Afrexai Demand Forecasting

# Demand Forecasting Framework

Build accurate demand forecasts using multiple methodologies. Combines statistical models with market intelligence for actionable predictions.

## When to Use
- Quarterly/annual demand planning
- New product launch forecasting
- Inventory optimization
- Capacity planning decisions
- Budget cycle preparation

## Forecasting Methodologies

### 1. Time Series Analysis
Best for: Established products with 24+ months of history.

```
Decompose into: Trend + Seasonality + Cyclical + Residual

Moving Average (3-month):
  Forecast = (Month_n + Month_n-1 + Month_n-2) / 3

Weighted Moving Average:
  Forecast = (0.5 × Month_n) + (0.3 × Month_n-1) + (0.2 × Month_n-2)

Exponential Smoothing (α = 0.3):
  Forecast_t+1 = α × Actual_t + (1-α) × Forecast_t
```

### 2. Causal / Regression Models
Best for: Products where external factors drive demand.

Key drivers to model:
- **Price elasticity**: % demand change per 1% price change
- **Marketing spend**: Lag effect (typically 2-6 weeks)
- **Seasonality index**: Monthly coefficient vs annual average
- **Economic indicators**: GDP growth, consumer confidence, industry PMI
- **Competitor actions**: New entrants, price changes, promotions

```
Demand = β₀ + β₁(Price) + β₂(Marketing) + β₃(Season) + β₄(Economic) + ε
```

### 3. Judgmental / Qualitative
Best for: New products, market disruptions, limited data.

Methods:
- **Delphi method**: 3+ expert rounds, anonymous, converging estimates
- **Sales force composite**: Bottom-up from territory reps (apply 15-20% optimism correction)
- **Market research**: Survey-based purchase intent (apply 30-40% intent-to-purchase conversion)
- **Analogous forecasting**: Map to similar product launch curves

### 4. Blended Forecast (Recommended)
Combine methods using confidence-weighted average:

| Method | Weight (Mature Product) | Weight (New Product) |
|--------|------------------------|---------------------|
| Time Series | 50% | 10% |
| Causal | 30% | 20% |
| Judgmental |...

# Demand Forecasting Framework

Build accurate demand forecasts using statistical models, causal analysis, and expert judgment — blended into actionable predictions.

## What's Inside

- **4 forecasting methodologies** — time series, causal/regression, judgmental, and blended approach
- **ABC-XYZ demand segmentation** — match inventory strategy to volume × variability
- **Accuracy metrics** — MAPE, bias, tracking signal with industry targets
- **Safety stock calculator** — service-level-based formula with lead time adjustment
- **Scenario planning** — bear/base/bull with probability weighting
- **Industry benchmarks** — typical MAPE and forecast horizons for 6 sectors
- **Monthly planning cycle** — 4-week cadence from statistical generation to consensus
- **Red flag checklist** — catch model drift, sandbagging, and copy-paste forecasts

## Who It's For

Operations leaders, supply chain managers, FP&A teams, and anyone responsible for matching supply to demand. Works for SaaS pipeline forecasting through to physical goods inventory planning.

## ROI

A 5-point MAPE improvement on $10M revenue typically saves $450K-$1.15M annually through reduced inventory, fewer stockouts, and better capacity utilization.

## Get the Full Picture

This skill covers demand forecasting fundamentals. For complete, industry-specific agent configurations:

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