Realizing the Promise of the Internet of Things9 Sep, 2019 By: Jill Newberg
Taking advantage of the IoT can require product developers to invest in new design methodologies and IT infrastructures, but the potential payoff is huge: better products, improved product development processes, and mutually beneficial customer relationships.
With the advent of Internet of Things (IoT) technologies, products can tell us about their usage, status, and operating conditions; they can alert us when they experience failure, or when they’re about to. This potential offers new opportunities to companies that provide products and services, including new value for their customers, new revenue streams, and new business models. But how far do these opportunities reach, and what will it take to deliver them?
Strategic Product Development
Better customer experiences help companies gain or maintain competitive positioning. Delightful products, hassle-free maintenance, and long, predictable operating cycles boost demand, improve customer loyalty, and drive repeat business. Preventing failures before they occur can lead to more sustainable products with better safety, less risk, extended uptime, and peak performance. But to remove the burden of service from customers, OEMs must manage more of the product’s lifecycle. They must manage the longest phase, in fact, and the one with which they are historically the least familiar: service and use.
Building products that can measure and interpret their own performance requires building in greater complexity, in the form of sensors, communication devices, and onboard and/or cloud-based computing. It also requires knowing up front what should be measured and what findings mean. Systems engineering, which begins early in the concept phase of the product lifecycle, creates a single, unified model of the product’s definition and requirements across its software, hardware, electrical/electronic, and mechanical components. Visibility throughout product development between all these domains and the system model helps teams identify requirements and analyze whether they are met as the digital product definition evolves. This visibility also helps teams balance trade-offs between competing requirements and manage changes, both across disciplines and among requirements themselves, in response to new information.
Leveraging systems models, collaborating across new teams and their tools, and tracking the digital thread of a product’s definition as it evolves across the many engineering domains responsible for it, both internal and external to the organization, can require companies to change their IT infrastructures. These changes, however, are essential. If a product’s digital definition can be measured and tracked as it evolves, and its changes well-coordinated across teams, then the same infrastructure and information can be leveraged to analyze and communicate findings from IoT data obtained from the physical product in the field, with faster, more effective actions generated in response to new learnings.
Strategic Product Sustainability
Leveraging IoT, OEMs can extend the customer relationship beyond a “sell one and done” model to realize new, recurring revenue opportunities and evolve their business models. Smart, connected products, equipped with sensors and computing to flag unexpected performance, can evolve beyond a traditional maintenance cycle of failures and fixes — and thereby advance the relationship that OEMs can have with their customers.
OEMs that can better predict and prevent failures can choose to retain ownership of products throughout their operational and service lifecycles, even as those products reside with their customers. They can realize recurring revenue by selling “power by the hour” to customers, the way aircraft engine providers today structure contracts with aircraft OEMs, assuming responsibility for maintenance while guaranteeing performance and uptime by monitoring and acting on data flowing back from their engines. Providers can sell access to the value their products deliver: for example, guaranteeing comfort levels for buildings rather than selling expensive, hard-to-maintain climate control equipment. And, they can adopt new, subscription sales models with options for tiered levels, as when medical imaging equipment providers charge customers by the image and guarantee equipment readiness on-demand.
Procedures to correct impending issues before products fail — known as preventive maintenance — are scheduled so they won’t interfere with the customer experience, while they maximize efficiency for providers. Leveraging the IoT to further evolve these benefits for both customers and providers relies on the concept that, despite their similarities, each product configuration in a unique environment experiences different operating conditions, usage patterns, and service procedures, resulting in its own, unique set of product information, or digital twin.
Unique failure rates and failure behaviors can be identified from each digital twin — rather than drawing on pre-defined assumptions or generalizations taken from devices in different settings — enabling users to personalize maintenance intervals and procedures, and thereby improve the accuracy and efficiency of preventive maintenance. This evolution, known as predictive maintenance, helps reduce the cost that preventive maintenance can incur when it is performed at too-frequent (or too-infrequent) intervals, adding to the benefits that companies can realize when they retain ownership of their products, and of the data they produce, end-to-end across the lifecycle.
Strategic Product Improvements
Retaining ownership of end-to-end product lifecycle information — from the earliest parts of concept development through digital twin data flowing back from each unique product configuration in the field — provides a wealth of knowledge, not just about product failures and their prevention, but about real-world operating conditions, unanticipated use cases, customers’ preferred features, and more. This information can help companies improve their offerings, and their customers’ experiences, in a number of ways.
Staying connected to IoT-enabled products in the field is central to improving them. With software on board so many of today’s products, over-the-air updates are increasingly used to enhance functionality. And with the IoT, information about what to improve can now be gleaned from products themselves and from the customers who use them. Getting this information to the in-house or external teams that can make improvements, and then validating changes across the other engineering domains they affect, is vital to ensuring that updates will improve, not erode, the customer experience.
Leveraging a single technology platform to connect upstream processes from the digital thread with downstream data generated by the digital twin can be critical to improving designs, both during operation and in next-generation products. For example, with access to simulation processes typically performed upstream, teams from maintenance or quality can visualize current, as-maintained configurations of the digital twin’s geometry and better pinpoint the root cause of failures or performance issues. Teams can learn more about products in the field by leveraging “virtual sensors”—measurements taken at positions in a simulation where physical sensors don’t exist. And, simulation can be used to digitally test potential upgrades for current products, or even new features for next-generation products, by leveraging real-world configurations and operating conditions gathered from digital twins to ensure upgrades will work as intended under actual circumstances.
Platform technologies are increasingly popular for their openness: OEMs can allow new teams, tools, and processes to leverage product information end-to-end across the lifecycle in new ways, introducing new technologies like machine learning and artificial intelligence (AI) to their existing processes. Products themselves can even be connected to platforms, and thereby to one another — merging IT and OT in applications like smart manufacturing and smart cities. Smart factories rely on platforms as a single source for monitoring and responding to outputs across equipment from many different vendors, tracking their performance, manufacturing results, and quality metrics in one place.
Manufacturers are increasingly leveraging platform technologies to connect with other manufacturers, inviting not just value chain partners but also collaborators from within and across industries onto technology platforms to exchange performance and field data, share IP, systematize collaborations, and communicate lessons learned in ways that can boost industries, or even completely redefine them. With the potential for platforms to connect products, equipment, supply chain partners, industry collaborators, and even customers with end-to-end product lifecycle information and analyses, the platform itself can be a vital differentiator for OEMs.
How far will the potential of IoT take companies and their products? Provided companies can connect and manage the digital thread of their products’ information, leverage the digital twin data produced by each unique product in the field, add emerging tools and technologies as needed to take advantage of this end-to-end data stream, and maintain strong collaborations across the many internal and external teams that can use this information, they will be well-equipped to take the potential of IoT as far as their customers demand.