Introduction

This content is currently WIP. Diagrams, content, and structure are subject to change.

Introduction

The C3 AI Demand Forecasting application helps organizations predict future demand for products and services with high accuracy. By leveraging time series analysis, machine learning algorithms, and external factors, the application enables businesses to optimize inventory, reduce costs, and improve customer satisfaction through better planning and resource allocation.

Key Features in Version 8.7

• Improved Forecasting Algorithms: Enhanced ML models for accurate predictions
• Hierarchical Forecasting: Predictions at different product hierarchy levels
• External Factor Analysis: Incorporate key external variables into forecasts
• Basic What-If Analysis: Test simple scenarios and their impact on demand
• Model Comparison: Compare performance of different forecasting models
• Forecast Insights: Basic insights into forecast drivers
• Standard Dashboards: Visualizations and standard reports

Key Demand Forecasting Terms

Term	Definition
Time Series	Sequential data points collected over time intervals
Seasonality	Regular and predictable patterns that repeat over time
Forecast Horizon	Future time period for which predictions are made
Forecast Accuracy	Measure of how close predictions are to actual values
External Factors	Variables outside the historical demand that influence future demand
Demand Drivers	Key factors that significantly impact demand patterns
Forecast Adjustment	Manual or automated modifications to statistical forecasts

Getting Started

To get started with the C3 AI Demand Forecasting application, see the following sections:

• Data Model Overview
• Setting Up a Test App
• Application Logic