demand-forecasting-engine

AI-powered demand prediction skill using historical data, market signals, and external factors for improved forecast accuracy

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技能内容

Demand Forecasting Engine

Overview

The Demand Forecasting Engine is an AI-powered skill that generates accurate demand predictions using historical data, market signals, and external factors. It employs multiple forecasting methods including time series analysis and machine learning models to improve forecast accuracy and support inventory planning decisions.

Capabilities

Time Series Forecasting (ARIMA, Prophet, etc.): Apply classical and modern time series methods for demand prediction
Machine Learning Demand Models: Use ML algorithms to capture complex demand patterns and relationships
Promotional Lift Modeling: Incorporate promotional calendar and estimate promotional demand lift
External Factor Integration (Weather, Events): Include weather, events, and economic indicators in forecasts
Forecast Accuracy Measurement: Track and report forecast accuracy using standard metrics (MAPE, bias, etc.)
Demand Sensing with POS Data: Incorporate point-of-sale data for short-term demand adjustments
New Product Forecasting: Generate forecasts for new products using analogous items or market research

Tools and Libraries

Prophet
statsmodels
scikit-learn
TensorFlow/PyTorch
Demand Planning Platforms

Used By Processes

Demand Forecasting
Reorder Point Calculation
ABC-XYZ Analysis

Usage

skill: demand-forecasting-engine
inputs:
  item:
    sku: "SKU001"
    category: "Consumer Electronics"
    lifecycle_stage: "mature"
  historical_data:
    frequency: "weekly"
    periods: 104  # 2 years
    data: [...]  # Weekly demand values
  external_factors:
    include_seasonality: true
    include_promotions: true
    promotion_calendar:
      - date: "2026-02-14"
        type: "price_reduction"
        expected_lift: 1.5
    include_weather: false
  forecast_parameters:
    horizon_periods: 12
    confidence_level: 95
    methods: ["prophet", "arima", "ml_ensemble"]
outputs:
  forecasts:
    method: "ml_ensemble"  # Best performing method
    predictions:
      - period: "2026-W05"
        forecast: 1250
        lower_bound: 1125
        upper_bound: 1375
      - period: "2026-W06"
        forecast: 1180
        lower_bound: 1062
        upper_bound: 1298
  accuracy_metrics:
    historical_mape: 8.5
    historical_bias: -2.1
    tracking_signal: 0.3
  method_comparison:
    prophet: { mape: 9.2, bias: -1.5 }
    arima: { mape: 10.1, bias: 2.3 }
    ml_ensemble: { mape: 8.5, bias: -2.1 }
  recommendations:
    best_method: "ml_ensemble"
    forecast_review_flag: false
    anomalies_detected: []