitiously, it provides comprehensive “crop yield management:” 

  • Digital cultivation schedule
  • Light strategies based on plant physiology and life cycle
  • Automatic light adjustment based on predictive analytics (e.g. weather forecast, energy prices, produce prices)

The app even allows predictive maintenance, predicting bulbs’ life expectancy and notifying maintenance to replace them in time to avoid disruptions in operations.

In the days when we suffered from what I call “Collective Blindness,” when we lacked the tools to “see” inside products to m0nitor and perhaps fix them based on real-time operating data, it made sense to sell products and provide hit-or-miss maintenance when they broke down.

Now that we can monitor them 24/7 and get early enough warning to instead provide predictive maintenance, it makes equal sense to switching to marketing them as services, with mutual benefits including:

  • increased customer satisfaction because of less down-time
  • new revenues from selling customers services based on availability of the real-time data, which in turn allows them more operating precision
  • increased customer loyalty, because the customer is less likely to actually go on the open market and buy a competing product
  • the opportunity to improve operations through software upgrades to the product.

Servitization: ugly word, but smart strategy. Hope you’ll join us on the 2nd!

Sound’s emerging IoT role

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.

IoT Intangibles: Increased Customer Loyalty

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.

Siemens’s MindSphere: from automation to digitalization

Perhaps the most important component of a successful IoT transformation is building it on a robust platform, because that alone can let your company go beyond random IoT experiments to achieve an integrated IoT strategy that can add new components systematically and create synergistic benefits by combining the various aspects of the program.

A good starting point for discussion of such platforms is a description of the eight key platform components as detailed by IoT Analytics:

  1. “Connectivity & normalization: brings different protocols and different data formats into one ‘software’  interface ensuring accurate data streaming and interaction with all devices.
  2. Device management: ensures the connected ‘things’ are working properly, seamlessly running patches and updates for software and applications running on the device or edge gateways.
  3. Database: scalable storage of device data brings the requirements for hybrid cloud-based databases to a new level in terms of data volume, variety, velocity and veracity.
  4. Processing & action management: brings data to life with rule-based event-action-triggers enabling execution of ‘smart’ actions based on specific sensor data.
  5. Analytics: performs a range of complex analysis from basic data clustering and deep machine learning to predictive analytics extracting the most value out of the IoT data-stream.
  6. Visualization: enables humans to see patterns and observe trends from visualization dashboards where data is vividly portrayed through line-, stacked-, or pie charts, 2D- or even 3D-models.
  7. Additional tools: allow IoT developers prototype, test and market the IoT use case creating platform ecosystem apps for visualizing, managing and controlling connected devices.
  8. External interfaces: integrate with 3rd-party systems and the rest of the wider IT-ecosystem via built-in application programming interfaces (API), software development kits (SDK), and gateways.”

Despite (or because of, the complexity,) I think this is a decent description, because a robust IoT platf0rm really must encompass so many functions. The eight points give a basis for deciding whether what a company hawks as an IoT platform really deserves that title or really constitutes only part of the necessary whole (Aside: it’s also a great illustration of my Essential Truth that, instead of hoarding data as in the past, we must begin to ask “who else can use this data?” either inside the company or, potentially, outside, then use technology such as an IoT platform to integrate all those data uses productively.).

During my recent Barcelona trip (disclaimer: Siemens paid my way and arranged special access to some of its key decision makers, but made no attempt to limit my editorial judgment) I interviewed the company’s Chief Strategy Officer, Dr. Horst J. Kayser, who made it clear (as I mentioned in my earlier post about Siemens) that one of the advantages the company has over pure-play software firms is that it can apply its software offerings internally first and tweak them there, because of its 169-year heritage as a manufacturer, and “sits on a vast program of automation.”

