IoT Intangibles: Increased Customer Loyalty

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

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

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

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

Why’s that important?

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

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

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

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

 

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

Examples include:

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

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

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

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

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

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.

Distributed Manufacturing by 3D Printing Revolution for IoT Comes of Age!

Two major developments in the 3-D printing world, from Fictiv and (who woulda thunk it!) UPS, make me think the time has come for “distributing manufacturing” and getting away from the old massive, manufacturing mentality exemplified by Ford’s River Rouge plant.

OK, first a confession and a little history. Being short & named David, I’ve always had a fascination with David & Goliath, and you can bet who I’d root for. I also was deeply touched by two visionaries in my past:

  • Steve Clay-Young, who used to run the workshop at the old Boston Architectural Center & turned me on to a neat, nearly-forgotten bit of WWII history: either Popular Science or Popular Mechanix (can’t remember which), organized a network of hobbyists with metal lathes, who played a major role in the war effort. The magazine published plans for turning metal for munitions, and these guys each worked in their workshops to make them.
  • Eric Drexler, the nano-tech guru, spoke at the Eco-Tech conference in the ’90s about his vision of a bread-box-size gizmo on your kitchen counter that would churn out all sorts of customized products for you.

Now, it’s all taking place, and I suspect 3D printing will be a crucial element in the IoT-based transformation of the economy.

 

                                   Fictiv distributed manufacturing model

Fictiv is a startup founded to “democratize manufacturing,” which just went public with its new “distributed manufacturing” service using a nationwide network of 3D high quality printers and CNC machines:

 

“We route parts to machine with open capacity so you don’t wait 5 days for a part that takes 5 hours…. We aggregate orders so every customer receives the benefits of large purchasing power….”

Perhaps coolest, “Parts are produced as close to customers as possible to reduce inefficiencies in logistics and shipping lead-time”  so that (for an extra charge) they’re fabricated and delivered in 24 hours, and otherwise delivered in two days.  I suspect that, just as having sensors on their products that results in real-time feedback allowing GE to compress the design cycle, especially upgrades, that this proximity and quick turn-around will allow designers to radically alter the design process by “failing rapidly,” just the way early spread-sheet software allowed business managers to do “what-if” hypotheticals for the first time.

By bundling orders, they give startups the bargaining power of large companies.As co-founder Dave Evans, an experienced product design pro, says, distributed, local manufacturing can even the playing field for smaller companies, especially startups just designing their first products:

“When ordering from a large manufacturing company, parts need to navigate through their complex system and then be shipped from the machine warehouse direct to the customer, increasing lead times.

From an engineer’s perspective, when you’re in the prototyping and ideation stages, time is everything and even a 1-2 day loss from a 3PL (third-party logistics player) matters significantly.

What’s important to consider here is that in manufacturing, things can and will go wrong. So when remote manufacturers inevitably have to manage errors, there’s a lot of complexity to deal with …. This is very evident in overseas mass manufacturing, which is why companies put engineers as close to the source as possible. It’s amazing how few companies consider the same principles during the early prototyping stages of a product when time is everything.

The beauty in working with smaller, local manufacturers on the other hand, is that parts can be picked up as soon as they’re ready or delivered via same-day courier, saving you the 1-2 days of shipping. In addition, if things go wrong (they always do), smaller shops have more agility, fewer organizational layers, and in general can respond more quickly compared with their larger counterparts.”

              3D printing at The UPS Store

Equally important is the continuing stream of 3D services being offered by UPS. which recently announced a nationwide on-demand 3D printing network.  The network will combine 3D printers at more than 60 The UPS Stores® and Fast Radius’ On Demand Production Platform™ and 3D printing factory in Louisville, KY. My friends at SAP will marry its SAP’s extended supply chain solutions will be integrated with the UPS 3D network and — most important — its global logistics network “to simplify the industrial manufacturing process from digitization, certification, order-to-manufacturing and delivery.”

