Enable Smarter Operational Productivity with
Productivity Through AI & Digitalization
Focuses on improving industrial productivity through IoT data integration, real-time OEE monitoring and AI-driven predictive insights for better operational performance decisions.
Why Digital Productivity Monitoring Matters in Modern Industry
Many organisations still rely on manual performance tracking and reactive maintenance approaches to manage operational efficiency. This can reduce visibility into productivity losses, equipment utilisation and improvement opportunities.
Digitalisation and AI-driven analytics enable teams to monitor performance indicators continuously and support more proactive productivity improvement decisions.
3 Key Operational Challenges
Limited Visibility into Productivity Performance
Operational efficiency metrics such as equipment utilisation and downtime are often reviewed after issues occur, reducing responsiveness to performance gaps.
Difficulty Integrating Legacy Equipment Data
Older machines may not provide structured digital data, making it challenging to monitor performance indicators consistently.
Reactive Decision-Making in Operations
Without predictive insights, improvement actions are often based on past issues rather than forward-looking performance analysis.
How This Training Addresses Those Challenges
Real-Time OEE Data Monitoring Setup
Participants learn to integrate sensors and gateway devices to capture equipment performance data and improve operational visibility.
Digital Productivity Dashboard Development
Participants visualise performance indicators through dashboards that support monitoring of utilisation trends and productivity metrics.
AI-Based Predictive Insight Application
AI techniques are introduced to analyse performance data and support forward-looking operational improvement strategies.
What Participants Will Do Over 3 Days
HRDCorp claimable | Customisable training delivery
Day 1 — Understand Productivity Concepts and Digitalisation
What participants will do:
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Explore Industry 4.0 concepts and productivity measurement approaches
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Identify operational productivity challenges and improvement opportunities
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Analyse processes and performance indicators
Outcome by end of Day 1:
Participants understand how digitalisation supports productivity monitoring and process improvement initiatives.
Day 2 — Set Up IoT-Based OEE Monitoring System
What participants will do:
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Configure Raspberry Pi and Arduino as monitoring gateways
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Integrate sensors to collect equipment performance data
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Analyse OEE data in real-time
Outcome by end of Day 2:
Participants establish a working productivity monitoring setup using IoT-enabled data collection.
Day 2 — Apply AI for Predictive Productivity Insights
What participants will do:
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Prepare and transform operational datasets
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Develop predictive models for performance insights
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Visualise predicted outputs through dashboards
Outcome by end of Day 2:
Participants implement an integrated digital productivity monitoring workflow with AI-supported insights.
This Training Is Suitable For:
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Operations and production professionals
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Maintenance and engineering personnel
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Industry 4.0 and digital transformation practitioners
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Process improvement and continuous improvement teams
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Technical educators introducing productivity analytics concepts
