Disruption and productivity claims

A delay claim asks for the cost of the project taking longer. A disruption claim asks for the cost of the work becoming less efficient. The two often travel together, but they are different. A crew can be disrupted without the project finishing late: if the tile installers on Ashford have to work around other trades in a crowded natatorium, they install fewer square meters per hour, the tiling costs more, and yet the critical path may be unaffected. Conversely, a project can be delayed without any crew becoming less productive, if work simply stops and restarts.

Disruption is the hardest kind of claim to prove. Lost productivity is invisible on any single day; it shows up only when actual hours are compared with what the hours should have been. And there are always several possible causes, some of them the contractor's own. This lesson explains the causes of lost productivity, the hierarchy of methods for measuring it, the measured mile in detail, and the weaker methods that are used when better evidence is not available. Lesson 8 covers total cost methods, which are the last resort.

What disrupts productivity

Productivity is output per unit of input, usually installed quantity per labor hour. A crew's productivity depends on the conditions in which it works, and many owner actions can change those conditions.

Several causes usually act at once, and some are the contractor's responsibility: poor supervision, under-skilled workers, late material deliveries it arranged, its own scheduling errors. A credible disruption claim separates the owner-caused loss from the rest. If it cannot, it is likely to fail.

The ripple effect of many changes, sometimes called cumulative impact, is the most contested form. The argument is that each change was priced and paid individually, but together they disrupted the project in a way no single change order captured. Owners respond that the contractor accepted each change order, often with language saying the price covered all direct and indirect costs, and so has given up any further claim. Whether a cumulative impact claim survives depends heavily on the wording of the change orders, the reservation of rights the contractor made when signing them, and the evidence. Lesson 2 discussed reservation language; this is where it pays off.

The hierarchy of methods

Industry guidance, including AACE International's recommended practice on estimating lost labor productivity (25R-03) and the Society of Construction Law Delay and Disruption Protocol (2nd edition, 2017), ranks methods by how directly they rely on project records. The more a method relies on the project's own data and isolates the owner-caused cause, the more persuasive it is.

The measured mile

The measured mile compares the productivity of the same work on the same project in a period that was not affected by the disruption with the productivity in the period that was. Because the crew, the work, the site and the contractor's management are the same, the main difference between the periods is the disruption. The method has been accepted by courts and boards in Canada and the US and is generally the preferred approach where the records support it.

To use it, the contractor needs reliable records of installed quantities and labor hours by activity and period. That means cost codes that separate the activity, foremen's reports of quantities installed each day or week, and payroll hours charged to the right codes. A contractor that records only total hours for the whole trade cannot do a measured mile. This is one of the main reasons lesson 12 emphasizes daily records.

import random
random.seed(420)
# Weekly tiling records on Ashford: (week, impacted?, installed m2, crew hours)
weeks = []
for w in range(1, 19):
    impacted = 7 <= w <= 15          # weeks of trade stacking after the late tile approval and pool changes
    rate = random.gauss(1.05 if impacted else 1.42, 0.06)
    hours = random.choice([280, 320, 360])
    weeks.append((w, impacted, round(rate * hours), hours))
un = [(q, h) for w, i, q, h in weeks if not i]
im = [(q, h) for w, i, q, h in weeks if i]
r_un = sum(q for q, h in un) / sum(h for q, h in un)
r_im = sum(q for q, h in im) / sum(h for q, h in im)
q_im = sum(q for q, h in im); h_im = sum(h for q, h in im)
earned = q_im / r_un
lost = h_im - earned
print(f"Unimpacted rate: {r_un:.2f} m2/h over {len(un)} weeks")
print(f"Impacted rate:   {r_im:.2f} m2/h over {len(im)} weeks")
print(f"Impacted work: {q_im:,} m2 in {h_im:,} h; should have taken {earned:,.0f} h")
print(f"Lost hours: {lost:,.0f} ({lost / h_im:.0%} of impacted hours); at $68/h = ${lost * 68:,.0f}")
print("Weekly rates:", " ".join(f"{q/h:.2f}{'*' if i else ''}" for w, i, q, h in weeks))

Unimpacted rate: 1.46 m2/h over 9 weeks
Impacted rate:   1.07 m2/h over 9 weeks
Impacted work: 3,040 m2 in 2,840 h; should have taken 2,088 h
Lost hours: 752 (26% of impacted hours); at $68/h = $51,142
Weekly rates: 1.52 1.44 1.49 1.47 1.47 1.43 1.06* 0.97* 1.10* 1.04* 1.09* 0.97* 1.09* 1.13* 1.21* 1.43 1.43 1.42

The tiling crew achieved about 1.46 m² per hour in the unimpacted weeks before and after the disruption and about 1.07 m² per hour during it. The 3,040 m² installed in the impacted weeks should have taken about 2,088 hours at the unimpacted rate; it took 2,840. The difference of about 752 hours, roughly a quarter of the impacted hours, is the measured loss. At an illustrative burdened rate of $68 per hour, the loss is about $51,000 before markup.

