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11-19-2025 04:11 AM - last edited 11-19-2025 04:17 AM
This pattern creates a base solution for Composite KPI Score by normalizing multiple KPIs to a 0–1 scale and applying user-defined weights via What-If parameters(numeric field parameters). The result is a single, comparable performance indicator that works across different KPI types and units, fully powered by Visual Calculations.
Because KPIs have different scales (money, percentages, counts), each KPI is first normalized, then multiplied by a weight.
The final composite score is a weighted sum of all normalized KPIs.
1. Min–Max Normalization (Higher Is Better)
This method maps all values into the range 0 to 1, where 0 is the minimum and 1 is the maximum:
Use this when higher values represent better performance (e.g., Sales, Orders, Margin, CSAT).
2. Min–Max Normalization (Lower Is Better)
Some KPIs—such as Total Cost, Defects, Error Rate, or Response Time—improve when the value is lower.
In these cases, the formula must be inverted so that the lowest value receives the highest normalized score:
This keeps everything in the same 0–1 range, while correctly rewarding lower values.
3. Z-Score Normalization (Standardization)
Alternatively, you can normalize using Z-scores, which measure how far a value is from the mean:
Z-scores:
For KPIs where lower is better, you simply invert the sign:
example code is provided for Z normalization in the file but not used in the composition
By Hadi Kheireddine