# Send Demand Forecasting Framework to your agent
Hand the extracted package to your coding agent with a concrete install brief instead of figuring it out manually.
## Fast path
- 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.
## Suggested prompts
### New install

```text
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

```text
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.
```
## Machine-readable fields
```json
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      "primaryActionLabel": "Download for OpenClaw",
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    "validation": {
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        "Use the Yavira download entry.",
        "Review SKILL.md after the package is downloaded.",
        "Confirm the extracted package contains the expected setup assets."
      ],
      "postInstallChecks": [
        "Confirm the extracted package includes the expected docs or setup files.",
        "Validate the skill or prompts are available in your target agent workspace.",
        "Capture any manual follow-up steps the agent could not complete."
      ]
    }
  },
  "links": {
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    "downloadUrl": "https://openagent3.xyz/downloads/afrexai-demand-forecasting",
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    "briefUrl": "https://openagent3.xyz/skills/afrexai-demand-forecasting/agent.md"
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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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## Trust
- Source: tencent
- Verification: Indexed source record
- Publisher: 1kalin
- Version: 1.0.0
## Source health
- Status: healthy
- Source download looks usable.
- Yavira can redirect you to the upstream package for this source.
- Health scope: source
- Reason: direct_download_ok
- Checked at: 2026-04-23T16:43:11.935Z
- Expires at: 2026-04-30T16:43:11.935Z
- Recommended action: Download for OpenClaw
## Links
- [Detail page](https://openagent3.xyz/skills/afrexai-demand-forecasting)
- [Send to Agent page](https://openagent3.xyz/skills/afrexai-demand-forecasting/agent)
- [JSON manifest](https://openagent3.xyz/skills/afrexai-demand-forecasting/agent.json)
- [Markdown brief](https://openagent3.xyz/skills/afrexai-demand-forecasting/agent.md)
- [Download page](https://openagent3.xyz/downloads/afrexai-demand-forecasting)