Advanced operating models
Jul 26, 2018

Looking beyond traditional remote monitoring – IoT use cases for smart assets

Technology and connectivity are combining to provide massive efficiencies

The future is here. Things we take for granted nowadays—video calling, automatic language translation, autonomous vehicles, advanced robotics, computing powerhouses in cell phones—were just the stuff of science fiction fantasy a mere 15 to 20 years ago. Today, technology is faster, better, and more accessible. At the same time, computing itself has become cheaper, as have transferring data, bandwidth and storage.

The speed with which this technology has progressed, along with inexpensive and widespread connectivity, has produced an abundance of smart devices—especially in the consumer space. Wearables, fitness trackers, AI-enabled and connected speakers, security cameras, smart thermostats, and other appliances have become part of our everyday life.

In some cases, however, product designers focus so keenly on connectivity that the primary function of a product slips into background, raising questions about practicality (does anyone really need an app-enabled, connected toothbrush?).

In other instances, the drive for so-called product 'smartification' and rushed timelines lead to disastrous results regarding security. So we end up with IoT-enabled kettles that leak wifi passwords to anyone who bothers to snoop on the radio spectrum, or baby monitors that stream live video of infants on the dark web. That's an unfortunate side effect of affordable and accessible technology available to any product manufacturer in the world.

In any event, estimates are that the number of connected products will be anywhere between 20 and 50 billion by 2020. Whatever the exact figure, though, it's important that the new generation of connected and intelligent products bring additional value to the customer.

An explosion of smart and connected assets

The scenario for industry is another thing. Its reason for creating connected and intelligent products differs from that of consumers. Consumers typically want products with a single function—health and fitness tracking, heating and other smart home applications, security surveillance, and behavioral analytics, for example. These products deliver specialized vertical solutions or services (smartphones and tablets, which are multi-purpose IoT devices, are the exception). In industry, by contrast, connected assets typically help cut operational costs, are generally more efficient, and add to top-line improvements. They decrease maintenance expenses, reduce truck rolls, and keep spare parts inventories in check. As well, they can make sure field service has the best first-visit-fix ratio, and that leadership has better visibility into the manufacturing process or deployed assets performance.

In today's post I'll focus on industrial assets that are making rapid strides into the digital era. For these purposes I'm using the word asset to refer to products, machines and devices with a life span of 10 to 20 years or more. I'm also talking about highly valued assets with complex bills of material (BOM). That covers everything from wind turbines, power generators, oil rig pumps, industrial printers, MRI scanners to earth moving bulldozers, locomotives, and agricultural equipment.

I'll also look at typical use cases and provide brief insights about the applicability of smart assets. As well, I'll suggest ways some assets can help mature a firm's overall strategy. I'm saving a discussion about smart cities for another time because the topic deserves its own post.

Where are my assets?

The first concern many people have had is about making the install base more visible. Universally, maintenance heads have wanted to know where their assets are, who owns them, and what is the installed configuration. Asset management systems were born to answer these needs, but they haven't addressed all the problems. Data populated at the point of sale or shipment can remain static after assets change ownership, locations or have been reconfigured. That makes asset management more complicated as the time goes by.

In the smart asset era, though, firms can rectify most of these issues. One reason: new industrial assets come out of the factory with the intelligence baked in thanks to cheap computing, instrumented components, and affordable connectivity. Another reason: existing assets can be retrofitted with add-on computing capabilities, sensors, actuators, encoders and data acquisition modules. That means an asset can now report its location, configuration and additional meta-data, such as ownership information, license status, and so on. That achieves the goal of having visibility into the install base.

How are my assets doing?

Once the plumbing is done—sensors are present and relevant data is flowing to the cloud—a door opens for the next use case that allows for remote monitoring of devices.

As software becomes more prevalent, remote troubleshooting can resolve many issues. Systems can perform recalibrations, configuration changes, software and firmware patching—or simply reboot—remotely. What's more, customer care teams can now potentially perform diagnostics using cloud-based apps that send commands down to the asset. These are the core functionalities of an IoT remote monitoring and diagnostic (RM&D) solution.

Most manufacturers of smart products and industrial devices nowadays have reached maturity with RM&D. Obviously, this type of IoT-powered solution optimizes costs and is tremendously valuable. Service departments are particularly aware of how RM&D reduces on-site visits, produces a better ratio of fix on first visit and improves customer satisfaction.

