GE & IBM make it official: IoT is here & now & you ignore it at your own risk!

Pardon my absence while doing the annual IRS dance.

While I was preoccupied, GE and IBM put the last nail in the coffin of those who are waiting to launch IoT initiatives and revise their strategy until the Internet of Things is more ….. (supply your favorite dismissive wishy-washy adjective here).

It’s official: the IoT is here, substantive, and profitable.

Deal with it.

To wit:

The two blue-chips’ moves were decisive and unambiguous. If you aren’t following suit, you’re in trouble.

The companies accompanied these bold strategic moves with targeted ones that illustrate how they plan to transform their companies and services based on the IoT and related technologies such as 3-D printing and Big Data:

  • GE, which has become a leader in 3-D printing, announced its first FAA-approved 3-D jet engine part, housing a jet’s compressor inlet temperature sensor. Sensors and 3-D printing: a killer combination.
  • IBM, commercializing its gee-whiz Watson big data processing system, launched Watson Health in conjunction with Apple and Johnson & Johnson, calling it “our moonshot” in health care, hoping to transform the industry.  Chair Ginny Rometty said that:

“The Watson Health Cloud platform will ‘enable secure access to individualized insights and a more complete picture of the many factors that can affect people’s health,’ IBM says each person generates one million gigabytes of health-related data across his or her lifetime, the equivalent of more than 300 million books.”

There can no longer be any doubt that the Internet of Things is a here-and-now reality. What is your company doing to catch up to the leaders and share in the benefits?


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?

Remember: The IoT Is Primarily About Small Data, Not Big

Posted on 16th March 2015 in data, Internet of Things, M2M, management, manufacturing, open data

In one of my fav examples of how the IoT can actually save lives, sensors on only eight preemies’ incubators at Toronto’s Hospital for Sick Children yield an eye-popping 90 million data points a day!  If all 90 million data points get relayed on to the “data pool,” the docs would be drowning in data, not saving sick preemies.

Enter “small data.”

Writing in Forbes, Mike Kavis has a worthwhile reminder that the essence of much of the Internet of Things isn’t big data, but small. By that, he means:

a dataset that contains very specific attributes. Small data is used to determine current states and conditions  or may be generated by analyzing larger data sets.

“When we talk about smart devices being deployed on wind turbines, small packages, on valves and pipes, or attached to drones, we are talking about collecting small datasets. Small data tell us about location, temperature, wetness, pressure, vibration, or even whether an item has been opened or not. Sensors give us small datasets in real time that we ingest into big data sets which provide a historical view.”

Usually, instead of aggregating  ALL of the data from all of the sensors (think about what that would mean for GE’s Durathon battery plant, where 10,000 sensors dot the assembly line!), the data is originally analyzed at “the edge,” i.e., at or near the point where the data is collected. Then only the data that deviates from the norm (i.e., is significant)  is passed on to to the centralized data bases and processing.  That’s why I’m so excited about Egburt, and its “fog computing” sensors.

As with sooo many aspects of the IoT, it’s the real-time aspect of small data that makes it so valuable, and so different from past practices, where much of the potential was never collected at all, or, if it was, was only collected, analyzed and acted upon historically. Hence, the “Collective Blindness” that I’ve written about before, which limited our decision-making abilities in the past. Again, Kavis:

“Small data can trigger events based on what is happening now. Those events can be merged with behavioral or trending information derived from machine learning algorithms run against big data datasets.”

As examples of the interplay of small and large data, he cites:

  • real-time data from wind turbines that is used immediately to adjust the blades for maximum efficiency. The relevant data is then passed along to the data lake, “..where machine-learning algorithms begin to understand patterns. These patterns can reveal performance of certain mechanisms based on their historical maintenance record, like how wind and weather conditions effect wear and tear on various components, and what the life expectancy is of a particular part.”
  • medicine containers with smart labels. “Small data can be used to determine where the medicine is located, its remaining shelf life, if the seal of the bottle has been broken, and the current temperature conditions in an effort to prevent spoilage. Big data can be used to look at this information over time to examine root cause analysis of why drugs are expiring or spoiling. Is it due to a certain shipping company or a certain retailer? Are there re-occurring patterns that can point to problems in the supply chain that can help determine how to minimize these events?”

