About The Client
A large-scale manufacturer operating multiple production facilities across the United States and Canada, requiring intelligent asset management and maintenance optimization.
Case Context / Overview
- The client faced significant unplanned downtime across manufacturing plants due to reactive maintenance practices.
- Lack of real-time visibility into equipment health led to costly emergency repairs and production losses.
- The client sought to transition from reactive to predictive maintenance to reduce costs and improve operational reliability.
Key Solution Elements
- Implementation of an IoT-enabled Predictive Maintenance platform integrating sensor data from critical manufacturing equipment.
- Machine learning models developed to predict equipment failures before they occur, enabling proactive intervention.
- Integration with SAP Plant Maintenance (PM) for seamless work order creation and execution.
- Real-time dashboards for maintenance managers and plant operators to monitor asset health across all facilities.
Scope of Services
- IoT sensor deployment strategy and data architecture design.
- Predictive analytics model development and training using historical failure data.
- SAP PM integration for automated work order generation.
- Change management and training for maintenance teams across US and Canada facilities.
Benefits and Value Delivered
- Significant reduction in unplanned downtime across all manufacturing plants.
- Lower maintenance costs through targeted, condition-based interventions instead of scheduled replacements.
- Improved equipment lifespan and reliability through early fault detection.
- Enhanced visibility into asset health enabling better capital planning decisions.
