An equipment manager asked if we knew of a national database of machine expected life. He doubted such a reference existed, but he figured we would know. I thanked him for the vote of confidence, and then I confirmed his hunch.
Construction equipment operates in too many vocations, too many geographic locations, and too many applications to enable the creation of such a database. It’s impossible to make general conclusions about how long a machine can perform without having answers to each of those three conditions, and probably several more. On top of that, dozens of manufacturers play in each machine category, so there are brand-specific performance variables.
To be sure, OEMs and distributors can provide some answers, but their numbers are much like the Pirate’s Code, they’re more like guidelines. Distributors have, of course, plenty of experience with machine performance over time, and they have direct lines to their OEMs. The major OEMs, through their proprietary telematics systems, have a plethora of data points on hundreds of machines and probably have the best data for a specific brand and model.
I suspect few fleets have access to that data, though.
So what’s a manager to do?
1) Understand that machine expected life depends on your fleet’s circumstances: Application, geography, vocation, preventative maintenance procedures, and more.
2) Identify how circumstances affect performance and what information measures performance.
3) Acknowledge that performance data must be collected, and determine how to collect it.
4) Recognize that a helpful collection of accurate age and performance data can only be built over time, and determine how you’re going to store it and analyze it.
Machine technology is quickly evolving into a rich source of machine data. But the industry lags in its adoption of same. For some, it’s investment dollars or lack of understanding of how it works. For others, it’s implementation into either the fleet itself or the organization’s fleet-management systems.
But for others, machine data means nothing because they don’t understand its value for making sound fleet decisions based on historical data. Before an equipment manager can begin building a historical database, they must first recognize the need to know machine expected life.
