Data-Driven Predictive Maintenance and Optimal Plan
About This Course
This non-WSQ course is designed for manufacturing, operations, and engineering professionals who want to apply data-driven, artificial intelligence (AI), and data-mining and modelling techniques to improve predictive quality, predictive maintenance and shopfloor optimisation. It is intended for intermediate to advanced learners with basic exposure to manufacturing operations and data analysis.
The course guides learners through the full lifecycle of a data- and AI-enabled Proof-of-Concept (PoC), from problem framing and data definition to predictive modelling, evaluation, and optimisation. Learners will develop practical capabilities in data mining and modelling to identify trends and patterns, build predictive models, and support forecasting and decision-making using production, equipment, and quality data.
By the end of the course, learners will be able to design and justify AI-enabled predictive shopfloor strategies and optimisation plans, improving yield, equipment availability, reliability, and operational performance in manufacturing environments.
What You'll Learn
- Analyse manufacturing shopfloor issues and define a data-driven, AI-enabled Proof-of-Concept (PoC) by articulating problem statements, selecting suitable AI approaches, and establishing clear objectives and expected impacts.
- Define predictive modelling scope by identifying output improvement targets, influential input variables, and appropriate data sources required for data-mining and AI-based analysis.
- Prepare manufacturing datasets for data-mining, modelling, and AI applications by collecting relevant quality, equipment, and process data, transforming datasets into model-ready formats, and applying clustering and feature selection techniques to identify trends and patterns.
- Develop and apply predictive models by building regression, machine learning and AI models to support predictive analytics including predictive quality, predictive maintenance, shopfloor performance analytics and process optimization.
- Evaluate model performance and translate PoC outcomes into predictive shopfloor decisions by selecting suitable models for process optimisation and smart Design of Experiments (DOE), and formulating recommendations for next-phase implementation.
Entry Requirements
Diploma with relevant experience or degree