Siemens’s IoT platform, MindSphere  is a collaboration with SAP, using the latter’s vast HANA cloud.  It ties together all components of Siemens’s IoT offerings, including data analytics, connectivity capabilities, developers’ tools, applications and services. MindSphere focuses on monitoring manufacturing assets’ real-time status, to evaluate and use customers’ data, producing insights that can cut production costs, improve performance, and even switch to predictive maintenance. Its Mind Connect Nano collects data from the assets and transferring it to MindSphere.

The “digital twin” is integrated throughout the MindSphere platform. Kayser says that “there’s a digital twin of the entire process, from conception through the manufacturing and maintenance, and it feeds the data back into the model.” In fact,  one dramatic example of the concept in action is the new Maserati Ghibli, created in 16 months instead of 30 — almost 50% less time than for prior models.  Using the Teamcenter PLM software, the team was able to virtually develop and extensively test the car before anything was created physically.

IMHO, Mindsphere and components such as Teamware might really be the key to actualizing my dream of the circular company, in this case with the IoT-based real-time digital twin at the heart of the enterprise — as Kayser said, “everything is done through one consistent data set.)” I hope to explore my concept, and the benefits I think it can produce, more with the Siemens strategists in the future!  I tried the idea out on several of them in Barcelona, and no one laughed, so we’ll see…

As with the company’s rail digitization services that I mentioned in my earlier post, there’s an in-house guinea pig for MindSphere as well: the company’s “Factory of the Future” in Amberg. The plant manufactures Simatic controllers, the key to the company’s automation products and services, to which digitalization is now being added as part of the company’s Industrie 4.0 IoT plan for manufacturing (paralleling GE’s “Industrial Internet.”). As you may be aware, Siemens’s efforts in this area are a subset of a formal German government/industry initiative — I  doubt seriously we’ll see this in the U.S. under Trump.

The results of digitalization at Amberg are astonishing by any measure, especially the ultimate accomplishment: a  99.9988 percent rate (no typo!!), which is even more incredible when you realize this is not mass production with long, uniform production runs: the plant manufactures more than 1,000 varieties of the controllers, with a total volume of 12 million Simatic products each year, or about one per second.  Here are some of the other benefits of what they call an emphasis on optimizing the entire value chain:

  • shorter delivery time: 24 hours from order.
  • time to market reduced by up to 50%.
  • cost savings of up to 25%

Of course there are several other robust IoT platforms, including GE’s Predix and PTC’s Thingworx, but my analysis shows that Mindsphere meets IoT Analytics’ criteria, and, combined with the company’s long background in manufacturing and automation, should make it a real player in the industrial internet. Bravo!

When Philips’s Hue Bulbs Are Attacked, IoT Security Becomes Even Bigger Issue

OK, what will it take to make security (and privacy) job #1 for the IoT industry?

The recent Mirai DDoS attack should have been enough to get IoT device companies to increase their security and privacy efforts.

Now we hear that the Hue bulbs from Philips, a global electronics and IoT leader that DOES emphasize security and doesn’t cut corners, have been the focus of a potentially devastating attack (um, just wonderin’: how does triggering mass epileptic seizures through your light bulbs grab you?).

Since it’s abundantly clear that the US president-elect would rather cut regulations than add needed ones (just announcing that, for every new regulation, two must be cut), the burden of improving IoT security will lie squarely on the shoulders of the industry itself. BTW:kudos in parting to outgoing FTC Chair Edith Ramirez, who has made intelligent, workable IoT regulations in collaboration with self-help efforts by the industry a priority. Will we be up to the security challenge, or, as I’ve warned before, will security and privacy lapses totally undermine the IoT in its adolescence by losing the public and corporate confidence and trust that is so crucial in this particular industry?

Count me among the dubious.

Here’s what happened in this truly scary episode, which, for the first time, presages making the focus of an IoT hack an entire city, by exploiting what might otherwise be a smart city/smart grid virtue: a large installed base of smart bulbs, all within communication distance of each other. The weapons? An off-the-shelf drone and an USB stick (the same team found that a car will also do nicely as an attack vector). Fortunately, the perpetrators in this case were a group of white-hat hackers from the Weizmann Institute of Science in Israel and Dalhousie University in Canada, who reported it to Philips so they could implement additional protections, which the company did.