Big data is often irrelevant in IoT systems’ functioning: all that’s needed is the real-time small data to trigger an action:

“In many instances, knowing the current state of a handful of attributes is all that is required to trigger a desired event. Are the patient’s blood sugar levels too high? Are the containers in the refrigerated truck at the optimal temperature? Does the soil have the right mixture of nutrients? Is the valve leaking?”

In a future post, I’ll address the growing role of data scientists in the IoT — and the need to educate workers on all levels on how to deal effectively with data. For now, just remember that E.F. Schumacher was right: “small is beautiful.”


Gartner study confirms senior managers don’t understand IoT

Posted on 21st February 2015 in Internet of Things, M2M, management, manufacturing, marketing, strategy

The “Managing the Internet of Things Revolution” e-guide I wrote for SAP was aimed at C-level executives. Even though it’s proven popular enough that the company is translating it into several languages, it appears we need to redouble our efforts to Managing_the_Internet_of_Things_Revolutionbuild IoT awareness among executives.

I say that because Gartner has just come out with a survey confirming my suspicions: even though a lot of companies now think the IoT will have a major effect on them, they’re clueless about how to manage it and most have yet to launch major IoT initiatives.

In fact, “many survey respondents felt that the senior levels of their organizations don’t yet have a good understanding of the potential impact of the IoT.” (my emphasis)


That’s despite the fact that a key conclusion of my guide was that (even though the IoT is a long way from full maturity) companies can and should begin their IoT strategies and implementation now, because they can already achieve significant savings in operating costs, improve marketing, and create new revenue streams with the current early stage sensors and analytical tools. Getting started will also build their confidence and familiarity with IoT tools and strategy before they begin more dramatic transformational strategies.

Consider these findings from the survey of 463 business and IT leaders:

  • 40% of companies think the IoT will at least bring new short-term revenue and cost reduction opportunities in the next three years — or perhaps even transform them. More than 60% think that will be true over 5 years or more.
  • Fewer than 25% said their company had “established clear business leadership for the IoT,” — even among the companies predicting a significant  – this includes those who said they expect the IoT to have a significant or transformational impact, says Gartner (however, 35% of them came from this group).
  • Yet, few have delegated specific responsibility for IoT strategy and management: “… less than one-quarter of survey respondents has established clear business leadership for the IoT, either in the form of a single organizational unit owning the issue or multiple business units taking ownership of separate IoT efforts.”
  • “attitudes toward the IoT vary widely by industry. For example, board of directors’ understanding of the IoT was rated as particularly weak in government, education, banking and insurance, whereas the communications and services industries scored above-average ratings for senior executive understanding of the IoT.”

Gartner concluded most companies have yet to really create IoT strategies:

“‘The survey confirmed that the IoT is very immature, and many organizations have only just started experimenting with it,’ said Nick Jones, vice president and distinguished analyst at Gartner. ‘Only a small minority have deployed solutions in a production environment. However, the falling costs of networking and processing mean that there are few economic inhibitors to adding sensing and communications to products costing as little as a few tens of dollars. The real challenge of the IoT is less in making products ‘smart’ and more in understanding the business opportunities enabled by smart products and new ecosystems.’ However, a lack of clear business or technical leadership is holding back investment in the technology.” (my emphasis)

In line with my current preoccupation, privacy and security, the survey did show companies are concerned with both issues, as well as with finding talented new staff who understand the IoT and how to benefit from it. According to Steve Kleyhans, Gartner’s research vp:

 “While a single leader for the IoT is not essential, leadership and vision are important, even in the form of several leaders from different business units. We expect that over the next three years, more organizations will establish clear leadership, and more will recognize the value of some form of an IoT center of excellence because of the need to master a wide range of new technologies and skills.”

If you haven’t launched any IoT projects or begun to create a strategy, the writing’s on the wall: get going!

Carpe diem: I take this survey as an omen that there’s a desperate need for When Things Can Talk: profiting from the Internet of Things revolution,” my proposed full-length book on IoT corporate strategy. Let me know if you can suggest a possible publisher!

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.

Lifting the Veil After the Sale: another IoT “Essential Truth”

Count me among those who believe the Internet of Things will affect every aspect of corporate operations, from manufacturing to customer relations.

Perhaps one of the most dramatic impacts will be on the range of activities that take place after the sale, including maintenance, product liability, product upgrades and customer relations.

