Outcomes at a Glance

• 83% of estimates within ±10% of actuals
• 91% of estimates within ±20% of actuals
• Calibration based on 50+ historical projects
• Completed 35 validated estimates after calibration
• Improved accuracy for all projects >30 FP (100% within ±10%)

Context

A major systems integrator wanted to strengthen estimation accuracy for a large multinational account — and standardize on function points as the sizing method. Multiple internal tools had been tried, but none delivered consistent, reliable results. The organization was also rolling out SLIM‑Estimate globally and needed a calibrated, account‑specific implementation.

Barriers

• Inconsistent estimation methods across teams
• Lack of historical, project‑level data for accurate calibration
• Need to convert function point sizing into ESLOC using reliable gearing factors
• No way to validate early estimates against past performance
• Prior internally built tools failed to deliver accuracy or repeatability

What QSM Delivered

The integrator launched a 5‑month pilot with two staff members to implement and calibrate SLIM‑Estimate:

• Built a historical dataset of 50+ completed projects
• Performed extensive calibration, representing 60% of total pilot effort
• Empirically determined FP/ESLOC gearing factors
• Ran 35 SLIM‑Estimate models and compared each to actuals

Results confirmed the value of calibration:

• 29 of 35 estimates (83%) were within ±10%
• 32 of 35 estimates (91%) were within ±20%
• Smaller projects (<30 FP) showed typical variability
• Larger projects (>30 FP) showed excellent accuracy (100% within ±10%)

By grounding SLIM‑Estimate in real, locally relevant historical data, the organization achieved highly reliable estimates — proving that calibration and proper sizing models dramatically improve estimation performance.