The Internet of Things (IoT) shakes up the traditional rules of manufacturing and business, as companies combine equipment, sensors, and data to deliver new levels of value to customers. Management guru Michael Porter says IoT will be the “Third Wave of IT-Driven Competition” that will “unleash another leap in productivity.” Gartner predicts that the number of connected devices will grow from 6 billion in 2016 to 21 billion in 2020. GE, which has staked its future growth on the Industrial Internet, as it refers to the new landscape of connected devices and machinery, estimates this market will add between $10 and $15 trillion to the global GDP within 20 years.
Much of the value created by the IoT today relates to using data to improving operating efficiencies. Companies such as GE and Intel have done so by streamlining or automating factory operations, for example. Others have optimized their marketing and supply chain efforts (for example, using sensor data to shape marketing tactics for a specific customer segment). Far fewer companies have harnessed IoT devices and data to generate incremental revenue with customers, as GE has by creating new data-driven services. In another example, HP put sensors in select home printers, turning them into connected IoT devices that sense when ink cartridges run low. Once a printer owner signs up for HP's Instant Ink program, HP automatically ships replacement cartridges at the time ink supply dwindles – and HP captures cartridge revenue that might otherwise go to third-parties. However, not all companies have made as much progress as GE and HP.
Many manufacturing companies we've worked with are just starting to collect data from their installed base of sensor-equipped products, and some have concluded initial pilot projects to prove the IoT value proposition. These companies often struggle to determine how to monetize their IoT offerings. In fact, a poll we conducted recently across 12 industries (at a conference for business and IT executives) found that 30% of respondents have not captured any value from their IoT efforts. A 2015 Tata Consultancy Services’ global study of large companies found that 79% have IoT projects in place, and these companies realized an average revenue gain of 16% in 2014, in the area of the business relevant to the IoT project.
However, companies logging that level of success have traveled further down the IoT path. In FTI's experience, manufacturing firms in the early stages of the IoT evolution generate less than 5% of their revenue from IoT-related services; many make less than 1%.
Why haven't more manufacturing companies capitalized on the promise of IoT? First, lured by the promise of a new industrial revolution and strong revenue growth, many companies choose a sub-optimal business model for new IoT offerings, or underestimate how much change will be required, both internally and externally, with customers. For example, IoT business models that shift a company's portfolio from a transactional model to a lifetime revenue model (with a combination of physical products and services,) involve significant organizational change. In addition, legacy go-to-market strategies can sink IoT offerings, for example, if salespeople call on their traditional prospects rather than the true decision-makers for an IoT offering. In our experience, these decision-makers include operations team members in addition to (traditional) finance team members, requiring sales teams to have a deep understanding of the customer's product use cases and cost of operations. Companies also face a keen battle to attract the right development talent, such as analytics experts, who bring crucial skills to IoT teams.
Given these and other challenges, it is time for manufacturing companies to rethink their traditional strategy and development processes in the age of IoT. Let's explore three key differences in strategy definition and strategy execution respectively, that companies should understand and address.
IoT Strategy Definition
1. Market focus
Let's start with defining the market for your IoT product or service. Traditional strategies segment the market around similarities in buyer needs; say family car buyers versus sportscar enthusiasts. Companies develop products/services and go-to-market approaches tailored to each segment in order to maximize revenue. But in the IoT world, one thing companies find themselves selling is data-driven insight -- unlocked by combining data streams generated in the course of business. In this case, the most attractive market segment is where the company can create the most value for customers and where it has the largest data advantage. For some data-driven services, the largest customers may no longer be the most attractive market segment, as they may have the internal capabilities and scale to collect and analyze their own sensor data, whereas midmarket customers may have more appetite for the data analysis service.