In the past, this has been a prime example of the “Collective Blindness” that afflicted us before the IoT, because we basically had no idea what happened with our products once they left the factory floor.

In fact, what little data we did have probably served to distort our impressions of how products were actually used. Because there was no direct way to find out how the products were actually used, negative data was probably given exaggerated weight: we heard negative comments (warrantee claims, returns, liability lawsuits, etc.), loud and clear, but there was no way to find out how the majority of customers who were pleased with their products used them.

That has all changed with the IoT.

Now, we have to think about products  in totally new ways to capitalize on the IoT, and I think this merits another “Essential Truth” about the IoT:

Everything is cyclical.

Think about products — and industrial processes in general — in the old industrial system. Everything was linear: perhaps best exemplified by Henry Ford’s massive River Rouge Complex, the world’s largest integrated factory, and the epitome of integrated production.

Ford River Rouge Complex

“Ford was attempting to control and coordinate all of the necessary resources to produce complete automobiles.  Although Ford’s vision was never completely realized, no one else has come so close, especially on such a large scale.  His vision was certainly a success, one indication of this is the term Fordism, which refers to his style of mass-production, characterized by vertical integration, standardized products and assembly-line production”

At “The Rouge,” raw materials (literally: it had its own coke ovens and foundry!)  flowed in one side, and completed cars flowed out the other, bound for who knows where. Once the cars were in customers’ hands, the company’s contact was limited to whatever knowledge could be gleaned from owners’ visits to dealers’ service departments, irate calls from customers who had problems, and (in later days) safety recalls and/or multi-million dollar class-action lawsuits.

That linear thinking led to a terrible example of the “Collective Blindness” phenomenon that I’ve written about in the past: who knew how customers actually thought about their Model T’s? How did they actually drive them? Were there consistent patterns of performance issues that might not have resulted in major problems, but did irritate customers?

Sure, you could guess, or try to make inferences based on limited data, but no one really knew.

Fast forward to the newest auto manufacturer, Tesla, and its factory in Fremont, California (aside: this massive building — Tesla only uses a portion, used to be the NUMMI factory, where Chevy built Novas and Toyota built Corollas. Loved the perceptual irony: exactly the same American workers built mechanically identical cars [only the sheet metal varied] but the Toyotas commanded much higher prices, because of the perception of “Japanese quality.” LOL. But I digress….).

Tesla doesn’t lose track of its customers once the cars leave the plant.

Tesla assembly line

In fact, as I’ve written before, these “iPhones on wheels” are part of a massive cyclical process, where the cars’ on-board communications constantly send back data to the company about how the cars are actually doing on the road. And, when need be, as I mentioned in that prior post, the company was able to solve a potentially dangerous problem by simply sending out a software patch that was implemented while owners slept, without requiring customer trips to a repair shop!

I imagine that the company’s design engineers also pour over this data to discern patterns that might indicate elements of the physical design to tweak as well.

Of course, what would a blog post by me about IoT paradigm shifts be without a gratuitous reference to General Electric and its Durathon battery plant (aside to GE accounting: where should I send my W-9 and invoice so you can send me massive check for all the free PR I’ve given you? LOL)?

I can’t think of a better example of this switch to cyclical thinking:

  • including sensors into the batteries at the beginning of the production process rather than slapping them on at the end means that the company is actually able to monitor, and fine tune, the manufacturing process to optimize the critical chemical reaction. The same data allows the workers to remove defective batteries from the assembly line, so that every battery that ships works.
  • once in the field (and, remember: these batteries are deployed in incredibly remote areas where it might take days for a repair crew to reach and either service or repair them) the same sensors send back data on how the batteries are functioning. I don’t know about the specifics in the case of these batteries, but GE has actually created new revenue streams with other continuously-monitored devices by selling this data to customers who can use it (because the data is shared on a real-time basis, not just historically) to optimize performance.

Elsewhere, as I’ve mentioned before, General Electric’s William Ruh has said that being able to lift the veil of “Collective Blindness” through feedback from how customers actually use their products has even revolutionized their product design process:

“… G.E. is adopting practices like releasing stripped-down products quickly, monitoring usage and rapidly changing designs depending on how things are used by customers. These approaches follow the ‘lean start-up’ style at many software-intensive Internet companies. “’We’re getting these offerings done in three, six, nine months,’ he (Ruh) said. ‘It used to take three years.’”

