Digital Twins: the Ultimate in Internet of Things Real-Time Monitoring

Get ready for the age when every product will have a “digital twin” back at the manufacturer, a perfect copy of not just the product as it left the factory floor, but as it is functioning in the field right now. That will be yet another IoT game-changer in terms of my 4th IoT Essential Truth, “rethink products.”

Oh, and did I forget to mention that we’ll each have a personal body twin from birth, to improve our health?

For the first time we’ll really understand products, how they work, what’s needed to improve them, and even how they may be tweaked once they’re thousands of miles from the factory, to add new features, fix problems, and/or optimize efficiency.

Key to circular organizations

Even better, the twin can play a critical role in accomplishing my vision of new circular organizations (replacing obsolete hierarchies and linear processes), in which all relevant departments and functions (and even supply chain members, distribution networks and customers, where relevant) form a continuous circle with real-time IoT data as the hub).  Think of the twin as one of those manifestations of the real-time data to which all departments will have simultaneous access.

GE Digital Twin visualization

               GE Digital Twin visualization

I’ve often remarked how incredible it was that companies (especially manufacturers) were able to function as well as they did and produce products as functional as they were despite the inability to peek inside them and really understand their operations and/or problems. Bravo, industrial pioneers!

However, that’s no longer good enough, and that’s where digital twins come in.  In a WSJ blog post this week, General Electric’s William Ruh, my fav IoT visionary/pragmatist, talked about how the company, as part of its “Industrial Internet” transformation, is making digital twins a key tool:

“Every product out there will have one, and there will be an ability to connect a system, or systems of digital twins, easily. The digital twin is a model of an asset, a product such as a jet engine or a model of the blades in a jet engine. Sensors on those blades pull the data off and feed them into the digital twin. The digital twin is kept current with the data that is run off the sensors. It is in sync with the reality of the blade. Now we can ask what is the best time to change the blade, how the blade performs, options to get greater efficiency.”

Proof of the pudding?

Ruh says they’ve created a wind turbine and twin they call the “Digital Windfarm,” which generates 20% more electricity than a nearby conventional turbine.

PTC is also working on digital twins. According to the company’s Executive VP for Digital Twin, Mike Campbell,:  “It’s a model that uniquely represents a physical occurrence in the real world. This one-­to­one mapping is important. You create a relationship between the digital data and a unique product occurrence from a variety of sources: sensors, enterprise data on how it was made, what its configuration was, its geometry, how it is being used, and how it is being serviced.”

Predix

The key to digital twins is GE’s “Predix” predictive analytics software platform, which the company is extending across its entire product line. As always, the key is a constant stream of real-time data:

“weather, component messages, service reports, performance of similar models in GE’s fleets—a predictive model is built and the data collected is turned into actionable insights. This model can perform advanced planning, such as forecasting a ‘plan of the day’ for turbine operation, determining a highly efficient strategy to execute planned maintenance activities, and providing warnings about upcoming unplanned maintenance events, all of which ultimately generates more output and revenue for the customer.”

Digital doppelgängers

Here’s where the really sci-fi part kicks in: Ruh also predicts (Predix??, LOL) that GE’s medical division will soon create digital twins for you and me — at birth!

“I believe we will have a digital twin at birth, and it will take data off of the sensors everybody is running, and that digital twin will predict things for us about disease and cancer and other things. I believe we will end up with health care being the ultimate digital twin. Without it, I believe we will have data but with no outcome, or value.”

And, frankly, there’s also a spooky aspect to what GE’s doing, working with retailers to create psychographic models of customers based on their buying preferences. I’m dubious on that account: I do appreciate some suggestion about what might interest me, especially books, based on my past purchases. On the other hand, a couple of weeks I shopped for — but didn’t buy — biz cards online. Now, I get AdSense ads for these cards everywhere — even on this homepage (sorry for stuff that isn’t IoT, dear reader) Get over it, OK? Count me out when it get’s down to really granular psychographic profiles — too many risks with privacy and security.

I suspect digital twins will become a staple of the IoT, yielding critical real-time info on product status that will enable predictive maintenance and, as Ruh has written elsewhere, speeding the product upgrade process because, for the first time, designers will know exactly how the products are functioning in the field, as opposed to the total lack of information that used to be the norm. Stay tuned.

Day 2, Live Blogging from SAP’s IoT2016 Internet of Things Event

I’m up first this morning, & hope to lift attendees’ vision of what can be achieved with the Internet of Things: sure, cool devices and greater efficiency are great, but there’s so much more: how about total transformation of businesses and the economy, to make them more creative, precise, and even environmentally sustainable?

