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Software AG
PREDICTIVE MAINTENANCE - Software AG Industrial IoT Case Study
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)
Equipment & Machinery
Predictive Maintenance

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.

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Equipment manufacturers
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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.

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Asset Performance, Machine Performance, Machine Utilization Rate, Total Effective Equipment Performance (TEEP)
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[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.

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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