CASE STUDY
Accelerating Build Timelines with Data: Montgomery Homes Timeframes Dashboard
Industry: Residential Construction
Solution Area: Project Delivery Performance
Client: Montgomery Homes (NSW, Australia)
Tools Used: ClickHome, Microsoft SQL Server, Power BI
Business Impact
50% Faster Delay Detection
Median vs. actual comparisons helped supervisors identify stage-level bottlenecks early.
Regional Benchmarking
Cross-region (Sydney, Central, Newcastle) visibility improved scheduling accountability.
Data-Driven Planning
Leadership used historical build trends to forecast workloads and allocate trades effectively.
The Challenge
Montgomery Homes managed hundreds of active construction projects across NSW using the ClickHome platform.
While ClickHome captured detailed progress, contract, and milestone data, it was difficult to consolidate this information into a unified analytical view. Leadership lacked visibility to answer:
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How long are builds taking from Commencement → PP5?
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Which supervisors or regions are performing above or below the median timeframe?
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Do higher contract values lead to longer delivery durations?
Fragmented data limited transparency, making it challenging to plan resources, monitor progress, or set realistic delivery expectations.
Our Solution
GrowthBI designed the Timeframes Analysis Dashboard, integrating ClickHome’s project data into Microsoft SQL Server and visualizing key insights in Power BI. The solution helped measure build efficiency across projects, stages, and supervisors—creating a single source of truth for operational performance.
Key Features:
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Stage-Wise Duration Analysis: Tracks average and median weeks between milestones (Commencement → PP5).
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Regional & Supervisor Comparison: Enables benchmarking across Sydney, Central, and Newcastle.
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Contract Value Correlation: Scatterplots reveal how contract value impacts completion time.
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Supervisor Leaderboard: Ranks supervisors by build efficiency to promote accountability.
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Interactive Filters: Drill-down by job series (Single, Double, Downhill) and view individual contract details.
Dashboard Walkthrough
1. Median vs Actual Duration by Stage
Visualizes each milestone (Commencement → PP1 → PP5) with median and actual build durations, helping identify where delays frequently occur.
2. Regional & Supervisor Comparison
Compares performance across Sydney, Central, and Newcastle regions. Supervisors can see how their build durations rank against regional medians.
3. Contract Value vs Time to Completion (Scatterplot)
Plots each job by contract value and total build weeks, revealing whether higher-value contracts correspond with longer timelines.
4. Supervisor Leaderboard
Ranks supervisors by average build duration, highlighting top performers and areas requiring support.
5. Job Series Filters (Single, Double, Downhill)
Allows users to segment performance by house type, with drill-through to individual contract details for deeper analysis.
Technical Architecture & Process
Data Flow:
ClickHome → Microsoft SQL Server (ETL & logic) → Power BI (visualization layer)
SQL Example:
SELECT
job_number,
DATEDIFF(WEEK, commencement_date, pp1_date) AS weeks_to_pp1,
DATEDIFF(WEEK, pp1_date, pp2_date) AS weeks_pp1_pp2,
DATEDIFF(WEEK, pp2_date, pp3_date) AS weeks_pp2_pp3,
DATEDIFF(WEEK, pp3_date, pp4_date) AS weeks_pp3_pp4,
DATEDIFF(WEEK, pp4_date, pp5_date) AS weeks_pp4_pp5
FROM clickhome_projects;
Power BI Measures:
MedianWeeks = MEDIAN('Projects'[WeeksToCompletion])
Variance = [ActualWeeks] - [MedianWeeks]
SupervisorRank = RANKX(ALL('Supervisor'[Name]), [MedianWeeks], , ASC)
Outcome
The Timeframes Dashboard consolidated multiple ClickHome datasets into one unified analytical model—transforming raw data into actionable insights.
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Transparency: Leadership gained real-time visibility into build timelines.
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Efficiency: Supervisors identified consistent delay points by stage.
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Forecasting: Historical patterns now guide realistic timeframe targets and capacity planning.
This integration empowered Montgomery Homes to manage delivery performance proactively—driving operational efficiency and improving build predictability across all regions.