If unit-measure estimates assume efficiency,

most schedules institutionalize that assumption.

They do it quietly, convincingly, and with great confidence.

The problem isn’t that schedules are wrong.

It’s that they are logically correct and physically impossible at the same time.

The Shared Blind Spot Between Estimates and Schedules

Unit-measure estimates and traditional schedules fail for the same reason:

They model work, not capacity.

Estimates assume activities can be executed efficiently.

Schedules assume those activities can be sequenced cleanly.

Neither asks the most important question:

“Can the system physically absorb this much work, with this many people, at the same time?”

Logic Is Not Capacity

Modern schedules are excellent at representing logic:

Finish-to-start relationships Critical paths Milestones Float and recovery narratives

What they don’t represent is crowding.

A schedule can show ten trades working in parallel and still look healthy.

The schedule doesn’t know:

How many people can fit in the space How many crews can be supervised effectively How much access, staging, and logistics capacity exists How productivity degrades as density increases

So the schedule optimizes sequence…

while the site absorbs chaos.

Why Schedules Make Bad Assumptions Look “Managed”

When resource congestion appears, schedules don’t fail — they adapt.

They:

Resequenced work Add parallel activities Compress logic Pull recovery levers

On paper, the schedule recovers.

On site, efficiency collapses.

The schedule says:

“Work can happen in parallel.”

The site says:

“Not with this many people.”

The Cost Signal Comes Too Late

Here’s the dangerous loop:

The estimate assumes efficiency The schedule assumes clean sequencing Crews are loaded in parallel Congestion erodes productivity Duration extends Cost increases Leadership asks, “What went wrong?”

Nothing went wrong.

The system behaved exactly as designed.

Why Resource Loading Isn’t Enough

Many teams respond by “resource-loading the schedule.”

This helps — but it doesn’t solve the core issue.

Resource loading answers:

Who is assigned to activities

It does not answer:

How many people can work in the same space When capacity limits are exceeded Where productivity collapse begins What efficiency loss costs over time

Without capacity constraints, resource loading just gives false precision.

A Better Question for Project Controls

Instead of asking:

“Is the schedule logic sound?”

Project controls should be asking:

“Is this plan physically executable with the resources available?”

That requires shifting focus from:

Activities → systems Logic → capacity Outputs → utilization

Until that shift happens, schedules will continue to look credible — right up until they aren’t.

The CRU Position

CRU challenges a foundational belief in project delivery:

You cannot schedule your way out of a constrained system.

If efficiency is lost because too many crews are forced into parallel execution, the solution is not better logic.

It’s better capacity governance.

Estimates, schedules, and controls must align around one truth:

Cost, time, and performance are consequences of how resources interact inside real constraints.

Final Thought

A logic-correct schedule can still be physically impossible.

And when it is, the schedule doesn’t protect the project —

it accelerates failure by making bad assumptions look planned.

If your plan ignores capacity,

your controls will only tell you the truth after it’s too late.