RM&D is a great starting point, but it's just the beginning. I strongly believe the next set of use cases can unlock enormous business value.

How can I keep my assets up and running?

Today's technology is such that you can apply predictive maintenance (PM) and avoid unplanned downtime thanks to advances in machine learning and data science. We can now combine information from multiple sources, such as streamed machine data, operational parameters, and asset management inputs. When we add that to historical maintenance records, root-cause- analysis reports, and expertise in mechanical engineering, it's possible to spot anomalies and correlate them with early symptoms of future failures. Self-learning models improve with every iteration and retrain when new data is available making them even more accurate over time.

What's more, we can now use a wide range of data-science-related technologies to build asset reliability models that are the basis of predictive maintenance solutions. But I'll cover that topic in a separate post. 

At any rate, predictive maintenance is a game changer for many maintenance and service organizations. It saves millions by avoiding unplanned downtime for industrial assets such as energy-producing generators, mining operations, aircraft engines, oil pumps, and locomotives. And when it comes to health equipment and human safety, it also helps prevent collateral damage and saves lives before small failures escalate into full-blown catastrophes.

And this is not the end of journey. When a service organization combines the power of IoT and analytics by deploying RM&D and PM solutions, it can realize even more business benefits.

Figure 1. Data science technologies

How many spare parts do I need?

At this point, we've discussed how RM&D has improved the way assets perform in the field. We've also described how we can, with significant accuracy, predict failures in all critical components thanks to PM. The next logical step: Optimizing spare-parts inventory to free up working capital by avoiding overstocking. Understocking, too, can have significant consequences and endanger business continuity. This solution predicts the lifespan of critical components across a fleet of assets and generates buy/no-buy decisions.

This use case is especially important for organizations dealing with assets with overly complex BOMs, with long lead times, such as manufacturing, transport or installation, or with made-to-order parts that are typically expensive.

Where to go next?

I focused on one benefit in each of the use cases I just discussed, but in fact, the spectrum of benefits is broad and rich. Depending on your organization, business priorities, maturity levels and strategy you may well select other starting use cases for your smart assets management solution. But it's best to keep the big picture in mind. Strategy is the guiding light of your overall digital transformation journey.

Here's a map of example solutions and typical transitions that most organizations follow as their IoT maturity evolves:

Figure 2. Typical IIoT transition and maturity model

Clearly, I haven't dealt with the wide range of the options these new technologies make possible. In fact, IoT and industrial analytics can offer so much more. Here's a quick look at a few prospects. In future posts, I'll go into greater detail.

New business models and alternative revenue streams
Connected devices are creating a wave of new businesses under the collective name product as a service (PaaS). In many cases PaaS replaces tangible products, providing power by the hour for aircraft engines or compressed air as a service for industrial applications. At a recent Hannover Messe exhibition I even saw bearings as a service. These new business models can live in parallel to traditional products but offer a different experience to the end customer. In many cases, they'll also lower the initial cost of entry.

Warranty optimization
Using industrial analytics, organizations can take a critical look at suppliers and their performance. They can identify troublesome components in their smart assets and learn the operational cost implications of their current warranties. Should asset manufacturers extend a warranty with their suppliers, paying more for a longer SLA but saving in the long run on device maintenance? Or should they seek shorter warranties, paying less to the supplier and risking extra costs if components fail quicker—but getting benefits if components prove to be reliable? Machine performance data delivers these insights, and by applying industrial analytics, can convert to actions.

Augmented and virtual reality
AR and VR can help train field service, new staff, and users. We are already seeing the first applications of AR replacing traditional user manuals.

Blockchain is a great asset life cycle management tool. It's especially useful for high-ticket assets that change ownership over time. On another hand, blockchain contracts in a digitalized supply chain are fully traceable and auditable through every device, component, part, and material.

A flight of fantasy?

Over a time, everything we've discussed here will produce really smart assets and will bring us devices that are autonomous, intelligent, self-healing, and self-improving. Science fiction fantasy? Maybe. But remember what we said about automatic language translation or autonomous vehicles 20 years ago.

About the author

Maciej Redel

Maciej Redel

Assistant Vice President, SME

Follow Maciej Redel on LinkedIn