How Industrial IoT Analytics Can Enhance Business

Rich analytics help managers and leaders at every level drive the decisions and realize the improvements that the Internet of Things promises.

- Based on the name alone, the Internet of Things may sound like the focus should be on devices. The ‘T’ in IoT is “things,” after all.

But in reality, the IoT is really about data – the data captured by those devices, and then reduced and processed into the rich analytics that help managers and leaders at every level drive the decisions and realize the improvements that the IoT promises in the first place.

What can these analytics actually do for your business? That depends on the reasons behind why you are implementing an IoT to begin with.

Setting goals

The IoT is a general-purpose tool which you can wield in myriad ways. Here’s an overview of the many applications for industrial IoT analytics to enhance your business.

One of the most common goals that businesses cite when implementing an IoT is to reduce operating costs.

Before the rise of detailed, large-scale data about equipment operating on the line, it was very difficult to understand where inefficiencies would lie. But the IoT allows you to instrument and monitor virtually everything.

That means it’s now possible to track power consumption and use analytics to determine where all manner of waste is occurring. It’s easy to see when equipment is running idle, or production equipment is not running optimal loads.

These insights all you to correlate actual production runs with energy usage. Likewise, instrumentation can give actionable feedback about raw materials being consumed and wasted in very granular detail.

Improving efficiency

It goes well beyond that as well. You can ask questions like, “Is the building’s HVAC system in sync with building usage?”

If you only staff facilities during certain shifts, for example, you might change the climate controls accordingly, or implement a demand control ventilation system. Even the lighting can be better synchronized with usage by scheduling lighting based on shift usage and installing motion sensors that trigger lighting and environmental controls when appropriate.

Hand in hand with operating costs is improving operating efficiency, and many businesses cite this as a primary driver in deploying an IoT. After all, a commonly touted advantage of the IoT is predictive maintenance.

Predicting failures

Instead of maintaining your equipment on a proscribed schedule and needing to contend with failures on the line as if they were acts of god – unexpected, unpredictable catastrophes that stop work until it’s fixed – analytics from fully instrumented hardware can predict imminent failures and let you make predictive maintenance routine, solving problems before anything fails.

Taking the concept of predictive maintenance to the next level, IoT data makes it possible to build digital twins – virtual replicas of hardware and even processes, so you can simulate systems and make improvements to processes, procedures, maintenance, and more.

Generating revenue

What’s most surprising, though, is that analytics have tremendous potential to be used not to simply save money through reducing costs and increasing efficiency but as a means to increase revenue.

Analytics can help engineers improve products – both by collecting data about failure modes in the field, but also how users interact with the product.

Again, digital twinning can yield a treasure trove of information about real-world product behavior if those products are designed to connect or synchronize and send performance data home for review.

The same analytics can create entirely new business models and lines of businesses as well. Through analytics, a business can migrate from selling its product outright, for example, to licensing it and selling it as a service instead.

It’s also possible that products may be conducive to using analytics to offering subscriptions for consumables, technical support, maintenance, or operations management services. Many, if not all, of these potential lines of business would be impossible if data about the product in the field wasn’t captured and sent home for analysis.

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