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Predictive Maintenance Automation in Manufacturing

Minimizing Downtime with AI Monitoring Tools

Predictive Maintenance Automation in Manufacturing

Regal Swiss supported the integration of AI for predictive maintenance in small Asian manufacturing plants, modernizing legacy equipment monitoring. Our process involved sensor data analysis, using Zapier to automate alerts from IoT devices and OpenAI to predict failures via historical logs. N8N linked maintenance schedules with production ERP for seamless workflows. We incorporated downtime simulations and compliance with Asian safety standards. Training sessions equipped technicians with dashboard usage. This proactive approach extends asset life and reduces interruptions. Ideal for legacy manufacturers, our implementations lower repair costs, enhance safety, and optimize production, helping Asian firms maintain competitive output in labor-intensive industries.

Project Info

Client:

SDICOM

Technologies:

Zapier, OpenAI, N8N, IoT, Predictive Maintenance, ERP

Duration:

Predictive maintenance automation implementation

Team:

Regal Swiss manufacturing automation and IoT integration team

Challenges

Analyzing sensor data from outdated manufacturing equipment

Predicting failures accurately in high-vibration environments

Integrating with legacy ERP for maintenance scheduling

Complying with Asian industrial safety regulations

Solutions

Utilized OpenAI for log-based failure predictions

Deployed Zapier for IoT alert automations

Configured N8N for ERP-maintenance integrations

Incorporated safety-compliant simulation modules

Results & Impact

Decreased unplanned downtime by 45% proactively

Extended equipment lifespan by 30% on average

Reduced maintenance costs by 25% through efficiency

Improved safety compliance scores significantly

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frontdesk@regalswiss.com