I’ve just revised my 4 IoT Essential Truths, the heart of my presentation, bumping make privacy and security the highest priority from number 4 to number 1 because of the factors I cited last week. I’ll draw on my background in crisis management to explain to the engineers in attendance, who I’ve found have a problem with accepting fear because it isn’t fact-based, how losing public trust could kill the IoT Golden Goose.

I’ll go on to explain the three other Essential Truths:

  • Share Data (instead of hoarding it, as in the past)
  • Close the Loop (feed that data back so there are no loose ends, and devices become self-regulating
  • Rethink Products so they will contain sensors to feed back data about the products’ real-time status, and/or can now be marketed not as products that are simply sold, but services that both provide additional benefits to customers while also creating new revenue streams for the manufacturer.

I’ll stress that these aren’t just truisms, but really difficult paradigm shifts to accomplish. They’re worth it, however, because making these changes a reality will allow us to leave behind old hierarchical and linear organizational structures that made sense in an age of limited and hard-t0-share data. Instead, we can follow the lead of W.L. Gore and its cyclical “lattice management,” in which — for the first time — everyone can get the real-time data they need to do their jobs better and make better decisions. Equally important, everyone can share this data in real time, breaking down information silos and encouraging collaboration, both within a company and with its supply chain and distribution network — and even with customers.

Amen.


Back with Michael Lynch of SAP!

  • we can change the world and enhance our understanding greater than ever.
  • can help us solve global warming.
  • great case study on heavy truck predictive maintenance in GoldCorp Canadian gold mines.
  • IoT maturity curve:
  • Critical question: who are you in a connected future?  Can lead to re-imaginging your corporate role.
  • UnderArmour is now embedding monitors into clothing.
  • Tennant makes cleaning equipment. Big problem with lost machines, now can find them quickly.
  • Asset Intelligence Network — Facebook for heavy equipment — SAP will launch soon.
  • example of a tractor company that’s moving to a “solutions-based enterprise.” What is the smallest increment of what you do that you could charge customer. Like the turbine companies charging for thrust.

SAP strategy:

  • “Our solution strategy is to grow by IoT-enabling core industry, and providing next generation solutions for millions of human users, while expanding our platform market by adding devices.”
  • they have an amazing next-gen. digital platform. More data flow through there than Alibaba & Amazon!
  • CenterPoint Energy — correlating all sorts of data such as smart meter & weather. Better forecasting.
  • Doing a new home-based diabetes monitoring system with Roche.
  • Doing a lot of predictive maintenance.
  • Connected mining.
  • Building blocks:
    • Connect (SAP IoT Starter Kit)
    • Transform
    • Re-imagine

Ending the day with my presentation on first steps for companies to take in beginning an IoT strategy, with special emphasis on applying analytical tools such as HANA to your current operations, and building “precision operations” by giving everyone who needs it real-time data to improve their job performance and decision-making. Much of the presentation will focus on GE, with its “Brilliant Factories” initiative!

Live Blogging from SAP’s HANA IoT event

Hmm. Never been to Vegas before: seems designed to bring out the New England Puritan in me. I’ll pass on opulence, thank you very much…

 SAP HANA/ IoT Conference

SAP HANA/ IoT Conference

Up front, very interested in a handout from Deloitte, “Beyond Linear,” which really is in line with speech I’ll give here tomorrow on the IoT “Essential Truths,” in which one of my four key points will be that we need to abandon the old, linear flow of data for a continuous cyclical one.  According to Deloitte’s Jag Bandia,

“Among users with a complete, 360-degree view of relevant data for each specific process can help avoid missed opportunities. The ‘all data’ approach means relevant data can and should come from anywhere — any application, any system, any process — not just the traditional channels associated with the process.”

Bravo!

First speaker: SAP Global Customers Operations CTO Ifran Khan:

  • “digital disruption”: catalyst for change & imperative to go digital.
  • digression about running going digital (I put in my 30 minutes this morning!!!), creating a totally new way of exercising (fits beautifully with “Smart Aging“!)
  • new macro tech trends are enabling digitalizations: hyper-connectivity, super computing, cloud computing, smart world, and cybersecurity (horrifying stat about how many USB sticks were left in dry cleaning!)
  • those who don’t go digital will go under…. (like John Chambers’ warning about IoT).
  • new opportunities in wide range of industries
  • need new digital architectures — “driving locality of data, integrated as deep as possible into the engine.
  • HOLY COW! He starts talking about a circular, digitally-centered concept, with a buckyball visual.  Yikes: great minds think alike.
  • sez HANA allows a single platform for all digital enterprise computing.
  • running things in real-time, with no latency — music to my ears!

Jayne Landry, SAP:

  • too few in enterprise have real-time access to analytics — oh yeah!
  • “analytics for everyone”
  • “own the outcome”
  • “be the one to know”
  • SAP Cloud for Analytics — “all analytics capabilities in one product.” real-time, embedded, consumer-grade user experience, cloud-based. Looking forward to seeing this one!
  • “Digital Boardroom” — instant insight. Same info available to board also available to shopfloor — oh yeah — democratizing data!

