forecast-accuracy-analyzer

Testing & Quality

Forecast accuracy measurement and improvement skill with error decomposition

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Forecast Accuracy Analyzer

Overview

The Forecast Accuracy Analyzer provides comprehensive forecast accuracy measurement, error decomposition, and improvement recommendation capabilities. It supports continuous forecast quality improvement through root cause analysis and model performance comparison.

Capabilities

MAPE, WMAPE, Bias Calculation: Standard accuracy metrics
Forecast Error Decomposition: Breakdown by error source
SKU-Level Accuracy Tracking: Granular accuracy monitoring
Forecast Value-Add (FVA) Analysis: Contribution of forecast steps
Root Cause Categorization: Error driver classification
Model Performance Comparison: Multi-model accuracy benchmarking
Improvement Recommendation Generation: Data-driven suggestions
Accuracy Trend Monitoring: Historical accuracy tracking

Input Schema

forecast_accuracy_request:
  forecast_data:
    forecasts: array
      - sku_id: string
        period: string
        forecast_value: float
        forecast_source: string
    period_range:
      start: date
      end: date
  actual_data:
    actuals: array
      - sku_id: string
        period: string
        actual_value: float
  analysis_parameters:
    metrics: array                # MAPE, WMAPE, Bias, etc.
    aggregation_levels: array     # SKU, category, total
    fva_steps: array              # Statistical, sales input, etc.
  segmentation:
    by_category: boolean
    by_volume: boolean
    by_variability: boolean

Output Schema

forecast_accuracy_output:
  accuracy_metrics:
    overall:
      mape: float
      wmape: float
      bias: float
      mpe: float
    by_segment: array
    by_sku: array
  error_decomposition:
    systematic_error: float
    random_error: float
    outlier_impact: float
    by_source: object
  fva_analysis:
    steps: array
      - step_name: string
        value_add: float
        before_accuracy: float
        after_accuracy: float
    recommendations: array
  root_cause_analysis:
    error_categories: array
      - category: string
        frequency: integer
        impact: float
    top_drivers: array
  model_comparison:
    models: array
      - model_name: string
        accuracy: float
        best_for: array
  improvement_recommendations: array
    - recommendation: string
      expected_improvement: float
      implementation_effort: string
  trends:
    accuracy_over_time: object
    bias_trend: object

Usage

Monthly Accuracy Review

Input: Previous month's forecasts and actuals
Process: Calculate accuracy metrics by segment
Output: Accuracy report with performance analysis

Forecast Value-Add Analysis

Input: Forecast at each process step (statistical, sales, consensus)
Process: Measure value added at each step
Output: FVA report identifying low-value steps

Root Cause Investigation

Input: High-error SKUs, demand patterns
Process: Categorize and analyze error drivers
Output: Root cause report with recommendations

Integration Points

Planning Systems: Forecast and actual data
BI Platforms: Accuracy dashboards
Statistical Tools: Advanced analysis
Tools/Libraries: Statistical analysis, visualization

Process Dependencies

Forecast Accuracy Analysis and Improvement
Demand Forecasting and Planning
Sales and Operations Planning (S&OP)

Best Practices

Measure accuracy at multiple aggregation levels
Use weighted metrics for volume importance
Investigate outliers before concluding
Compare models on like-for-like basis
Set realistic improvement targets
Share accuracy results with stakeholders