Understanding Benchmarked Autophasing Cash Flow in UniPhi
- 3 days ago
- 3 min read
Leveraging Historical Data for Financial Projections
UniPhi 20 (and subsequent versions such as UniPhi 21) includes a benchmarked autophasing cash flow capability. While not a self-learning AI, it is an advanced algorithm that acts as a precursor to machine learning by using historical data to generate accurate projections.
This feature enables informed decision-making by creating realistic spend profiles and timelines based on comparable past projects. In effect, it turns your organisation’s historical project data into a powerful forecasting tool.
Key Concepts
Parametric Estimating: This capability builds on UniPhi’s long-standing portfolio-first approach, which already includes benchmarking and parametric modelling for more accurate estimates.
Accurate Spend Profiles: By drawing on historical data, the algorithm can generate remarkably precise earned value profiles for projects (even with relatively few data points) particularly in industries like construction, where productivity profiles have remained stable over time.
Baseline for Performance: The phased cash flow serves as a baseline to monitor your builder’s performance and quickly identify emerging risks. It can also be used in cost–benefit analyses during feasibility studies.
Steps to Use Benchmarked Autophasing Cash Flow
Access the Budget Screen
Open a specific project (e.g., Cheerful Views) and navigate to the budget screen.
Enter Project Estimate
Input your estimated total cost: for example, the projected cost of a new commercial tower.
Go to the Autophasing Section
Select the Autophasing option within UniPhi.
Select the Benchmarking Algorithm
Choose the benchmarking phasing algorithm
Filter Relevant Projects
The system automatically searches for completed projects in the same sector, project type, and service line as your current project.
You can refine the dataset by manually ticking or unticking projects to exclude irrelevant examples. This effectively trains the algorithm by ensuring only comparable historical projects are used.
Filters such as project size, floor area, or other scope criteria can help further narrow the selection.
Review Project Information
Access details like floor area, net leasable area, or GFA to inform your decision on which projects to include.
Set the Project Timeline
Define the start and finish dates for your project.
Generate the Phased Timeline
Click Save to phase the project’s timeline and automatically generate the spend profile.
View the Cash Flow
The phased cash flow can be viewed on the dashboard or in the manual phasing screen.
UniPhi 21 Enhancement: New phasing screens allow you to view the entire profile ( 0whether one year or 100 years) on a single, scrollable screen.
Real-World Example
A property developer is assessing the feasibility of a mixed-use building. By entering the estimated cost and selecting relevant historical projects in UniPhi’s benchmarked autophasing tool, they generate a realistic 18-month spend profile.
This profile is then used to:
Compare the projected spend against the builder’s actual progress claims.
Identify early if the project is running ahead or behind schedule.
Integrate the spend profile into a feasibility study to assess return on investment.
The result? Faster, data-driven decisions at both feasibility and delivery stages.
Important Considerations
Data Import
UniPhi does not come preloaded with historical data. You can import actuals for past projects either by extracting them to Excel from existing databases or using direct integrations with systems UniPhi connects to. This historical data is essential for training the forecasting algorithm.
Breadth and Quality of Data
To unlock the full potential of UniPhi’s algorithms (many of which act as stepping stones towards machine learning) it’s important to use the full breadth of project management functionality and maintain high data quality.
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