IoT: What Can You Do That You Couldn’t? Heavy Construction

Not sure why, but I’m particularly fascinated by how the IoT can transform parts of the economy that have been around for more than 100 years, such as the way the Union Pacific uses it to reduce derailments — and worse.

One of those tradition-bound industries where the IoT Essential TruthWhat Can You Do Now That You Couldn’t Do Before” is starting to revolutionize both daily practices and strategy is heavy construction, both for buildings and public works.

First of all, heavy construction is inherently dangerous, so anything that can be done to manage that danger is beneficial.

Lots of very heavy machinery; many people, frequently on foot; almost impossible to coordinate all of them in the past, especially as vehicles enter and leave the site.  According to OSHA, in the US alone, 796, or 20.3% of all workers killed on the job in 2003 were killed on construction sites, primarily through falls, struck by objects, electrocution or “caught-in-between.” Of those, lack of coordination probably resulted in most of the struck by objects and “caught-in-between” deaths.

One of the most exciting developments in that regard is SAP’s demonstration program with SK Solutions, which makes anti-collision software, on a construction site in Dubai. They are capitalizing on new construction cranes and construction vehicle  that have sensors built in so their real-time location can be determined instantly. SAP and SK Solutions combine sensor-based data – such as 3-D motion control, location, load weight, equipment usage and wind speed – to avoid collisions with trucks  to enhance worker safety, improve productivity and reduce costs. The site and project managers monitor the equipment via a dashboard.

Less dramatic than collision avoidance is the way that construction companies are using real-time data from the equipment to maximize operating efficiency and reduce maintenance costs through innovations such as “predictive maintenance.”  As my Boston IoT MeetUp co-director Chris Rezendes of INEX Advisors discussed at the recent Association of Equipment Management Professionals Asset Management Symposium, “instrumentation of assets” through digital plans and models, sensors, data and embedded communication devices in buildings and bridges is becoming a key differentiator in the industry. According to Rezendes:

““Everybody in tech wants to instrument your assets, inventories, operations, people and processes… They are looking at instrumenting all manner of industrial machines, equipment and more. And they’re doing it really well…. You should feel threatened, at least a little bit, by big technology companies trying to instrument your assets for you, maybe to you… I’m going to tell it to you straight: He or she who controls the intelligence–the data about those assets, inventories and areas of operation–will control that market, the customer, the regulatory environment and the supply chain. They will control you.”

What a seismic shift from the old days of heavy construction, which was largely a matter of brute force and difficult demands on operators to remain always vigilant in the midst of loud noises.  Add in the sensors that these construction crews are now embedding in bridges’ structure and in buildings to monitor a wide range of stresses and environmental conditions, and the conclusion is inescapable: every industry can and will be fundamentally altered in the coming decade as equipment and processes begin switch the requirements from brawn to brains.

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 (IFTTT.com, 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!

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!

Egburt: key tool to make IoT pay off NOW

Posted on 31st October 2014 in data, energy, Internet of Things, maintenance, management, retail

As I’ve remarked before, writing the Managing the Internet of Things Revolution e-guide to IoT strategy for SAP was an eye-opener for me, shifting my attention from the eye-popping opportunities for radical reinvention through the IoT (products as services, user-customizable products, seamless smart phone-car integration, etc.) to very practical ways the IoT could begin optimizing companies’ current operations TODAY (BTW: much-deserved shout-out to SAP’s Mahira Kalim: it was dialogue with her that led to this insight!).

Egburt

In that vein, I was blown away at this week’s IoT Global Summit by the roll-out of Egburt by Camgian.

Egburt stresses two crucial, inter-related obstacles to widespread IoT solution deployment by mainstream businesses:

  • low cost-of-ownership sensing (by using very little energy, thereby extending battery life)
  • reducing potentially huge cloud-computing costs (because of the sheer volume of 24/7 sensor data) by allowing “fog computing,” where the processing would be done right at the collection process, with only the small amount of really relevant data being passed on to a central location.

The highlight of the product launch was a live demo of Egburt in real-time use at a chain of dollar stores in the south, monitoring a wide range of factors, from floor traffic to freezer operation (Camgian pointed out the system paid for itself in the first month of operation when it recorded failure of a freezer when the store was unoccupied, in time for immediate repairs to avoid loss of frozen foods).

Think about it: the very volume of Big Data possible with constant monitoring by a whole range of sensors can also be the IoT’s undoing. Since all that’s of interest in many cases is data that deviates from the norm, doesn’t it make sense to process that data at the collection point, then only pass on the deviations?

The company has targeted three IoT segments:

  • retail to reduce heating and lighting, and maximize sales through tracking foot traffic patterns to optimize product placement.
  • infrastructure: with sensors at key points such as bridges that will detect flooding and stress.
  • smart cities: optimizing emergency response.