2. Non-traditional competitors
Many companies are starting to realize that the IoT market introduces non-traditional competitors. Companies with advanced analytics capabilities can enter your market and provide IoT services to your customers. Your company's historical advantage can erode as these new rivals move in. A good example is the smart energy grid area, where both Google and Apple are developing software for the smart grid and smart home devices such as Google's Nest thermostat. Airline industry mainstay Boeing has also received grants from the government as it actively pursues Smart Grid initiatives.
For IoT products and services, competitive positioning is not defined through superior product attributes and features, but rather through the technology stack and where value can be created (see figure 1). Consider your company's position in the IoT technology stack (which includes elements like applications, data storage, analytics, and connectivity tools). Our recent IoT poll showed that most respondents found the most value in the data & analytics layer of that stack.
Alternatively, your company may have an advantage based on the object (or “thing” itself,) and the range of things with which your device can communicate. The Apple smartwatch comes to mind here. In terms of reach, most companies apply IoT services to their own products, while others have moved beyond that. For example, GE, after decades of providing predictive maintenance analytics on its engines, now offers a wider set of analytics services, for the set of systems running an entire airplane. Long-term monitoring (and predictive modeling) of how customers interact with your product or service will help you fine-tune it -- and increase its business value with time. What aspects are customers ignoring? Where do customers struggle? Connected devices report back the real picture about how customers use your offering.
Finally, your company may find a business advantage through an IoT offering that has multiple industry applications. For example, sensors and software tools that help monitor energy efficiency in “smart buildings” might also help analyze data from connected health devices. That is one reason Intel is targeting applications at markets ranging from cars to medical devices. Intel has shown that data on the number of times lights go on and off in a house every day can do more than just help customers save money on electricity. That data, connected to the right devices and apps, could serve as an early warning system that your elderly relative is not moving around his house as usual.
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3. Business model
The third key difference for companies defining IoT strategy: It is a whole new world of business models. This is where most companies we work with struggle. As in the early days of Internet commerce, the key questions are “how does this product/service make money” and "how do we maximize revenue?" In our experience, companies do not understand how to choose the best option among four different business models (see figure 2).
Some companies start with giving away IoT data-driven services to differentiate their products and encourage hardware sales. This makes sense, but does not provide a sustainable competitive advantage. Good examples of this are heavy equipment manufacturers, who have provided value-added analytics on their equipment to help enhance the value their customers receive from using the product, and to differentiate themselves in the market. We recommend companies either skip this step or move through it quickly.
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Another business model is the digital add-on. Here the business value increases as users opt to activate new services -- much like the apps model we are all used to on our mobile devices. The long-term business value does not come from the device itself but from the installed base of users who will buy associated services. In one example, cable company Comcast is positioning its Xfinity service as an IoT device platform in homes. The customer signs up for Internet and cable service first, then Comcast can offer a slew of additional services later. To this end, Comcast has also struck partnerships with makers of smart home locks, sprinkler systems, and pet monitors, to let consumers control IoT devices via Xfinity.
Next, the Product–as-a-Service (PaaS) model can help businesses establish an installed base quickly. Our recent IoT poll found this to be the most popular IoT approach today, with more growth expected. Here, the company makes an initial investment in IoT-enabled hardware to build an installed base of product users. Value for the OEM accumulates over time as users pay usage/service fees. This is sometimes called ‘pay per use’ or ‘power by the hour’ and has been used in the aerospace industry for decades. In another example, companies with medical laboratories can now pay for access to lab instruments rather than having to own them. An additional benefit the service provider can offer: Statistics that show how well the lab rates on specific measures as compared to its peers.
A similar model is the subscription-based service. Nest and Vue are two good examples of this business model. Here, as with the product-as-a-service model, the value of the offering increases linearly with the number of users. Companies charge for the hardware as a traditional product sale or as part of a monthly subscription fee.
For the digital add-on and PaaS models, business value and growth may increase as the number of users increases, in what's known as the network effect. This effect does not apply to every case, but when it does kick in, it is powerful. Think of the subscription example of predictive maintenance analytics: As more users sign up, more equipment data is collected, and the accuracy of the data service rises (as does the customer's perceived value of the service).