Back in the ’90’s, I used to lecture and consult on what I called “Natural Wealth,” a paradigm shift in which we’d find all the inspiration we needed for an information-based economy in a table-top terrarium that embodies billion-year-old  principles of nature:

  • embrace chaos, don’t try to control it. (i.e., use open systems rather than proprietary ones)
  • create symbiosis: balance competition with cooperation (, where you release your APIs to create synergistic mashups with others).
  • close the loop.

With the IoT, we can finally put that last principle into practice, substituting cyclical processes for linear ones.  At long last, the “systems dynamics” thinking pioneered by Jay Forrester and his disciple, Peter Senge, can become a reality. Here’s a closing tip to make that possible: in addition to SAP’s HANA or other analytics packages, look to systems dynamics software such as isee systems’  iThink to model your processes and transform linear into cyclical ones. Now get going: close the loop!

I’ll be on SAP Radio Again Today: the IoT and Big Data

I’ll be on SAP’s “Coffee Breaks With Game Changers” radio again today, live @ 2 EST, appearing again with SAP’s David Jonker, again talking about the IoT and Big Data.  This time I plan to speak about:

  • Integrating real-time and historic data in decision-making:  in the past, it was so hard to glean real-time operating data that we had to operate on the basis of inferring about how to manage the future based on analysis of past data.  Now we have a more difficult challenge: learn to balance past and real-time data.
  • Sharing data in real-time: In the past, data trickled down from top management and might (or might not) eventually get to operators on the shop floor.  Now, everyone can get immediate access to it. Will senior managers continue to be the gatekeepers, or will everyone have real-time access to the data that might allow them to do their jobs more effectively (for example, fine-tuning production processes).

  • Revolutionizing decision-making: Decision-making will also change, because of everyone being able to have simultaneous access to data. Does it really make sense any more for sequential decision-making by various siloed departments when they might all benefit by making the decisions simultaneously and collaboratively, based on the data?

Tune in!

My #IoT predictions for 2015

I was on a live edition of “Coffee Break With Game-Changers” a few hours ago with panelists Sherryanne Meyer of Air Products and Chemicals and Sven Denecken of SAP, talking about tech projections for 2015.

Here’s what I said about my prognostications:

“I predict that 2015 will be the year that the Internet of Things penetrates consumer consciousness — because of the Apple Watch. The watch will unite both health and smart home apps and devices, and that will mean you’ll be able to access all that usability just by looking at your watch, without having to fumble for your phone and open a specific app.

If Apple chooses to share the watch’s API on the IFTTT – If This Then That — site, the Apple phone’s adoption – and usability — will go into warp speed. We won’t have to wait for Apple or developers to come up with novel ways of using the phone and the related devices — makers and just plain folks using IFTTT will contribute their own “recipes” linking them. This “democratization of data” is one of the most powerful – and under-appreciated – aspects of the IoT. In fact, Sherryanne, I think one of the most interesting IoT strategy questions for business is going to be that we now have the ability to share real time data with everyone in the company who needs it – and even with supply chain and distribution networks – and we’ll start to see some discussion of how we’ll have to change management practices to capitalize on this this instant ability to share.

(Sven will be interested in this one) In 2015, the IoT is also going to speed the development of fog computing, where the vast quantities of data generated by the IoT will mean a switch to processing data “at the edge,” and only passing on relevant data to the cloud, rather than overwhelming it with data – most of which is irrelevant.

In 2015 the IoT is also going to become more of a factor in the manufacturing world. The success of GE’s Durathon battery plant and German “Industry 4.0” manufacturers such as Siemans will mean that more companies will develop incremental IoT strategies, where they’ll begin to implement things such as sensors on the assembly line to allow real-time adjustments, then build on that familiarity with the IoT to eventually bring about revolutionary changes in every aspect of their operations.

2015 will also be the year when we really get serious about IoT security and privacy, driven by the increasing public concern about the erosion of privacy. I predict that if anything can hold back the IoT at this point, it will be failure to take privacy and security seriously. The public trust is extremely fragile: if even some fledgling startup is responsible for a privacy breach, the public will tend to tar the entire industry with the same brush, and that could be disastrous for all IoT firms. Look for the FTC to start scrutinizing IoT claims and levying more fines for insufficient security.”