In a sponsored white paper by ABI Research, “Evolution of the Internet of Things: from connected to intelligent devices,” they documented the benefits of going beyond first-generation, “connected,” IoT devices that were just sensors collecting and passing on data, to a second generation of “intelligent ones” such as Egburt the combine sensors and processing and offer not only lower operating costs but also — critically — more data security:

  • “Communication Latency: Handling more processing at the network’s edge reduces latency from the device’s actions. Use cases that are highly time-sensitive and require immediate analysis of, or response to, the collected sensor data are, in general, unfeasible under cloud- centric IoT architectures, especially if the data are sent over long distances.
  • “Data Security: By and large, sensitive and business-critical operational data are safer when encrypted adequately on the endpoint level. Unintelligent devices transmitting frequent and badly secured payloads to the cloud are generally more vulnerable to hacking and interception by unauthorized parties. Additionally, many enterprises may need to secure and control their machine data on the edge level for compliance reasons.
  • “Total Cost of Ownership: Perhaps most significantly, the paradigm shift can reduce the IoT systems’ total cost of ownership, or TCO. Intelligent devices are usually more expensive than less sophisticated alternatives, but their TCO over a long service life can be substantially lower.”

IMHO, for the IoT to be widely deployed, especially in SMEs, devices such as Egburt that reduce the cost of collecting and processing data are a critical component.


(PROMINENT DISCLAIMER: I actually won a FitBit in Camgian’s drawing at the conference. That has no impact on this review. Had I won the iPhone 6 that they also gave away, I would have totally been in the bag, LOL…)

GE & Accenture provide detailed picture of current IoT strategy & deployment

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Read it, or be left in the dust!


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

Why It’s So Hard to Predict Internet of Things’ Full Impact: “Collective Blindness”

I’ve been trying to come up with a layman’s analogy to use in explaining to skeptical executives about how dramatic the Internet of Things’ impact will be on every aspect of business and our lives, and why, if anything, it will be even more dramatic than experts’ predictions so far (see Postscapes‘ roundup of the projections).

See whether you thing “Collective Blindness” does justice to the potential for change?

 

What if there was a universal malady known as Collective Blindness, whose symptoms were that we humans simply could not see much of what was in the world?

Even worse, because everyone suffered from the condition, we wouldn’t even be aware of it as a problem, so no one would research how to end it. Instead, for millennia we’d just come up with coping mechanisms to work around the problem.

Collective Blindness would be a stupendous obstacle to full realization of a whole range of human activities (but, of course, we couldn’t quantify the problem’s impact because we weren’t even aware that it existed).

Collective Blindness has been a reality, because vast areas of our daily reality have been unknowable in the past, to the extent that we have just accepted it as a condition of reality.

Consider how Collective Blindness has limited our business horizons.

We couldn’t tell when a key piece of machinery was going to fail because of metal fatigue.

We couldn’t tell how efficiently an entire assembly line was operating, or how to fully optimize its performance.

We couldn’t tell whether a delivery truck would be stuck in traffic.

We couldn’t tell exactly when we’d need a parts shipment from a supplier, nor would the supplier know exactly when to do a new production run to be read.

We couldn’t tell how customers actually used our products.

That’s all changing now. Collective Blindness is ending, …. and will be eradified by the Internet of Things.

What do you think? Useful analogy?

Why the Internet of Things Will Bring Fundamental Change “What Can You Do Now That You Couldn’t Do Before?”

The great Eric Bonabeau has chiseled it into my consciousness that the test of whether a new technology really brings about fundamental change is to always ask “What can you do now that you couldn’t do before?

Tesla Roadster

That’s certainly the case for the Tesla alternative last winter to a costly, time-consuming, and reputation-staining recall  (dunno: I must have been hiding under a rock at the time to have not heard about it).

In reporting the company’s action, Wired‘s story’s subtitle was “best example yet of the Internet of Things?”

I’d have to agree it was.

Coming at the same time as the godawful Chevy recall that’s still playing out and still dragging down the company, Tesla promptly and decisively response solved another potentially dangerous situation:

 

“‘Not to worry,’ said Tesla, and completed the fix for its 29,222 vehicle owners via software update. What’s more, this wasn’t the first time Tesla has used such updates to enhance the performance of its cars. Last year it changed the suspension settings to give the car more clearance at high speeds, due to issues that had surfaced in certain collisions.”

Think of it: because Tesla has basically converted cars into computers with four wheels, modifying key parts by building in sensors and two-way communications, it has also fundamentally changed its relationship with customers: it can remain in constant contact with them, rather than losing contact between the time the customer drives off the lot and when the customer remembers (hopefully..) to schedule a service appointment, and many modifications that used to require costly and hard-to-install replacement parts now are done with a few lines of code!