Very funny bit by Ty Miller on using SAP Cloud for Analytics to analyze Area 51 data. Woo Woo!

Ifran Khan again:

  • how to bring it to the masses? Because it’s expensive and difficult to maintain on the premises, extend and build in cloud! Add new “micro services” to SAP HANA cloud platform: SAP Application Integration, Tax Service, Procurement, Customer Engagement, Predictive, and, ta da, IoT.
  • video of Hamburg Port Authority. Absolutely love that and what they’re doing with construction sites!

Jan Jackman, IBM:

  • customers want speed. Cloud is essential. IBM & HANA are partners in cloud…

This guy is sooo neat: Michael Lynch, IoT Extended Supply Chain for SAP (and former opera student!):

  • “Connecting information, people, and things is greatest resource ever to drive insightful action.”
  • “big deal is the big data processing potential is real & chips are cheaper, so you can build actual business solutions”
  • STILL gmbh (forklifts) great example!
  • phase 1: connect w/ billions of internet-enabled things to gain new insights
  • phase II: transform the way you make decisions and take action
  • phase III: re-imagine your customer’s experience.
  • they do design thinking workshops — would luv one of those!
  • great paradigm shift: Hagleitner commercial bathroom supplies
  • Kaeser compressors: re-imaging customer service
  • working with several German car companies on enabling connected driving
  • once again, the  Hamburg Port Authority!!

SAP’s strategy:

  • offers IoT apps. platforms, and facilitates extensions of IoT solutions
  • work closely with Siemens: he’s talked with them about turbine business.
  • SAP has several solutions for IoT
  • Cloud-based predictive maintenance!
  • “social network for assets”: Asset Intelligence Network
  • They did the Harley York PA plant! — one line, 21-day per bike to 6 hrs.  (displays all around the plant with KPIs)
  • 5 layers of connectivity in manufacturing “shop floor to top floor”  SAP Connected Manufacturing
  • They have a IoT Starter Kit — neat
  • SAP Manufacturing Integration and Intelligence
  • SAP Plant Connectivity
  • SAP Event Stream Processor
  • SAP MobiLink
  • SAP SQL Anywhere/SAP ultralite
  • 3rd Party IoT Device Cloud (had never heard of “device cloud” concept — specialize in various industry verticals).

“Becoming an Insight-Driven Organization”  Speakers: Jag Bandla and Chris Dinkel of Deloitte.

  • Deloitte is using these techniques internally to make Deloitte “insight-driven”
  • “an insight-driven organization (IDO) is one which embeds analysis, data, and reasoning into every step of the decision-making process.” music to my ears!
  • emphasis on actionable insight
  • “when humans rely on their own experiences and knowledge, augmented by a stream of analytics-driven insights, the impact on value can be exponential”
  • benefits to becoming an IDO:
    • faster decisions
    • increased revenue
    • decreased cost of decision making
  • challenges:
    • lack of proper tech to capture
    • oooh: leaders who don’t understand the data…
  • 5 enabling capabilities:
    • strategy
    • people
    • process
    • data
    • tech
  • developing vision for analytics
  • Key questions: (only get a few..)
    • what are key purchase drivers for our customers?
    • how should we promote customer loyalty?
    • what customer sentiments are being expressed on social media?
    • how much should we invest in innovation?
  • Value drivers:
    • strategic alignment
    • revenue growth
    • cost reduction
    • margin improvement
    • tech
    • regulation/compliance
  • Organize for success (hmm: I don’t agree with any of these: want to decentralize while everyone is linked on a real-time basis):
    • centralized (don’t like this one, with all analyzed in one central group.. decentralize and empower!)
    • consulting: analysts are centralized, but act as internal consultants
    • center of excellence: central entity coordinates community of analysts across company
    • functional: analysts in functions such as marketing & supply chain
    • dispersed: analysts scattered across organization, little coordination
  • Hire right people! “Professionals who can deliver data-backed insights that create business value — and not just crunch numbers — are the lifeblood of an Insight-Driven Organization”
    • strong quantitative skills
    • strong biz & content skills (understand content and context)
    • strong data modeling & management skills
    • strong IT skills
    • strong creative design skills (yea: techies often overlook the cool design guys & gals)
  • Change the mindset (critical, IMHO!):
    • Communicate: build compelling picture of future to steer people in right direction.
    • Advocate: develop cohort of leaders to advocate for program.
    • Active Engagement: engage key figures to create pull for the program
    • Mobilize: mobilize right team across the organization.
  • How do you actually do it? 
    • improve insight-to-impact with “Exponential Biz Processes” — must rebuild existing business processes!  Involves digital user experience, biz process management, enterprise science, all data, and IT modernization.
      • re-engineer processes from ground up
      • develop intuitive, smart processes
      • enable exception-based management
  • Data:
    • “dark data:” digital exhaust, etc. might be hidden somewhere, but still actionable.
      • they use it for IoT: predictive personalization (not sure I get that straight…).
    • want to have well-defined data governance organization: standards, data quality, etc.
  • Technology: digital core (workforce engagement, big data & IoT, supplier collaboration, customer experience
    • HANA
  • Switch to digital delivery: visualizations are key!
    • allow for faster observations of trends & patterns
    • improve understanding & retention of info
    • empower embedded feeds and user engagement

