Predictive Maintenance

Unplanned downtime remains one of the most costly challenges in equipment performance, affecting productivity, service costs and time to market. According to Deloitte, reactive maintenance leaves manufacturers choosing between two costly extremes:

• Risking significant damage by running a machine until it fails
• Incurring significant costs by replacing components too early

Predictive maintenance, also known as PdM, solves this problem. Leveraging equipment lifecycle management, the Internet of Things (IoT) data and predictive analytics upholds optimum equipment performance in manufacturing. The adoption of PdM strategies is a proactive approach that empowers manufacturers to service equipment based on actual usage and condition, rather than relying on fixed schedules or planned maintenance.

The value of predictive maintenance increases through the virtual twin experience.

The Installed Base Virtual Twin (IVBT) models an asset in operation, simulates what-if scenarios and detects potential anomalies earlier. It equips manufacturers with a competitive edge to validate corrective actions before potential issues related to asset failures occur. Through a combination of artificial intelligence (AI) and data science, predictive maintenance becomes a strategic ability enabling  Equipment as a Service (EaaS) models .

A unified platform unlocks significant results by integrating data across assets, strengthening collaboration within teams and accelerating decision-making processes throughout the organization.

As organizations differ in maintenance maturity, these proven steps ensure a structured, measurable and resilient predictive maintenance rollout within an organization.

• Step 1: Determine business KPIs
  Organizations must prioritize areas for improvement to guide predictive maintenance strategies. Clear KPIs ensure informed decisions, better cost-benefit tradeoffs and alignment across an organization.
• Step 2: Identify and validate data quality
  Predictive analytics rely on real-time data from IoT sensors, condition monitoring tools and vibration analysis, which are integrated on a single unified platform to minimize unplanned downtime.
• Step 3: Extract actionable insights
  A unified platform consolidates predictive analytics data and AI-driven insights, enabling maintenance teams to detect patterns, classify anomalies and make faster maintenance decisions.
• Step 4: Leverage virtual twin technology
  Virtual twin technology enables organizations to simulate what-if scenarios, assess risks and understand potential issues before production. This improves maintenance planning and asset utilization.
• Step 5: Optimize maintenance decisions
  Use predictive insights to schedule maintenance intelligently and adapt to unexpected changes. This ensures lower maintenance costs, improved productivity and stronger lifecycle performance.

Manufacturers achieve the strongest predictive maintenance outcomes when supported by a resilient, integrated digital platform that offers a suite of collaborative solutions to optimize workflows between multidisciplinary teams.

The 3DEXPERIENCE platform acts as a single source of truth that scales with businesses and achieves positive returns on investment (ROI), regardless of maintenance maturity levels, enabling: 

• Greater maintenance efficiency through data-driven insights
• Higher customer satisfaction with reliable asset performance
• Seamless collaboration through shared visibility of asset health