Not only can Tesla streamline recalls, but it can even enhance the customer experience after the car is bought: I remember reading somewhere that car companies may start offering customer choice on engine performance: it could offer various software configurations to maximize performance or to maximize fuel savings — and continue to tweak those settings in the future, just as computers get updated operating systems. That’s much like the transformation of many other IoT-enhanced products into services, where the customer may willingly pay more over a long term for a not just a hunk of metal, but also a continuing data stream that will help optimize efficiency and reduce operating costs.

Wired went on to talk about how the engineering/management paradigm shift represented a real change:

  • “In nearly all instances, the main job of the IoT — the reason it ever came to be — is to facilitate removal of non-value add activity from the course of daily life, whether at work or in private. In the case of Tesla, this role is clear. Rather than having the tiresome task of an unplanned trip to the dealer put upon them, Tesla owners can go about their day while the car ‘fixes itself.’
  • Sustainable value – The real challenge for the ‘consumer-facing’ Internet of Things is that applications will always be fighting for a tightly squeezed share of disposable consumer income. The value proposition must provide tangible worth over time. For Tesla, the prospect of getting one’s vehicle fixed without ‘taking it to the shop’ is instantly meaningful for the would-be buyer – and the differentiator only becomes stronger over time as proud new Tesla owners laugh while their friends must continue heading to the dealer to iron out typical bug fixes for a new car. In other words, there is immediate monetary value and technology expands brand differentiation. As for Tesla dealers, they must be delighted to avoid having to make such needling repairs to irritated customers – they can merely enjoy the positive PR halo effect that a paradigm changing event like this creates for the brand – and therefore their businesses.
  • Setting new precedents – Two factors really helped push Tesla’s capability into the news cycle: involvement by NHTSA and the word ‘recall.’ At its issuance, CEO Elon Musk argued that the fix should not technically be a ‘recall’ because the necessary changes did not require customers find time to have the work performed. And, despite Musk’s feather-ruffling remarks over word choice, the stage appears to have been set for bifurcation in the future by the governing bodies. Former NHTSA administrator David Strickland admitted that Musk was ‘partially right’ and that the event could be ‘precedent-setting’ for regulators.”

That’s why I’m convinced that Internet of Things technologies such as sensors and tiny radios may be the easy part of the revolution: the hard part is going to be fundamental management changes that require new thinking and new questions.

What can you do now that you couldn’t do before??

BTW: Musk’s argument that its software upgrade shouldn’t be considered a traditional “recall” meshes nicely with my call for IoT-based “real-time regulation.”  As I wrote, it’s a win-win, because the same data that could be used for enforcement can also be used to enhance the product and its performance:

  • by installing the sensors and monitoring them all the time (typically, only the exceptions to the norm would be reported, to reduce data processing and required attention to the data) the company would be able to optimize production and distribution all the time (see my piece on ‘precision manufacturing’).
  • repair costs would be lower: “predictive maintenance” based on real-time information on equipment’s status is cheaper than emergency repairs. the public interest would be protected, because many situations that have resulted in disasters in the past would instead be avoided, or at least minimized.
  • the cost of regulation would be reduced while its effectiveness would be increased: at present, we must rely on insufficient numbers of inspectors who make infrequent visits: catching a violation is largely a matter of luck. Instead, the inspectors could monitor the real-time data and intervene instantly– hopefully in time to avoid an incident. “

Capgemini Report: dramatic proof most big companies lag on IoT strategy!

In writing the SAP “Managing the Internet of Things Revolution” i-guide to IoT strategy for C-level executives, my research led me to believe that most big companies were still clueless about the IoT and how it would revolutionize every aspect of their operations.  Now a great report by Capgemini, “The Internet of Things: Are Organizations Ready for a Multi-Trillion Dollar Prize?” seems to answer its own question with a resounding “No!” It’s a must read, whether you’re late to the game, or if you’re looking for entrepreneurial opportunities. Let’s start with the conclusion:

The IoT represents the next evolution of the digital universe. The speed at which nimble startups and Internet players are capturing IoT opportunities should serve as a wake-up call to larger, traditional organizations. Analyst estimates point to a world where startups will dominate the IoT market. Fifty percent of IoT solutions are expected to originate in startups less than 3 years old, by 201732. They may be less nimble, but bigger organizations need to step up to the plate. As with all digital disruptions, being an organization that is in catch-up mode will be a deeply uncomfortable place to be. ” (my emphasis)

Earlier, it emphasizes that success will require both a paradigm shift and mastering new technologies such as big data analysis:

The IoT prize will be won by those who achieve a change in mindset, from a product world to a service world. However, that fundamental mind-shift is not the only requirement. Organizations need to get the right IT infrastructure in place, quickly acquire capabilities in analytics, and strengthen a whole host of functional capabilities. “

Got your attention yet?

