This is the sixth and final installment of my blog series focusing on different approaches that make the development of IoT solutions more efficient. If you have been following this blog series, you probably have a good overview of how each of these different approaches influences the development process and how to apply them to your IoT project. One resource that I haven’t mentioned before, but would like to introduce here in my final post, is the utilization of developer zones.

Imagine the following scenario: your old phone has reached the end of its life and it is time to get a new one. Since you are most likely going to use this new phone for several years, you want to avoid making any hasty decisions. Therefore, you have done your research and know all about the phone’s technology, as well as the community of developers supporting it with apps and new content. You have reviewed the different compatible gadgets and accessories that are available on the market and finally cut your choices to two buying options. While you have researched every possible aspect of those two phones, you haven’t had a chance yet to actually get your hands on the phone and put it to the test without a sales person looking over your shoulder. Wouldn’t it be great if you could take those phones home with you for a few days to get a real feeling for how well they perform?

The more complex the product is, the more the customer wants to put the product to the test before making purchase decisions. In the IT sector, developer zones have become a common tool to offer potential customers exactly that kind of experience. The general idea of a developer zone is to give users access to the product, offer additional educational content on how to use it, and allow them to explore the product.

As the IoT market continues to grow and technologies become more and more elaborate, vendors and technology providers are starting to offer similar services. Especially in the IoT, where users typically do not get the opportunity to experience the new technologies in advance, it is even more important to provide novice users with a guided experience that will help them get started and explore all the different aspects of the technology. Some developer zones offer packaged software kits that are equipped with sample software or applications, as well as instructions and tutorials to enable users to rapidly build a sample application and connect their first device.

Before making any purchase decisions, try to get a real feel for the IoT technologies you are evaluating. You must make sure your technology provides the user experience you require and promised functionality works and integrates seamlessly with the technologies you already have in place as well as the ones you are planning to use. It is also always a good idea to ask what resources are available and whether you will be able to draw from pre-built components or the expertise of a trained and experienced developer community. While developer zones are becoming more readily available, having the opportunity to ‘test drive’ a solution, just might sway your final decision.

Image by Robert Couse-Baker on Flickr (CC BY 2.0)

Related Articles:

• Don’t Underestimate Collaboration to Create IoT Value
• Finding the Perfect Match: Compatibility in the IoT 
• No One Company Can Do It All: The Power of the IoT Ecosystem
• Developing Future-Proof IoT Solutions
• Removing Obstacles to Make IoT Development More Efficient

The UK has installed wind power generating capacity equal to 30 coal power generating plants. These plants are located offshore, largely in Scotland where winds are often the highest. But the biggest power demand is hundreds of miles away in Southern England. While the power transmission lines connecting Scotland and England typically operate below their thermal limit—the maximum amount of power before heating up, sagging and possibly hitting an obstruction—getting the power from where it is generated to where it is needed is not easy.

Electrical power is delivered using alternating current, with power flow constantly alternating directions. System operators controlling the network have to keep all of the online power generators operating in lockstep so all change direction at the same time. One technical concern is that transmission lines shift the phase – the position on the waveform cycle — of the electricity they are transporting. If the phase difference or angle between two generators at any point on the grid becomes too great, one or both may suffer serious damage. Generators are set up with pole trip detection systems designed to instantly take them off-line when phase differences approach dangerous values. The demand being met by that generator will then instantly be dumped onto other generators. In the worst case scenario, one generator after another trips, causing a power blackout that takes hours or even days to bring the grid back online.

System operators monitoring transmission lines have to make quick decisions. The risk is managed by a combination of offline study and experience that is used to determine whether or not lines are able to handle low-cost sources of power such as wind when it becomes available. The phase angles will always vary but as long as it is an amount that approaches the critical value for any generator that is currently online, there is nothing to fear. Today’s monitoring systems are not capable of measuring the phase shifts that are occurring throughout the network and transmitting that information hundreds of miles to a national control center. So, operators don’t know if the phase shift at various points in the grid is approaching dangerous values. They rely upon operating rules that are determined by studies performed in a simulation environment.

Without live information on the state of the grid, operating rules have to be very conservative. “Getting stability wrong can have catastrophic consequences,” says Peter Haigh, ‎Senior Power Systems Engineer for National Grid, a major supplier of electricity and gas in the UK and Northeastern US and operator of the UK’s electric power transmission system. “We play it safe by adding appropriate margins of safety to the operating rules. Occasionally, this can mean that renewable power sources in Scotland must be taken off-line and replaced by fossil fuel plants in England to avoid the possibility of overstressing transmission lines.”

National Grid is applying the Internet of Things to address this challenge. It is installing 110 grid monitoring sensors at substations and has built 26 portable units. Each sensor is synchronized with a Global Positioning System (GPS) time source so they can make extremely accurate phase angle measurements. The monitors also measure a wide range of other values like voltage waveform quality which is becoming increasingly important because renewable resources can lower the quality of the voltage waveform by introducing harmonic distortion. These distortions can be managed with the help of the measurements from these devices. Each monitoring unit is equipped with National Instrument’s CompactRIO controller which provides control, data logging, processing and network communications. The controllers connect to a central server via National Grid’s secure virtual local area network (VLAN).

The database is currently available to engineers and managers in the company who use it to gain a better understanding of the condition and performance of the power transmission network and to make better decisions such as prioritizing capital investment. In the near future, the phase angle and other key measurements at any point in the grid will be integrated into the control panel’s information system.. This will enable system operators in national control centers to make power dispatch decisions based on actual real-time conditions rather than just relying on the operating rules.

“By knowing the phase angle of voltages at critical points in the transmission network our operators can understand where the actual stability limit is and operate closer to it,” says Haigh. “In practice, this means that in some instances they will be able to take fossil fuel plants offline and replace them with renewable resources that operate at a lower cost and with fewer emissions. As our network evolves, real-time monitoring will enable our system operators to use the lowest cost resources available to keep energy prices low and reduce our carbon footprint.”

Image by SwaloPhoto on Flickr (CC BY 2.0)

Related Articles:

• IoT-Based Smart Energy Systems Power Sustainability
• Path to a Smarter Grid Comes With Challenges

According to a recent study by German-based industry consultant Staufen AG, only 10 percent of Chinese firms have started to make the transition towards the web-based, real-time networking of objects, machines and people, and a third of Chinese companies have not dealt with Industry 4.0 in any way whatsoever.

But while China may lag behind Germany, North America, and Japan in terms of its technical prowess, it remains a manufacturing powerhouse with a robust supply chain and infrastructure, a cheap bill of materials, and a government willing to invest countless millions to maintain a top spot in the global market.

Despite a slow move into Industry 4.0, China-based companies recognize its potential and believe their business models and workforce will change considerably in the next five years because of it, says Staufen. A majority anticipate a shift to smart production will bring fresh economic gains.

Made in China 2025, a ten-year government-sponsored program aimed at putting China on equal footing with Western industrial nations, should help pave the way.

The plan is strongly focused around automation and the development of cyber physical systems that will boost lean production and cut dependency on an outdated cheap-labor model.

A shining example sits in Dongguan city, north of Shenzhen.

Dongguan is a center for Chinese manufacturing and home to the country’s first ‘unmanned’ factory run by computer-controlled robots, computer numerical control machining equipment, unmanned transport trucks, and automated warehouse equipment.

The factory owner, Changying Precision Technology Company, a cell phone module manufacturer, says its robots can make better products faster. It claims to have replaced 600 human assembly-line workers with 60 robots, resulting in a fivefold reduction in manufacturing errors and an increase in production of over 250 percent.

And improving the bottom-line isn’t the only benefit to factory automation. It may also offer a solution to China’s looming workforce crisis – currently 200 million of China’s population are above the age of 60 and this is set to rise sharply in the coming years.

But China must take care not to automate factories where unnecessary, experts warn.

Although automation can overcome labor issues and cut costs, there are instances when wage hikes are more cost-effective than replacing workers with robots.

In Japan, for example, where labor costs are five times that of China, automation is a less lucrative proposition.

A Japanese company could afford to use two robots to replace one worker, says Deng Qiuwei, general manager at Shenzhen-based Rapoo Robotics Applications. “But in China, it’s only worth considering when one robot can replace three workers.”

In a related and somewhat ironic move, China’s neighbor South Korea recently announced a $16.75 billion investment in robotics technology to reduce its dependence on rising-cost Chinese labor. In partnership with industry giant Samsung, it hopes to build 600 smart factories for starters. And that’s just the tip of the iceberg.

“We are pushing to build 10,000 smart factories by 2020 for the smart ecosystem of the country’s manufacturing,” says Vice Minister Lee Kwan Sup of South Korea’s Ministry of Trade, Industry, and Energy.

It’s hoped that once affordable robots reach the South Korean market and are more widely used in smart factories it will bring about far-reaching innovations to that manufacturing sector.

Further east, Japan’s factory floor doesn’t need the same level of overhaul, say industry experts.

According to a recent study by Cisco, smart factories represent a $139 billion value stake for Japan, much lower than the rest of the world. This is because Japan already has an advanced manufacturing sector which includes a good mix of technologies.

For Japan, Industry 4.0 has less to do with technology and more to do with mentality.

Industry 4.0 is premised on openness and connectivity, something Japanese manufacturers are less inclined towards, preferring instead to rely upon proprietary technology and a black-box approach. Yet Industry 4.0 depends not only on networking and interlinking across machines and manufacturing facilities, but also across businesses and people.

Other countries like South Korea and Singapore do a much better job of sharing information and collaborating on Industry 4.0 initiatives on a global scale, something Japanese businesses are gradually waking up to.

Perhaps a step in the right direction is the recently launched ‘Industrial Value Chain Initiative’—whose members include Mitsubishi, Fujitsu and Panasonic—which aims to connect factories worldwide and reposition Japanese businesses in a more open system.

Image by JoshEwwAhh on Flickr (CC BY 2.0)

Related posts:

• Building a Path to Smart factory Value
• Smart Factories Boost Economic Growth at Home

Larrea explains that as a society, we assign specific functionality to discrete spaces, resulting in all kinds of rooms – a living room, a dining room, a bedroom, a bathroom. And, this is how people have been designing homes for more than 2,000 years. MorphLab intends to change this paradigm by augmenting spaces through architectural robots, so that “space killers” such as sofas and beds don’t take up space when we don’t need them. The interview with Hasier Larrea follows:

Tell us a little bit about your background and how you came to work with the MIT Accelerator?

I’m from the Basque region of Spain and I recently completed my Master’s in Media Arts and Sciences from MIT. My team and I joined the MIT Global Founder’s Skills Accelerator program over the summer to explore how all our research and technology could be applied to the market right now. So, we spun off MorphLab from the MIT Media Lab with the Accelerator’s help. And now we are out in the wild raising money and trying to get some traction.

