Equinix Case Studies IoT-Driven Predictive Maintenance: Siemens' Real-Time Data Solution

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	IoT-Driven Predictive Maintenance: Siemens' Real-Time Data Solution Equinix

IoT-Driven Predictive Maintenance: Siemens' Real-Time Data Solution

Equinix

Technology Category	Infrastructure as a Service (IaaS) - Hybrid Cloud Platform as a Service (PaaS) - Application Development Platforms
Applicable Industries	Railway & Metro Transportation
Applicable Functions	Logistics & Transportation Maintenance
Use Cases	Predictive Maintenance Vehicle-to-Infrastructure
Services	Cloud Planning, Design & Implementation Services Training
Challenge	Across Europe, billions of Euros are being invested in upgrading rail infrastructure with the aim of carrying more passengers, on more trains, more regularly, with on-time arrival, at a lower cost. Siemens, a leader in engineering solutions for the rail industry, is at the forefront of this transformation. The company uses data collected from over 300 sensors on each train, combined with historical data, to predict when components might fail. This IoT-driven approach, dubbed the 'Internet of Trains', ensures greater uptime for train operators, fewer delays for passengers, and more cost-effective maintenance. However, the challenge for Siemens lies not in the collection, but in the storage, management, and analytics of this vast variety of data. Furthermore, as an international company, Siemens must ensure that the data is stored according to local laws in the most cost-effective way possible. Read More
About Customer	Siemens AG, based in Berlin and Munich, is a technology company focused on industry, infrastructure, transport, and healthcare. The company is committed to creating more resource-efficient factories, resilient supply chains, smarter buildings and grids, cleaner and more comfortable transportation, and advanced healthcare. Siemens is a leader in engineering solutions for the rail industry, offering a wide range of systems and services for signaling, control, and communication for main line, metro, and light rail. The company is the first to provide digitalization in rail transport and to operate a special data analytics center, led by Siemens Mobility Data Services. Read More
Customer Name	Siemens AG Read More
Solution	To address these challenges, Siemens partnered with Teradata and Equinix. Teradata, a provider of business analytics solutions, data and analytics solutions, and hybrid cloud products and services, helped Siemens unite varied and multiple silos of data and extract valuable insights. Equinix, on the other hand, enabled Teradata to directly and securely interconnect to leading network and cloud providers for greater performance at a lower cost. By deploying on Platform Equinix®, Siemens re-architected its IT service management infrastructure for a digital edge by integrating digital and cloud technologies, while optimizing automation, improving reliability, and boosting performance. The Teradata Analytics Platform provides an exceptional range of analytic tools and capabilities to evaluate the combined IoT data from different perspectives in near real time. It can predict engine problems and identify failed elements that triggered the malfunction of other components as much as three days in advance of the problem. Read More Log in to view content
Contents

Technology Category

Infrastructure as a Service (IaaS) - Hybrid Cloud

Platform as a Service (PaaS) - Application Development Platforms

Applicable Industries

Railway & Metro

Transportation

Applicable Functions

Logistics & Transportation

Maintenance

Use Cases

Predictive Maintenance

Vehicle-to-Infrastructure

Services

Cloud Planning, Design & Implementation Services

Training

Challenge

Across Europe, billions of Euros are being invested in upgrading rail infrastructure with the aim of carrying more passengers, on more trains, more regularly, with on-time arrival, at a lower cost. Siemens, a leader in engineering solutions for the rail industry, is at the forefront of this transformation. The company uses data collected from over 300 sensors on each train, combined with historical data, to predict when components might fail. This IoT-driven approach, dubbed the 'Internet of Trains', ensures greater uptime for train operators, fewer delays for passengers, and more cost-effective maintenance. However, the challenge for Siemens lies not in the collection, but in the storage, management, and analytics of this vast variety of data. Furthermore, as an international company, Siemens must ensure that the data is stored according to local laws in the most cost-effective way possible.

About Customer

Siemens AG, based in Berlin and Munich, is a technology company focused on industry, infrastructure, transport, and healthcare. The company is committed to creating more resource-efficient factories, resilient supply chains, smarter buildings and grids, cleaner and more comfortable transportation, and advanced healthcare. Siemens is a leader in engineering solutions for the rail industry, offering a wide range of systems and services for signaling, control, and communication for main line, metro, and light rail. The company is the first to provide digitalization in rail transport and to operate a special data analytics center, led by Siemens Mobility Data Services.

Customer Name

Siemens AG

Solution

To address these challenges, Siemens partnered with Teradata and Equinix. Teradata, a provider of business analytics solutions, data and analytics solutions, and hybrid cloud products and services, helped Siemens unite varied and multiple silos of data and extract valuable insights. Equinix, on the other hand, enabled Teradata to directly and securely interconnect to leading network and cloud providers for greater performance at a lower cost. By deploying on Platform Equinix®, Siemens re-architected its IT service management infrastructure for a digital edge by integrating digital and cloud technologies, while optimizing automation, improving reliability, and boosting performance. The Teradata Analytics Platform provides an exceptional range of analytic tools and capabilities to evaluate the combined IoT data from different perspectives in near real time. It can predict engine problems and identify failed elements that triggered the malfunction of other components as much as three days in advance of the problem.

Impact #1	The partnership with Teradata and Equinix has enabled Siemens to scale its worldwide business quickly, with access to leading cloud and network partners. This continues to allow Teradata to meet local data privacy requirements and ensure regulatory compliance. The Teradata relationship with Equinix supports a long-term strategic view of the way big data customers will approach the market. Teradata research suggested that 90% of its customers would adopt a hybrid deployment by 2020 via a hybrid solution of on-premises and cloud resources, while 85% were expected to want this as a service. This solution has not only improved the reliability and performance of Siemens' rail services but also opened up new business model opportunities through predictive maintenance.

Impact #1

The partnership with Teradata and Equinix has enabled Siemens to scale its worldwide business quickly, with access to leading cloud and network partners. This continues to allow Teradata to meet local data privacy requirements and ensure regulatory compliance. The Teradata relationship with Equinix supports a long-term strategic view of the way big data customers will approach the market. Teradata research suggested that 90% of its customers would adopt a hybrid deployment by 2020 via a hybrid solution of on-premises and cloud resources, while 85% were expected to want this as a service. This solution has not only improved the reliability and performance of Siemens' rail services but also opened up new business model opportunities through predictive maintenance.

Benefit #1	Real-time data availability supports predictive maintenance planning and supply chain optimization.
Benefit #2	Service differentiators create the opportunity to leverage predictive maintenance to evolve into a new business model.
Benefit #3	Easy and flexible scaling allows for the addition of locations for data capture and scaling IT capacity up or down as required.

Benefit #1

Real-time data availability supports predictive maintenance planning and supply chain optimization.

Benefit #2

Service differentiators create the opportunity to leverage predictive maintenance to evolve into a new business model.

Benefit #3

Easy and flexible scaling allows for the addition of locations for data capture and scaling IT capacity up or down as required.

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Overview

IoT-Driven Predictive Maintenance: Siemens' Real-Time Data Solution

Operational Impact

Quantitative Benefit