What’s your take on the year ahead? Would love your comments!

Is GE the future of manufacturing? IoT + nanotech + 3D-printing

The specific impetus for this post was an article in The Boston Globe about heart stents that fit perfectly because they’re 3-D printed individuallly for each patient.

GE jet engine 3-D-printed fuel nozzle

That prompted me to think of how manufacturing may change when three of my favorite technologies — nanotech, 3-D printing and the Internet of Things — are fully mature and synergies begin (as I’m sure they will) to emerge between the three.

I’m convinced we’ll see an unprecedented combination of:

  • waste elimination: we’ll no longer do subtractive processes, where a rough item is progressively refined until it is usable.  Instead, products will be built atom-by-atom, in additive processes where they will emerge exactly in the form they’re sold.
  • as with the stents, products will increasingly be customized to the customer’s exact specifications.
  • the products will be further fine-tuned based on a constant flow of data from the field about how customers actually use them.

Guess what?  The same company is in on the cutting edge of all three: General Electric (no, I’m not on their payroll, despite all my fawning attention to them!):

  • Their Industrial Internet IoT initiative is resulting in dramatic changes to their products, with built-in sensors that relay data constantly to GE and the customer about the product’s current status, allowing predictive maintenance practices that cuts repair costs, optimizing the device’s performance for more economical operations, and even allowing GE to switch from selling products to leasing them, with the lease price determined dynamically using factors such as how many hours the products are actually used.  Not only that, but they practice what they preach, with 10,000 sensors on the assembly line at their Durathon battery plant in Schenectady, plus sensors in the batteries themselves, allowing managers to roam the plant with an iPad to get instant readings on the assembly line’s real-time operation, to fine-tune the processes, and to be able to spot defective batteries while they are still in production, so that 100% of the batteries shipped will work.
    They’re also able to push products out the door more rapidly and updating them quicker based on the huge volumes of data they gather from sensors built into the products: “… G.E. is adopting practices like releasing stripped-down products quickly, monitoring usage and rapidly changing designs depending on how things are used by customers. These approaches follow the ‘lean start-up’ style at many software-intensive Internet companies. “’We’re getting these offerings done in three, six, nine months,’ he [William Ruh] said. ‘It used to take three years.’”
  • They’ve made a major commitment to 3-D printing, with 100,000 3-D printed parts scheduled to be built into their precision LEAP jet engines — a big deal, since there’s not a great deal of fault tolerance in something that may plunge to the earth if it malfunctions! As Bloomberg reported, “The finished product is stronger and lighter than those made on the assembly line and can withstand the extreme temperatures (up to 2,400F) inside an engine.”  They’re making major investments to boost the 3-D printers’ capacity and speed.  Oh, and did I mention their precedent-setting contest to crowd-source the invention of a 3-D printed engine mount?
  • They’re also partnering with New York State on perhaps the most visionary technology of all, nanotech, which manipulates materials on the molecular level. GE will focus on cheap silicon carbide wafers, which beat silicon chips in terms of efficiency and power, leading to smaller and lighter devices.

GE is the only member of the original Dow-Jones Index (in 1884) that still exists. As I’ve said before, I’m astounded that they not only get it about IoT technology, but also the new management practices such as sharing data that will be required to fully capitalize on it.

Thomas A. Edison is alive and well!

Interview w/ Echelon for its IoT blog

Just finished a delightful interview with three Echelon staffers for a forthcoming piece on its blog about my prognostications for the Industrial Internet of Things (AKA “Industrial Internet” ien GE-marketing speak).  They’ve been around in this field since the dark ages — 1988, and are now focusing on industrial applications.

My main point to them was the one I made in the SAP “Managing the Internet of Things Revolution” e-guide,  that even though the IoT hasn’t realized its full potential yet, that smart companies would begin creating and executing an IoT strategy now, “to connect their existing infrastructure and enhance key foundational IoT technologies,” optimizing their operating efficiency. Then they could build on that experience to make more fundamental transformations.

We touched 0n several other examples how the IoT could increase operating efficiency or make fundamental transformations:

At any rate, a fun time was had by all, and I’ll let you know when their blog post is up!