Real-time demand signal integration from POS, channel data, and external signals for short-term forecast enhancement
Integrates real-time demand signals from multiple sources to enhance short-term forecasting accuracy.
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The Demand Sensing Integrator captures and processes real-time demand signals from multiple sources including point-of-sale data, channel inventory, weather patterns, social media sentiment, and economic indicators. It enables short-term forecast enhancement by detecting demand pattern changes faster than traditional forecasting methods.
sensing_request:
signal_sources:
pos_data: object # Point-of-sale feeds
channel_inventory: object # Inventory by channel
weather_data: object # Weather forecasts/actuals
social_signals: object # Social media data
economic_indicators: object # Economic data feeds
baseline_forecast: object # Current forecast to adjust
sensing_horizon: integer # Days/weeks to sense
sensitivity_thresholds: object # Signal detection thresholds
sensing_output:
adjusted_forecast: object
- period: string
baseline: float
sensed_adjustment: float
final_forecast: float
signal_contributions: object
detected_signals: array
- signal_type: string
magnitude: float
confidence: float
source: string
recommendations: array
Input: Daily POS data from retail channels
Process: Compare actual sales velocity to forecast, detect deviations
Output: Adjusted near-term forecast with POS-based corrections
Input: 10-day weather forecast + historical weather-demand correlation
Process: Calculate weather impact on category demand
Output: Weather-adjusted demand forecast by location
Input: Social media mentions, review sentiment trends
Process: Correlate sentiment changes with demand patterns
Output: Sentiment-influenced demand adjustments
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