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

Data Model and Types

Application Logic

Setup and Configuration

User Interface

Machine Learning

Introduction

Key Features in Version 8.7

Key Demand Forecasting Terms

Getting Started

​Introduction

​Key Features in Version 8.7

​Key Demand Forecasting Terms

​Getting Started

Introduction

Key Features in Version 8.7

Key Demand Forecasting Terms

Getting Started