Can you briefly describe the MorphLab concept, and the problem your technology solves?

At MorphLab, we are focused on creating hyper-efficient and responsive urban living spaces. We’re leveraging the smart and connected aspects of the Internet of Things to disrupt the real estate industry.

Today, more than 54% of the world’s population lives in cities, but space is limited and costs are skyrocketing. Many of us want to live in vibrant, workable, central locations where the action happens – but many people are all being priced out of these vibrant cities. (Larrea explains this problem further in his TEDx Cambridge talk.)

Since square footage is the biggest cost in real estate, solutions as micro-units have become more popular, but no one wants to live in that type of a cramped environment. During our research at the MIT Media Lab, we uncovered two basic things. One, we don’t need as much space as we think (because we are surrounded by these “space killers” such as sofas and beds and tables); and two, space augmentation through robotics can create larger spaces.

MorphLab’s goal is to deploy this idea at scale by creating tools and systems for architects and designers, almost like Legos, that are smart and connected.

Do you have early validation of your idea?

After four years of research, we posted a video for our prototype – this shows how a 200 square foot space can be used as a space three times larger – the video got over a million views in YouTube in less than a month! So, we saw that there was this appetite because there were big problems that need to be solved in the space of robotics and architecture.

Your video shows walls and beds moving around, but how does that actually work?

The idea here is to bring the world of robotics and architecture together. We tried to think about all the systems that have been applied in many other different systems in places like cars, appliances, and how we could start thinking about those components in the context of furniture and architectural elements.   How do we turn static and dumb architectural elements and furniture into dynamic and intelligent components of your living space?

We started thinking about appliances like garage door openers and how we could apply technologies that are already in mainstream, to systems like walls, tables, and beds. But – and this is very important – this is not about just creating a new closet that moves or a bed that drops from the ceiling because there are examples of that already. We are trying to design on a scalable strategy by creating a platform.

What are some of your challenges?

We are trying to design those components, those mechanical systems, those electronics so that we can have basically an infrastructure that could allow the design of endless possibilities. So, again, we are not claiming to be the first ones doing a moving wall or a dropdown bed, but we are actually creating a new ecosystem that allows us to create many of these systems very quickly and be ahead of the curve for understanding the needs of the users. So, I would say scalability is a challenge, as well as acceptance in the real estate industry that isn’t used to innovation.

 Can you explain how the Internet of Things is involved and what that connection is?

Yes, the first part is the dynamic system – how do you effortlessly transform the space? But, the second part is how do you make all these systems intelligent and smart? And that’s where the Internet of Things comes into play.

I believe the smart home is still in its infancy, meaning we are still talking about the peripherals in the home – such as connected thermostats, connected lights, connected speaker systems. But, no one is talking about connected architectural elements or connected furniture. And if you think about your home, arguably those are the things that really give personality to your home, the things that are more predominant in your space.

By connecting to the Internet of Things, all of a sudden your couch, your table, your closet, your wall could also talk to other connected devices, and with the idea that they not only have the potential to connect, but they also have the potential to be a hub, to connect other smart devices.

We’re creating a platform that allows the designers, developers, and software developers in the world to start playing with those hardware devices and start developing apps, it opens up a whole app ecosystem, the same way mobile phones did it. We believe the home of the future is going to be a platform, and everyone will be able to customize their experience.

What are your go-to-market plans for MorphLab, and who are you targeting as your customers?

We are a B2B company and we are going to sell to real estate developers with the idea that developers could purchase some of the systems, and they would rent them at a premium. For example, the “creative class” is getting priced out of the market. Instead of turning to micro-units, which are dysfunctional, we can bring technology to make a smaller space act like the bigger studio but with a lower cost.

Any ideas on where your first installations will be?

Our plan is to start in Boston. A good example of a neighborhood that is pushing the envelope with the smaller units is the Seaport District. In 2016, we’ll bring the pilot program to other cities, like New York, DC, and maybe San Francisco. The whole plan is to use these first apartments as a pilot – a “living laboratory” – so that we can have people interacting with them, and giving us feedback. We want to record some of the usage of the systems to see how valuable they are, to see what things work, and what things don’t work so well so we can keep refining the product.

In essence, we want to use architectural robots to augment the capabilities of our homes – whether small or larger – to change the way people relate to and experience their living spaces.

Photos courtesy of MorphLab.

In the article, How Smart Connected Products are Transforming Companies, co-authors PTC CEO, Jim Heppelmann, and Harvard Business School professor, Michael Porter, describe the powerful insights gained by combining data from SCPs with data from other sources, and the implications on companies operations and organizational structure. This continuous data stream produced by SCPs creates an opportunity for manufacturers and customers to know each other better.  Marketing and sales teams must become extremely data-savvy to realize any benefits. It’s a challenging hurdle. But once it is cleared, SCPs open the door to unique and innovative customer engagement.

• Product-as-media: When a new engagement channel emerges, the first word out of marketing’s mouth is almost always, “Ads!” SCPs will be no different. New screens are just too seductive.  More ads could swell the tidal wave of irrelevant interruptions people get every day. But what if the offer was personalized and arrived at the point of need instead?  These messages become no longer an interruption, but a helpful convenience. For example, if a smart appliance sensed a user consistently struggling with an operation, it could offer advice or a coupon for a helpful accessory. SCPs offer the opportunity for true concierge-level service.
• Product-as-a-brand avatar: Marketers think of themselves as brand stewards. Sales believes they own the customer relationship. But the #1 element contributing to loyalty is product behavior. Starbucks is a well-loved brand, but if their coffee didn’t perform as expected – delivering the caffeine kick, the iconic brand would struggle. SCPs can do much more than just perform their basic function. Wearable technology could make social sharing more convenient. Office equipment could use gaming to reward teams who are most efficient. SCPs offer the opportunity to contribute more deeply to the emotional bond that a good brand achieves.
• Product-as-a-captivator: The more we learn about the way customers really buy, the more antiquated the old sales funnel becomes. A fresher approach is to turn the funnel upside down – build loyalty first, then figure out how to monetize that loyalty.  In the words of marketing guru, Seth Godin, companies should “make strangers into friends and then friends into customers.” Park visitors with Disney World’s Magic Band on their wrists, for example, can not only instantly pay for services, but they can also receive personalized recommendations for attractions based on their patterns of use. SCPs, with their interactive capability, are a powerful tool for captivating customers in the brand’s orbit.

Implications for the marketing organization:

• Increase integration. To change the engagement game, marketing must break down silos. Everything must be integrated — data, content, and methods of interaction. This integration must occur not only within marketing, but also between functions such as sales, service, logistics, and manufacturing. Companies must pioneer new organizational structures, leadership styles, and reward systems, as well as invest in collaborative technologies.
• Increase agility. Real-time engagement requires that companies be ready for anything. Marketing and sales should adopt practices from dynamic teams such as community first responders, surgical teams, software development, and gaming. Keys to success include a customer-centric mission, data-driven feedback, and empowered cross-functional teams. Technologies such as cognitive computing and predictive analytics will also take on new importance.

This is the sixth installment in a series of guest posts by leading industry analysts covering topics found in the new Harvard Business Review article, How Smart Connected Products are Transforming Companies, co-authored by PTC CEO, Jim Heppelmann, and Harvard Business School professor, Michael Porter.

Related Articles:

• The Analytical Center-of-Excellence in a World of Connected Data
• IoT Makes “As Good as the Day I Bought It” a Thing of the Past
• Services and Customer Success Collide in the IoT
• Voice of the Product: Smart, Connected Products Transform Marketing
• Enabling Manufacturing Transformation with the IIoT

LNS Research agrees with the duo in that Smart, Connected Products and the Industrial Internet of Things (IIoT) will transform the internal operations of companies and how companies interact with the rest of the value chain. In a recent LNS Research blog post, I highlighted the IIoT Platform, Big Data Analytics, Business Model Transformation, and Manufacturing Systems Transformation, and Organizational Structure Transformation as the top areas that will be impacted. In this article I will drill down into one of the most important areas highlighted by Porter and Heppelmann: How manufacturing will transform and the emergence of Smart Factories.

Traditional Manufacturing System Architecture

For more than 20 years manufacturing organizations have been attempting to create “shop floor to top floor” connectivity through a rigid, tightly integrated, and hierarchical model depicted by the often referenced Purdue or ISA-95 model. In large part these attempts have fallen short, but not for lack of effort or well-funded and intentioned projects. Rather, these endeavors have been doomed from the beginning because of the very architecture itself.

The above image is an LNS Research adaptation of the traditional models. Although at first glance it seems like a logical approach, there are a number of shortcomings limiting integration, including:

• As a temporal model, the data structure and decisions made at lower levels have much higher degrees of fidelity and granularity than higher level systems. Because of these differences in data models, if all information flowed through from the bottom to top, higher level systems would be quickly overwhelmed. Subsequently, only limited amounts of data actually flow.
• Since different systems, at different levels, have different organizational owners, there has been a homogeneous adoption of systems, i.e. there has been broad adoption of ERP, PLM, and Plant Automation systems but limited adoption of MES.
• Due to some systems that reside on plant networks (like data historians or HMI/SCADA) and some systems that reside on enterprise networks (like ERP and PLM), there a number of security concerns between tight integration between the different networks
• Because so many end-user companies have pursued tight integration strategies across the levels and some vendors have attempted to pursue single vendor lock-in at multiple levels, there has been a proliferation of integration standards, many of which are still proprietary.

Next Generation Manufacturing System Architecture

As described in the Porter and Heppelmann article, the Internet of Things is a transformative set of technologies that will change products, operations, and service delivery. To capture the value of the IoT in an industrial setting, manufacturers need to deploy a new technology stack, an IIoT Platform.

As companies deploy IIoT Platforms, it will have a number of impacts on traditional systems and architecture.

• First, many legacy system providers like ERP, PLM, and MES vendors will begin to re-platform legacy applications onto IIoT Platforms; think SAP on HANA, GE on Predix, or PTC on ThingWorx.
• Second, we will see the lower levels of the model converge to enable Smart Connected Assets that are delivered to Smart Factories as plug and play; think selling compressed air instead of air compressors or selling holes instead of drills.
• Third, and most importantly, a new set of consumer-grade lightweight applications that are IIoT enabled will begin to span the entire model. This enables data from anywhere to anywhere and workflows that move across the entire value chain; think closed-loop quality or capable to promise.

It is truly an exciting time in the manufacturing technology market place. Not since almost 20 years ago, when many of these original legacy systems were implemented, has there been so much innovation, opportunity, and momentum for investment and creating new business value.

