What My 25 Year Old Truck Can Teach You About Intelligent Assets

Richard Howells,
Mar 21 · 4 min read
My 1994 Toyota Truck

As my father always told me “If it ain’t broke, don’t fix it.” Obviously, I took his advice in at least one respect: sitting in my driveway is a twenty-five year old pickup truck I bought new in 1994.

But as with most axioms of this sort, there’s always a counterpoint to consider. Take, for example, today’s business world, where companies can’t afford to let their multi-thousand-dollar business-critical capital asset break down.

Companies depend on these assets to run their businesses, do more with less, and even drive new business models. Under such circumstances — where running until failure is simply too disruptive for the business and its customers — companies often adhere to a different adage.

“Better safe than sorry” — Preventive Maintenance

Many companies approach the management of assets with a planned or preventative maintenance strategy — where maintenance is routinely performed on a piece of equipment to lessen the likelihood of it failing. Frankly, I follow the same approach with my pickup truck. Every 6,000 miles, or 6 months, I bring it in for service — whether it needs it or not.

For me — a guy holding on to a truck for reasons that are practical (dump runs) and sentimental (I just love the old beater) — this approach works fine enough. It keeps the truck on the road and the expense isn’t exactly preventing me from paying the mortgage.

But for companies that need to squeeze every bit of cost efficiency out of their assets, “better safe than sorry” no longer cuts it. Preventive maintenance, in fact has been calculated to consume nearly as much of a typical facility’s operating budget as utility costs — amounting to more than one-third of total operating expenses.

“Expect (or detect) the unexpected” — Predictive Maintenance

The Dashboard of my 1994 Truck

My pickup has three gauges: speed, temperature, and gas. But in today’s world of pervasive Internet of Things (IoT) technology, a new model would have hundreds of built in sensors that enable real-time condition monitoring.

The same, of course, is true for leading companies managing large deployments of critical assets. Almost every asset deployed in the field or in a factory is designed and manufactured with built-in sensors to provide data on equipment status and.

With the ability to analyze this data within the context of their businesses, companies can expect (or detect) the unexpected — predicting issues before they arise. This puts you in the position to take swift, preemptive, and cost-effective action to fix them. In other words, companies can now perform maintenance only when required. This maximizes the lifetime value of parts, optimizes technician time, and helps to deliver a better customer experience.

“Just what the doctor ordered” — Prescriptive Maintenance

But there’s still more. Today, we see examples of companies moving beyond simply predicating what will happen next. Leveraging machine learning and predictive analytics, companies can now produce outcome-based recommendations for the machine to follow. After the predictive analytics tells you that a problem is imminent, the prescriptive part kicks in to serve up a selection of actions and scenarios to choose from.

Let’s say my 1994 pickup truck suddenly has prescriptive maintenance capabilities. One day on my way to the dump, my temperature gauge starts inching upward — indicating that my truck will soon overheat. Predictive analytics will look at the temperature history of not only my truck but others of the same design (if there are any more still on the road). Based on this data set, it can calculate the probability of break-down if conditions remain the same. The prescriptive logic may then determine that if I drive just ten miles per hour slower, I could double the “time to failure” — which would allow me enough time to get to a mechanic before I blow a head gasket. As I plan to drive my truck for another twenty-five years, this is advice I’d surely take!

Of course, asset maintenance at this level requires an integrated and intelligent asset management system with support for intelligent technologies — including IoT, predictive analytics, and machine learning. In fact, according to an SAP performance benchmark, companies can expect a 17% return on assets where asset management systems are fully integrated. All of which might very well lead to a new adage with a slight twist on what my father used to tell me so many years ago — something like: “If it ain’t broke, isn’t it time to fix it?”

To learn more about intelligent asset management, download this new white paper from ARC Advisory Group.

SAP Innovation Spotlight

Richard Howells,

Written by

Richard Howells has been in the supply chain management & manufacturing space for over 25 years. He is the Global VP of IoT & Digital Supply Chain at SAP.

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