Time Utilization Models: What mining got right about data

The mining industry figured out how to drive accountability and common business definitions in reporting decades ago. Most other industries are still catching up.

The Time Utilization Model is mining's answer to a definitions problem that most businesses still haven't solved.

How mining defined common KPIs across every team

How did mining get it so right? By implementing strict definitions for KPIs that mean the same thing across teams and sites, because the stakes are high enough to force the discipline.

Time Utilization Models (like the GMG standard) establish common, well-documented definitions that everyone in mining uses consistently.

Every activity, delay, and status gets classified into universally defined categories:

Calendar Time → Total time available

Scheduled Time → Equipment is required and assigned

Available Time → Equipment is ready to operate (not broken)

Operating Time → Equipment is under control and running

Working Time → Equipment is performing its intended function

Productive Time → Equipment is directly contributing to production

Now when operations say "75% Use of Availability," everyone knows they mean Operating Time ÷ Available Time.

When maintenance says "68% Physical Availability," they mean Available Time ÷ Scheduled Time.

When finance says "82% Operating Utilisation," they mean Operating Time ÷ Scheduled Time.

All three numbers are right simultaneously because they're measuring different aspects of performance with clear, documented definitions.

Most other industries are still arguing about what "available" means.

What Time Utilization Models actually solve in mining

Arguments stop

When operations and maintenance disagree about equipment performance, it's usually because they're using different denominators. Common definitions eliminate the argument. Both teams can be right, measuring different aspects of the same reality.

Data becomes a common language

Engineering speaks to finance. Operations speak to maintenance. Planning speaks to operations. Everyone's measuring performance consistently across the entire business.

No more "wait, which spreadsheet has the right numbers?" No more "your data doesn't match mine." No more meetings spent arguing about whose version is correct.

KPIs actually mean something

"Improve utilization by 5%" is meaningless if nobody agrees what utilization means.

With a standard framework:

• Physical Availability measures maintenance impact

• Use of Availability measures operational effectiveness

• Operating Efficiency measures delay impact

Everyone knows exactly what they're measuring, why it matters, and how to improve it.

You can identify real improvement opportunities

When you break down where time goes with consistent categories, patterns emerge:

• High Operating Delay at Site 1 → dispatch optimization problem

• High Standby at Site 2 → workforce scheduling problem

• Low Productive Time at Site 3 → too much non-productive activity (repositioning, cleanup)

Without consistent definitions, these patterns stay invisible.

Benchmarking actually works

You can't compare performance to other operations (or even between your own sites) when everyone's measuring different things.

"Our haul truck utilization is 72%" means nothing if Site A measures it differently than Site B.

Standardized definitions make benchmarking meaningful. You're finally comparing apples to apples.

The lesson for other industries

Mining operations got this right because the stakes were high enough to force the discipline.

When a single haul truck costs millions and delays cost thousands per hour, you need to know exactly where time goes and why.

But agriculture, logistics, manufacturing? Most are still flying blind because finance and operations are measuring "efficiency" in completely different ways.

The data exists. The tracking exists. What's missing are common definitions that let everyone speak the same language.

Does this apply to you?

If different teams in your business report different performance numbers for the same operation, you don't have a data problem. You have a definitions problem.

Mining solved this decades ago.

The blueprint exists. It just needs applying to your business.

Reach out for a chat about getting your teams speaking the same language.