Predictive operations for streamlined logistics
Unplanned outages can significantly disrupt production and cause customer dissatisfaction. They send costs soaring and bruise reputations – not only in the manufacturing industry, but in logistics, and passenger and freight transportation. In Germany, for example, approximately a quarter of all trains arrive behind schedule. This is a major inconvenience for passengers and freight customers. Moreover, delays in extremely tight schedules and crowded networks have a ripple effect, affecting timetables throughout the entire system. Studies show that more than a third of initial delays stem from defects in rolling stock and infrastructure components; in other words, they arise from unexpected faults in metros, trains, trucks, and buses.
Predictive maintenance: targeted servicing and monitoring
Against this background, transportation companies are looking for new and improved ways to reduce downtime, and make best possible use of available resources. Regularly scheduled maintenance, where equipment is serviced within pre-defined windows, has its limitations. It is geared to typical experience, with a generous safety margin, not to actual wear and tear. One effective answer is preventative maintenance – the monitoring of assets, and targeted servicing in response to detected anomalies.
Predictive Maintenance Solution (PMS)
The Locomotive Predictive Maintenance Solution enables railway companies to anticipate possible breakdowns at an early stage. This helps companies to pre-plan maintenance on your equipment with minimal interruptions of your regular schedule.
Prevention is better than expensive cure
Predictive maintenance leverages sensors on vehicles and other equipment. These sensors capture data on the condition of individual components, and transmit this information wirelessly to a central software system. The software analyzes the data in near real time, and displays the results on an online dashboard. As a result, any deterioration can be pinpointed and remediated before it causes unplanned downtime. Predictive maintenance enables better, more cost-effective planning of regular servicing and repairs. For example, Union Pacific, a major USA railroad corporation with nearly 8,000 locomotives in operation, estimates that predictive maintenance saves it approximately 100 million dollars per year.
Predictive analytics can be applied to many manufacturing and logistics processes – in fact, any scenario where large volumes of data on the condition of individual components are continuously captured. This data can be harnessed to identify trends and predict events – and to streamline and refine operational processes in real time. To this end, T-Systems’ end-to-end offering includes solutions for sensors, connectivity, data science, and cloud-based big data platforms, plus extensive industry-specific expertise.
Big data analytics via a secure cloud
T-Systems deploys powerful SAP HANA and Hadoop technologies for data analytics. Where required, these services can be provisioned from the cloud. As a result, businesses do need to shoulder the costs of building and operating their own data centers or software infrastructure. This is particularly attractive where load fluctuates markedly, requiring highly scalable resources. In addition, all T-Systems’ cloud-based services for predictive maintenance and predictive analytics are hosted in certified, highly secure German data centers in line with the nation’s strict data protection standards.