 

IoT and the Data-Driven Enterprise: Bob Mahoney, Red Hat & Sid Sipes, Sr. Director of Edge Computing, SAP

  • What’s driving enterprise IoT?
    • more connected devices
    • non-traditional interactions such as M2M and H2M
    • ubiquitous internet connectivity
    • affordable bandwidth
    • cloud computing
    • standards-based and open-source software
  • Biz benefits:
    • economic gains
    • new revenue streams (such as sale of jet turbine data)
    • regulatory compliance
    • efficiencies and productivity
    • ecological impact
    • customer satisfaction
  • example of Positive Train Control systems to avert collisions. Now, that can be replaced by “smarter train tech”
  • SAP and edge computing (can’t move all of HANA to edge, but..)
    • improve security in transmission
    • reduce bandwidth need
    • what if connection goes down
    • actual analysis at the edge
    • allows much quicker response than sending it to corporate, analyzing & send it back
    • keep it simple
    • focused on, but not limited to, IoT
  • they can run SQL anywhere on IoT, including edge: SQL Anywhere
  • Red Hat & SAP doing interesting combination for retail, with iBeacons, video heat map & location tracking: yields real insights into consumer behavior.

Testing the IoT Waters: 1st Steps in Creating an IoT Corporate Strategy

What if you’re interested in the Internet of Things, but are a little scared of making a major commitment and making major expenditures until you build your familiarity level and start to enjoy some tangible results?

That concern is understandable, especially when prognosticators such as I emphasize what a transformational impact the IoT will have on every aspect of your operations and strategy.

So where to begin?

I’ll speak on this issue at SAP’s  IoT 2016 Conference, Feb. 16-19, in Las Vegas, and hope you can attend. But, if not, or if a teaser might convince you to make the plunge, here’s a summary of my major points, which I hope will motivate you to act sooner, rather than later!

Managing_the_Internet_of_Things_RevolutionThis is an issue that I first visited with my “Managing the Internet of Things Revolution” e-guide to IoT strategy for C-level executives, which I wrote in 2014 for SAP, and which has been successful enough that they’ve translated it into eight languages.

I suggested that the best reason to begin now on creating and executing an IoT strategy was that a lot of the requisite tools for an IoT strategy were also critical to optimize your current operations:

  • invest now in analytical tools (such as SAP’s HANA!), so that you can make sense of the rapidly-expanding amount of data (especially unstructured data) that you are already collecting, with new benefits including predictive analytics that allow you to better predict the future.
  • even before capital equipment is redesigned to incorporate sensors that will yield 24/7 real-time data on their operations and status, consider add-on sensors where available, so you can take the guesswork out of operations.
  • where possible, process sensor data “at the edge,” so that only the relevant data will be conveyed to your processing hub, reducing storage and central processing demands.
  • develop or contract for cloud storage, to handle vastly increased data.
GE Brilliant Factory benefits

GE Brilliant Factory benefits

As I’ll explain my speech, even without launching any major IoT projects such as product redesign or converting products into services, initial IoT projects such as these will dramatically boost your profits and efficiency by allowing unprecedented precision in operations.  I’ll emphasize the example of GE, whose “Brilliant Factory” initiative is aimed at increasing both its own manufacturing efficiency and its customers’ as well. They make a modest, but astonishing claim:

“GE estimates that a 1% improvement in its productivity across its global manufacturing base translates to $500 million in annual savings. Worldwide, GE thinks a 1% improvement in industrial productivity could add $10 trillion to $15 trillion to worldwide GDP over the next 15 years.”

Remember: that’s not exploiting the full potential of the IoT, but simply using it to boost operating efficiency. I see this as bringing about an era of “Precision Manufacturing,” because everyone who needs real-time data about the assembly line and production machinery will be able to share it instantly — including not only all departments within your company but also your supply chain and your distribution network.

In many cases, resupply will be automatic, through M2M processes where data from the assembly line will automatically trigger supply re-orders (and may lead to reshoring of jobs, because the advantages of true “just-in-time” delivery of parts from a supplier located a few miles away will outweigh the benefits of using one on the other side of the world, where delivery times are measured in weeks).  Instead of the current linear progression from supply chain to factory floor to distribution network, we’ll have a continuous loop uniting all of those components, with real-time IoT data as the “hub.”

