{ "schemaVersion": "1.0", "item": { "slug": "afrexai-demand-forecasting", "name": "Demand Forecasting Framework", "source": "tencent", "type": "skill", "category": "数据分析", "sourceUrl": "https://clawhub.ai/1kalin/afrexai-demand-forecasting", "canonicalUrl": "https://clawhub.ai/1kalin/afrexai-demand-forecasting", "targetPlatform": "OpenClaw" }, "install": { "downloadMode": "redirect", "downloadUrl": "/downloads/afrexai-demand-forecasting", "sourceDownloadUrl": "https://wry-manatee-359.convex.site/api/v1/download?slug=afrexai-demand-forecasting", "sourcePlatform": "tencent", "targetPlatform": "OpenClaw", "installMethod": "Manual import", "extraction": "Extract archive", "prerequisites": [ "OpenClaw" ], "packageFormat": "ZIP package", "includedAssets": [ "README.md", "SKILL.md" ], "primaryDoc": "SKILL.md", "quickSetup": [ "Download the package from Yavira.", "Extract the archive and review SKILL.md first.", "Import or place the package into your OpenClaw setup." ], "agentAssist": { "summary": "Hand the extracted package to your coding agent with a concrete install brief instead of figuring it out manually.", "steps": [ "Download the package from Yavira.", "Extract it into a folder your agent can access.", "Paste one of the prompts below and point your agent at the extracted folder." ], "prompts": [ { "label": "New install", "body": "I downloaded a skill package from Yavira. 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Read SKILL.md from the extracted folder and install it by following the included instructions. Then review README.md for any prerequisites, environment setup, or post-install checks. Tell me what you changed and call out any manual steps you could not complete." }, { "label": "Upgrade existing", "body": "I downloaded an updated skill package from Yavira. Read SKILL.md from the extracted folder, compare it with my current installation, and upgrade it while preserving any custom configuration unless the package docs explicitly say otherwise. Then review README.md for any prerequisites, environment setup, or post-install checks. Summarize what changed and any follow-up checks I should run." } ] }, "documentation": { "source": "clawhub", "primaryDoc": "SKILL.md", "sections": [ { "title": "Demand Forecasting Framework", "body": "Build accurate demand forecasts using multiple methodologies. Combines statistical models with market intelligence for actionable predictions." }, { "title": "When to Use", "body": "Quarterly/annual demand planning\nNew product launch forecasting\nInventory optimization\nCapacity planning decisions\nBudget cycle preparation" }, { "title": "1. Time Series Analysis", "body": "Best for: Established products with 24+ months of history.\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" }, { "title": "2. Causal / Regression Models", "body": "Best for: Products where external factors drive demand.\n\nKey drivers to model:\n\nPrice elasticity: % demand change per 1% price change\nMarketing spend: Lag effect (typically 2-6 weeks)\nSeasonality index: Monthly coefficient vs annual average\nEconomic indicators: GDP growth, consumer confidence, industry PMI\nCompetitor actions: New entrants, price changes, promotions\n\nDemand = β₀ + β₁(Price) + β₂(Marketing) + β₃(Season) + β₄(Economic) + ε" }, { "title": "3. Judgmental / Qualitative", "body": "Best for: New products, market disruptions, limited data.