Analysis of construction hours: Dropia automates productivity control by site.
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Automated analysis of construction hours with Dropia: productivity control by site, detection of discrepancies, real-time optimization of profitability.
Hour control is fundamental in the construction industry. Every poorly allocated hour, every unproductive hour directly impacts profitability. Dropia fully automates hour analysis, transforming timesheets into actionable intelligence to optimize your productivity.
Why hour analysis is crucial in construction
Hours account for 40-60% of a project's cost
In most construction projects:
Labor: 40-60% of total cost
Materials: 30-45%
Other costs: 10-20%
A 10% variation in hours thus has more impact than a 10% variation in materials. It's the main lever of profitability.
Every hour counts
On a project budgeted at 500 hours:
10% excess hours = 50 additional hours
At €45/hour charged = €2,250 of additional cost
On a margin of €10,000, that's -22.5% profitability
Productivity varies greatly
Between workers, between tasks, between projects:
A senior worker can be 30-50% more productive than a junior
A poorly organized task can take 2x longer than necessary
A project with good access is 20% faster
Identifying these variations allows for optimizing assignments and organization.
The limitations of manual hour analysis
Time-consuming
Manually analyzing 500 hours spread over 5 workers and 10 tasks:
Consolidating timesheets: 30 minutes
Calculating totals by worker and by task: 20 minutes
Comparing with the forecast budget: 20 minutes
Identifying discrepancies: 15 minutes
Producing the report: 15 minutes
Total: 1 hour 40 minutes
For 10 projects per week: 16 hours of administrative work.
Always late
Manual analysis is done at best weekly, often monthly. Productivity issues are detected 1-4 weeks late.
Inaccurate
Between entry errors, misallocated times, and forgetfulness, the reliability of manual analysis is rarely optimal.
How Dropia automates hour analysis
Automatic collection and intelligent allocation
Instant mobile timesheets
Every worker logs hours from their smartphone with the Dropia app:
Project selection (optional geolocation)
Task selection
Start and end of work
Total time: 10 seconds
Automatic allocation
Hours are automatically:
Allocated to the correct project
Linked to the correct task
Assigned to the correct worker
Valued at the specific charged hourly rate
No re-entry, no allocation errors.
Automatic real-time calculations
Dropia calculates instantly:
By project:
Total hours consumed vs budget
Total labor cost vs forecast
Distribution by task
Distribution by worker
Discrepancies in hours and euros
By task:
Actual hours vs budgeted hours
Productivity ratio (budgeted/actual)
Actual cost vs forecast cost
Identification of drifting tasks
By worker:
Hours worked on each project
Relative productivity by type of task
Comparison with team average
Evolution over time
Automatic comparative analyses
Inter-project
For similar projects:
Which project consumes the most/least hours?
What are the reasons for these differences?
What lessons for the next ones?
Inter-period
Evolution of productivity over time:
Is productivity improving?
Impact of training
Effect of experience
Inter-worker
Anonymized benchmarking:
Who are the most productive by type of task?
Who needs training or support?
Optimization of future assignments
The 5 key automated analyses by Dropia
Analysis 1: Discrepancy of budgeted vs actual hours by task
Automatic table:
Immediate identification: major problem on framing
Action: In-depth investigation of this task
Which worker? Entire team underproductive or an individual?
What period? Recent problem or since the beginning?
What is the cause? Technical difficulty, organization, method?
Analysis 2: Productivity by worker on project
Automatic table for the "Villa Durand" project:
*Productivity = ratio between expected production and actual production
Insights:
Jean overperforming, candidate for complex projects
Luc underperforming on framing, needs training or reallocation
Marc slightly below on framing, investigate why
Analysis 3: Temporal evolution of hour consumption
Automatic graph
Curve showing week by week:
Hours consumed (blue bars)
Proportional budgeted hours (green line)
Immediate visual detection:
Weeks 1-3: consumption compliant
Week 4: peak consumption (+35%) → Investigation
Weeks 5-6: return to normal
Correlation with events
Dropia allows annotation of the graph:
Week 4: Late delivery of materials → unproductive wait times
Explanation of the discrepancy found
Lesson: improve delivery coordination
Analysis 4: Actual hourly cost per project
Automatic comparative table:
Cause analysis for Building C:
Dropia details automatically:
60% of the time: senior workers (high cost)
40% of the time: junior workers (normal cost)
Explanation: Complex project requiring more seniors than expected
Action: Future project estimates will integrate this realistic ratio
Analysis 5: Occupancy rate and unproductive time
Automatic analysis over a monthly period:
Total available hours: 12 workers × 151h = 1,812h
Automatic distribution by Dropia:
Billable project hours: 1,520h (84%) ✅
Travel hours: 145h (8%) ⚠️
Training hours: 40h (2%)
Waiting/unproductive hours: 107h (6%) 🔴
Insight: 6% unproductive hours = 107h × €45 = €4,815 in cost without value
Automatic investigation of causes:
Waiting for materials: 45h
Unresolved technical issues: 28h
Poor coordination: 34h
Identified corrective actions:
Improve delivery management (savings: ~€2,000/month)
Enhance technical assistance (savings: ~€1,250/month)
Automatic alerts on hour analysis
Alert for hour overruns by task
Trigger: hour consumption > budget + 10%
Notification sent to the project manager: "⚠️ Task 'Electricity' villa project: 88h consumed for 75h budgeted (+17%)"
Action required: Immediate investigation
Alert for abnormal productivity
Trigger: worker productivity < 75% over 3 consecutive days
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