Quality assurance (QA) is the set of processes designed to stop defects happening in the first place, as distinct from quality control, which is about catching them after they have.

Production planning decides what will be made, in what quantity and by when, balancing customer demand against the material, capacity and labour you actually have available.

Predictive maintenance uses machine condition data - vibration, temperature, run hours - to service equipment just before it is likely to fail, instead of on a fixed calendar or after a breakdown.

On-premise software runs on servers you own and maintain inside your own building, rather than being hosted by the vendor, so you control the hardware, the data and the upgrade timetable.

Order management covers everything that happens to a customer order from acceptance to despatch - converting it into works orders, tracking progress through production and confirming delivery.

OEE (overall equipment effectiveness) scores how well a machine is really used by multiplying three figures: availability, performance and quality. A score of 100 per cent means no stoppages, full speed and no defects.

A non-conformance report (NCR) is the formal record raised when a product, material or process fails to meet its specification, capturing what went wrong, what was done about it and why it happened.

Machine learning is a form of AI where a system improves its predictions by finding patterns in historical data, rather than being explicitly programmed with rules for every case.

Lot traceability records which lot of material went into which products, so a supplier defect can be traced forward to every affected item and backwards from a failure to its source.