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

Richard Howells

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 25-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 six months, I bring it in for service – whether it needs it or not.

For me – a guy holding onto 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 has been calculated to consume nearly as much as utility costs in a typical facility’s operating budget – amounting to more than one-third of total operating expenses.

“Expect (or detect) the unexpected” – Predictive maintenance

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.

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 predicting 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 dataset, it can calculate the probability of break-down if conditions remain the same. The prescriptive logic may then determine that if I drive just 10 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 25 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 whitepaper from ARC Advisory Group.


About Richard Howells

Richard Howells is a Vice President at SAP responsible for the positioning, messaging, AR , PR and go-to market activities for the SAP Supply Chain solutions.