Requirements
- Target platform
- OpenClaw
- Install method
- Manual import
- Extraction
- Extract archive
- Prerequisites
- OpenClaw
- Primary doc
- SKILL.md
Tencent SkillHub · Data Analysis
Demand Forecasting Framework
Build demand forecasts using time series, causal models, and expert judgment for planning, inventory, and capacity decisions with scenario analysis.
skill openclawclawhub Free
Build demand forecasts using time series, causal models, and expert judgment for planning, inventory, and capacity decisions with scenario analysis.
⬇ 0 downloads ★ 0 stars Unverified but indexed
Install for OpenClaw
Quick setup
- Download the package from Yavira.
- Extract the archive and review SKILL.md first.
- Import or place the package into your OpenClaw setup.
Package facts
- Download mode
- Yavira redirect
- Package format
- ZIP package
- Source platform
- Tencent SkillHub
- What's included
- README.md, SKILL.md
Validation
- Use the Yavira download entry.
- Review SKILL.md after the package is downloaded.
- Confirm the extracted package contains the expected setup assets.
Install with your agent
Agent handoff
Hand the extracted package to your coding agent with a concrete install brief instead of figuring it out manually.
- 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.
New install
I downloaded a skill package from Yavira. 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.
Upgrade existing
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.
Trust & source
Release facts
- Source
- Tencent SkillHub
- Verification
- Indexed source record
- Version
- 1.0.0
Provenance
- Publisher
- 1kalin
- Source page
- View original listing
- Canonical URL
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Documentation
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
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: MethodWeight (Mature Product)Weight (New Product)Time Series50%10%Causal30%20%Judgmental20%70%
Forecast Accuracy Metrics
MetricFormulaTargetMAPEAvg(Actual - ForecastBiasΣ(Forecast - Actual) / nNear 0Tracking SignalCumulative Error / MAD-4 to +4Weighted MAPERevenue-weighted MAPE<10% for top SKUs
Monthly Cycle
Week 1: Statistical forecast generation (auto-run models) Week 2: Market intelligence overlay (sales input, competitor intel) Week 3: Consensus meeting — align Sales, Marketing, Ops, Finance Week 4: Finalize, communicate to supply chain, track vs prior forecast
Demand Segmentation (ABC-XYZ)
SegmentVolumeVariabilityApproachAXHighLowAuto-replenish, tight safety stockAYHighMediumStatistical + review quarterlyAZHighHighCollaborative planning, buffer stockBXMediumLowStatistical, periodic reviewBYMediumMediumHybrid modelBZMediumHighJudgmental + safety stockCXLowLowMin/max rulesCYLowMediumPeriodic reviewCZLowHighMake-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: ScenarioProbabilityAssumptionsBear20%-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
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
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
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.
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Category context
Data access, storage, extraction, analysis, reporting, and insight generation.
Source: Tencent SkillHub
Largest current source with strong distribution and engagement signals.
Package contents
Included in package
2 Docs
- SKILL.md
- README.md