Over the coming years, we expect to see a dramatic increase in innovation among manufacturing organizations. For many companies this is taking the form of pilot projects, where companies are beginning to use internet technologies to remotely monitor assets, raw materials, production operations, and finished goods inventories across the global plant network to enable new levels of production visibility, optimization, and asset reliability. As these innovations and pilot projects begin to emerge as broadly deployed best practices, the industry will start to see the emergence of business model transformation and the visions of Industrie 4.0 and Smart Manufacturing will start to become a reality.

This is the fifth installment in a series of guest posts by leading industry analysts covering topics found in the new Harvard Business Review article, How Smart Connected Products are Transforming Companies, co-authored by PTC CEO, Jim Heppelmann, and Harvard Business School professor, Michael Porter.

Related Articles:

• The Analytical Center-of-Excellence in a World of Connected Data
• IoT Makes “As Good as the Day I Bought It” a Thing of the Past
• Services and Customer Success Collide in the IoT
• Voice of the Product: Smart, Connected Products Transform Marketing

This is the fifth installment in a series of six posts that focuses on key contributing factors that enable the efficient development of IoT solutions. All of my previous posts highlighted one unique approach and explained the role that each plays in developing an IoT solution as well as efficiency. Here is a review of what I have covered:

• IoT platforms, which provide the underlying technology for an IoT solution. They are typically equipped with tools to develop IoT solutions and support basic functionalities that are required for any IoT solutions.
• Ecosystems can provide additional value to any IoT project and allow companies and developers to leverage existing components and best in breed technology.
• 3^rd party integrations and value added services and applications enable companies to adapt their IoT solutions to future requirements while enhancing the usefulness and value of the IoT solution. IoT solutions are typically highly distributed systems with requirements that constantly change. The ecosystem is a powerful resource that can provide many components.
• Using compatible technology that is tested and certified to work with your IoT solution enables your project to be up and running in less time while making the most of your budget. The integration of 3^rd party hardware and software is part of almost any IoT project.

Looking at this list, it is clear that the ecosystem plays a major role in developing IoT solutions. Therefore, your ability to efficiently leverage the value of the IoT ecosystem has a significant impact on how efficiently you can build IoT solutions.

The co-creation of value

As explained in my second installment, the smartphone industry is a great example of how influential the ecosystem can be for a company’s success. The success of Apple and Android is primarily a result of their app stores, for which an entire ecosystem of partners and developers creates new applications and services. This adds additional value to the Apple and Android platforms to an extent that they could never have reached themselves.

Just like in the smartphone industry, the success of the IoT and the value of the individual IoT platform will rely on collaboration. Online exchanges such as Apple’s App Store or Google Play foster this kind of collaboration, enabling the co-creation of value and exchange between producers and consumers of apps, services and more.

As with any two-sided business in which consumers are the bait to get producers on board and vice versa, it is all about bringing both participants together and creating an exchange that is equally beneficial to both of them.

So how can consumers, as well as producers, benefit from an online marketplace for the IoT?

• Consumers: A marketplace for IoT apps, services and technologies gives consumers access to additional resources for their IoT solution all in one place. Not only does this simplify the search for compatible components, but it also allows consumers to develop new and enhanced existing IoT solutions from pre-built components.
• Producers: For producers, a marketplace becomes a key channel for gaining exposure and marketing applications and extensions to create additional revenue. The immediate benefit of offering apps or other services in a marketplace lies in the ability to market to an audience that is highly qualified and already in the midst of developing IoT solutions.

An online marketplace for IoT components allows consumers and producers to collaborate more closely and enables both parties to deliver early and quick wins. And the idea of co-creating value and sharing pre-built components accelerates IoT adoption, which in turn makes developing IoT solutions less time consuming and a lot easier.

Now that I have shared key considerations for more efficient approaches to IoT solution development, in my next and final post I will provide some tips that will help you quickly add value to your organization.

Related Articles:

• Removing Obstacles to Make IoT Development More Efficient
• No One Company Can Do It All: The Power of the IoT Ecosystem
• Developing Future-Proof IoT Solutions
• Finding the Perfect Match: Compatibility in the IoT

Image by Jamie McCraffrey on Flickr (CC BY 2.0)

Imagine all the data generated by those devices – surely there will be zettabytes or even yottabytes of information that must be stored and analyzed. The big question that is looming on everyone’s mind is this: Will data centers be able to handle all that data?

No, they won’t, says Manny Linhares, director of strategy for the data communications division of Legrand, a provider of products and systems for electrical installations and information networks. Linhares, a former mechanical engineer, is now providing IoT expertise to companies that are building out networks and data center infrastructures. And he has some interesting insights on how the IoT is forcing companies to rethink data centers and the management of big data.

“The world is just not going to be able to hold all the data,” Linhares says. He uses the increasing intelligence of lighting in buildings as an example. “Soon, Cisco will be one of the largest players in LED lighting. Who would have thought that a network giant would become a foremost leader in intelligent LED lighting?”

With Cisco networks powering up lighting, data lines will now become low-voltage power sources. “Rather than mechanically wired, lights in a building will be connected to the network via IoT devices. Software will be turning them on and off, rather than an electrical wire and traditional light switches typically used to control high-voltage power,” Linhares says. IoT lighting switches will be able to power lights on and off, change their color, adjust to moods, dim, and sense natural lighting. All of this will be over a network connection without the need for a separate power supply.

The good news is that there will be tremendous energy savings. But here’s the catch. As buildings become more intelligent with IoT devices, Linhares says data centers will need to respond to new multiple connections and massive amounts of data – maybe 10 to 20 times what they are managing today. Data is being generated at rates that never have been seen before, but where is all the data going to go?

“As a director of strategy, I get asked quite often the question, ‘are data centers going to get bigger because of IoT?’ My answer is almost instantaneous,” Linhares says. “And it’s ‘I don’t know.’ What I do know is that data centers will not be able to handle everything.”

To ease the pressure on data centers and data storage while preserving the benefit of IoT intelligence, Linhares has two suggestions. First, he recommends that companies look to decentralize their data center, using the concept of fog computing and gateway devices to capture IoT data locally rather than sending it all directly to the data center.

“With remote gateway devices near IoT devices, data centers can be flattened so they don’t need to get bigger,” Linhares says. “Think about a hospital. There could be a gateway device on a surgery wing that collects data from hundreds of IoT sensors and devices in the operating rooms. From the gateway, one cable line can run to the data center. This kind of one to ‘n’ connection can create enormous efficiencies both in networking and energy.” For instance, rather than adding a $100,000 switch in the data center, an organization could add several $50-500 gateways in several locations throughout the premise.

The next step Linhares recommends is for companies to scrutinize the data it captures and determine what is really of value. “It’s time for companies to rethink how data is being captured. For instance, at an ATM, a security camera doesn’t need to capture hours of inactivity – it only needs to provide intelligence on a change in activity or motion,” Linhares says. “Petabytes of data is generated when no one is there – and that data has no value. But if the sensors and built-in intelligence are cued up to show a change from inactivity to activity, then IoT images can capture data that is of real value.” With this approach, companies can not only increase the value of IoT data, but also save tremendously on storage.

Both recommendations – the decentralization of the data center and greater scrutiny over which IoT data to capture – are different ways to look at how things can be done today. But soon, with 8 billion connected devices in the world, companies won’t have much choice. They will have to do things differently.

The voice of SCPs provides marketers with masses of real-time data describing actual usage. Behavioral analysis based on this data is an exceptional tool for understanding customers and creating meaningful segments. Human behavior is very complex.  Marketers used to be stuck with inadequate strategies for segmentation — methods such as gut-feelings, past experience, and traditional rule-based methods like parsing by vertical markets. Without behavioral data, marketers will miss important insights (at best), or will be biased and wrong (at worst).

In the article, How Smart Connected Products are Transforming Companies, co-authors PTC CEO, Jim Heppelmann, and Harvard Business School professor, Michael Porter, describe the powerful insights gained by combining data from SCPs with data from other sources. Supplemental sources include operational systems (e.g., CRM, customer service, logistics, and finance) and customer interaction systems (e.g., click streams, search, and geographic data, downloads, or video views). Data can also be also accessed from third parties (e.g., list companies, media, or from databases such as SEC filings).

By integrating data from SCPs with other data, marketers will be able to discover hidden segments with a high propensity to buy, or act in certain useful patterns. They can then use these segments to design more effective pricing strategies, offers, and bundles. For example, a customer who is an active product user might enjoy offers for product extensions. A dormant user would just be irritated by these offers and needs other stimulus to resume use.

Implications for the marketing organization:

• Increase marketing’s role as a data scientist. The digital transformation has put marketing leaders on notice that their teams need new data skills, but SCPs greatly increase the urgency. Data skills of all types are already in short supply. Marketing teams need to use pooled resource teams and knowledge-sharing centers of excellence. In addition, marketers need partners with deep technical expertise. Best-practice companies form “zipper organizations” that link marketing with the information technology (IT) department at every level – starting with the chief marketing officer and chief information officer and moving down to the practitioners. Companies must also significantly invest in data technology, data quality, and especially data integration.
• Increase marketing’s role as an innovator. Marketers must lead their companies into the new territory that SCP data will illuminate. Insights from behavioral sources frequently topple conventional wisdom. However, it is the unexpectedness of the discovery that will deliver competitive advantage. Marketing leaders must create organizations that foster innovation and react to insights in inventive ways. Companies should increase funding and rewards for customer R&D. They must break down both internal silo walls and welcome customers and eco-system participants into the process.

This is the fourth installment in a series of guest posts by leading industry analysts covering topics found in the new Harvard Business Review article, How Smart Connected Products are Transforming Companies, co-authored by PTC CEO, Jim Heppelmann, and Harvard Business School professor, Michael Porter.

Related Articles:

• The Analytical Center-of-Excellence in a World of Connected Data
• IoT Makes “As Good as the Day I Bought It” a Thing of the Past
• Services and Customer Success Collide in the IoT
• Enabling Manufacturing Transformation with the IIoT

In my previous post, I provided guidance on the key functions of an IoT solution for a PaaS business model, including device usage data collection and entitlement management.

Now here is a list of considerations when selecting an appropriate charging package for your PaaS-enabled IoT solution. These may not provide the answers straight away, but are a good starting point for the discussion.

Industry alignment

The first point to address is industry alignment: Is the monetization engine able to support the smart, connected product’s industry in its native capability?

Most modern packages will have no problem adapting to the industry rather than forcing the manufacturer to compromise on the offering. However, some legacy ex-Telecom billing systems may struggle to cope without a large customization effort.

The cost of the charging engine will be a limiting factor and must be viewed in the context of the projected number of PaaS enabled products, and the forecasted revenue they will bring in over a period of time.

Pricing modeling capabilities

Pricing capabilities define if the package is able to build multiple product catalogs with support for multi-dimensional pricing. Usage qualifiers should be swappable in anticipation of the market situation changing and the charging should adopt quickly – i.e. move from charging per distance to charging per hours of use.

