Agent skill

forecast-accuracy-analyzer

Forecast accuracy measurement and improvement skill with error decomposition

View SKILL.md on GitHub Repository
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Install this agent skill to your Project

npx add-skill https://github.com/a5c-ai/babysitter/tree/main/library/specializations/domains/business/supply-chain/skills/forecast-accuracy-analyzer

Metadata

Additional technical details for this skill

domain
business
category
analytics
priority
standard
specialization
supply-chain

SKILL.md

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

yaml
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

yaml
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

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

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