From Hype To Reality: Data Analytics In Heavy Equipment Dealerships

Five years ago, having access to in-depth machine and customer data wasn’t as ubiquitous as it is today. Now, we’re living in a time where we have access to more data than ever before. It’s pretty much expected that companies of all kinds will incorporate data-driven insights into their business plans.

Despite this, business owners are still skeptical of the value of predictive analytics. Some view it as pure hype that will never deliver the results they need — and we totally understand. It’s easy to get caught up in the buzz of big data and jump into projects that seem exciting, yet don’t deliver any real value for your business.

But data done right — that’s a different story.

Toromont CAT, a long-time Uptake customer, has used predictive analytics to completely revolutionize the way it does business. Mike Cuddy, CIO and VP of Toromont Industries, was skeptical at first. He’d heard “big data” jargon thrown around at conferences and wasn’t impressed, but now he’s seeing real results within his business.

A new kind of heavy equipment dealership.

With their customer base becoming younger and more digitally-savvy, Cuddy knew customers would begin to expect more from them:

For example, instead of relying on OEM maintenance schedules to maintain expensive assets, Toromont can now use sensor and contextual data to recommend equipment maintenance and service scheduling for its customers. They don’t just sell equipment, they help customers get the most out of their machines.

Better equipment and maintenance insights.

Some of Toromont’s customers have fleets of haul trucks and one of the most expensive components to maintain in a haul truck is the final drive. It costs just under a million dollars to replace this single part.

By using predictive analytics, Toromont can give its customers a more accurate view of how the final drive is performing, as well as make more informed maintenance recommendations to extend the life of the component for the whole life of the haul truck. This can save a customer with say, thirty or forty haul trucks, millions of dollars.

Cuddy was able to achieve this by not only using equipment data but by also using contextual data and encoding information from SMEs on his team into his data models. He notes, “With sensors on the equipment we now understand what’s going on with the equipment in ways that no one has before. We can take the business rules that are in the heads of people about what makes a good machine [...] and we can encode all that. And so now the models produce better insights as to the value propositions we can take to customers.”

Cuddy makes it sound easy but his company went through a lot of change to get these kinds of results. To hear more about Toromont’s data story, check out the CNBC interview below:

See how OEMs and dealers can use data to deliver better results to customers in our white paper Manufacturing the Future.