Also, when selecting the charging solution it is important to ensure the pricing capabilities support multiple charge types (one-offs, recurring subscriptions, and event based charges, discounts based on percentage or volume, various discount types, commitments, and adjustments, and so on). Support for different charge types will allow the manufacturer of the product to adapt to the market by applying flexible pricing rules.

Support for global deployments

With the global reach of the smart, connected product, the manufacturer offering their product in a PaaS model must consider the following aspects:

• Is it possible to build offerings with support for multiple currencies? How will the FX rates be maintained in the system (manual updates vs. automatic FX import)?
• What about various taxation models? Is this a native capability or is there a need to integrate with an external tax package system? How is the tax point being recognized (at the device, at the point of service delivery, or at the point of service agreement)?

The taxation topic will require a deeper discussion with the tax subject matter experts.

Support for post- and pre-paid models

Depending on geography and the product itself, there may be need for a pre-paid or post-paid charging solution. Typically, a pre-paid solution will require some sort of cut-off mechanism being put in place to ensure that the product is not (over)used once the pre-paid balance is consumed. This also calls for almost real time monitoring of the product usage.

Support for IoT data streams

The IoT industry is well known for producing a large amount of device-originating data. Volume and velocity of that data will only get bigger with the increase of IoT adoption.

The selected monetization engine must be capable of processing data which comes from IoT devices (device usage statistics) as well as from the IoT platform (consolidated and aggregated data, behavior patterns, etc.). Depending on the amount of products, users, and pricing qualifiers, this stream may have different velocity – the charging system must be capable of dealing with it.

Also, it is worth pointing out that there are complementary business opportunities on the horizon – for example Data as a Currency. Once the enterprise recognizes it is in possession of valuable information assets, these can also be monetized – by trading with other enterprises or providing them through big data exchange hubs. For many organizations this can be an opportunity for complete redesign of their business models – from being primarily a manufacturer of the tangible product to the vendor of big data traded with other companies or municipalities.

I will cover the topic of Data as a Currency in greater detail in a future post.

Support for multi-way settlements

Support for the multi-way settlements will be essential in a scenario where, for example, the product manufacturer and owner are not the same as the one who provides service, content or application to the product user.

Not every monetization engine is capable of dealing with settlements, especially when more complex concepts are introduced (like minimum revenue commitments, etc.).

Multi-way settlements will become an essential feature of a PaaS-enabled IoT solution with the rise of the app economy. In the app economy, the manufacturer is responsible for the product and content delivery; however value to the end user comes from the additional applications or extra content which is provided by third parties. In this case, the lessee (or end user) pays for use to the product owner but this revenue must be split with the application, content or service provider.

Alternatively, the end user enters into the monetization relationship directly with the service provider, but in such a case, the service provider will most likely have to pay to the infrastructure or product manufacturer to share the revenue.

There are many monetization models related to the app economy and I will cover them in a future post.

Conclusion

I do not believe that new business models will replace traditional ones. However, I strongly believe they can co-exist for the foreseeable future. By designing and building the products, and the IoT solutions, in such way that both models can be supported is the strategic way to stay in front of the competition.

Related Articles

• How IoT Can Power the Next Wave of PaaS
• Building Your IoT Solution for a PaaS Business Model

Image by Sander van der Wel on Flickr (CC BY 2.0)

Smart, connected products with their embedded software, sensors and IP-enabled connectivity, whether it’s an airplane, washing machine, or other “thing” in the Internet of Things, are more complex to design, build and service than their pre-IoT predecessors. They demand more from engineering – bringing together multi-disciplinary teams to collaborate on the design of highly integrated hardware, software and connected systems.

What’s needed now is a modern approach to product lifecycle management

Traditional PLM can only support the early phases of the product lifecycle – design and development. Without IoT connectivity, this is all that can really be expected. It allows teams to collaborate on a digital version of the product (CAD, Bill of Materials, and other valuable data) that gets handed over to a factory to become a physical product. But the management of the product’s lifecycle stops there.

After the product is sold, the longest phase of the lifecycle begins – the use phase. During a time when tremendous insights could be gathered, the manufacturer can’t “see” how the product is doing out in the hands of the customer.

There’s a visibility gap between the digital world and the physical world. A customer has to call, or feedback has to come in for the manufacturer to gain knowledge about the product’s real-world performance and quality. This limitation impacts stakeholders across the company in product design and development, manufacturing, sales and marketing, customer operations, and after-sales services.

In today’s connected world there has to be a better way

And this is where PTC comes in. At PTC LiveWorx Europe this week, the company announced the release of PTC Windchill 11, the industry’s first PLM system purpose-built for the IoT era.

PTC Windchill is used by more than 1.5 million users around the world to manage and optimize their product development and lifecycle processes. Now PTC Windchill 11 offers a PLM system that can bridge the digital world and the physical world. It introduces new connectivity between products and the people who design, develop, test, build and service them, effectively closing the lifecycle loop with IoT data captured in real time during the use phase of products.

PTC ThingWorx is the IoT platform that enables PTC Windchill 11 to integrate data from physical products, web-based resources, and enterprise software systems to deliver valuable insights.

By gathering data from products while they are in use, manufacturers can answer important questions such as, ”What features and functions are actually being used?” and “Is the product performing up to par with our quality standards?”

“We are tremendously excited about the release of PTC Windchill 11 that combines IoT connectivity with improved PLM capabilities,” said Kevin Wrenn to an audience at PTC LiveWorx Europe. “For our customers this means that your product data, arguably the most valuable asset in your company, is now accessible to more roles across the organization in a way that is easier and more intuitive to use, to drive better decisions and get your products to market faster.”

Wrenn, who is the divisional general manager of the PLM segment at PTC, took the audience through an overview of PTC Windchill 11.

“Designing, managing, and leveraging smart, connected products is quickly becoming a reality for so many of the customers PTC works with,” said Wrenn. “With this release we wanted to address the key requirements for today’s PLM initiatives.”

PTC Windchill 11 is smart, connected, complete, and flexible

In summary, it delivers new technological advancements in product development data and processes, including:

• Smart – Role-based apps that provide access to relevant product information based on who you are and what you need to accomplish; new multi-faceted search capabilities that allow you to quickly filter and find specific product information; and PTC has incorporated OSLC standards for greater collaboration and connection to other systems.
• Connected – IoT data can be captured in real time when products are in use to close the lifecycle loop. Product planning, design, and quality teams can now learn from a product’s operational behavior to improve features that customers use most, configure offerings to usage patterns, and redesign parts or systems to improve quality, for example.
• Complete – Users can manage the complete digital product definition with a complete, multidimensional Bill of Materials that extends back into requirements processes and out into service.
• Flexible – New deployment offerings that include SaaS and fully hosted PLM Cloud, and a subscription model that enables you to scale up or down as program and project needs change.

Product lifecycle management no longer has to end with the engineering team. PTC Windchill 11 extends valuable product data to more of the organization. And, in an industry first, PLM can now manage smart, connected product data from the field to drive better, faster improvements to a product’s quality and design.

“Our customers can create smart, connected products, put them out in the marketplace and truly achieve the idea of closed-loop lifecycle management,” concluded Wrenn. “It’s a breakthrough in PLM.”

Set to open its doors this month, it’s hoped the $73-million facility will reduce design expenses and save on sourcing and manufacturing to the tune of $100 million over the next three years. GE will mix advanced manufacturing techniques with the latest materials science and production processes, as well employ state-of-the-art data analytics, it says.

The Greenville plant is GE’s version of the smart factory—which extends from design through manufacturing and supply chain—and it’s just the tip of the iceberg.

The company could be eyeing as much as $10 billion productivity savings globally through its various smart factory initiatives. It’s already opened brilliant factories in Poland and India.

Like other manufacturers worldwide, the company is utilizing 3D printing as part of its smart factory mix, and it’s also experimenting with ceramic matrix composites—or CMCs—to replace the nickel super-alloys in its gas turbines.

But other less tangible technologies are at play.

The Internet of Things, the cloud, big data, and plant floor analytics are all players on the Brilliant Factory floor.

In fact, connecting machine to machine, machine to network, and machine to human is creating a whole new world of opportunities for a vast number of manufacturers.

“IoT really provides visibility,” says Jim Heppelmann, CEO of PTC, a Boston-based IoT company that recently partnered with GE to help support it’s Brilliant Manufacturing Suite.

“A Brilliant Factory is a place where all the assets are being completely managed, and like any value that’s created in the industrial internet, it starts with asset performance management.”

That, says Heppelmann, who recently co-authored two landmark papers on IoT with Harvard’s Michael Porter, means knowing every single thing about your asset. It’s being able to do analytics on top of data to drive insights that can change your behavior and maintenance schedules to eliminate downtime.

This type of connectivity is set to drive tremendous value.

McKinsey Global Institute estimates that the IoT will have a $1.2 to $3.7 trillion economic impact on factories by 2025, $1.7 trillion in operations optimization alone.

“Now is the time to act,” says Bill Ruh, vice president of GE Software. “But if you don’t have the right sensors and the right data you can’t even begin to get on this journey. The data is where the insight is going to come. The technology price has come down substantially. The cost of computing has become very low, the cost of storage low, and our ability to integrate machines has become much greater.”

Easy for the $250 billion GE to say, perhaps.

Realizing value from IoT depends on how effectively manufacturers can connect their factory floor machines. For starters, connectivity requires investing in high bandwidth industrial networks—costing upwards $200,000—and deploying tens of thousands of sensors. It also requires aggregating, storing and analyzing vast quantities of data.

According to a recent study, only 35 percent of U.S. manufacturers collect and use data generated from sensors to improve manufacturing and operating processes, but despite obvious challenges, another 41 percent have plans to use sensors to collect data.

A thousand miles west of GE’s Brilliant Factory, food giant Tyson, is one such company.

Its Fort Worth T.X.-based factory has already installed 1,500 sensors to collect data on everything from raw meat inventories to wastewater and electrical usage, and it’s partnered with Rockwell Automation to give remote plant managers access to that data anytime they choose.

Using IoT technology, any Tyson plant manager has the ability to troubleshoot production delays from his mobile device before they become a serious issue, saving valuable time and costs. And while the thought of efficiently made chicken nuggets might not set the heart racing for some, remote operations is a lucrative proposition, particularly when the “factory” environment is inhospitable, far-flung, or dangerous. Energy, mining, and agriculture are three that spring to mind.

Rockwell chief executive Keith Nosbusch says about 14 percent of U.S. manufacturers—including GE—have synced their production equipment into automated networks like those at Tyson, leaving massive room for growth.

In a new era of manufacturing, remote operations and predictive maintenance are perhaps two of the more obvious money makers, or, more accurately, money savers.

