Babysitter demand-sensing-integrator

Real-time demand signal integration from POS, channel data, and external signals for short-term forecast enhancement

install

source · Clone the upstream repo

git clone https://github.com/a5c-ai/babysitter

Claude Code · Install into ~/.claude/skills/

T=$(mktemp -d) && git clone --depth=1 https://github.com/a5c-ai/babysitter "$T" && mkdir -p ~/.claude/skills && cp -r "$T/library/specializations/domains/business/supply-chain/skills/demand-sensing-integrator" ~/.claude/skills/a5c-ai-babysitter-demand-sensing-integrator-bbecc3 && rm -rf "$T"

manifest: library/specializations/domains/business/supply-chain/skills/demand-sensing-integrator/SKILL.md

Demand Sensing Integrator

Overview

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.

Capabilities

POS Data Ingestion: Real-time point-of-sale data collection and cleansing
Channel Inventory Visibility: Multi-channel inventory position integration
Weather Impact Correlation: Weather-driven demand adjustments
Social Media Sentiment Analysis: Consumer sentiment signal extraction
Economic Indicator Integration: Macro-economic factor incorporation
Market Intelligence Feeds: Competitor and market signal processing
Near-Term Demand Adjustment: Short-horizon forecast corrections
Signal-to-Noise Filtering: Distinguish meaningful signals from noise

Input Schema

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

Output Schema

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

Usage

Real-Time POS Integration

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

Weather-Driven Adjustment

Input: 10-day weather forecast + historical weather-demand correlation
Process: Calculate weather impact on category demand
Output: Weather-adjusted demand forecast by location

Sentiment-Based Demand Signal

Input: Social media mentions, review sentiment trends
Process: Correlate sentiment changes with demand patterns
Output: Sentiment-influenced demand adjustments

Integration Points

Data Pipelines: Apache Kafka, real-time streaming platforms
External APIs: Weather services, social media APIs, economic data providers
Planning Systems: Integration with demand planning platforms
Tools/Libraries: Stream processing frameworks, NLP libraries

Process Dependencies

Demand Forecasting and Planning
Sales and Operations Planning (S&OP)
Supply Chain Disruption Response

Best Practices

Establish clear signal latency requirements
Implement robust data quality checks on incoming signals
Calibrate signal weights based on historical accuracy
Monitor signal source reliability continuously
Balance responsiveness with forecast stability
Document signal sources and transformation logic