Demand Forecaster
Overview
The Demand Forecaster skill provides comprehensive capabilities for generating and managing demand forecasts. It supports multiple forecasting methods, accuracy measurement, bias correction, and integration of statistical and judgmental inputs.
Capabilities
- Time series forecasting (ARIMA, exponential smoothing)
- Causal modeling
- Machine learning forecasts
- Forecast accuracy metrics (MAPE, MAE, bias)
- Collaborative forecasting
- Demand sensing
- Seasonality adjustment
- New product forecasting
Used By Processes
- CAP-004: Demand Forecasting and Analysis
- CAP-003: Sales and Operations Planning
- CAP-001: Capacity Requirements Planning
Tools and Libraries
- Python statsmodels
- Prophet
- ML libraries (scikit-learn, TensorFlow)
- Demand planning systems
Usage
skill: demand-forecaster
inputs:
historical_data:
- period: "2025-01"
demand: 10500
- period: "2025-02"
demand: 11200
# ... additional history
forecast_horizon: 12 # months
method: "auto" # auto | arima | exponential | ml | ensemble
external_factors:
- name: "gdp_growth"
coefficient: 0.5
- name: "marketing_spend"
coefficient: 0.3
adjustments:
- period: "2026-06"
type: "promotion"
lift: 15 # percent
outputs:
- point_forecast
- confidence_intervals
- accuracy_metrics
- bias_analysis
- seasonality_factors
- recommendations
Forecasting Methods
Time Series Methods
| Method | Best For | Complexity | |--------|----------|------------| | Moving Average | Stable demand | Low | | Exponential Smoothing | Trends and seasonality | Medium | | ARIMA | Complex patterns | High | | Prophet | Multiple seasonalities | Medium |
Causal Methods
| Method | Use Case | |--------|----------| | Regression | Known drivers | | Econometric | Market factors | | Machine Learning | Complex relationships |
Accuracy Metrics
MAPE = (1/n) x Sum(|Actual - Forecast| / Actual) x 100
MAE = (1/n) x Sum(|Actual - Forecast|)
Bias = (1/n) x Sum(Forecast - Actual)
Accuracy Benchmarks
| MAPE | Interpretation | |------|----------------| | < 10% | Excellent | | 10-20% | Good | | 20-30% | Acceptable | | 30-50% | Poor | | > 50% | Very poor |
Forecast Value Added (FVA)
Compare accuracy at each step:
- Naive forecast (prior period)
- Statistical forecast
- Analyst adjustments
- Sales/customer input
- Final consensus
Only keep adjustments that improve accuracy.
Integration Points
- ERP/demand planning systems
- CRM systems
- Point of sale data
- Economic data feeds