If I’m correct, the UPS network will concentrate on prototyping at this point, but it’s easy to see that it could soon have a dramatic impact on the replacement parts industry. Why should the manufacturer warehouse a large supply of spare parts, just because they might be needed, when they could instead simply transmit the part’s digital file to the nearest UPS 3D printer, generate the part, and use UPS to deliver it in a fraction of the time.

Combine that with the predictive maintenance possible with feedback from sensors on products, and you truly have a revolution in product design and maintenance as well as manufacturing. It would also foster the IoT-based circular company vision that I’ve been pushing, because supply chain, manufacturing, distribution, and maintenance would all be linked in a great circle.

Sweet!

 

 

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

You may remember I wrote several months ago about a collaboration between SAP and SK Solutions in Dubai (interesting factoid: Dubai is home to almost 25% of the world’s cranes [assume most of the rest nest at Sand Hill, LOL], and they are increasingly huge, and that makes them difficult to choreograph.

I’m returning to the subject today, with a slightly broader emphasis on how the IoT might manage a range of dangerous job sites, such as mining and off-shore oil rigs, allowing us to do now that we couldn’t do before, one of my IoT Essential Truths.

I’m driven in part by home-town preoccupation with Boston’s bid for the 2024 Olympics, and the inevitable questions that raises on the part of those still smarting from our totally-botched handling of the last big construction project in these parts, the infamous “Big Dig” tunnel and highway project.

I’m one of those incurable optimists who think that part of ensuring that the Olympics would have a positive “legacy” (another big pre-occupation in these parts) would be to transform the city and state into the leading example of large-scale Internet of Things implementation.

There are a couple of lessons from SAP and SK Solutions’ collaboration in Dubai that would be relevant here:

    • The system is real-time: the only way the Boston Olympic sites could be finished in time would be through maximizing efficiency every day. Think how hard that is with a major construction project: as with “for want of a nail the kingdom was lost,” the sensitive interdependence between every truck and subcontractor on the site — many of which might be too small to invest in automation themselves — is critical. If information about one sub being late isn’t shared, in real-time, with all the other players, the delays — and potential collisions — will only pile up. The system includes an auto-pilot that makes immediate adjustments to eliminate operator errors. By contrast, historical data that’s only analyzed after the fact won’t be helpful, because there’s no do-overs, no 2025 Olympics!
    • The data is shared: that’s another key IoT Essential Truth.  “Decision-makers using SK Solutions on a daily basis span the entire organization. Besides health and safety officers, people responsible for logistics, human resources, operations and maintenance are among the typical users.”  The more former information silos share the data, the more likely they are to find synergistic solutions.
    • The system is inclusive, both in terms of data collection and benefits: SK Solutions’ Founder and Inventor Séverin Kezeu, came up with his collision-avoidance software pre-IoT, but when the IoT became practical he partnered with SAP, Cisco, and Honeywell to integrate and slice and dice the data yielded by the sensors they installed on cranes and vehicles and other sources.  For example, the height of these cranes makes them vulnerable to sudden weather changes, so weather data such as wind speed and direction must be factored in, as well as the “machinery’s position, movement, weight, and inertia…. The information is delivered on dashboards and mobile devices, visualized with live 3-D images with customizable views. It’s also incredibly precise.”As a result, by using SAP’s HANA platform, a system developed to reduce construction accidents also makes predictive maintenance of the cranes and other equipment, and lets the construction companies monitor Key Performance Indicators (KPIs) such as asset saturation, usage rates, and collisions avoided.  McKinsey reports that construction site efficiency could improve dramatically due to better coordination: “One study found that buffers built into construction project schedules allowed for unexpected delays resulting in 70 to 80 percent idle time at the worksite.Visibility alone can allow for shorter buffers to be built into the construction process.”

