Trend Analysis is an analytical method that uses mathematical models to forecast future outcomes based on historical results.
It identifies patterns over time in performance data—such as cost, schedule, or quality metrics—to anticipate future results and inform decision-making. Trend analysis helps project teams detect early signs of deviation, assess long-term impact, and determine whether corrective actions are required.
Key Characteristics
- Data-Driven – Based on historical performance metrics
- Forecast-Oriented – Used to predict likely future conditions
- Supports Corrective Action – Highlights where adjustments may be needed
- Applicable Across Domains – Used for cost, schedule, risk, quality, and resource trends
Example Scenarios
- Tracking the Schedule Performance Index (SPI) over time to predict project delay
- Monitoring test defect rates across iterations to forecast quality improvement
- Observing cost variance trends to identify risk of budget overrun
Example: Cost Performance Trend Analysis
Click to expand Cost Performance Trend Analysis code
import matplotlib.pyplot as plt
import pandas as pd
# Sample trend analysis data (CPI over time)
data = {
"Week": ["Week 1", "Week 2", "Week 3", "Week 4", "Week 5", "Week 6"],
"EV": [10000, 22000, 35000, 48000, 62000, 75000],
"AC": [11000, 23000, 36000, 49000, 62000, 74000]
}
# Create DataFrame
df = pd.DataFrame(data)
df["CPI"] = df["EV"] / df["AC"]
# Plot CPI over time
plt.figure(figsize=(10, 6))
plt.plot(df["Week"], df["CPI"], marker='o', linewidth=2)
plt.title("Cost Performance Index (CPI) Trend Over Time")
plt.xlabel("Reporting Period")
plt.ylabel("CPI")
plt.ylim(0.8, 1.1)
plt.grid(True)
plt.axhline(y=1.0, color='gray', linestyle='--', linewidth=1)
plt.tight_layout()
plt.show()| Reporting Period | Earned Value (EV) | Actual Cost (AC) | Cost Performance Index (CPI) | Trend |
|---|---|---|---|---|
| Week 1 | $10,000 | $11,000 | 0.91 | Below target |
| Week 2 | $22,000 | $23,000 | 0.96 | Slight improvement |
| Week 3 | $35,000 | $36,000 | 0.97 | Holding steady |
| Week 4 | $48,000 | $49,000 | 0.98 | Gradual improvement |
| Week 5 | $62,000 | $62,000 | 1.00 | On track |
| Week 6 | $75,000 | $74,000 | 1.01 | Slightly ahead |
Role in Monitoring and Forecasting
- Enables Early Detection – Spots performance degradation or improvement
- Improves Planning Accuracy – Enhances forecasts by leveraging past behavior
- Supports Stakeholder Communication – Provides evidence-based progress updates
- Feeds into Control Processes – Used alongside variance analysis to guide decisions
See also: Variance Analysis, Forecasting, Performance Measurement Baseline, Schedule Forecasts, To-Complete Performance Index (TCPI).