Digital Twins: Could They Have Uncovered the Boeing 737 Problems Sooner? — and how might they benefit your company

Posted on 20th March 2019 in Augmented Reality, design, Internet of Things, maintenance, management, manufacturing, marketing, sensors, strategy, supply chain

Tragically, the Boeing 737-Max includes an Internet of Things technology — the “digital twin” — that might have avoided the current tragedies, if only its use had been extended from the engines to the entire plane’s operations. Even if your products don’t involve such high stakes as a jet plane does, your company should seriously consider adding the digital twin to your services, because it can benefit every aspect of your operations and strategy.

Let me explain.

A joint venture company between GE Aviation and a French company makes the Max’s CFM International LEAP engines.  Every one of them is paired to a digital representation of the engine, a digital twin, that allows an engineer thousands of miles away to instantly monitor an inflight engine’s current operations.  That, among many other benefits, allows GE to replace the preventive maintenance of the past, which had to rely on after-the-fact data about when the average engine needed to be maintained, with “predictive maintenance,” which uses data about the earliest evidence of a possible problem to trigger a sequence to intervene early on, well before a major problem. At its most extreme, that could mean that when a plane whose sensors have detected an engine anomaly lands at its destination, the mechanic would be ready to go, knowing not only where in the engine the problem existed, but also having had the replacement part automatically dispatched from a distribution hub, so the replacement could be done quickly and at least cost.  No wonder GE has created more than hundreds of thousands of digital twins for products ranging from the jet engines to medical devices.

What if Boeing had extended the “digital twin” concept to the entire plane’s operations, including the navigation software?  The real essence of the Internet of Things — of which the digital twin is perhaps the ideal example of how the IoT merges the digital and physical — is that it allows everyone who needs it to instantly share (the verb is critical!) that real-time data — something that was impossible in the past.  In this case, that could mean real-time sharing between the cockpit, the airline, and Boeing, rather than after the fact harvesting of data from a plane’s “black box.” Hypothetically, the ability to share the real-time cockpit data would have meant that all those parties could have collaboratively brainstormed solutions to possible in-flight problems such as the maneuvering characteristics augmentation system, (MCAS) that has been fingered the possible cause of the problems.

OK, how does this ability to share real-time data from a digital twin apply to your company’s products and operations? 

The potential benefits extend to every aspect of your operations:

More radically, the digital twin’s benefits in terms of reliability and improved design can allow a fundamental change in your business model, away from selling products to leasing them, with the customer’s actual cost based on use of the product (if the turbine is sitting on the ground being repaired, it ain’t bringing the manufacturer any revenue!).  All three major jet turbine manufacturers — GE, Pratt & Whitney, and Rolls-Royce — have made this switch. They can even create new revenue streams by selling the real-time in-flight data to airlines, which can mash it up with atmospheric conditions, fuel prices, etc., to maximize flight efficiency.  Similarly, Hortilux, which makes greenhouse lights, now provides artificial light, plus data on growing conditions, rather than selling the bulbs.

Even more important than the potential to improve individual aspects of operations through digital twins is the potential for innovation and creativity if all departments (and, if you choose, even supply chain and distribution network partners and, conceivably, your customers) look at and can discuss the data—ground truth—simultaneously.

Determination of exactly what happened and why with the 737-Maxes will require intense scrutiny, but your company can benefit from the wake-up call these tragedies have provided to the IoT’s new-found ability to gather and share operating data instantly, with the result being improving every aspect of your operations and strategy.

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

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

Huzzah!

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

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

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

 

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

 

Railigent

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

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

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

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

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

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

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

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

The new Mobility Services approach’s results are dramatic:

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

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

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

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

 

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

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IoT Intangibles: Increased Customer Loyalty

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

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

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

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

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

Why’s that important?

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

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

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

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

 

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

Examples include:

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

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

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

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

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Siemens’s MindSphere: from automation to digitalization

Posted on 30th November 2016 in automotive, circular company, data, Internet of Things, M2M, manufacturing, open data

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!

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

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

George Stephenson’s Killingworth locomotive
Source: Project Gutenberg

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

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

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

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

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

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

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

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

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

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

The results of the new approach are dramatic.

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

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

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

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

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

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

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2nd day liveblogging, Gartner ITxpo, Barcelona

Posted on 8th November 2016 in aging, agriculture, circular company, circular economy, collaboration, communication, data, design, energy, Essential Truths, health, home automation, Internet of Things, m-health, maintenance, management, open data, paradigm shift, privacy, Quantified Self, security, services, strategy

Accelerating Digital Business Transformation With IoT Saptarshi Routh Angelo Marotta
(arrived late, mea culpa)

  • case study (didn’t mention name, but just moved headquarters to Boston. Hmmmmm).
  • you will be disrupted by IoT.
  • market fragmented now.

Toshiba: How is IoT Redefining Relationships Between Customers and Suppliers, Damien Jaume, president, Toshiba Client Solutions, Europe:

