AIM-Ready: Predictive Maintenance and Shopfloor Analytics
About This Course
This intermediate-level course equips manufacturing professionals to assess and plan the adoption of predictive shopfloor analytics in manufacturing environments.
Participants will learn to identify suitable use cases across predictive maintenance, automated operational monitoring, quality monitoring and process performance improvement. They will translate operational pain points into clear problem statements, measurable key performance indicators and practical implementation requirements.
Through case studies, guided discussions, a readiness assessment and project-based learning, participants will assess shopfloor data readiness, Internet of Things and sensor connectivity, data-mining and modelling requirements, systems integration, dashboarding, business value and implementation risks. They will also develop an implementation roadmap and adoption proposal for a selected predictive shopfloor use case.
The course is relevant to manufacturing leaders, operations managers, maintenance managers, quality engineers, process engineers, automation engineers, IT/OT leads, digital transformation teams and project owners involved in improving equipment reliability, operational visibility, productivity, quality and process performance. Prior coding or machine learning experience is not required, but participants should have basic knowledge of manufacturing operations, maintenance, quality, automation or digital transformation.
What You'll Learn
- Analyse predictive shopfloor opportunities across maintenance, quality, production and operations use cases.
- Frame operational challenges into AI-ready use cases with clear problem statements, performance baselines, target outcomes and measurable key performance indicators.
- Assess shopfloor data readiness by evaluating data sources, quality, volume, connectivity, labelling needs and historical records for predictive analytics.
- Evaluate Internet of Things and sensor connectivity requirements for machine data capture, equipment signals and shopfloor data collection.
- Assess systems integration requirements across machine, maintenance, quality and enterprise systems to support predictive shopfloor analytics.
- Compare analytics approaches such as condition monitoring, anomaly detection, trend analysis, data-mining and predictive modelling for manufacturing use cases.
- Evaluate business value and adoption risks by considering productivity, downtime, quality, maintenance, implementation effort and operational constraints.
- Develop an implementation roadmap and adoption proposal covering pilot scope, timeline, roles, validation approach, risks and next steps.
Entry Requirements
Min. Diploma