Software AG Case Studies PREDICTIVE MAINTENANCE

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	PREDICTIVE MAINTENANCE Software AG

PREDICTIVE MAINTENANCE

Software AG

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
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. Read More
About Customer	Equipment manufacturers Read More
Customer Name	Undisclosed Read More
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. Read More Log in to view content
Data Collected	Asset Performance, Machine Performance, Machine Utilization Rate, Total Effective Equipment Performance (TEEP) Read More Log in to view content
Contents

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

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 Customer

Equipment manufacturers

Customer Name

Undisclosed

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)

Impact #1	[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.
Impact #2	[Data Management - Data Analysis] Flexible use of operating data in the context of process capacities and customer requirements.

Impact #1

[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.

Impact #2

[Data Management - Data Analysis]
Flexible use of operating data in the context of process capacities and customer requirements.

Benefit #1	Reduced technician costs and improved service levels.
Benefit #2	Deliver more competitive service contracts at a lower cost.
Benefit #3	Increased real-time visibility into field service technician tasks and performance.

Benefit #1

Reduced technician costs and improved service levels.

Benefit #2

Deliver more competitive service contracts at a lower cost.

Benefit #3

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

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Overview

PREDICTIVE MAINTENANCE

Operational Impact

Quantitative Benefit