Here’s what they wrote about their plan of attack:

“In this paper we describe a new type of threat in which adjacent IoT devices will infect each other with a worm that will spread explosively over large areas in a kind of nuclear chain reaction (my emphasis), provided that the density of compatible IoT devices exceeds a certain critical mass. In particular, we developed and verified such an infection using the popular Philips Hue smart lamps as a platform.

“The worm spreads by jumping directly from one lamp to its neighbors, using only their built-in ZigBee wireless connectivity and their physical proximity. The attack can start by plugging in a single infected bulb anywhere in the city, and then catastrophically spread everywhere within minutes, enabling the attacker to turn all the city lights on or off, permanently brick them, or exploit them in a massive DDOS attack (my emphasis). To demonstrate the risks involved, we use results from percolation theory to estimate the critical mass of installed devices for a typical city such as Paris whose area is about 105 square kilometers: The chain reaction will fizzle if there are fewer than about 15,000 randomly located smart lights in the whole city, but will spread everywhere when the number exceeds this critical mass (which had almost certainly been surpassed already (my emphasis).

“To make such an attack possible, we had to find a way to remotely yank already installed lamps from their current networks, and to perform over-the-air firmware updates. We overcame the first problem by discovering and exploiting a major bug in the implementation of the Touchlink part of the ZigBee Light Link protocol, which is supposed to stop such attempts with a proximity test. To solve the second problem, we developed a new version of a side channel attack to extract the global AES-CCM key that Philips uses to encrypt and authenticate new firmware. We used only readily available equipment costing a few hundred dollars, and managed to find this key without seeing any actual updates. This demonstrates once again how difficult it is to get security right even for a large company that uses standard cryptographic techniques to protect a major product.”

Again, this wasn’t one of those fly-by-night Chinese manufacturers of low-end IoT devices, but Philips, a major, respected, and vigilant corporation.

As for the possible results? It could:

  •  jam WiFi connections
  • disturb the electric grid
  • brick devices making entire critical systems inoperable
  • and, as I mentioned before, cause mass epileptic seizures.

As for the specifics, according to TechHive, the researchers installed Hue bulbs in several offices in an office building in the Israeli city of Beer Sheva. In a nice flair for the ironic, the building housed several computer security firms and the Israeli Computer Emergency Response Team.  They attached the attack kit on the USB stick to a drone, and flew it toward the building from 350 meters away. When they got to the building they took over the bulbs and made them flash the SOS signal in Morse Code.

The researchers”were able to bypass any prohibitions against remote access of the networked light bulbs, and then install malicious firmware. At that point the researchers were able to block further wireless updates, which apparently made the infection irreversible. ‘There is no other method of reprogramming these [infected] devices without full disassemble (which is not feasible). Any old stock would also need to be recalled, as any devices with vulnerable firmware can be infected as soon as power is applied.’”

Worst of all, the attack was against Zigbee, one of the most robust and widely-used IoT protocols, an IoT favorite because Zigbee networks tend to be cheaper and simpler than WiFi or BlueTooth.

The attack points up one of the critical ambiguities about the IoT. On one hand, the fact that it allows networking of devices leads to “network effects,” where each device becomes more valuable because of the synergies with other IoT devices. On the other hand, that same networking and use of open standards means that penetrating one device can mean ultimately penetrating millions and compounding the damage.


I’m hoping against hope that when Trump’s team tries to implement cyber-warfare protections they’ll extend the scope to include the IoT because of this specific threat. If they do, they’ll realize that you can’t just say yes cyber-security and no, regulations. In the messy world of actually governing, rather than issuing categorical dictums, you sometimes have to embrace the messy world of ambiguity.  

What do you think?

 

http://www.stephensonstrategies.com/">Stephenson blogs on Internet of Things Internet of Things strategy, breakthroughs and management