---
name: demand-forecasting-engine-1b5764f2f5e2
description: AI-powered demand prediction skill using historical data, market signals, and external factors for improved forecast accuracy
allowed-tools:
  - Read
  - Write
  - Glob
  - Grep
  - Bash
  - WebFetch
metadata:
  specialization: logistics
  domain: business
  category: inventory
  priority: high
  shared-candidate: true
graph:
  domains: [domain:logistics]
  skillAreas: [skill-area:procurement-management, skill-area:statistical-analysis, skill-area:data-analytics]
  roles: [role:supply-chain-analyst, role:operations-analyst, role:data-analyst]
---

# 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

```yaml
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: []
```

## Integration Points

- Enterprise Resource Planning (ERP) Systems
- Demand Planning Systems
- Inventory Management Systems
- Point of Sale (POS) Systems
- External Data Providers

## Performance Metrics

- Forecast accuracy (MAPE)
- Forecast bias
- Tracking signal
- Value-added improvement
- Forecast coverage


---
Source attribution: a5c-ai
Original project: https://github.com/a5c-ai/babysitter
License: MIT
License information: https://opensource.org/license/mit
Original upstream instructions; downloaded from Mega AI Hub.

## Upstream license notice

MIT License

Copyright (c) 2026 Gabriel Moreira

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.



# Mega AI Hub resource metadata

Creator / publisher: a5c-ai
Original project: https://github.com/a5c-ai/babysitter
Attribution basis: Upstream repository explicitly recorded in package mirror path

Fields of work: Data & Analytics, Marketing, Operations, Product Management, Sales
Roles: Business Development, Data Analyst, Marketing Analyst, Operations Manager, Product Manager
Specialties: API, Backend
Tools: Prophet, Pytorch, Tensorflow, Scikit Learn, Statsmodels
Country applicability: Country not specified
Classification basis: Source catalog labels
Instruction SHA-256: 2906af0ecd1062a57e87ec59e3f31b341abe9d3e336c0ed27b67d376d4a230aa