Skill: 1kalin/afrexai-demand-forecasting (version 1.0.0)

# 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 | 20% | 70% | ## Forecast Accuracy Metrics | Metric | Formula | Target | |--------|---------|--------| | MAPE | Avg(|Actual - Forecast| / Actual) × 100 | <15% | | Bias | Σ(Forecast - Actual) / n | Near 0 | | Tracking Signal | Cumulative Error / MAD | -4 to +4 | | Weighted MAPE | Revenue-weighted MAPE | <10% for top SKUs | ## Demand Planning Process ### Monthly Cycle 1. **Week 1**: Statistical forecast generation (auto-run models) 2. **Week 2**: Market intelligence overlay (sales input, competitor intel) 3. **Week 3**: Consensus meeting — align Sales, Marketing, Ops, Finance 4. **Week 4**: Finalize, communicate to supply chain, track vs prior forecast ### Demand Segmentation (ABC-XYZ) | Segment | Volume | Variability | Approach | |---------|--------|-------------|----------| | AX | High | Low | Auto-replenish, tight safety stock | | AY | High | Medium | Statistical + review quarterly | | AZ | High | High | Collaborative planning, buffer stock | | BX | Medium | Low | Statistical, periodic review | | BY | Medium | Medium | Hybrid model | | BZ | Medium | High | Judgmental + safety stock | | CX | Low | Low | Min/max rules | | CY | Low | Medium | Periodic review | | CZ | Low | High | Make-to-order where possible | ## Safety Stock Calculation ``` Safety Stock = Z × σ_demand × √(Lead Time) Where: Z = Service level factor (95% = 1.65, 98% = 2.05, 99% = 2.33) σ_demand = Standard deviation of demand Lead Time = In same units as demand period ``` ## Scenario Planning For each forecast, generate three scenarios: | Scenario | Probability | Assumptions | |----------|-------------|-------------| | Bear | 20% | -15% to -25% vs base. Recession, market contraction, competitor disruption | | Base | 60% | Historical trends + known pipeline. Most likely outcome | | Bull | 20% | +15% to +25% vs base. Market expansion, product virality, competitor exit | ## Red Flags in Your Forecast - [ ] MAPE consistently >20% — model needs retraining - [ ] Persistent positive bias — sales team sandbagging - [ ] Persistent negative bias — over-optimism, check incentive structure - [ ] Tracking signal outside ±4 — systematic error, investigate root cause - [ ] Forecast never changes — "spreadsheet copy-paste" problem - [ ] No external inputs — pure statistical = blind to market shifts ## Industry Benchmarks | Industry | Typical MAPE | Forecast Horizon | Key Driver | |----------|-------------|-----------------|------------| | CPG/FMCG | 20-30% | 3-6 months | Promotions, seasonality | | Retail | 15-25% | 1-3 months | Trends, weather, events | | Manufacturing | 10-20% | 6-12 months | Orders, lead times | | SaaS | 10-15% | 12 months | Pipeline, churn, expansion | | Healthcare | 15-25% | 3-6 months | Regulation, demographics | | Construction | 20-35% | 12-24 months | Permits, economic cycle | ## ROI of Better Forecasting For a company doing $10M revenue: - **5% MAPE improvement** → $200K-$500K inventory savings - **Reduced stockouts** → 2-5% revenue recovery ($200K-$500K) - **Lower expediting costs** → $50K-$150K savings - **Better capacity utilization** → 3-8% OpEx reduction **Total impact: $450K-$1.15M annually from a 5-point MAPE improvement.** --- ## Full Industry Context Packs These frameworks scratch the surface. 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