The calculation is simple; the arguments are about whether the comparison is fair.

A sensitivity check strengthens the analysis. If using only the weeks before the disruption, or only the weeks after, or excluding the best and worst weeks changes the loss by a few percent, the result is robust. If it swings widely, the reviewer will notice, and the contractor should explain why.

When there is no clean measured mile

Sometimes the whole of an activity was impacted, or the records do not separate the activity. Then the contractor must use a weaker method and accept that it will carry less weight.

• Baseline productivity: Use the best sustained productivity achieved on the project, for example the best five consecutive weeks, as the benchmark even if those weeks were partly impacted. It tends to favor the contractor and needs careful explanation.
• Comparable projects: Compare with the same contractor's productivity on similar work on other projects, adjusted for known differences. The owner will ask for the full records of those projects.
• Earned value: Compare earned hours with actual hours, period by period, and identify where the gap opened. This shows when productivity fell, which helps with causation, but the loss is measured against the estimate, so a low estimate inflates it.
• Industry factors: Apply published loss percentages for conditions such as overtime, crew density, stacking and season. They are generic and are often criticized, but they can support or cross-check a project-based method.

Industry factor tables, such as those published by the Mechanical Contractors Association of America and the National Electrical Contractors Association, list conditions with minor, average and severe loss percentages. They were developed as estimating aids and their use to prove damages has been criticized because they are not based on the specific project and their research basis is limited. Courts and boards in both countries have accepted them in some cases and rejected them in others, often depending on whether the claimant also showed project-specific evidence. Studies of extended overtime, such as those by the Business Roundtable and later researchers, show productivity falling as weekly hours rise and the overtime continues for weeks, but the figures vary between studies.

# Illustrative factor-based cross-check for the Ashford tiling impacted period
# Percentages are placeholders in the style of published factor tables, not values from any table.
impacted_hours = 2_840
factors = {
    "Trade stacking (average)":       0.10,
    "Crew size above optimum (minor)": 0.05,
    "Out-of-sequence work (minor)":    0.05,
    "Extended overtime (minor)":       0.05,
}
combined = 1.0
for name, f in factors.items():
    combined *= (1 - f)
loss_share = 1 - combined
print(f"Combined loss share: {loss_share:.1%}")
print(f"Factor-based lost hours: {impacted_hours * loss_share:,.0f} (measured mile gave about 752)")

Combined loss share: 22.8%
Factor-based lost hours: 649 (measured mile gave about 752)

Combining the factors multiplicatively, rather than adding them, avoids overstating the loss when several conditions act at once. Here the factor method gives about 650 lost hours, somewhat lower than the measured mile's 752. That agreement is useful as a cross-check, but the measured mile, based on the project's own records, would carry the weight.

Causation and allocation

Measuring a productivity loss is half the job; the other half is showing that the owner caused it. The best evidence ties the impacted period to specific owner events: the date the late tile approval arrived, the date the deck redesign was issued, the period when the tile, mechanical and electrical crews were all in the natatorium. Daily reports that record crew sizes, areas worked and interference, photographs of congestion and correspondence complaining about it at the time are far more persuasive than an expert's reconstruction after the fact.

Where the contractor contributed to the loss, an honest claim allocates. If the tile crew's foreman changed in week 9 and the next two weeks were the worst in the impacted period, the contractor might accept responsibility for part of the loss in those weeks. Reviewers and tribunals tend to trust a claim that concedes what it should, and to discount everything in a claim that concedes nothing.

US courts and boards require proof of liability, causation and resulting injury, and accept a reasonable estimate of the amount once the fact of damage is shown. Federal boards have repeatedly endorsed the measured mile and treat industry factors with caution. A201-2017 change orders are agreements on the adjustment in contract sum and time, so a contractor that signs without reservation risks being found to have settled the disruption associated with that change; contractors commonly add reservation language for cumulative impact.