A sophisticated analytical framework for generating accurate demand predictions using a blend of statistical time series, causal regression, and qualitative methodologies.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install afrexai-demand-forecasting
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install afrexai-demand-forecasting using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is Demand Forecasting Framework?
The Demand Forecasting Framework is a technical system designed to provide organizations with actionable predictions by synthesizing historical data with market intelligence. As a vital part of the Openclaw Skills collection, it allows developers to build agents that handle complex planning cycles, from inventory optimization to capacity decision-making. The framework moves beyond basic projections by incorporating price elasticity, marketing spend lags, and economic indicators into its modeling logic.
By implementing this skill within the Openclaw Skills ecosystem, businesses can bridge the gap between raw data and consensus-based planning. It provides the mathematical rigor required for high-stakes financial forecasting while maintaining the flexibility to account for human judgment in new product launches or market disruptions.
Demand Forecasting Framework Use Cases
- Planning quarterly and annual demand cycles for enterprise-level resource allocation.
- Launching new products where historical data is limited and analogous forecasting is required.
- Optimizing inventory levels through ABC-XYZ segmentation and safety stock calculations.
- Aligning cross-functional teams in Sales, Marketing, and Finance during monthly consensus cycles.
- Managing supply chain risks by generating Bear, Base, and Bull market scenarios.
How Demand Forecasting Framework Works
- Generate a baseline statistical forecast using time series analysis such as Exponential Smoothing or Weighted Moving Averages.
- Apply causal regression models to account for external drivers like price elasticity, marketing spend, and macro-economic trends.
- Integrate qualitative inputs via methods like the Delphi technique or sales force composites to refine the data with human intelligence.
- Perform demand segmentation using the ABC-XYZ matrix to determine the optimal replenishment strategy for different SKU categories.
- Calculate safety stock requirements based on desired service levels and historical demand variability.
Demand Forecasting Framework Setup
To deploy this logic within your agentic workflows, define your forecasting parameters and historical data paths. Ensure your environment supports the Openclaw Skills standards for data processing.
# Structure your forecasting directories
mkdir -p ./demand-framework/{history,metrics,scenarios}
# Configure your service level factors (e.g., 95% = 1.65)
echo '{"service_level": 1.65, "alpha": 0.3}' > forecast_config.json
Demand Forecasting Framework Data Schema & Taxonomy
The framework utilizes a structured data schema to track performance and categorical importance across the product lifecycle.
| Component | Format | Key Attributes |
|---|---|---|
| Historical Demand | Time-Series Table | Date, SKU, Actual_Qty, Price, Promo_Spend |
| Segmentation | Matrix | Volume (A/B/C), Variability (X/Y/Z), Strategy |
| Accuracy Metrics | Dashboard | MAPE, Bias, Tracking Signal, Weighted MAPE |
| Scenarios | Probabilistic Models | Bear (20%), Base (60%), Bull (20%) |
Demand Forecasting Framework Advanced Features
- Blended forecasting weights that automatically shift based on whether a product is mature or new.
- Dynamic safety stock calculations that adjust based on lead time volatility and service level targets.
- Automated red flag detection for identifying persistent forecast bias or tracking signal errors.
- Revenue-weighted MAPE tracking to focus accuracy efforts on high-value SKUs within the Openclaw Skills environment.
- Integration-ready hooks for feeding forecast outputs directly into procurement or manufacturing agents.
SKILL.md
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