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Combines statistical models with market intelligence for actionable predictions.\n\n## When to Use\n- Quarterly/annual demand planning\n- New product launch forecasting\n- Inventory optimization\n- Capacity planning decisions\n- Budget cycle preparation\n\n## Forecasting Methodologies\n\n### 1. Time Series Analysis\nBest for: Established products with 24+ months of history.\n\n```\nDecompose into: Trend + Seasonality + Cyclical + Residual\n\nMoving Average (3-month):\n  Forecast = (Month_n + Month_n-1 + Month_n-2) / 3\n\nWeighted Moving Average:\n  Forecast = (0.5 × Month_n) + (0.3 × Month_n-1) + (0.2 × Month_n-2)\n\nExponential Smoothing (α = 0.3):\n  Forecast_t+1 = α × Actual_t + (1-α) × Forecast_t\n```\n\n### 2. Causal / Regression Models\nBest for: Products where external factors drive demand.\n\nKey drivers to model:\n- **Price elasticity**: % demand change per 1% price change\n- **Marketing spend**: Lag effect (typically 2-6 weeks)\n- **Seasonality index**: Monthly coefficient vs annual average\n- **Economic indicators**: GDP growth, consumer confidence, industry PMI\n- **Competitor actions**: New entrants, price changes, promotions\n\n```\nDemand = β₀ + β₁(Price) + β₂(Marketing) + β₃(Season) + β₄(Economic) + ε\n```\n\n### 3. Judgmental / Qualitative\nBest for: New products, market disruptions, limited data.\n\nMethods:\n- **Delphi method**: 3+ expert rounds, anonymous, converging estimates\n- **Sales force composite**: Bottom-up from territory reps (apply 15-20% optimism correction)\n- **Market research**: Survey-based purchase intent (apply 30-40% intent-to-purchase conversion)\n- **Analogous forecasting**: Map to similar product launch curves\n\n### 4. Blended Forecast (Recommended)\nCombine methods using confidence-weighted average:\n\n| Method | Weight (Mature Product) | Weight (New Product) |\n|--------|------------------------|---------------------|\n| Time Series | 50% | 10% |\n| Causal | 30% | 20% |\n| Judgmental | 20% | 70% |\n\n## Forecast Accuracy Metrics\n\n| Metric | Formula | Target |\n|--------|---------|--------|\n| MAPE | Avg(|Actual - Forecast| / Actual) × 100 | <15% |\n| Bias | Σ(Forecast - Actual) / n | Near 0 |\n| Tracking Signal | Cumulative Error / MAD | -4 to +4 |\n| Weighted MAPE | Revenue-weighted MAPE | <10% for top SKUs |\n\n## Demand Planning Process\n\n### Monthly Cycle\n1. **Week 1**: Statistical forecast generation (auto-run models)\n2. **Week 2**: Market intelligence overlay (sales input, competitor intel)\n3. **Week 3**: Consensus meeting — align Sales, Marketing, Ops, Finance\n4. **Week 4**: Finalize, communicate to supply chain, track vs prior forecast\n\n### Demand Segmentation (ABC-XYZ)\n\n| Segment | Volume | Variability | Approach |\n|---------|--------|-------------|----------|\n| AX | High | Low | Auto-replenish, tight safety stock |\n| AY | High | Medium | Statistical + review quarterly |\n| AZ | High | High | Collaborative planning, buffer stock |\n| BX | Medium | Low | Statistical, periodic review |\n| BY | Medium | Medium | Hybrid model |\n| BZ | Medium | High | Judgmental + safety stock |\n| CX | Low | Low | Min/max rules |\n| CY | Low | Medium | Periodic review |\n| CZ | Low | High | Make-to-order where possible |\n\n## Safety Stock Calculation\n\n```\nSafety Stock = Z × σ_demand × √(Lead Time)\n\nWhere:\n  Z = Service level factor (95% = 1.65, 98% = 2.05, 99% = 2.33)\n  σ_demand = Standard deviation of demand\n  Lead Time = In same units as demand period\n```\n\n## Scenario Planning\n\nFor each forecast, generate three scenarios:\n\n| Scenario | Probability | Assumptions |\n|----------|-------------|-------------|\n| Bear | 20% | -15% to -25% vs base. Recession, market contraction, competitor disruption |\n| Base | 60% | Historical trends + known pipeline. Most likely outcome |\n| Bull | 20% | +15% to +25% vs base. Market expansion, product virality, competitor exit |\n\n## Red Flags in Your Forecast\n\n- [ ] MAPE consistently >20% — model needs retraining\n- [ ] Persistent positive bias — sales team sandbagging\n- [ ] Persistent negative bias — over-optimism, check incentive structure\n- [ ] Tracking signal outside ±4 — systematic error, investigate root cause\n- [ ] Forecast never changes — \"spreadsheet copy-paste\" problem\n- [ ] No external inputs — pure statistical = blind to market shifts\n\n## Industry Benchmarks\n\n| Industry | Typical MAPE | Forecast Horizon | Key Driver |\n|----------|-------------|-----------------|------------|\n| CPG/FMCG | 20-30% | 3-6 months | Promotions, seasonality |\n| Retail | 15-25% | 1-3 months | Trends, weather, events |\n| Manufacturing | 10-20% | 6-12 months | Orders, lead times |\n| SaaS | 10-15% | 12 months | Pipeline, churn, expansion |\n| Healthcare | 15-25% | 3-6 months | Regulation, demographics |\n| Construction | 20-35% | 12-24 months | Permits, economic cycle |\n\n## ROI of Better Forecasting\n\nFor a company doing $10M revenue:\n- **5% MAPE improvement** → $200K-$500K inventory savings\n- **Reduced stockouts** → 2-5% revenue recovery ($200K-$500K)\n- **Lower expediting costs** → $50K-$150K savings\n- **Better capacity utilization** → 3-8% OpEx reduction\n\n**Total impact: $450K-$1.15M annually from a 5-point MAPE improvement.**\n\n---\n\n## Full Industry Context Packs\n\nThese frameworks scratch the surface. For complete, deployment-ready agent configurations tailored to your industry:\n\n**[AfrexAI Context Packs](https://afrexai-cto.github.io/context-packs/)** — $47 each\n\n- 🏗️ Construction | 🏥 Healthcare | ⚖️ Legal | 💰 Fintech\n- 🛒 Ecommerce | 💻 SaaS | 🏠 Real Estate | 👥 Recruitment\n- 🏭 Manufacturing | 📋 Professional Services\n\n**[AI Revenue Calculator](https://afrexai-cto.github.io/ai-revenue-calculator/)** — Find your automation ROI in 2 minutes\n\n**[Agent Setup Wizard](https://afrexai-cto.github.io/agent-setup/)** — Configure your AI agent stack\n\n### Bundles\n- **Pick 3** — $97 (save 31%)\n- **All 10** — $197 (save 58%)\n- **Everything Bundle** — $247 (all packs + playbook + wizard)\n","readmeExcerpt":"Demand Forecasting Framework Build accurate demand forecasts using multiple methodologies. Combines statistical models with market intelligence for actionable predictions. 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