Again, without making a full-fledged commitment to the IoT, another benefit that I’ll detail is how you’ll be able to dramatically improve workplace safety, especially inherently chaotic and fast-changing worksites such as construction projects and harbors, whose common elements include unpredictable schedules, many companies and contractors, many workers, and many vehicles — a recipe for disaster given current conditions!  However, the combination of simply putting location sensors on the equipment, vehicle, and people can radically decrease the risk. For example,  in Dubai — home to 25% of all construction cranes in the world — SAP partnered with a worldwide leader in construction site safety, SK Solutions. Sensors are located on machinery throughout every site, reporting real-time details about every activity: machinery’s position, movement, weight, and inertia and critical data from other sources (as with the GE Durathon factory’s use of weather data), including wind speed and direction, temperature, and more. Managers can detect potential collisions, and an auto-pilot makes instant adjustments to eliminate operator errors. “The information is delivered on dashboards and mobile devices, visualized with live 3-D images with customizable views.”

As I’ll tell the conference attendees,

“Equally incredible is the change at the Port of Hamburg, Germany’s biggest port, which must juggle 9 million containers and 12,000 vessels a year, not to mention a huge number of trucks and trains. You can imagine the potential for snarls and accidents. Since installing HANA, all of these components, including the drivers and other operators, are linked in real time.  Average waiting time for each truckload has been cut 5 minutes,  and there are 5,000 fewer truck hours daily. The coordination has gotten so precise that, if a trucker will be held up by a bridge opening, the nearby coffee shop will send a discount coupon to his iPad.”

I’ll conclude by mentioning a couple of the long-term components of an IoT strategy, such as redesigning products so that they can be controlled by apps and/or feedback constant information on their status, and considering whether to market products instead as services, where the customer only pays for the products when they’re actually being used, and creating optional data services that customers may choose to buy because they’ll allow the customer to optimize operating efficiency.

But the latter are the long-term challenges and benefits.  For now, I’ll tell the audience that the important thing is to begin now investing in the analytical tools and sensors that will help them boost efficiency.

Hope you can be there!


Oh yeah. Why get started on your IoT strategy now, rather than wait a few more years? Last year, former Cisco Chairman John Chambers said that 40% of the companies attending a recent seminar wouldn’t survive in a “meaningful way” within 10 years if they don’t begin now to embrace the IoT. Sobering, huh?

Data Is the Hub: How the IoT and Circular Economy Build Profits

Fasten your seatbelts! I think I’ve finally zeroed in on the Internet of Things’ (IoT’s) most important potential economic benefit and how it could simultaneously help us escape the growing global environmental crisis:

make real-time IoT data* the hub of a circular economy and management mentality. It’s both good for the bottom line and the planet.

I started writing about circular business models back in the 90’s, when I consulted on profitable environmental strategies, i.e., those that were good both for the corporate bottom line and the planet.  It galled me that executives who railed about eliminating inefficiency thought reducing waste was for tree-huggers. Semantics and lifestyle prejudices got in the way of good strategy.

Ford’s River Rouge Plant (1952 view)

I could see that it was vital that we get away from old, linear models that began with extracting resources and ended with abandoned products in landfills. Ford’s massive 1 x 1.6 mile River Rouge Plant, the world’s largest integrated factory, was the paradigm of this thinking: ore was deposited at one end, made into steel, and cars came out the other (Hank’s penchant for vertical integration even led him to buy rubber plantations! If you have any illusions about the ultimate impossibility of top-down control, watch the PBS documentary on Ford — he simply couldn’t share power, even with his own son — and it almost ruined the company). The linear model worked for a long time, and, truth to tell, it was probably the only one that was feasible in the era of paper-and-pencil information flow:  it was so hard to gather and transmit information that senior management controlled who got what information, and basically threw it over the transom to the next office.

As for any kind of real-time information about what was actually happening on the factory floor: fugetaboutit: all that was possible was for low-level functionaries to shuffle along the assembly line, taking scheduled readings from a few gauges and writing them on a clipboard. Who knew if anyone ever actually read the forms, let alone made adjustments to equipment based on the readings?

Fast forward to 2015, and everything’s changed!

The image of the circular corporation popped back into my head last week while I was searching for an image of how the IoT really can change every aspect of corporate operations, from product design to supply chain management.  I was happily surprised that when I Googled “circular economy” I found a large number of pieces, including ones from consulting gurus Accenture and McKinsey (the most comprehensive report on the concept is probably this one from the Ellen MacArthur Foundation), about the bottom-line and environmental benefits of switching from a linear (‘take-make-dispose’) pattern.

