Senseye > Case Studies > Scalable Predictive Maintenance in INSEE

Scalable Predictive Maintenance in INSEE

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 Scalable Predictive Maintenance in INSEE - IoT ONE Case Study
Technology Category
  • Analytics & Modeling - Predictive Analytics
  • Functional Applications - Computerized Maintenance Management Systems (CMMS)
  • Infrastructure as a Service (IaaS)
Applicable Industries
  • Construction & Infrastructure
Applicable Functions
  • Maintenance
Use Cases
  • Predictive Maintenance
The Challenge

SCCC had committed to running a showcase Digital Factory for the ASEAN region and had already invested heavily in smart factory equipment and sensors. They required a predictive maintenance system that would leverage their existing investments and integrate with their SAP PM maintenance system.

The Customer
About The Customer
INSEE Cement, also known as Siam City Cement Company (SCCC), is a leading cement manufacturer in Southeast Asia. It manufactures INSEE branded cement, Ready-Mix Concrete, construction aggregates and solutions to serve a widespread regional market that inc
The Solution

Working closely with existing suppliers, Senseye integrated with SCCC’s SAP PM system, allowing for an automated detection, diagnostic and prognostic system across all factory assets. The complete Computerized Maintenance Management System (CMMS) integration ensured a ‘closed loop’ learning system, requiring minimal training for end-users and improving maintenance accuracy

Operational Impact
  • [Process Optimization - Predictive Maintenance]

    Automated learning based on closed work-orders from SAP PM

  • [Data Management - Data Availability]

    Automated work-order creation based on prognostic insights

Quantitative Benefit
  • Expansion in progress to 10+ sites.

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