“We can drive massive amounts of waste out of everything,” says competition expert Michael Porter. “We don’t need to fix products before they need to be fixed anymore, we’ll know to the second how much a product is being used per day.”

A world of smart connected products, which begins on the factory floor, Porter says, “Is lean on steroids.”

But this is just the beginning.

“Over the next 10 years we believe there is going to be a major change in the way manufacturing is done,” says GE’s Ruh.

With changes driven by software and technologies like 3D printing, GE is bringing design and manufacturing closer together, producing and testing designs more quickly with fewer errors, Ruh says. “So too, we are completely rethinking what we do inside the factory – which is all based on data and the ability to get insight into processes in real-time.”

Connectivity throughout the entire lifecycle, including supply chain, is crucial, Ruh says.

This is the second installment of a four-part blog series taking a deep-dive into smart factories and the technological, operational and strategic changes necessary for success. Next, we’ll highlight Europe as we continue our global view of trends and outlooks.

Related Articles:

• Building a Path to Smart Factory Value
• China, Asia Carve Out Stake in Industry 4.0

Photo courtesy of GE.

This is the fourth installment in a series of six, focusing on different approaches that will make the development of IoT solutions more efficient. In my previous installment, I explained why an IoT solution’s ability to integrate and interoperate with existing and new technology will ultimately define its value and usefulness.

The requirements and scope of an IoT solution continually evolve as organizations start to integrate IoT into their business. New hardware or software might need to be added, existing business systems replaced or processes changed. IoT platforms are specifically designed to meet these volatile requirements. But even with the most flexible and versatile platform, you want to make sure your platform has a large inventory of pre-certified hardware and software components.

Let’s use again the example of a car manufacturer that wants to develop an IoT solution to optimize their manufacturing process. While the manufacturer will have to deal with integrations of legacy technologies, such as the organization’s ERP system or 3^rd party systems used by partners and customers, they will also be required to integrate new technologies. In order to get real-time insights into product availability, capacities and logistics processes, the manufacturer might have to implement new software, such as a predictive analytics tool, or retrofit existing equipment with sensors, controllers, asset trackers or other devices to make them IoT-enabled.

Aside from picking an IoT platform that meets your requirements, another important decision you’ll have to make is which technology to integrate into your IoT solution.

Cost and functionality of the technology will most likely be deciding factors that come to mind immediately. However, other factors that should be taken into consideration as well are whether the technology has been tested and proven to interoperate with the IoT platform you are using and how easily the technology can be integrated. Two sensors that seem to be similar at first, may vary entirely in the communication channels or data-encoding methods and protocols they use. Any purchase decision should therefore include the following considerations:

• Check if reports or success stories from other users that have successfully integrated the technology into the same IoT platform are available. The platform provider might even provide information on compatible and tested technology. This way you can avoid purchasing a technology that doesn’t function properly with the platform, which could potentially require an additional investment in a better matching technology.
• Will you have to develop the integration of the technology yourself or are there pre-built software components that allow for a quick and easy integration of the device into your IoT solution? Are there user guides available that provide guidance on how to set up the device with your IoT platform? Being able to draw from existing software components instead of developing relevant integration internally will save your organization valuable time – time that can be used to focus on creating innovative and unique features.

Ensuring that the technologies you are planning on using have been tested to work with your IoT platform, will enable your projects to be up and running in less time, while making the most of your IoT budget.

In this ever growing market of IoT-enabled technologies, it becomes more and more difficult to identify the best match for your business. What tools you can use to guide you through this process will be the focus of my next installment.

Related Articles:

• Removing Obstacles to Make IoT Development More Efficient
• No One Company Can Do It All: The Power of the IoT Ecosystem
• Developing Future-Proof IoT Solutions

Well, all of that is changing. In a recent Harvard Business Review article, How Smart Connected Products are Transforming Companies, the co-authors PTC CEO, Jim Heppelmann, and Harvard Business School professor, Michael Porter, highlight Customer Success Management as a key activity to support a smart, connected products strategy. They state, “This unit (Customer Success) performs roles that traditional sales and service organizations are not equipped for and don’t have incentives to adopt: monitoring product use and performance data to gauge the value customers capture and identifying ways to increase it. This new unit does not operate as a self-contained silo but collaborates on an ongoing basis with marketing, sales, and service.”

Customers, even of those organizations who support industrial or medical equipment, are seeking additional value from their service providers. No longer are break/fix support contracts seen as value-add. Third-party service providers who bring economies of scale to the customer can do a majority of non-critical break/fix work. Think of the convenience and cost benefit to the customer in relying on a single third-party for all non-critical assets on a plant floor or hospital as opposed to relying on a number of OEMs.  Proactive and predictive support is becoming table stakes. In order to keep up with the competition as a service provider, you must provide a guaranteed level of uptime and availability.

Differentiation comes in the form of value-added services and system optimization

It’s not just about the uptime of the serviceable product, but about getting the most out of the investment in that product – for the life of that product. To add to that, its not just about the performance of a single asset, but the optimization of that asset’s performance within an overall ecosystem. E.g. How can we get the most out of a chiller in our overall HVAC investment? How can we maximize the flow of people given our investment in escalators and elevators? What can we do to ensure the optimal performance of staff, patients, and doctors, within an operating theater? Customers want solutions, and expect their service providers to deliver on these solutions.

From a service provider’s perspective, these solutions are enabled with the aid of data. This data can be captured through customer complaints, customer activity, employee performance tracking, and more. More and more, this information and data is being captured and analyzed directly from smart, connected products. Paired with other performance data, this information from smart, connected products can provide the servicing organization with a bigger picture of how products are being utilized in a customer’s environment. This performance can also be compared with the asset usage across all customers.

Services aimed at enhancing the value seen by customers

This information and data available to service providers must be communicated back to the customers in a consumable form. At the base level, this information is provided back in reports. Sixty-one percent (61%) of respondents to a The Service Council survey regarding Internet of Things deployments indicate that they provide their customers with access to operational data of their serviceable products. For most, this information is made available via a portal, while others rely on frequent meetings and conversations to convey the information. On top of reporting, service providers use this information to develop and offer newer value-generating products. These typically fall in three buckets:

• Pay-per-use consumption models
• Training and consulting services
• Optimization services

These services are aimed at enhancing the value seen by customers. Essentially they are focused on enhancing success, both for the servicing organization as well as the customer. The value and impact of these services has to be consistently communicated to customers, as does the availability of newer services that are created as a result of co-innovation. Success agents or ambassadors can support with this communication. This is already being done at industrial organizations where success agents offer a means of constant communication and collaboration with the customer while offering a single point of contact for the customer.

We’re entering a realm where customer success management is no longer an investment to make to curb churn, but one that is made to improve customer value and ensure business sustainability.

This is the third installment in a series of guest posts by leading industry analysts covering topics found in the new Harvard Business Review article, How Smart Connected Products are Transforming Companies, co-authored by PTC CEO, Jim Heppelmann, and Harvard Business School professor, Michael Porter.

Related Articles:

• The Analytical Center-of-Excellence in a World of Connected Data
• IoT Makes “As Good as the Day I Bought It” a Thing of the Past
• Voice of the Product: Smart, Connected Products Transform Marketing
• Enabling Manufacturing Transformation with the IIoT

IDC research found that while 66% of discrete manufacturers are actively pursuing IoT initiatives, less than half (40%) of those discrete manufacturers have even begun a pilot. Why the delay?

There are four common challenges that slow the progress in creating and capturing IoT opportunities:

• Unfunded Organizations that struggle to define and prioritize IoT use cases and develop a business case to fund initial investment.
• Technology driven Organizations that define their strategy based on technical capabilities instead of a clear value proposition and monetization plan.
• Narrowly focused Organizations that are only exploring IoT in one business function, and have not evangelized the opportunity across the organization.
• Poorly aligned Organizations that have not linked their IoT initiatives with corporate value drivers, such as reducing costs or enabling new revenue streams.

In response to these common challenges, PTC has developed a new program to help companies define their own IoT strategy in an interactive, half-day workshop, facilitated by IoT business experts from PTC. These complimentary Innovation Workshops are fast and productive; participants exit with prioritized IoT use cases and an actionable strategy.

Whether you partner with PTC in an Innovation Workshop or lead a workshop within your own organization, these are the three key steps to accelerating your path to IoT success:

Assemble Cross-Functional Team

There are two critical reasons to begin your process with a cross-functional team, including representation from Product Management, R&D, IT, Sales, Marketing, Finance, Operations, and Service/Support. First, the value of the IoT comes in connecting things; products, enterprise systems, operations, and customers. As a result multiple functions in the organization will need to participate to make these connections or provide guidance in how they should be made. Second, is that the data generated insights about how the product is performing or how it is being used by the customer creates opportunities for all functions across the organization to improve operational effectiveness or create strategic differentiation.

Identify and Define IoT Use Cases

While there are an infinite number of things technically possible, the only projects worth pursuing create real value for the business or customer. Identifying a specific IoT use case for a defined stakeholder is the best way to define and achieve that value. Start by examining IoT use case examples from a variety of industries. Discuss how the capabilities and data generated by smart, connected products can create new opportunities to capture value. Then select a specific product, identify the relevant internal and external stakeholders for that product, and explore how the new capabilities or data generated could address each stakeholders needs. For example, if the Product Management leadership knows which product features are being used and which are not, they can better prioritize future R&D investment and improve product packaging and pricing.

Prioritize Projects and Build Practical Action Plans

Evaluate use cases against a consistent framework that defines the required resources and identifies pilot project milestones. Include customer (e.g. which new capabilities create value), strategic (e.g. should we change our business model), organizational (do we establish an IoT Center of Excellence), and technology (e.g. cost and risk associated with capturing product data) considerations in this analysis. At this time it is also critical to identify and define metrics for the specific use cases and goals, for example, to prove reduced service costs via a Remote Service application consider traditional metrics like mean time to repair (MTTR), but also define new connected metrics like the remote fix rate, or number of service trips avoided to capture the extent of the value created. Establish baselines to validate future cost savings or revenue gains.

Defining and prioritizing IoT use cases to pilot is the clearest path to creating value. If you’re ready to go from thinking about IoT to winning in the new competitive environment of smart, connected products, schedule an Innovation Workshop today.

Image by tableatney on Flickr (CC BY 2.0)

A vast network

Around 55,000 miles of crude oil trunk lines connect regional markets in the US. Oil wells connect to this backbone through 40,000 miles of gathering lines. And the end products of refineries travel through 95,000 miles of pipelines.

According to the U.S. Energy Information Administration, there are some 305,000 miles of interstate and intrastate natural gas transmission pipelines in the US, with an additional 1.25 million miles of natural gas distribution pipeline.