In cases like this, the more customer data the company can gather, the more valuable the data, and the more chances to spin the data into new service offerings. However, achieving the value of the network effect poses challenges. The biggest are agreeing with customers on the terms of data ownership, and acquiring enough customers to reach the inflection point where the data set becomes informative.
IoT Strategy Execution
As Morris Chang, Chairman of Taiwan Semiconductor Manufacturing Company, said “Without strategy, execution is aimless. Without execution, strategy is useless.” We have identified three areas in which the execution of an IoT strategy differs from a traditional product strategy.
1. Go-to-market approach
The sales force needs to be retrained to sell outcomes and understand how IoT applications provide value in their customer’s processes. Pricing will be based on the value created with the IoT solution and will be different for different customers. (This is not a new idea in pricing strategy, but is often a new idea for a manufacturing company's sales force.) For example, if the same IoT data-driven service provides more absolute value to larger customers, it should be priced in a corresponding manner.
Companies often need to rethink their prospects and coach their sales force accordingly. Especially in the case of capital-intensive products, the sales force is used to selling to senior management and finance executives. With IoT solutions, the prospects also typically include people on the operations side who will be using the product or service themselves. Salespeople will need a deep understanding of how their products are used in their customers' processes, so they can explain where IoT solutions create value. Finally, sales force incentive structures may need a considerable overhaul, for companies moving their focus from products to services, for example.
2. Organization and talent
Developing IoT solutions requires a different set of skills, including advanced data analytics, data science, and cyber security skills. To this end, many companies (including GE, Airbus, and others) have set up software development centers in Silicon Valley and Boston to attract the right talent. Other talent acquisition strategies: Coding contests (to identify and recruit rising stars); partnerships with local universities (to develop talent with targeted skills); and public speaking engagements at industry conferences (to show off your workplace as full of opportunity for sought-after developers.) Corporate brand image campaigns can also make recruits more aware of your company's potential.
People skills matter greatly on these teams as well, since solutions are not created in a laboratory, but together with customers. This is illustrated in job descriptions for IoT initiatives, which often stress the need for job candidates to have not only data science or technical expertise, but also the ability to understand and analyze customer operations and processes. These teams will do crucial change management work with the customers, advising customers on how to change their processes, given the possibilities that sensor-equipped products bring to light.
3. Ecosystem and partnerships
IoT solutions often arrive at market via an ecosystem of partners, each bringing their own unique skillset and strengths. Time to market matters more than vertical integration. As a result, partnerships are found in many different areas of the IoT, and across vertical industries, as in the Comcast example we cited earlier. In another example, in oil & gas, Schneider and Cisco are partnering on a “Connected Pipelines” offering that uses sensor data to warn energy companies of accidents (like leaks) or physical or cyber attacks. Car companies like Ford are not only building large internal teams around IoT but also seeking out partners who have strengths in mapping technology, music services, even health applications (think of a car dashboard that could warn you that you are getting drowsy at the wheel.) Remember you don’t have to provide all the puzzle pieces yourself. Companies need to determine what capabilities are required, then decide between buy/build/partner options.
Like other business transformations, a shift from traditional products and services to IoT products and services requires thoughtful change management and strong executive support. Instead of rushing in, companies should hit the “pause” button to think about the key differences in strategy and execution, as outlined above. It is important to understand your position in the IoT space, where in the technology stack and the ecosystem your company fits and provides the most value.
Now's the time for manufacturing companies to:
- Start with small, practical use cases to achieve quick wins, and fight organizational inertia around IoT.
- Examine results from early IoT implementations and build business models based on the value delivered to the customer.
- Pursue a collaborative process with customers: Iterate together to improve your IoT solution, as you both learn from the data and as the customer works the solution into his processes.
- Involve the sales organization early and identify the sales leaders who are most qualified to sell IoT solutions.
- Use multiple strategies to recruit software development and analytics talent.