\n\nMethods:\n\nDelphi method: 3+ expert rounds, anonymous, converging estimates\nSales force composite: Bottom-up from territory reps (apply 15-20% optimism correction)\nMarket research: Survey-based purchase intent (apply 30-40% intent-to-purchase conversion)\nAnalogous forecasting: Map to similar product launch curves" }, { "title": "4. Blended Forecast (Recommended)", "body": "Combine methods using confidence-weighted average:\n\nMethodWeight (Mature Product)Weight (New Product)Time Series50%10%Causal30%20%Judgmental20%70%" }, { "title": "Forecast Accuracy Metrics", "body": "MetricFormulaTargetMAPEAvg(Actual - ForecastBiasΣ(Forecast - Actual) / nNear 0Tracking SignalCumulative Error / MAD-4 to +4Weighted MAPERevenue-weighted MAPE<10% for top SKUs" }, { "title": "Monthly Cycle", "body": "Week 1: Statistical forecast generation (auto-run models)\nWeek 2: Market intelligence overlay (sales input, competitor intel)\nWeek 3: Consensus meeting — align Sales, Marketing, Ops, Finance\nWeek 4: Finalize, communicate to supply chain, track vs prior forecast" }, { "title": "Demand Segmentation (ABC-XYZ)", "body": "SegmentVolumeVariabilityApproachAXHighLowAuto-replenish, tight safety stockAYHighMediumStatistical + review quarterlyAZHighHighCollaborative planning, buffer stockBXMediumLowStatistical, periodic reviewBYMediumMediumHybrid modelBZMediumHighJudgmental + safety stockCXLowLowMin/max rulesCYLowMediumPeriodic reviewCZLowHighMake-to-order where possible" }, { "title": "Safety Stock Calculation", "body": "Safety 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" }, { "title": "Scenario Planning", "body": "For each forecast, generate three scenarios:\n\nScenarioProbabilityAssumptionsBear20%-15% to -25% vs base. Recession, market contraction, competitor disruptionBase60%Historical trends + known pipeline. Most likely outcomeBull20%+15% to +25% vs base. Market expansion, product virality, competitor exit" }, { "title": "Red Flags in Your Forecast", "body": "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" }, { "title": "Industry Benchmarks", "body": "IndustryTypical MAPEForecast HorizonKey DriverCPG/FMCG20-30%3-6 monthsPromotions, seasonalityRetail15-25%1-3 monthsTrends, weather, eventsManufacturing10-20%6-12 monthsOrders, lead timesSaaS10-15%12 monthsPipeline, churn, expansionHealthcare15-25%3-6 monthsRegulation, demographicsConstruction20-35%12-24 monthsPermits, economic cycle" }, { "title": "ROI of Better Forecasting", "body": "For a company doing $10M revenue:\n\n5% MAPE improvement → $200K-$500K inventory savings\nReduced stockouts → 2-5% revenue recovery ($200K-$500K)\nLower expediting costs → $50K-$150K savings\nBetter capacity utilization → 3-8% OpEx reduction\n\nTotal impact: $450K-$1.15M annually from a 5-point MAPE improvement." }, { "title": "Full Industry Context Packs", "body": "These frameworks scratch the surface. For complete, deployment-ready agent configurations tailored to your industry:\n\nAfrexAI Context Packs — $47 each\n\n🏗️ Construction | 🏥 Healthcare | ⚖️ Legal | 💰 Fintech\n🛒 Ecommerce | 💻 SaaS | 🏠 Real Estate | 👥 Recruitment\n🏭 Manufacturing | 📋 Professional Services\n\nAI Revenue Calculator — Find your automation ROI in 2 minutes\n\nAgent Setup Wizard — Configure your AI agent stack" }, { "title": "Bundles", "body": "Pick 3 — $97 (save 31%)\nAll 10 — $197 (save 58%)\nEverything Bundle — $247 (all packs + playbook + wizard)" } ], "body": "Demand Forecasting Framework\n\nBuild accurate demand forecasts using multiple methodologies. Combines statistical models with market intelligence for actionable predictions.\n\nWhen to Use\nQuarterly/annual demand planning\nNew product launch forecasting\nInventory optimization\nCapacity planning decisions\nBudget cycle preparation\nForecasting Methodologies\n1. Time Series Analysis\n\nBest for: Established products with 24+ months of history.\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\n2. Causal / Regression Models\n\nBest for: Products where external factors drive demand.\n\nKey drivers to model:\n\nPrice elasticity: % demand change per 1% price change\nMarketing spend: Lag effect (typically 2-6 weeks)\nSeasonality index: Monthly coefficient vs annual average\nEconomic indicators: GDP growth, consumer confidence, industry PMI\nCompetitor actions: New entrants, price changes, promotions\nDemand = β₀ + β₁(Price) + β₂(Marketing) + β₃(Season) + β₄(Economic) + ε\n\n3. Judgmental / Qualitative\n\nBest for: New products, market disruptions, limited data.