Nine-time project forecasting methods exist as a result of three forecast methods & three performance factors. 3 min read.
Time Forecasting Methods
The nine-time forecasting methods are the result of three fundamentally different methods proposed by three different authors, and each method is calculated under the three-performance factor (PF) situations as shown below,
The rows under ‘performance factor’ make a distinction between the three different project situations based on the PF classification. Each situation involves another assumption about the future performance of the work to finish the project.
- PF = 1: Assumes remaining work is done according to the baseline schedule.
- PF = SPI or SPI(t): Assumes remaining work is done according to SPI or SPI(t) trend
- PF = SCI or SCI(t): Assumes remaining work is done according to SCI or SCI(t) trend.
The columns under ‘forecasting method’ display the three methods to forecast the project’s final duration, known as,
- The planned value method (Ambari, 2003).
- The earned duration method (Jacob, 2003).
- The earned schedule method (Lipke, 2003).
Microsoft Project Demonstration Project
Shown below is a Microsoft Project demonstration project, which is fully resourced and costed. This project has a schedule duration of 9-weeks and a cost budget of $150. The delivered project and the resulting tracking information, show a final duration of 11-weeks and an actual cost of $210.
Planned Value Method
Shown below is the planned value forecasting results,
Earned Duration Method
Shown below is the earned duration forecasting results,
Earned Schedule Method
Shown below is the earned schedule forecasting results,
Comparing each of the forecast methods is it clear,
- Planned Value is the worst performing
- Earned Duration is in-between performing
- Earned Schedule is the best performing
Similarly, comparing each of the performance factors it is clear,
- PF = SCI or SCI(t) is the worst performing
- PF = SPI or SPI(t) is in-between performing
- PF = 1 is the best performing
Consequently, the Earned Schedule forecasting method calculated using the PF=1 is the most accurate forecasting method. This indicates that what is important for this schedule is measuring earned schedule and that performance patterns and trends can be ignored.
Forecast accuracy steadily improves over time, during the first third the project finish is underestimated, then during the middle the forecast finish continuously improves and finally during the last third accurately forecasts the project will finish by week 11.
Interestingly, SPI/SPI(t) outperforms SCI/SCI(t) indicating the dominant performance pattern and trend is time, and that cost is not relevant for time forecasting methods.
Unfortunately, conclusions drawn for this schedule can’t be generalised because each project is unique and characteristics from one project to another differ significantly.
If you would like to know more about leveraging data-driven actionable insights for your project schedule, then feel free to contact me on firstname.lastname@example.org.