How Big Data Makes Predictive Equipment Maintenance Possible

Picture this: You receive an alert on your phone that a critical component in one of your bulldozers’ engines is about to fail.

To generate the alert, your condition monitoring system assesses the bulldozer’s overall machine health and status, its oil sample results, its recent inspection data and an electronic copy of its full work order history. But that’s just the beginning.

The system then recommends when and how you should replace the part, incorporating weather data to suggest the optimal day to perform the necessary maintenance – identifying a window of time when rain is forecasted, since your projects are typically paused during precipitation.

In the background, the required part is automatically quoted, ordered and replaced—all in the matter of a few days, without jeopardizing the asset’s uptime or your project’s completion timeline.

Sound like life in the future? It’s not. It’s made possible by predictive technology that exists today. Let’s examine how.

Welcome to the next generation of equipment maintenance

The value of data in equipment maintenance cannot be understated. Without data-driven insights, maintenance processes are manual and difficult at best—at worst, they’re inefficient and costly in terms of time and money lost.

Traditionally, when parts break, someone at the dealership calls the customer, they go back and forth on how to proceed, the part is then manually quoted and ordered, and perhaps most importantly the machine is in the shop the entire time.

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Why has the world of maintenance run on such antiquated processes? For starters, machine health has long been a black box. And without visibility into real-time machine data — information that reveals the truth about the status and health of critical assets — those machines will keep on running until they break.

Knowing in advance when parts are beginning to fail — and identifying early on the best time to fix them — is infinitely more valuable than knowing when something has already failed. By then it’s too late.

But when it comes to the data, where does one begin to predict and prevent problems before they occur?

Not all equipment data is created equal

Simply put, not all equipment data is created equal. For example, visual inspection data isn’t sufficient for accurately assessing machine health, nor is it the only option. Let’s take a closer look at why this is the case.

Oil sampling and fluids analysis check for metals in oil, which can be the first indicator that something is wrong with equipment. The eyes and ears of technicians are certainly important, but manual inspections have traditionally been recorded with pen and paper — the ability to capture accurate information is limited, and the inspection results themselves exist in siloes.

Knowing in advance when parts are beginning to fail is infinitely more valuable than knowing when something has already failed.

With today’s available technology, inspection results can be automatically combined with GPS data, oil sampling and fluids analysis, and work order history — then aggregated with insights from other industrial sectors and even weather predictions — to create the most accurate, holistic view of machine health.

It doesn’t stop there. By applying data science to the above information, machines can be scored against each other to flag those with the most severe problems and prioritize them at the top of the maintenance list.

Data is the key to improving the customer-dealer relationship

Intelligent equipment maintenance can significantly improve the relationships between customers and dealers by fostering greater trust and transparency.

Loyal customers can be confident that dealerships are helping them save time and money, improving their operational efficiency and increasing the productivity of their machines.

Dealerships, meanwhile, can put data behind their maintenance recommendations, providing evidence-based insights that incentivize customers to stick around. And when trust builds, so do sales. Your bottom line can thank the data for that.

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