Implement a reliable and cost-effective approach to maintenance
Industries tend to invest in high-quality equipment and machinery with a long life cycle which must be managed and preserved effectively. Every company wants to remove the word “downtime” from their vocabulary and operations. However, 70% of companies still do not have visibility into when their equipment should be stopped and intervened to avoid unplanned stops.
An efficient strategy to optimize maintenance performance is to evolve the TPM model to apply data-driven methods so as to reduce downtime with a reliable and cost-effective approach.
A solution that combines best-in-class lean principles with digital tools
Sensorization and classification of every machine part’s impact on equipment reliability
Analytics and Machine Learning
Advanced Analytics and Machine Learning to predict the likelihood of failure
Computerized Maintenance Management System (CMMS) to support maintenance activities, including work orders, spare parts management, KPIs, etc.
AIDC for accessing machine information (history, handbooks) and tool tracking
Manufacturing Execution System
Manufacturing Execution System (MES) for machine data capture, alerts, and Autonomous Maintenance tasks management