Software AG > Case Studies > PREDICTIVE MAINTENANCE

PREDICTIVE MAINTENANCE

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Technology Category
  • Analytics & Modeling - Predictive Analytics
  • Analytics & Modeling - Process Analytics
  • Analytics & Modeling - Real Time Analytics
  • Application Infrastructure & Middleware - Data Exchange & Integration
  • Functional Applications - Enterprise Asset Management Systems (EAM)
  • Infrastructure as a Service (IaaS)
Applicable Industries
  • Equipment & Machinery
Applicable Functions
  • Maintenance
Use Cases
  • Predictive Maintenance
The Challenge

Manufacturers rightly focus on improving profit margins and growing revenue. Attracting new customers, selling more products and lean practices can help. However, as equipment sophistication increases, so does the ability to monitor equipment. Manufacturers can now develop revenue from maintenance services. Preventive maintenance has its advantages but to really drive uptime and maintain service levels, predictive maintenance is needed. Seamless IoT and machine sensor data integration is critical as well as a low-latency messaging backbone for scalable, fast and reliable transport. Delivering potentially large quantities of data at sub-second speeds is key to downstream activities. webMethods Integration, featuring Universal Messaging, addresses this need with an enterprise-grade service bus for connectivity, messaging, transformation and security of machine data for advanced real-time analytics.

About The Customer
Equipment manufacturers
The Solution

Software AG brings the Internet of Things (IoT), streaming analytics and process analytics into an integrated predictive maintenance solution for manufacturers. The IoT provides access to usage and status data directly from equipment. Streaming analytics combines with predictive analytics to predict machine failure. Process analytics, helps monitor and schedule field service technicians. The end result: reduced technician costs and improved service levels—enabling you to deliver more competitive service contracts at a lower cost.

Data Collected
Asset Performance, Machine Performance, Machine Utilization Rate, Total Effective Equipment Performance (TEEP)
Operational Impact
  • [Data Management - Real Time Data Analysis]
    Combined streaming and process analytics to understand changes in capacity, usage trends for both customers and service providers, obtaining diverse data types from multiple sources at speed to drive real-time analysis.
  • [Data Management - Data Analysis]
    Flexible use of operating data in the context of process capacities and customer requirements.
Quantitative Benefit
  • Reduced technician costs and improved service levels.

  • Deliver more competitive service contracts at a lower cost.

  • Increased real-time visibility into field service technician tasks and performance.

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