Manufacturing AIoT Implementation

Context

A mid-size manufacturing company was experiencing frequent unplanned equipment downtime, leading to production delays and increased maintenance costs. Their reactive maintenance approach was costly and unpredictable, impacting customer delivery commitments and operational efficiency.

Constraints

Legacy equipment with limited existing instrumentation. Maintenance team skeptical of new technology approaches. Budget constraints requiring clear ROI demonstration within 12 months. Need to maintain production during implementation without disrupting operations.

Actions

• Deployed IoT sensors across critical manufacturing equipment with edge computing capabilities
• Built machine learning models for predictive maintenance using historical failure data
• Implemented real-time monitoring dashboard with automated alerting and escalation procedures
• Trained maintenance teams on new predictive workflows and decision-making processes
• Established model monitoring and retraining procedures to maintain prediction accuracy
• Created change management programme to drive adoption across operations teams

Artefacts Delivered

• AIoT architecture with sensor deployment plan and data pipeline design
• Predictive maintenance models with performance monitoring and alerting systems
• Operations dashboard with real-time equipment health and maintenance scheduling
• Training materials and workflows for maintenance teams and operators
• Model governance framework with monitoring, validation, and retraining procedures
• Change management playbook with adoption metrics and feedback mechanisms