The report was most emphatic about an aspect of the IoT that I don’t think I’ve emphasized enough in the past, the shift from products to services. Once again, I look to GE as one big company that “gets it” about the IoT transition, building sensors into its products that rotate, then monetizing the investment by offering real-time data about the products’ operations to customers so that they can optimize their operations — and charging for that data.  The study said that within a year after GE began offering its “Predictivity” line of IoT services in 2012, it generated $290 million in revenues.

One of the reasons why I really like the analysis is that it zeros in on a range of management issues that executives must address to capitalize on the IoT.

The study of more than 100 US and European companies reported that most don’t have the in-house expertise to make the switch from selling products to offering services:

“They now need to be able to envision new services, develop commercial models and design service contracts that result in continuous revenue streams. Our discussions with senior executives revealed that these are not areas of strength for many product- centric organizations.”

In particular, it targeted salespeople as a problem area: “For IoT solutions, a sales force needs to be comfortable in articulating the value proposition and potential benefits, which is critical to convincing often-reluctant customers to pay for a new class of services.” Customer support will also need to be beefed up — and delivered faster to customers who come to expect real-time data.

 The research showed that most companies were only in the early stages of IoT implementation — if at all. Fewer than 30% support remote operation of devices, and fewer than 40% use sensor data to offer customers the kind of performance improvement insights that GE gives.

One major gap that jumped out to me is that most of the big companies just don’t get my “Essential Truth” that you have to begin asking “who else can use this data”?,” and begin opening up proprietary systems so that third parties will enrich your offerings by creating new combinations and complementary offerings. Fewer “than 15% of organizations offer IoT solutions that integrate with third-party products and services.” (my emphasis) If mighty GE can team with Quirky and Electric Imp, what’s your excuse? On the more positive side, the research revealed that nearly 60% use partnerships to develop IoT solutions, so there’s hope.

The gaps are technological as well as human. 67% of the respondents said they don’t have the technology (shout-out to SAP’s HANA) to handle the massive amounts of big data the IoT will generate.

Another obstacle that the report identified was one I’d not come across before: resistance from within. “An executive at a medical technology company outlined how resistance can come less from the customer – and more from within the organization, explaining, ‘We only have 20% resistance from the customer and 80% from our own organization. Consequently, it is a significant challenge to align our existing business processes with new IoT-based service offerings.’”

The final section is an action agenda to get companies up to speed on the IoT:

  1. Put the Right IT Infrastructure in Place and Acquire Data Analytics Capabilities.
  2. Strengthen Functional Capabilities across Product Management, Sales and Marketing and Customer Support
  3. Use Trainings and Incentives to Prepare the Sales Force to Sell IoT Solutions. Augment Product Management Capabilities with Services Expertise and Emphasize Ease-of-Use in Product Design
  4. Develop Customer Support Capabilities to Drive Real-Time Issue Resolution.

Bottom line, Capgemini concluded that a shocking 42% of all companies don’t provide any IoT services. That, in my mind, is a clarion call to action!

You simply must read this report — then act on it.

Failure to inspect oil rigs another argument for “real-time regulation”

The news that the Bureau of Land Management has failed to inspect thousands of fracking and other oil wells considered at high risk for contaminating water is Exhibit A for my argument we need Intnet of Things-based “real-time regulation” for a variety of risky regulated businesses.

According to a new GAO report obtained by AP:

“Investigators said weak control by the Interior Department’s Bureau of Land Management resulted from policies based on outdated science and from incomplete monitoring data….

“The audit also said the BLM did not coordinate effectively with state regulators in New Mexico, North Dakota, Oklahoma and Utah.”

Let’s face it: a regulatory scheme based on after-the-fact self-reporting by the companies themselves backed up by infrequent site visits by an inadequate number of inspectors will never adequately protect the public and the environment.  In this case, the GAO said that “…. the BLM had failed to conduct inspections on more than 2,100 of the 3,702 wells that it had specified as ‘high priority’ and drilled from 2009 through 2012. The agency considers a well ‘high priority’ based on a greater need to protect against possible water contamination and other environmental safety issues.”

By contrast, requiring that oil rigs and a range of other technology-based products, from jet engines to oil pipelines, have sensors attached (or, over time, built in) that would send real-time data to the companies should allow them to spot incipient problems at their earliest stages, in time to schedule early maintenance that would both reduce maintenance costs and reduce or even eliminate catastrophic failures. As I said before, this should be a win-win solution.

If problems still persisted after the companies had access to this real-time data, then more draconian steps could be required, such as also giving state and federal regulators real-time access to the same data — something that would be easy to do with IoT-based systems. There would have to be tight restrictions on access to the data that would protect proprietary corporate information, but companies that are chronic offenders would forfeit some of those protections to protect the public interest.