  • time of tremendous transformation
  • by end of ’17, will surpass PC, tabled & phone market combined
  • 30 billion connect  devices by 2020
  • health care IoT will be $117 billion by 2020
  • 38% of indiustry leaders disrupted by digitally-enabled competitors by 2018
  • certainty of customer-supplier relationship disruption will be greatest in manufacturing, but also every other market
    • farming: from product procurement to systems within systems. Smart, connected product will yield to integrated systems of systems.
  • not selling product, but how to feed into whole IoT ecosystem
  • security paramount on every level
  • risk to suppliers from new entrants w/ lean start-up costs.
  • transition from low engagement, low trust to high engagement, high trust.
  • Improving efficiencies
  • ELIMINATE MIDDLEMAN — NO LONGER RELEVANT
  • 4 critical success factors:
    • real-time performance pre-requisite
    • robustness — no downtime
    • scalability
    • security
  • case studies: energy & connected home, insurance & health & social care (Neil Bramley, business unit director for clients solutions
    • increase depth of engagement with customer. Tailored information
    • real-time performance is key, esp. in energy & health
    • 20 million smart homes underway in GB by 2020:
      • digitally empowering consumers
      • engaging consumers
      • Transforming relationships among all players
      • Transforming homes
      • Digital readiness
    • car insurance: real-time telematics.
      • real-time telematics data
      • fleet management: training to reduce accidents. Working  w/ Sompo Japan car insurance:
    • Birmingham NHS Trust for health (Ciaron Hoye, head of digital) :
      • move to health promotion paradigm
      • pro-actively treat patients
      • security first
      • asynchronous communications to “nudge” behavior.
      • avoiding hip fractures
      • changing relationship w/ the patient: making them stakeholders, involving in discussion, strategy
      • use game theory to change relationship

One-on-one w/ Christian Steenstrup, Gartner IoT analyst. ABSOLUTE VISIONARY — I’LL BE INTERVIEWING HIM AT LENGTH IN FUTURE:

  • industrial emphasis
  • applications more ROI driven, tangible benefits
  • case study: mining & heavy industry
    • mining in Australia, automating entire value train. Driverless. Driverless trains. Sensors. Caterpillar. Collateral benefits: 10% increase in productivity. Less payroll.  Lower maintenance. Less damage means less repairs.
    • he downplays AR in industrial setting: walking in industrial setting with lithium battery strapped to your head is dangerous.
    • big benefit: less capital expense when they build next mine. For example, building the town for the operators — so eliminate the town!
  • take existing processes & small improvements, but IoT-centric biz, eliminating people, might eliminate people. Such as a human-less warehouse. No more pumping huge amount of air underground. Huge reduction with new system.  Mine of future: smaller holes. Possibility  of under-sea mining.
  • mining has only had incremental change.
  • BHP mining’s railroad — Western Australia. No one else is involved. “Massive experiment.”
  • Sound sensing can be important in industrial maintenance.  All sorts of real-time info. 
  • Digital twins: must give complete info — 1 thing missing & it doesn’t work.
  • Future: 3rd party data brokers for equipment data.
  • Privacy rights of equipment.
  • “communism model” of info sharing — twist on Lenin.

 

Accelerating Digital Transformation with Microsoft Azure IoT Suite (Charlie Lagervik):

  • value networking approach
  • customer at center of everything: customer conversation
  • 4 imperatives:
    • engage customers
    • transform products
    • empower employees
    • optmize operations
  • their def. of IoT combines things/connectivity/data/analytics/action  Need feedback loop for change
  • they focus on B2B because of efficiency gains.
  • Problems: difficult to maintain security, time-consuming to launch, incompatible with current infrastructure, and hard to scale.
  • Azure built on cloud.
  • InternetofYourThings.com

 

Afternoon panel on “IoT of Moving Things” starts with all sorts of incredible factoids (“since Aug., Singapore residents have had access to self=driving taxis”/ “By 2030, owning a car will be an expensive self-indulgence and will no longer be legal.”

  • vehicles now have broader range of connectivity now
  • do we really want others to know where we are? — privacy again!
  • who owns the data?
  • what challenges do we need to overcome to turn data into information & valuable insight that will help network and city operators maximize efficiency & drive improvement across our transportation network?
  • think of evolution: now car will be software driven, then will become living room or office.
  • data is still just data, needs context & location gives context.
  • cities have to re-engineer streets to become intelligent streets.
  • must create trust among those who aren’t IT saavy.
  • do we need to invest in physical infrastructure, or will it all be digital?
  • case study: one car company w/ engine failures in 1 of 3 cars gave the consultants data to decide on what was the problem.
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My O’Reilly blog post about how the IoT will transform manufacturing

Posted on 29th April 2014 in 3-D printing, Internet of Things, M2M, manufacturing

Woopiedoo! I have a post in today’s O’Reilly SOLID blog (which is, among other things, promoting their SOLID conference in SF next month) about how the Internet of Things will transform manufacturing.

In it, I emphasized the manufacturing variation on the two transformative aspects of the IoT that I think will characterize its effect on every aspect of our lives and economy:

  1. for the first time, we will have real-time information on the current state of all sorts of things
  2. we will also be able to share that information, again, on a real-time basis, with everyone who could benefit from that information.

We’re already starting to see signs of that transformation, with GE’s Durathon battery factory (with 10,000 sensors on the assembly line plus others designed into the batteries themselves), SAP’s Future Factory, and Siemens’ Electronic Works factory.  As the price, size and energy demands of sensors continues to plummet, the trend will accelerate.

As a result, manufacturing will no longer be isolated from real-time activities in the rest of the enterprise:

  • “Designing sensors into products, rather than adding them on retroactively, will allow companies to identify defective products immediately, rather than waiting for post-production testing.
  • The built-in sensors will also allow companies to create new revenue streams. They will be able to sell customers real-time data on product operations that will allow the customers to optimize their use, and they may also choose, instead of selling the products, to lease them, with the price determined dynamically based on how much the product is actually used — take, for instance, jet turbines that are now priced on the basis of how many hours they actually operate.
  • The product design cycle will accelerate. Companies will be able to monitor a product’s actual usage in the field, then implement more rapid upgrades.
  • ‘Just-in-time’ supply chains will become even more efficient as real-time production data triggers resupply orders, just as distribution systems will become more closely integrated on the other end of the production cycle.”

The SOLID conference focuses on the convergence of hardware and software. It’s about time the two are fully integrated, and the results will be incredible!

 

 

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