But how to make the circular economy really function? That’s where the IoT comes in, and, in my estimation, is THE crucial element.

Visualize everything a company does as a circle, with IoT-gathered real-time data as its hub. That’s crucial, because everything in a profitable circular company revolves around this data, shared in real time by all who need it.

When that happens, a number of crucial changes that were impossible in the era of linear operations and thinking and limited data became possible for the first time:

  • you can optimize assembly line efficiency because all components of the factory are monitored by sensors in real time, and one process can activate and regulate another, and/or managers and assembly-line workers can fine-tune processes (think of the 10,000 sensors on the GE Durathon battery assembly line).
  • you can integrate the assembly line with the supply chain and distribution and sales network as never before (provided that you share the real-time data with them), so materials are delivered on a just-in-time basis) and production is dictated by real-time data on sales (the SAP smart vending machine, integrated with logistics, is a great example).
  • you can optimize product redesign and upgrades and speed the process, because sensor data from the products as they are actually used in the field is immediately fed back to the designers, so they have objective evidence of what does, and doesn’t work properly (think of how GE has improved its product upgrade process). No more ignorance of how your products are actually used!
  • from an environmental standpoint, having sensors on key components can make it possible for you to recover and profitably remanufacture them (closing the loop) rather than having them landfilled (I was excited to learn that Caterpillar has been doing this for 40 years (!) through its Reman Program, which “reduces costs, waste, greenhouse gas emissions and need for raw inputs.”).
  • you can create new revenue streams, by substituting services for actual sales of products.  I’ve written before about how GE and RollsRoyce do this with jet engines, helping clients be more efficient by providing them with real-time data from jet turbines in return for new fees, and Deere does it with data feeds from its tractors. Now I learn that Phillips does this, with industrial lighting, retaining ownership of the lighting: the customers only pay for the actual use of the lights. Phillips also closes the loop by taking the lights back at the end of their life and/or upgrading them.

As I’ve written before, creating the real-time data is perhaps the easier part: what’s harder is the paradigm shift the circular economy requires, of managers learning to share real-time data with everyone inside the enterprise (and, preferably, with the supply chain, distribution network, retailers, and, yes, even customers). When that happens, we will have unprecedented corporate efficiency, new revenue streams, satisfied customers, and, equally important reduce our use of finite resources, cut pollution, and tread lightly on the earth.  There you have it: the secret to 21st-century profitability is:

real-time IoT data, at the hub of the circular enterprise.


*Oh yeah, please don’t drop a dime on me with the grammar police about the title: in fact, I’m a retired colonel in the Massachusetts Grammar Police, but I’ve given up the fight on “data.” From my Latin training, I know that data are the plural form of datum, but datum is used so infrequently now and data with a singular verb has become so common that I’ve given up the fight and use it as a singular noun.  You can see the issue debated ad nauseum here

I’ll Speak Twice at Internet of Things Global Summit Next Week

I always love the Internet of Things Global Summit in DC because it’s the only IoT conference I know of that places equal emphasis on both IoT technology and public policy, especially on issues such as security and privacy.

At this year’s conference, on the  26th and 27th, I’ll speak twice, on “Smart Aging” and on the IoT in retailing.

2015_IoT_SummitIn the past, the event was used to launch major IoT regulatory initiatives by the FTC, the only branch of the federal government that seems to really take the IoT seriously, and understand the need to protect personal privacy and security. My other fav component of last year’s summit was Camgian’s introduction of its Egburt, which combines “fog computing,” to analyze IoT data at “the edge,” and low power consumption. Camgian’s Gary Butler will be on the retail panel with me and with Rob van Kranenburg, one of the IoT’s real thought leaders.

This year’s program again combines a heady mix of IoT innovations and regulatory concerns. Some of the topics are:

  • The Internet of Things in Financial Services and the Insurance sector (panel includes my buddy Chris Rezendes of INEX).
  • Monetizing the Internet of Things and a look at what the new business models will be
  • The Connected Car
  • Connected living – at home and in the city
  • IoT as an enabler for industrial growth and competition
  • Privacy in a Connected World – a continuing balancing act

The speakers are a great cross-section of technology and policy leaders.

There’s still time to register.  Hope to see you there!

 

 

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

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.

McKinsey IoT Report Nails It: Interoperability is Key!

I’ll be posting on various aspects of McKinsey’s new “The Internet of Things: Mapping the Value Beyond the Hype” report for quite some time.

First of all, it’s big: 148 pages in the online edition, making it the longest IoT analysis I’ve seen! Second, it’s exhaustive and insightful. Third, as with several other IoT landmarks, such as Google’s purchase of Nest and GE’s divestiture of its non-industrial internet division, the fact that a leading consulting firm would put such an emphasis on the IoT has tremendous symbolic importance.