\n\nMethods:\n\nDelphi method: 3+ expert rounds, anonymous, converging estimates\nSales force composite: Bottom-up from territory reps (apply 15-20% optimism correction)\nMarket research: Survey-based purchase intent (apply 30-40% intent-to-purchase conversion)\nAnalogous forecasting: Map to similar product launch curves\n4. Blended Forecast (Recommended)\n\nCombine methods using confidence-weighted average:\n\nMethod\tWeight (Mature Product)\tWeight (New Product)\nTime Series\t50%\t10%\nCausal\t30%\t20%\nJudgmental\t20%\t70%\nForecast Accuracy Metrics\nMetric\tFormula\tTarget\nMAPE\tAvg(\tActual - Forecast\nBias\tΣ(Forecast - Actual) / n\tNear 0\nTracking Signal\tCumulative Error / MAD\t-4 to +4\nWeighted MAPE\tRevenue-weighted MAPE\t<10% for top SKUs\nDemand Planning Process\nMonthly Cycle\nWeek 1: Statistical forecast generation (auto-run models)\nWeek 2: Market intelligence overlay (sales input, competitor intel)\nWeek 3: Consensus meeting — align Sales, Marketing, Ops, Finance\nWeek 4: Finalize, communicate to supply chain, track vs prior forecast\nDemand Segmentation (ABC-XYZ)\nSegment\tVolume\tVariability\tApproach\nAX\tHigh\tLow\tAuto-replenish, tight safety stock\nAY\tHigh\tMedium\tStatistical + review quarterly\nAZ\tHigh\tHigh\tCollaborative planning, buffer stock\nBX\tMedium\tLow\tStatistical, periodic review\nBY\tMedium\tMedium\tHybrid model\nBZ\tMedium\tHigh\tJudgmental + safety stock\nCX\tLow\tLow\tMin/max rules\nCY\tLow\tMedium\tPeriodic review\nCZ\tLow\tHigh\tMake-to-order where possible\nSafety Stock Calculation\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\nScenario Planning\n\nFor each forecast, generate three scenarios:\n\nScenario\tProbability\tAssumptions\nBear\t20%\t-15% to -25% vs base. Recession, market contraction, competitor disruption\nBase\t60%\tHistorical trends + known pipeline. Most likely outcome\nBull\t20%\t+15% to +25% vs base. Market expansion, product virality, competitor exit\nRed Flags in Your Forecast\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\nIndustry Benchmarks\nIndustry\tTypical MAPE\tForecast Horizon\tKey Driver\nCPG/FMCG\t20-30%\t3-6 months\tPromotions, seasonality\nRetail\t15-25%\t1-3 months\tTrends, weather, events\nManufacturing\t10-20%\t6-12 months\tOrders, lead times\nSaaS\t10-15%\t12 months\tPipeline, churn, expansion\nHealthcare\t15-25%\t3-6 months\tRegulation, demographics\nConstruction\t20-35%\t12-24 months\tPermits, economic cycle\nROI of Better Forecasting\n\nFor a company doing $10M revenue:\n\n5% MAPE improvement → $200K-$500K inventory savings\nReduced stockouts → 2-5% revenue recovery ($200K-$500K)\nLower expediting costs → $50K-$150K savings\nBetter capacity utilization → 3-8% OpEx reduction\n\nTotal impact: $450K-$1.15M annually from a 5-point MAPE improvement.\n\nFull Industry Context Packs\n\nThese frameworks scratch the surface. For complete, deployment-ready agent configurations tailored to your industry:\n\nAfrexAI Context Packs — $47 each\n\n🏗️ Construction | 🏥 Healthcare | ⚖️ Legal | 💰 Fintech\n🛒 Ecommerce | 💻 SaaS | 🏠 Real Estate | 👥 Recruitment\n🏭 Manufacturing | 📋 Professional Services\n\nAI Revenue Calculator — Find your automation ROI in 2 minutes\n\nAgent Setup Wizard — Configure your AI agent stack\n\nBundles\nPick 3 — $97 (save 31%)\nAll 10 — $197 (save 58%)\nEverything Bundle — $247 (all packs + playbook + wizard)" }, "trust": { "sourceLabel": "tencent", "provenanceUrl": "https://clawhub.ai/1kalin/afrexai-demand-forecasting", "publisherUrl": "https://clawhub.ai/1kalin/afrexai-demand-forecasting", "owner": "1kalin", "version": "1.0.0", "license": null, "verificationStatus": "Indexed source record" }, "links": { "detailUrl": "https://openagent3.xyz/skills/afrexai-demand-forecasting", "downloadUrl": "https://openagent3.xyz/downloads/afrexai-demand-forecasting", "agentUrl": "https://openagent3.xyz/skills/afrexai-demand-forecasting/agent", "manifestUrl": "https://openagent3.xyz/skills/afrexai-demand-forecasting/agent.json", "briefUrl": "https://openagent3.xyz/skills/afrexai-demand-forecasting/agent.md" } }