demand-forecaster

name	demand-forecaster
description	Demand forecasting skill with quantitative and qualitative methods, accuracy measurement, and bias correction
allowed-tools	["Read","Write","Glob","Grep","Edit"]
metadata	{"specialization":"operations","domain":"business","category":"capacity-planning"}
graph	{"domains":["domain:operations"],"skillAreas":["skill-area:statistical-analysis","skill-area:data-analytics","skill-area:quantitative-modeling"],"workflows":["workflow:vendor-onboarding","workflow:vendor-evaluation"],"roles":["role:operations-analyst","role:data-analyst","role:procurement-manager"]}

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:

1. Naive forecast (prior period)
2. Statistical forecast
3. Analyst adjustments
4. Sales/customer input
5. Final consensus

Only keep adjustments that improve accuracy.

Integration Points

• ERP/demand planning systems
• CRM systems
• Point of sale data
• Economic data feeds