Today at Sapphire, I dropped into a session to hear from Urs Gehrig, Senior Consultant at Swiss Federal Railways SBB, the national railway company of Switzerland. Urs explained how Swiss Federal has successfully implemented reliability-centered maintenance (RCM) processes for its trains by empowering its workforce to access information in real time and predict potential operational problems ahead of time.
The focus of reliability-centered maintenance is, of course, reliability. On a daily basis, Swiss Federal transports 1.25 million passengers on more than 6,000 trains. Keeping these trains running in a reliable fashion is a top priority.
To get the job done, Swiss Federal powers its RCM processes with SAP Intelligent Asset Management solutions. Specialist technical knowledge is pooled across disciplines and mixed with operational context. The result is RCM analysis based on comprehensive data – and a fully justified maintenance file presented in a standardized format.
RCM, of course, is hardly a new idea in asset management. What separates Swiss Federal is an intelligence mix of asset maintenance strategies to optimize customer comfort, availability and cost reduction.
One key strategy is predictive maintenance – enabled in part by incoming diagnostic data regarding cycle, temperature, pressure, and other asset-related KPIs. Such data helps Swiss Federal transcend costly “run-to-failure” strategies with feedback loops that identify failure modes, associated risks, and countermeasures required.
Digitization and proper data management are critical for making all of this happen. After all, the more you digitize, the more data you have available, and the more you can move to predictive maintenance practices.
With SAP Intelligent Asset management, Swiss Federal has defined a vehicle structure that allows for pervasive data integration – from the vehicle fleet level down to the bill of materials for individual assets.
Based on a solid asset data foundation, Swiss Federal is also moving toward creating sophisticated digital representations (digital twins) of each asset in SAP Asset Intelligence Network (AIN) – with links to the failure-modes of parts to improve failure discovery.
With on-board vehicle monitoring systems, the company also collects conditional coupling cycle data. It then adds this data to SAP Predictive Maintenance and Service (PdMS) to build rule-sets. What are these rule sets used for? To automatically generate maintenance notifications – based on operational context – for staff to take action as required.
RCM. Predictive maintenance. Networked digital twin technology with automated maintenance notifications. Quite sophisticated! I wonder what’s coming next for Swiss Federal.
Learn more about SAP Intelligent Asset Management and register for the ARC whitepaper, Intelligent Asset Management improves asset performance.
The focus of reliability-centered maintenance is, of course, reliability. On a daily basis, Swiss Federal transports 1.25 million passengers on more than 6,000 trains. Keeping these trains running in a reliable fashion is a top priority.
To get the job done, Swiss Federal powers its RCM processes with SAP Intelligent Asset Management solutions. Specialist technical knowledge is pooled across disciplines and mixed with operational context. The result is RCM analysis based on comprehensive data – and a fully justified maintenance file presented in a standardized format.
Turning “run to failure” into “run to success”
RCM, of course, is hardly a new idea in asset management. What separates Swiss Federal is an intelligence mix of asset maintenance strategies to optimize customer comfort, availability and cost reduction.
One key strategy is predictive maintenance – enabled in part by incoming diagnostic data regarding cycle, temperature, pressure, and other asset-related KPIs. Such data helps Swiss Federal transcend costly “run-to-failure” strategies with feedback loops that identify failure modes, associated risks, and countermeasures required.
Digitization and proper data management are critical for making all of this happen. After all, the more you digitize, the more data you have available, and the more you can move to predictive maintenance practices.
With SAP Intelligent Asset management, Swiss Federal has defined a vehicle structure that allows for pervasive data integration – from the vehicle fleet level down to the bill of materials for individual assets.
Based on a solid asset data foundation, Swiss Federal is also moving toward creating sophisticated digital representations (digital twins) of each asset in SAP Asset Intelligence Network (AIN) – with links to the failure-modes of parts to improve failure discovery.
With on-board vehicle monitoring systems, the company also collects conditional coupling cycle data. It then adds this data to SAP Predictive Maintenance and Service (PdMS) to build rule-sets. What are these rule sets used for? To automatically generate maintenance notifications – based on operational context – for staff to take action as required.
RCM. Predictive maintenance. Networked digital twin technology with automated maintenance notifications. Quite sophisticated! I wonder what’s coming next for Swiss Federal.
Learn more about SAP Intelligent Asset Management and register for the ARC whitepaper, Intelligent Asset Management improves asset performance.
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