The pipelines are mostly coated steel pipe buried underground. Oil pipelines typically transport liquid at pressures between 600 and 1000 psi, while natural gas pipelines go up to 1500 psi. These high pressures are why ruptures can be so serious, and why monitoring and detecting flaws in advance is so important, particularly given the age of some of these pipes. According to the US DOT, more than half are at least 50 years old.

Ruptures and leaks

Pipelines tend to enter the public consciousness only when there is a leak, leading to a toxic spill, or even an explosion that costs lives.

Yet pipelines are by far the safest way to move large amounts of petroleum, and really the only way to transport natural gas. But an accident, when it happens, can be serious. While a derailed tanker train can only spill as much oil as it is carrying, a ruptured pipeline can continue to pump. Thus, prompt detection and shutdown are essential.

The state of the pipe

The industry is incorporating sensing technology to monitor pressure, flow, compressor condition, temperature, density, and other variables. Large ruptures often start as pinhole leaks, that visual inspection can easily miss until they become serious. Acoustic sensors can detect a breach by a variation in the acoustic signature. Fiber optic sensors detect deformations in the pipe walls.

Sensors are also sent down the pipes for inspection. The most popular is a robotic instrument called a smart pig. The name comes from the squealing noise the original models, wire-wrapped straw used for cleaning out wax and other contaminants, made as they traveled down the pipe. Depending on the model, smart pigs detect cracks and weld defects through magnetic flux leakage or shear wave ultrasound, mechanically measure the roundness of the pipe to detect crushing, or measure pipe wall thickness and metal loss through compression wave ultrasound.

From SCADA to IoT

The system that integrates this information on an operational level is called SCADA (Supervisory Control and Data Acquisition).  It is used to gather and monitor data and then to do something like turn a valve or change the set point on a flow controller. SCADA is common in industrial operations that require real-time control of system operations.

In industrial implementations, the Internet of Things  develops on top of the already existing system, allowing for a move from “monitor and respond” to a predictive and proactive approach supporting improved decision making.

Moving up from data: the example of PG&E

Over the past five years, Pacific Gas & Electric (PG&E), which operates 6,700 miles of gas transmission pipeline and 42,000 miles of gas distribution pipeline in northern and central California, has worked intensively to become predictive and proactive in the way it manages its network.  According to Mel Christopher, Senior Director of Gas Systems Operations, this will be a multistep journey. These steps can serve as a model for all such IoT implementations.

Situational awareness creates intelligence out of the data, with better visualizations that enable operators in the Gas Control Center to see changes in the system quickly.

Situational intelligence follows, and integrates geospatial and temporal data to give a precise understanding of specific events as they happen.

Predictive analytics finally take all of that real-time data and pull out patterns that signal approaching abnormal events, allowing for proactive responsiveness.

Their own pipeline sensors can’t provide all the needed data. PG&E recognizes the importance of gathering data from outside sources to gain the broadest view of developing risks. Data from the Army Corps of Engineers, Caltrans, CAL FIRE, and other third parties is also an input to PG&E’s proprietary system, called TAMI (tactical analysis mapping integration).

“In late September 2015, three huge fires, the Valley, Butte, and Rough Fires, were close to our pipelines and facilities,” said Christopher. “We used TAMI to monitor the movement of the fire lines and the wind direction, and it provided alerts whenever the fire line was within a certain distance of a facility. This would trigger an isolation plan we had already built using data from TAMI.”

Invisible system, visible improvements

Continuously monitoring hundreds of thousands of miles of pipeline is no easy task, nor is responding effectively to ruptures and other malfunctions. For the oil and gas industry, the IoT makes that task easier.

Image by Maureen on Flickr (CC BY 2.0)

In my previous post, I highlighted the key components of a PaaS-enabled IoT solution: the connected product (connected device or other service endpoint), an entitlement management system that orchestrates the product usage eligibility or level of service, a CRM, and a monetization engine, which fulfills charging and billing functions. Let’s take a closer look at these components.

Device usage data collection

The manufacturer of a smart, connected product has already incorporated data collection into the product itself. Collected data has many purposes: it allows for remote monitoring and diagnostics, it feeds back into R&D for product improvements, and it is also used as part of the preventive maintenance process.

However, with the onset of the PaaS business model, additional data must be collected from the device – data that describes the usage of the product for the charging purposes. It can be anything from the distance of the journey, number of printouts, counted hours of engine thrust, to the number of analyzed samples or MRI jobs performed. In many cases these data points will be used as input to the monetization engine which will apply price points to be used in the final settlement.

When designing the smart, connected product for the PaaS model, the following aspects of data collection must be considered:

• What are the key data points on which the pay-per-use pricing will be based? Depending on the nature of the product and the industry it may be a single qualifier (number of MRI scan jobs) or multiple points (distance driven and number of days). Ideally the product should collect multiple usage related data points to allow for building competitive pricing models depending on changing market conditions, customer demographics or competition.
• Collecting auxiliary or complimentary usage related data points – these will play a key role in monetizing any additional services. For instance, the hospital pays the device manufacturer for each MRI scan performed; an example of an add-on service is the consultancy of the scan result with the external specialist using the device’s built-in desktop sharing capability. Such add-on service must generate a specific event on the device (or in the cloud) which will be associated with the asset and then priced accordingly.

The IoT connectivity is able to address the concerns mentioned above. An IoT agent (software running on the device responsible for exchanging the data with the IoT platform) shall be modified to read the usage data from the device and then transmit it back to the IoT platform where the usage aggregation is going to happen.

Secondly, it is recommended to design the device in such a way that it provides constant feedback to the end user. Ideally the PaaS-enabled IoT solution will signal the local user with the current state of usage related counters. This will serve the following purposes:

• Allow the user to understand how much of daily, weekly, monthly, etc. “quota” has been consumed;
• Avoid bill shock by over using the device;
• Avoid the situation when the service is cut off (end of entitlement) without previous clear warnings.

Currently available techniques (alarms, events, real time data monitoring) can enhance the user experience; for example keep the user informed if abnormally high usage is detected. For example, the number of MRI scan jobs are above running average or industry standard, etc.

Lastly, the IoT solution by design requires the products to be connected to the network and transmit to and from the IoT platform. However, the design should incorporate mechanisms to cater to the situations when connectivity is down due to infrastructure issues or due to policies (i.e. hospital allowing the device to be connected to the network only outside of business hours). The device must be equipped with a queuing mechanism to continue collecting the usage statistics despite the interruptions in connectivity.

Entitlement Management

The entitlement management system controls which product’s features a given user is eligible to use over a unit of time. Depending on industry, product type, criticality of the product function, the entitlement management can be realized on various levels of technical sophistication.

On one side of the spectrum it may be implemented via a straight forward on and off flag directly in the IoT platform. The account manager can set this flag on a device (or set of devices) once negotiation with the lessee has been completed. The IoT platform will propagate the flag down to the IoT agent and the device itself. As a result, the device will enable or disable its features available to the end user.

More advanced implementation will involve a more dedicated entitlement management or license management system which, most likely will integrate with a CRM, and will control when and for which devices the features shall be enabled or disabled. The complex hierarchy of features and sub-features can be modelled with such an approach but the end result will be the same – the device will automatically receive information from the IoT platform when the license is active, when the license has expired and potentially when it has been renewed.

When implementing the entitlement management, the following aspects must be considered:

• How critical is the product function? If the license expires does it make sense to cut off the service, or limit product function, or shutdown the device automatically? In many cases this will not be a viable approach if the result would be endangerment of life, health or property.
• What technical measures shall be implemented to warn the end user or lessee about potential entitlement approaching its end? End user can receive the warning distributed by the IoT Platform while the lessee will most likely be notified by the CRM system.

Monetization Engine

The monetization engine will play key role in a PaaS-enabled IoT deployment – this will be the system responsible for generating finance documents – invoices for product users and settlements with 3^rd party providers (application, service or content).

Selection of the appropriate monetization engine (or rating and billing package) will play a key role in the way that the PaaS offerings are going to be priced and marketed.

It is important to keep the cost of the charging system in line with expected ROI per connected device and, on the other hand, it should offer a flexible pricing structure to model future offerings without major redesign of the solution.

The rating and billing market is a very crowded space. There are very large players, who for decades have been entrenched in telecom or cable industries with legacy packages primarily targeted to Communication Service Providers. On the other side there is a large amount of medium and small players providing packages geared towards specific industries. One thing all these vendors have in common is the desire to put their foot in the IoT and M2M space.

Next steps

In my next post I will focus on the monetization engine and the essential qualities it must possess in order to support PaaS-enabled IoT deployment. Stay tuned for more!

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Image by Liz West on Flickr (CC BY 2.0)

This is the third installment in a series of six on different approaches that will make IoT development more efficient. In my previous installments, I talked about how companies can benefit from leveraging IoT platforms and the power of the ecosystem when building IoT solutions.

One challenge that shouldn’t be overlooked during the building process is that IoT solutions are typically not closed systems but highly distributed, with requirements that constantly change.

So how can companies efficiently evolve their IoT solutions?

The value of an IoT solution is immediately dependent on its interoperability and integration with business systems, devices, services and other data sources. If we look at a car manufacturer that wants to develop an IoT solution to optimize their manufacturing process, for example, there are many different elements they would need to integrate.

In a first step, the car manufacturer might look at integrations with customers to get real-time insights into demands as well as integrations with suppliers and partners to automatically pull and push order information. Moving forward, the manufacturer might want to get real-time insights into product availability and capacities, in order to automatically trigger subsequent processes, such as order confirmations or logistics processes. Therefore, they would need to incorporate things like manufacturing line status, output created, and current inventories. Adding services such as predictive analytics, they would eventually be able to create much more accurate forecasts on future demands and output requirements.

The list of possible integrations is endless, and of course depends on the use case at hand. This example shows, however, that the scope and requirements for an IoT solution continually evolve as companies move forward and incorporate IoT into their business.

How well an IoT solution can integrate and interoperate with new elements will ultimately define its usefulness and value.

The IoT ecosystem is a powerful resource that can provide many pieces of an IoT solution that companies would normally need to spend time on developing in-house, which is why using the ecosystem is a strategic move.

It is also critical that the underlying technology used to build the solution supports such a flexible and diverse architecture. IoT platforms are specifically designed to meet these requirements. In order to find the right platform that will not only meet the requirements of your current use case, but scale in future projects as well, ask yourself the following questions:

• Does the platform enable easy integration with 3^rd party systems, cloud services, and hardware components?
• How well does it support interoperability with other applications such as predictive analytics or big data services?
• What does the process for changing or adding elements in the future look like?
• Is the platform supported by an ecosystem of partners and developers that offer complementary technology and out of the box functionality that can be plugged directly into the platform?
• Are there already existing pre-built components? Can they be accessed via a central app store or marketplace?