McKinsey report — The IoT: Mapping the Value Beyond the Hype

My favorite finding:

“Interoperability is critical to maximizing the value of the Internet of Things. On average, 40 percent of the total value that can be unlocked requires different IoT systems to work together. Without these benefits, the maximum value of the applications we size would be only about $7 trillion per year in 2025, rather than $11.1 trillion.” (my emphasis)

This goes along with my most basic IoT Essential Truth, “share data.”  I’ve been preaching this mantra since my 2011 book, Data Dynamite (which, if I may toot my own horn, I believe remains the only book to focus on the sweeping benefits of a paradigm shift from hoarding data to sharing it).

I was excited to see that the specific example they zeroed in on was offshore oil rigs, which I focused on in my op-ed on “real-time regulations,” because sharing the data from the rig’s sensors could both boost operating efficiency and reduce the chance of catastrophic failure. The paper points out that there can be 30,000 sensors on an rig, but most of them function in isolation, to monitor a single machine or system:

“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.”

Yet, the researchers found that only about 1% of the rig data was being used, because it rarely was shared off the rig with other in the company and its ecosystem!

The section on interoperability goes on to talk about the benefits — and challenges — of linking sensor systems in examples such as urban traffic regulation, that could link not only data from stationary sensors and cameras, but also thousands of real-time feeds from individual cars and trucks, parking meters — and even non-traffic data that could have a huge impact on performance, such as weather forecasts.  

While more work needs to be done on the technical side to increase the ease of interoperability, either through the growing number of interface standards or middleware, it seems to me that a shift in management mindset is as critical as sensor and analysis technology to take advantage of this huge increase in data:

“A critical challenge is to use the flood of big data generated by IoT devices for prediction and optimization. Where IoT data are being used, they are often used only for anomaly detection or real-time control, rather than for optimization or prediction, which we know from our study of big data is where much additional value can be derived. For example, in manufacturing, an increasing number of machines are ‘wired,’ but this instrumentation is used primarily to control the tools or to send alarms when it detects something out of tolerance. The data from these tools are often not analyzed (or even collected in a place where they could be analyzed), even though the data could be used to optimize processes and head off disruptions.”

I urge you to download the whole report. I’ll blog more about it in coming weeks.

The Internet of Things’ Essential Truths

I’ve been writing about what I call the Internet of Things’ “Essential Truths” for three years now, and decided the time was long overview to codify them and present them in a single post to make them easy to refer to.

As I’ve said, the IoT really will bring about a total paradigm shift, because, for the the first time, it will be possible for everyone who needs it to share real-time information instantly. That really does change everything, obliterating the “Collective Blindness” that has hampered both daily operations and long-term strategy in the past. As a result, we must rethink a wide range of management shibboleths (OK, OK, that was gratuitous, but I’ve always wanted to use the word, and it seemed relevant here, LOL):

  1. First, we must share data. Tesla leads the way with its patent sharing. In the past, proprietary knowledge led to wealth: your win was my loss. Now, we must automatically ask “who else can use this information?” and, even in the case of competitors, “can we mutually profit from sharing this information?” Closed systems and proprietary standards are the biggest obstacle to the IoT.
  2. Second, we must use the Internet of Things to empower workers. With the IoT, it is technically possible for everyone who could do their job better because of access to real-time information to share it instantly, so management must begin with a new premise: information should be shared with the entire workforce. Limiting access must be justified.
  3. Third, we must close the loop. We must redesign our data management processes to capitalize on new information, creating continuous feedback loops.
  4. Fourth, we must rethink products’ roles. Rolls-Royce jet engines feed back a constant stream of real-time data on their operations. Real-time field data lets companies have a sustained dialogue with products and their customers, increasingly allowing them to market products as services, with benefits including new revenue streams.
  5. Fifth, we must develop new skills to listen to products and understand their signals. IBM scientists and medical experts jointly analyzed data from sick preemies’ bassinettes & realized they could diagnose infections a day before there was any visible sign. It’s not enough to have vast data streams: we need to understand them.
  6. Sixth, we must democratize innovation. The wildly-popular IFTTT web site allows anyone to create new “recipes” to exploit unforeseen aspects of IoT products – and doesn’t require any tech skills to use. By sharing IoT data, we empower everyone who has access to develop new ways to capitalize on that data, speading the IoT’s development.
  7. Seventh, and perhaps most important, we must take privacy and security seriously. What responsible parent would put an IoT baby monitor in their baby’s room after the highly-publicized incident when a hacker exploited the manufacturer’s disregard for privacy and spewed a string of obscenities at the baby? Unless everyone in the field takes privacy and security seriously, the public may lose faith in the IoT.

There you have ’em: my best analysis of how the Internet of Things will require a revolution not just in technology, but also management strategy and practices. What do you think?

