A machine-based system that can, for a given set of objectives, make predictions, recommendations, or decisions that influence real or virtual environments.

In a cement plant: AI can predict kiln instability, recommend fuel adjustments, or automate quality monitoring.

A component of an information system that applies AI technology using computational, statistical, or machine-learning techniques to produce outputs from a given set of inputs.

In a cement plant: An AI model might take in raw mix chemistry, kiln temperature, and fuel flow data, then suggest the best setpoints for stable clinker formation.

A field that combines domain expertise, programming, mathematics, and statistics to extract meaningful insights from data.

In a cement plant: Data science helps analyze trends in energy consumption, predict equipment failures, and identify optimization opportunities.

As cement plants move toward digital transformation, understanding AI and data science becomes critical. These technologies, when applied correctly, can boost efficiency, reliability, and sustainability. This glossary provides a foundation for exploring how AI-driven tools are shaping the future of manufacturing.