Using an IoT platform that is designed to integrate components provided by the ecosystem as well as extend the functionality with additional components, enables companies to efficiently adapt their IoT solutions to future requirements and gives them the flexibility to benefit from new developments in IoT technology.

The question I will be addressing in my next installment is how to enable interoperability between different products and technologies.

Related Articles:

• Removing Obstacles to Make IoT Development More Efficient
• No One Company Can Do It All: The Power of the IoT Ecosystem

Image by  Gabriel Saldana on Flickr (CC BY 2.0)

The article shared two examples that, to me, explain why manufacturers need to pay attention to this discipline. In two separate instances, Tesla set themselves apart from traditional automakers using evergreen principles. In the first, they identified a hazardous driving condition that was leading to fires and remotely upgraded all existing cars’ suspensions to prevent the scenario from occurring. They made the car safer to drive and avoided an expensive recall. In another, they included “autopilot” capabilities into cars while the feature was still a work in progress. They avoided the difficult, traditional tradeoff of either omitting a new feature in a new model or delaying product introduction. They included what they could with plans to continually improve it and introduce the feature when it was ready.

So why should manufacturers pay attention to evergreen design? Let’s step back a bit.

Remember when the day you bought a product was the best it would ever be? With the exception of a few anomalies like blue jeans or a baseball glove that getter better with use, the brand new product was at its best. Then, after a while it slowed down, got weaker, wasn’t as responsive, would stop doing a function, or maybe a part broke off. Performance and features declined with time. The best you could possibly hope for was “as good as the day I got it.” Maybe your products are still that way.

But I distinctly remember the day that changed for me. I upgraded my BlackBerry to a new operating system and it turned into a brand-new and improved device! The user interface was much more graphical. I was giddy. It was like a new phone for free!  The way of the future for equipment has started to look a lot more like an upgradeable computer instead of a piece of equipment that could only deteriorate over time. The smarter devices have become, the more they can improve, evolve, and gain value as you own them.

But the real reason manufacturers have to pay attention to evergreen concepts is that it fundamentally changes the relationship between a manufacturer, the products they make, and the customers that use them.

The more connected products have become, the more engaged the manufacturer (or service provider) can stay in the product while it’s in the customers’ hands. This is why smart, connected products should be viewed as a major disruption as opposed to a simple step change in product capabilities. Now manufacturers can offer new services, like adding live traffic information or restaurant reviews to a GPS.  They can fix problems with devices before customers know they exist. They can leverage new design practices like soft buttons and controls that allow them to add new interfaces and functions to products in the field that the original designer may not have been able to anticipate.  They allow the manufacturer to experience what customers are doing with products in the field to gain new insights.

Let’s not celebrate this brave new world too quickly, though. There are plenty of obstacles for manufacturers to address.

I also remember the first device that “bricked” on me. I downloaded a new operating system and my phone downgraded itself into a paperweight. In that case, permanently. I also remember the fist time my car bricked on me. A sensor mistakenly determined that some mechanical parts were going to clash so it shut the motor off leaving my family stranded at a kid’s baseball game. That was a wakeup call on the potential pitfalls of smarter products.

The point is, designing smart products is hard. The industry has started to make strides in developing and validating “mechatronic” products that contain mechanical, electrical, and software components. But evergreen design takes advantage of smart products (systems) connected to other systems. It leverages connectivity to data sources in the cloud. It relies on feedback from devices to get to the manufacturer and make a round trip with updates. And it all has to work together. That’s a lot to design and validate. It’s even more difficult to manage as things change!  If manufacturers push updates to products running in the field, they have to be able to simulate and validate their behavior to avoid turning a potential positive upgrade into unplanned downtime.

The industry still has a lot to learn about how to effectively develop systems, and now we are pushing the boundaries further. Why? Because the value is there. Products can now get better over time. The relationship between the manufacturer and the user can be closer. That’s simply too compelling of an opportunity. The complexity has to be – and will be – met by new design disciplines and tools. Now, maybe we will love our old equipment as much as we love our old blue jeans and baseball gloves.

This is the second installment in a series of guest posts by leading industry analysts covering topics found in the new Harvard Business Review article, How Smart Connected Products are Transforming Companies, co-authored by PTC CEO, Jim Heppelmann, and Harvard Business School professor, Michael Porter.

Related Articles:

• The Analytical Center-of-Excellence in a World of Connected Data
• Services and Customer Success Collide in the IoT
• Voice of the Product: Smart, Connected Products Transform Marketing
• Enabling Manufacturing Transformation with the IIoT

But where are the biggest growth areas within the smart factory itself? Which countries and industries are leading the charge? And is there a set of best practices manufacturers can follow when developing their own smart factory plan?

There are some excellent examples of large manufacturers who are already leading with smart factory initiatives—General Dynamics, Honeywell, Mitsubishi Electric, Rockwell Automation, Schneider Electric, GE, and Bosch—as well as smaller companies like UK-based Gripple and Tap Biosytems.

Each have implemented their smart factories in different ways to reach a variety of goals.

In fact, if you ask manufacturing leaders what a smart factory means to them, you’re likely to get a different answer from each. Responses will vary depending on industry, role, and geographic location, although there will be core common values and objectives shared across the board.

For some, smart factories are about sustainability, next-generation materials, and zero-waste production.

For others it might mean improved simulation and modeling, the merging of digital and physical worlds, or the integration of manufacturing and engineering processes.

Still others will envision agile facilities and systems that can be reconfigured based on market demand, while some will talk about optimizing the entire value chain, streamlining the flow of materials, and dabbling in value-added services and Big Data.

Many will include all of the above in their smart factory vision.

Automation of the factory floor, robotics, M2M, and remote operations are all components of the smart factory accelerated by the rise of the Internet of Things and the networking of sensors, motors, switches, and other sundry gadgets.

And right at the center of all this technology is an intrinsically human story. A changing workforce, job loss, and job creation. The smart factory requires a new type of worker, highly skilled, creative, well-educated, and IT literate – a workforce that focusses less on repetitive manual tasks and more on product design, optimization, and the monitoring and controlling of processes.

Across the globe, different countries have differing definitions of the smart factory and smart manufacturing. Sustainable manufacturing is a goal shared by all. The United States is focused on simulation and modelling tools for design, advanced robotics and intelligent manufacturing systems, strategic standards development, and responsive, distributed design and production systems.

Germany’s High Tech Strategy 2020 addresses energy and resource-efficient manufacturing, advanced automation, robotics and the human-machine interface. Other goals include the development of flexible production networks and systems for customized production. Industry 4.0 is focused on embedded systems, seamless digital networks, decentralized control of production, virtual planning of products, and production and remote maintenance.

Asia-Pacific is a leader in smart factories—due essentially to the large number of manufacturing industries in the region—and is expected to continue its growth between 2015 and 2020.

Japan’s innovation program focuses on new production technologies for an aging workforce, energy conservation, smart manufacturing and visualization technologies, and integration of IT systems and analytics.

Korea is focusing on green tech, the smart grid, cloud computing, and value-added services.

China is an interesting study. Like other nations, it is concerned with resource-efficient and eco-friendly manufacturing, new-generation IT, and digital and intelligent design and manufacturing. But its impending demographic crisis—the country has nearly 200 million people above 60 years of age—makes advanced automation of the factory floor a high priority.

However, the transition to a truly smart factory, one that includes rapidly developing IoT technologies, is not an easy one to make and manufacturers face many hurdles.

This four-part blog series takes a deep-dive into smart factories and the technological, operational and strategic changes necessary for success. We’ll highlight global trends and outlooks and showcase leaders in smart manufacturing, beginning in the U.S.

IDC Manufacturing Insights Research Manager, Heather Ashton, recommends an IoT roadmap that includes two important steps: 1) Determine Use Case, and 2) Prove ROI.

“Because IoT technology is still emerging and can enable thousands of use cases, it’s critical to define and prioritize the use case that will create the most value to the organization, and then balance that with the risk and cost of the initial investment,” says Ashton in a recent video. She provides some questions to help you get started:

• How can connected product data transform business processes?
• What’s the use case and value proposition for the business or customer?
• What are the inherent risks and associated costs with this use case?

Keep in mind that as you evaluate the value and costs of a use case, it’s important to establish metrics so that you can later objectively validate the ROI, advises Ashton. Project leaders should identify and define metrics up front for each IoT use case.

One example is for service. If you’re considering a remote service use case to reduce service costs, establish baselines and calculate changes for  existing  service metrics like mean time to repair and first time fix rate.

In addition to establishing metrics, “Also, ensure customer and business stakeholders understand the value proposition and proactively manage and communicate the value they create,” concludes Ashton.

For more insights from Heather Ashton, watch the full video series by IDC: Reshaping the Manufacturing and Operations Landscape.

Related Article:

• How IoT Transforms Discrete and Process Manufacturing
• Top Drivers for IoT in Discrete Manufacturing
• The Rise of IoT in Process Manufacturing
• Top Five IoT Challenges for Manufacturers

The Internet of Things domain does not stay impartial to the business model changes. Manufacturers of smart, connected products must adapt to an environment where customers are shifting from a need to own the “product” towards the need for only the product’s “outcome”.

This trend is happening across different markets:

• Millennials living in metropolis centers often don’t want to own a car (and all the hassle related to it), they want access to a flexible transportation solution instead (Zipcar, Uber or Boris-bike);
• Owning CDs is replaced with music streaming services;
• Jet engine manufacturers are leasing the engines to the airlines; airlines are buying the outcome instead – jet-thrust per hour;
• Hospitals rent the equipment and pay per use to avoid paying the full price upfront of an otherwise expensive device;
• Office space providers use smart-building solutions and pay for the outcome – percentage of electricity, and other benefits.

As the examples above illustrate, the change in customer behavior is a result of one or more of the following factors: to avoid the commitment to product ownership and related responsibilities, a focus on product outcome, an expectation of service delivery, and access to new services.

IoT as a PaaS enabler

Before the IoT-era the PaaS model was limited to specific industries (telecoms), also it was not “real time enough” and license management was not easy, especially in the case of products which were not connected. Now, with ubiquitous connectivity it is much easier to bring the PaaS model to existing or new products. IoT solutions and “always on” connectivity are the key elements responsible for popularizing the subscription-based business models.

How do all of the above impact manufacturers of smart, connected products? How must their IoT solutions be adapted to support PaaS concepts? And what are the top capabilities to include in the design?

In this first installment of a two part series of posts on best practices for IoT solutions for a PaaS business model, I will provide a technology architecture blueprint overview to help you understand the capabilities you will need.

High level architecture of an IoT solution for a PaaS business model

An IoT solution capable of supporting a PaaS business model will contain the following components: the connected product (connected device or other service endpoint) connected to an IoT platform via Internet, an entitlement management system, a CRM, and a monetization engine.

What are the main functions of the IoT-enabled PaaS solution?