Management Challenge: Lifeguards in the IoT Data Lake

In their Harvard Business Review November cover story, How Smart, Connected Products Are Transforming Competition, PTC CEO Jim Heppelmann and Professor Michael Porter make a critical strategic point about the Internet of Things that’s obscured by just focusing on IoT technology: “…What makes smart, connected products fundamentally different is not the internet, but the changing nature of the “things.”

In the past, “things” were largely inscrutable. We couldn’t peer inside massive assembly line machinery or inside cars once they left the factory, forcing companies to base much of both strategy and daily operations on inferences about these things and their behavior from limited data (data which was also often gathered only after the fact).

Now that lack of information is being removed. The Internet of Things creates two unprecedented opportunities regarding data about things:

  • data will be available instantly, as it is generated by the things
  • it can also be shared instantly by everyone who needs it.

This real-time knowledge of things presents both real opportunities and significant management challenges.

Each opportunity carries with it the challenge of crafting new policies on how to manage access to the vast new amounts of data and the forms in which it can be accessed.

For example: with the Internet of Things we will be able to bring about optimal manufacturing efficiency as well as unprecedented integration of supply chains and distribution networks. Why? Because we will now be able to “see” inside assembly line machinery, and the various parts of the assembly line will be able to automatically regulate each other without human intervention (M2M) to optimize each other’s efficiency, and/or workers will be able to fine-tune their operation based on this data.

Equally important, because of the second new opportunity, the exact same assembly line data can also be shared in real time with supply chain and distribution network partners. Each of them can use the data to trigger their own processes to optimize their efficiency and integration with the factory and its production schedule.

But that possibility also creates a challenge for management.

When data was hard to get, limited in scope, and largely gathered historically rather than in the moment, what data was available flowed in a linear, top-down fashion. Senior management had first access, then they passed on to individual departments only what they decided was relevant. Departments had no chance to simultaneously examine the raw data and have round-table discussions of its significance and improve decision-making. Everything was sequential. Relevant real-time data that they could use to do their jobs better almost never reached workers on the factory floor.

That all potentially changes with the IoT – but will it, or will the old tight control of data remain?

Managers must learn to ask a new question that’s so contrary to old top-down control of information: who else can use this data?

To answer that question they will have to consider the concept of a “data lake” created by the IoT.

“In broad terms, data lakes are marketed as enterprise wide data management platforms for analyzing disparate sources of data in its native format,” Nick Heudecker, research director at Gartner, says. “The idea is simple: instead of placing data in a purpose-built data store, you move it into a data lake in its original format. This eliminates the upfront costs of data ingestion, like transformation. Once data is placed into the lake, it’s available for analysis by everyone in the organization.”

Essentially, data that has been collected and stored in a data lake repository remains in the state it was gathered and is available to anyone, versus being structured, tagged with metadata, and having limited access.

That is a critical distinction and can make the data far more valuable, because the volume and variety will allow more cross-fertilization and serendipitous discovery.

At the same time, it’s also possible to “drown” in so much data, so C-level management must create new, deft policies – to serve as lifeguards, as it were. They must govern data lake access if we are to, on one hand, avoid drowning due to the sheer volume of data, and, on the other, to capitalize on its full value:

  • Senior management must resist the temptation to analyze the data first and then pass on only what they deem of value. They too will have a crack at the analysis, but the value of real-time data is getting it when it can still be acted on in the moment, rather than just in historical analyses (BTW, that’s not to say historical perspective won’t have value going forward: it will still provide valuable perspective).
  • There will need to be limits to data access, but they must be commonsense ones. For example, production line workers won’t need access to marketing data, just real-time data from the factory floor.
  • Perhaps most important, access shouldn’t be limited based on pre-conceptions of what might be relevant to a given function or department. For example, a prototype vending machine uses Near Field Communication to learn customers’ preferences over time, then offers them special deals based on those choices. However, by thinking inclusively about data from the machine, rather than just limiting access to the marketing department, the company shared the real-time information with its distribution network, so trucks were automatically rerouted to resupply machines that were running low due to factors such as summer heat.
  • Similarly, they will have to relax arbitrary boundaries between departments to encourage mutually-beneficial collaboration. When multiple departments not only share but also get to discuss the same data set, undoubtedly synergies will emerge among them (such as the vending machine ones) that no one department could have discovered on its own.
  • They will need to challenge their analytics software suppliers to create new software and dashboards specifically designed to make such a wide range of data easily digested and actionable.

Make no mistake about it: the simple creation of vast data lakes won’t automatically cure companies’ varied problems. But C-level managers who realize that if they are willing to give up control over data flow, real-time sharing of real-time data can create possibilities that were impossible to visualize in the past, will make data lakes safe, navigable – and profitable.