Several other great IoT solutions come to mind at the same time, both relating to dangerous industries. Off-shore oil rigs and mining were treated at length in the recent McKinsey omnibus IoT forecast, “The Internet of Things: Mapping the Value Beyond the Hype:”

  • off-shore rigs: “Much of the data collected by these sensors [30,000 on some rigs] today is used to monitor discrete machines or systems. Individual equipment manufacturers collect performance data from their own machines and the data can be used to schedule maintenance. Interoperability would significantly improve performance by combining sensor data from different machines and systems to provide decision makers with an integrated view of performance across an entire factory or oil rig. Our research shows that more than half of the potential issues that can be identified by predictive analysis in such environments require data from multiple IoT systems. Oil and gas experts interviewed for this research estimate that interoperability could improve the effectiveness of equipment maintenance in their industry by 100 to 200 percent.” (my emphasis). 
  • mining: “In one mining case study, using automated equipment in an underground mine increased productivity by 25 percent. A breakdown of underground mining activity indicates that teleremote hauling can increase active production time in mines by as much as nine hours every day by eliminating the need for shift changes of car operators and reducing the downtime for the blasting process. Another source of operating efficiency is the use of real-time data to manage IoT systems across different worksites, an example of the need for interoperability. In the most advanced implementations, dashboards optimized for smartphones are used to present output from sophisticated algorithms that perform complex, real-time optimizations. In one case study from the Canadian tar sands, advanced analytics raised daily production by 5 to 8 percent, by allowing managers to schedule and allocate staff and equipment more effectively. In another example, when Rio Tinto’s (one mine) crews are preparing a new site for blasting, they are collecting information on the geological formation where they are working. Operations managers can provide blasting crews with detailed information to calibrate their use of explosives better, allowing them to adjust for the characteristics of the ore in different parts of the pit.”
 In all of these cases, the safety and productivity problems — and solutions are intertwined.  As McKinsey puts it:
“Downtime, whether from repairs, breakdowns, or maintenance, can keep machinery out of use 40 percent of the time or more. The unique requirements of each job make it difficult to streamline work with simple, repeatable steps, which is how processes are optimized in other industries. Finally, worksite operations involve complex supply chains, which in mining and oil and gas often extend to remote and harsh locations.”
Could it be that the IoT will finally tame these most extreme work situations, and bring order, safety, and increased profitability?  I’m betting on it.

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!

Calculating Internet of Things ROI — important tool

Just came across this video while researching how to calculate ROI on Internet of Things investments for the e-book I’m writing, and felt compelled to share it.

That’s because it may be hard to calculate ROI fully and accurately for IoT investments if you aren’t thinking in terms of what my friend/patron Eric Bonabeau always pounds into my head: what can you do now that you couldn’t do before?

In the case of the IoT, there are  several things, such as “predictive maintenance,” that weren’t possible before and thus we don’t automatically think of calculating these benefits. It will require a conscious change in figuring ROI to account for them.

According to Axeda CMO Bill Zujewski, there are 6 levels of M2M/IoT implementation, and there are both cost savings and revenue enhancements as you move up the curve:

  1. Unconnected: this is where most firms are today. No M2M/IoT investments.
  2. Connected, pulling data for future use: No return yet.
  3. Service: the investment begins to pay off, primarily because of lower service costs.
    1. Cost reductions:
      1. fewer repair visits  Now that you’re harvesting real-time information about products’ condition, you may be able to optimize operating conditions remotely.
      2. first-time fix rate increases: Now you may know what the problem is before you leave, and can also take the proper replacement parts.
      3. reduced call length: You may know the problem in advance, rather than having to tinker once you’re there to discover it.
    2. Higher Revenues:
      1. Greater customer satisfaction. Customer doesn’t have to pay as much for repairs, down-time is reduced.
  4. Analyze: Putting data into BI and other analysis tools to get greater insights. For example, understand what are bad parts, when they’re failing.
    1. Cost reductions:
      1. fewer service visits: instead of monthly service you may be able to switch to quarterly.
      2. lowering returns
      3. improve product design
    2. Higher Revenues:
      1. Increase product up-time: due to better design and more effective maintenance, longer mean-time-to-failure.
  5. Data integration: begin to integrate data with business processes.
    1. Cost reductions:
      1. warrantees (especially for industrial equipment): fewer claims if you can monitor equipment’s operations, warn owner if they’re using it improperly.
      2. recalls: reduced.
    2. Higher revenues:
      1. pay-as-you-go leases: as we’ve discussed earlier, you may be able to increase revenues by leasing products based on how much the customer actually uses them (which you can now document), rather than selling them.
      2. increased sales of consumables: you’ll be able to know exactly when the customer needs them.
  6. Reinvent the customer experience: According to Zujewski, this is where you “put machine data into the end users’ hands” through a smartphone app, for example, that gives them access to the information.
    1. Cost reductions:
      1. reduced calls to call center: the end user will be able to initiate service and troubleshoot themselves.
    2. Higher revenues:
      1. increases sales: your product will be enhanced, leading to more successful sales calls. You also may be able to charge for some of the new data access services that make the product better.

Zujewski concludes by saying that all of these changes combine into 4 major benefits:

  1. world-class service
  2. business insights (such as better understanding of how your customers are using your products) from all the data and analysis
  3. improve business processes: integrating data allows you to improve the way you perform current processes
  4. highly-differentiated offering due to to the apps and information you can provide users. “You end up demo-ing your apps vs. just the machines”

I was really impressed with this presentation, and it makes sense to me as a framework for calculating ROI on Internet of Things investments (I want to think about other benefits of the IoT that were impossible before to see if there are any other factors that should also be calculated).

I’d be really interested in your reaction: is this a valid methodology? what other factors would you also include?

Agriculture and the Internet of Things

Posted on 21st October 2013 in agriculture, environmental, Internet of Things, M2M, management

I’m particularly interested in how very traditional businesses will make the transition to the Internet of Things (Exhibit A: see my post from last year about the incredible way the Union Pacific Railroad has been able to switch to “predictive maintenance” by stringing sensors all along its tracks).

What could be older, and more basic, than agriculture?

Lance Donny, the founder and ceo of On Farm Systems, gave an overview of the potential of the Internet of Things to radically increase productivity and cut costs in agriculture at last week’s GigaOM Mobilize conference, saying that agriculture is a “sleeping giant,” when it comes to real-time data.

At present, the industry is handicapped by the high cost of sensors — they can run hundreds of dollars apiece — and lack of infrastructure — there’s no wi-fi, and, more often than not, bandwidth on the farm is limited.

Despite that, On Farm and other firms in the field (ooh, bad pun) are already helping farmers, especially with the critical issue of managing water use. He said there are already 14 million “connected farms” in the US and Europe, and by 2020 there will be 70 million connected devices on farms (interest in the technology is also increasing in developing nations.

Donny mentioned that a big issue with really serving farmers’ needs is that since they’re operating in a moving tractor, “you can’t give them too much data,” but must pay a lot of attention to the user interface, and only give them limited amounts of actionable data.

This “precision agriculture” yields tremendous volumes of data, and one of the problems facing IT firms in agriculture is that there are no common platforms (On Farm uses ThinkWorx), so adding a new data source from another provider requires contacting them directly and then connect to their API.

He said that reducing water usage by pinpointing when it is needed and how much is the biggest challenge, pointing out that 70% of fresh water usage is for agriculture —  even a 5% reduction in use could have tremendous implications not only for farmers, but a world with inadequate water supplies.

Donny said that Monsanto’s recent purchase of Climate Corp., which underwrites weather insurance for farms, for nearly a billion dollars “started a data war” in agriculture. He said that the goal will be to combine enough real-time data so that farmers would have 90% or more accurate 5-day weather forecasts in order to manage water better.

If the IoT is emerging as a priority for as basic an industry as farming, can other mainstream businesses be far behind?