Let’s start with the connected product itself. In its primary function this product provides a value or an outcome to the end user. It can be a tangible product or just a service endpoint. However, in the subscription model it is important for the product to provide, in almost real time, its usage statistics. Also, the product must be aware of the entitlements granted to the end user. Based on the entitlement (think license) the product’s capabilities will be gradually switched on or off, or the service will change its levels.

The second main function of the solution is the entitlement management system. This is where the eligibility for product features and functions will be associated to the end user. This is also the system which will provide updates to the license information (entitlement) on the connected devices in situations when the entitlement changes (new license purchased, service level upgraded or downgraded, license eligibility elapsed, etc.).

The third function is the monetization engine, which serves as a pricing and billing solution. Depending on the sophistication of the charging package, it could also address (directly or via additional integrations) other concerns which are applicable in the PaaS model: taxation, currency conversions, discounting, invoicing, payments processing and accounts receivable. These will be especially important in the global deployments.

The next function is the CRM. Its role in the PaaS context will include additional functions (potentially absent in non-PaaS deployments) – management of the entitlement sales, early warnings when subscriptions are about to expire, and management of the communications about the product use to the end users. The CRM will be integrated with the entitlement management system as well as with the monetization engine to support a subscription-based business model.

And finally, the IoT platform function in the PaaS solution will play the centerpiece role – it will be the enabler of bi-directional communication with the connected product. It will collect usage statistics which will be fed into the monetization engine for charging purposes. The IoT platform also receives from the entitlement management system updates to the license information and will provide that to the connected product or service endpoint.

Next steps

In my next post I will provide further guidance on the key functions of an IoT solution for a PaaS business model. I will focus on the following areas: device usage data collection and entitlement management. Also, I will provide a detailed look into the selection process of the monetization engine, and key capabilities that will be essential for a successful a global IoT PaaS deployment.

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Image by Farhan Amoor on Flickr (CC BY 2.0)

This is the second installment in a series of six on different approaches that will make IoT development more efficient. In my previous installment, I talked about how IoT platforms can help organizations build IoT solutions more efficiently. However, there are other pieces – namely compatible hardware, extensions, integrations into 3^rd party systems, apps, etc. – necessary to create IoT solutions that meet the requirements of different industries, companies and people.

Following the mobile industry’s approach of user generated innovation

The question we need to address is: how are we going to fill in those pieces, while making it as easy and cost-effective as possible for companies and developers to build IoT solutions? One solution to this problem lies in the power of the ecosystem. The power of an ecosystem is demonstrated best by taking a closer look at the smartphone industry.

With the release of the iPhone in 2007, the mobile phone industry underwent a major change. Mobile phones, whose functionality was up to then mostly limited to making calls or sending text messages, transitioned to be smart devices with features similar to those of a personal computer. More disrupting however was the way in which mobile phone manufacturers developed the content for these phones, which ended up changing the business model for an entire industry. The key to it was to leverage the power of the ecosystem to extend the value of the mobile phone (i.e. the platform).

An ecosystem of partners and developers

As smartphones took over the mobile phone market, apps became commonplace. Not only did apps make it suddenly possible to configure a phone to anyone’s individual needs, they ended up adding incredible value to the core functionality of the underlying smartphone platform.

However, the apps were not necessarily developed by the smartphone manufacturer or creator of the smartphone’s operating system. In fact, most of them were developed by an ecosystem of partners and developers. A large part of the innovation in the smartphone industry was – and still is – created in a joint effort.

Adoption exceeded everyone’s expectations as user needs were not only addressed by one single company, but instead by an entire ecosystem that was capable of handling this ever-growing demand.

The example of the smartphone ecosystem certainly applies to the IoT as well. Ecosystems have the ability to extend and enhance existing solutions, offering developers more out of the box functionality. This way, companies can leverage existing components provided by the ecosystem instead of spending their resources on building all those separate components in-house.

There are two major benefits to this approach. First, it is much more efficient and second, it allows each partner in the ecosystem to focus on what they can do best, thus yielding higher quality IoT solutions as the end result.

Using an IoT platform and leveraging existing, best in breed technology in the IoT ecosystem at the same time, will help companies get their IoT solution delivered in less time and cut their development costs to help realize a greater ROI.

In my next installment, I’ll talk about the distributed nature of IoT apps, and how extensibility plays a major role in developing IoT solutions.

Related Articles:

• Removing Obstacles to Make IoT Development More Efficient
• Developing Future-Proof IoT Solutions

Image by Sonny Abesamis on Flickr (CC BY 2.0)

However, this analytical nirvana is not achieved in a vacuum. While users are generally becoming more savvy and adept at handling data, the richness and diversity of information available has only grown. Aberdeen’s recent report, Data-Driven Manufacturing in the Age of Insight, demonstrated that those in the manufacturing sector in particular are actively using several different types of data from the traditional to the emerging. See chart below:

A Diversity of Data in Use

In an effort to exploit the inherent value of this data rather than drowning in its rising tide, more companies today are considering a centralized way of managing and understanding data and its best usage – an internal organization often referred to as a Business Intelligence Competency Center (BICC) or an analytical Center-of-Excellence (CoE).

Similar concepts are explored in a recent Harvard Business Review article which did an in-depth investigation into connected devices and their impact on the operations and organizational structure of today’s companies. The article makes reference to the concept of a Unified Data Organization, which shares several of the ideals and overarching goals of an analytical CoE.

In parallel to this discussion and analysis, Aberdeen’s research examined the activities and commonalities of 33 organizations reportedly using an analytical CoE, closely tied to the Unified Data Organization discussed in the HBR article. Among other things, these organizations seemed to share four key characteristics:

• Cross-functional representation. Rarely does a critical decision rest upon data or knowledge from one specific area of the business. An effective CoE includes active representation from core areas of the business such as IT, marketing, sales, product development, or service.
• Data sharing and collaboration. In order to make better cross-functional decisions, users need access to a broader and richer base of information to support those decisions. Companies with an analytical CoE in place are seventy-four percent more likely to break down unnecessary barriers to information exchange and share data across functions.
• Controls and oversight of data usage. As a vital counterpart to the sharing of information, companies need to maintain tight controls and oversight of who can access data and how it is used, lest a variety of data quality and security issues arise. Companies using a CoE are eighty-eight percent more likely to have policies and/or tools in place for better data governance.
• Ongoing monitoring of analytical activity. Nothing makes an IT professional (or CFO) cringe more than the concept of underutilized technology (i.e. “shelfware”).  Engaging more users requires more job-role relevant data to be available and better tailored analytical capabilities. Those using an analytical CoE are more than twice as likely to track the adoption and utilization of their analytical tools.

With these puzzle pieces in place and a strong analytical CoE taking shape, companies are in an advantageous position to spread analytical activity and better data-driven decisions across the organization. In fact, Aberdeen’s research demonstrates that this enhanced analytical pervasiveness is a key benefit for organizations using a CoE. See chart below:

Spreading Analytics Across a Connected Organization

The HBR article makes a strong case for the value and importance of the data generated by connected products and how top companies are transforming strategy to adapt and perform in this new environment of data. The article then extends this point in talking about the value to an organization of combining this product-generated IoT data with other more traditional sources, “This new product data is valuable by itself, yet its value increases exponentially when it is integrated with other data…”

Aberdeen’s research also makes the case, not just for connected data, but for connected people and enhanced analytical activity as critical components to this environment. If a product manager is mapping out the features, functionality, and long-term road map for a particular product, she might need supply chain data to factor sourcing viability into the decision. She might need customer data to tailor the product specifications to a particular demographic or customer type. She might need financial data to perform a proper cost accounting analysis and forecast profit margins. She might also need machine-generated sensor data to take into account the performance and durability of existing products into her feature updates. Decisions like these require not only connected data from the Internet of Things, but shared knowledge, a collective data-driven curiosity, and elevated analytical activity across multiple areas of the business.

Leaning on the ideals of an analytical CoE, companies can increase data fluidity, broaden analytical activity and raise the organizational bar for decision efficiency. Time and again, Aberdeen’s research demonstrates that these qualities lead to the identification of more salient business opportunities and, ultimately, enhanced business performance.

This is the first installment in a series of guest posts by leading industry analysts covering topics found in the new Harvard Business Review article, How Smart Connected Products are Transforming Companies, co-authored by PTC CEO, Jim Heppelmann, and Harvard Business School professor, Michael Porter.

Related Articles:

• IoT Makes “As Good as the Day I Bought It” a Thing of the Past
• Services and Customer Success Collide in the IoT
• Voice of the Product: Smart, Connected Products Transform Marketing
• Enabling Manufacturing Transformation with the IIoT

It’s getting easier and faster to collect data from connected devices in the Internet of Things-era, requiring new questions and resisting an impulse to assume causes or connections until effective analysis is done.

Managing how people and machines co-exist is another IoT balancing act.

It’s a topic Martin Baker has been watching closely as senior digital content manger at Hershey Corp. He suggests the book Leading Digital: Turning Technology Into Business Transformation, written by three MIT business experts. Successful companies – guided by so-called Digital Masters — are committed to using data and analytics throughout an organization for sustainable competitive edge.

Want proof? According to the book’s research, Digital Masters are twenty-six percent more profitable than industry peers and generate nice percent higher revenue from their physical assets.

Machines do what they do best, and humans focus on creative, personal tasks where they excel. Together, people and data bring a digital transformation that neither can manage alone, Baker added.

Mining data for knowledge

Are you sifting through tons of data to find a trend? There’s a data warehouse app for that. Then, people need to discuss possible outcomes or responses. They may query the data repeatedly looking for underlying causes or patterns.

“What you know is more important than what you own. But traditional business thinking views money spent on knowledge – people, networks, data, software and processes – as an expense, not an investment. So businesses need new tools to identify, measure and communicate the value of their knowledge,” said Mary Adams.

She founded a consulting group, Smarter Companies, to help executives identify knowledge resources and value them – or the cost of replacement – on a company balance sheet.

For instance, Toyota Corp. executives developed a “5 Why” method of analyzing situations. Asking “Why?” to question a particular circumstance five layers deep led to a deeper understanding of what could be changed – and what couldn’t – in trying to improve daily business operations.

Knowledge sharing and collaboration

“Think of data as the ‘know what’ and knowledge as the ‘know how’ or ‘know why’ – there are lots of things you can do with those raw materials,” said Robert Armacost Jr., a director at iKnow LLC, a New Jersey-based consulting firm. He has led collaboration and knowledge sharing efforts for global enterprises such as KPMG.

Machines share data when programmed, but people need incentives to collaborate and, over time, build trust and knowledge of relationships. Can you get different offices or departments to exchange information or learn from each other? That’s one field where managers aren’t likely to be replaced by an algorithm.

At least not yet.

Image by Saad Faruque on Flickr (CC BY 2.0)