IoT Case Study Database - Page 1 of 1 | IoT ONE Digital Transformation Advisors

Siemens

Mercedes-Benz and his partner GAZ chose Siemens to be its maintenance partner at a new engine plant in Yaroslavl, Russia. The new plant offers a capacity to manufacture diesel engines for the Russian market, for locally produced Sprinter Classic. In addition to engines for the local market, the Yaroslavl plant will also produce spare parts. Mercedes-Benz Russia and his partner needed a service partner in order to ensure the operation of these lines in a maintenance partnership arrangement. The challenges included coordinating the entire maintenance management operation, in particular inspections, corrective and predictive maintenance activities, and the optimizing spare parts management. Siemens developed a customized maintenance solution that includes all electronic and mechanical maintenance activities (Integral Plant Maintenance).

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Cutting-edge Predictive Analytics for HIROTEC Group

ThingWorx

Hirotec needed to ensure continuous operations and to minimize unplanned downtime in its manufacturing facilities. Unplanned downtime is costly and compromises Hirotec's ability to deliver its goods to customers on time.

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Porsche Announces Augmented Reality at Scale, Powered by Atheer

Atheer

The usual practice for car repairs at a Porsche car dealership is to have a factory representative or regional engineer visit to help diagnose the problem, and sometimes a faulty assembly is shipped back to company HQ for damage analysis. All that costs time and money for customers and dealers alike.

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Scalable Predictive Maintenance in Nissan

Senseye

With an abundance of data and insufficient skilled resources to perform analysis, Nissan were keen to expand the benefits of using data to influence maintenance. It decided to embark on a Condition Based maintenance programme to reduce production downtime by up to 50% across thousands of diverse assets. It was attracted to Senseye by its strong prognostics offering underpinned by machine learning.

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Big Data and Predictive Maintenance

Endian

Predictive maintenance refers to techniques that help determine the condition of in-service equipment in order to predict and/or optimize when maintenance should be performed. Predictive maintenance is one of the most important benefits of the Industry 4.0 revolution.

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Bilia's New Deal

Telia

While mechanical service is still needed, incre- asing digitalization of cars has led to the need for rmware and software updates to complement service and repairs. Instead of repairing cars once they have broken down, real-time data from connected cars enables predictive and proactive service. This creates new opportunities for OEMs to increase the direct relationship to the end customer. Conversely, repair shops need to adapt to these new conditions and innovate new business models to ensure future competitiveness.

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Parts Quality Gets Robotic Boost

Intel

When manufacturers, such as the world's top car makers and automotive parts suppliers, produce components in their factories, traditional QA testing has been limited to verifying the quality of random parts pulled off the line throughout the day.It was time consuming to perform the detailed tests required, and defective parts could get through despite randomized tests.If a defective part caused a recall or accident, manufacturers could face costly litigation or irreparable damage to their reputation.

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Predictive Maintenance For Connected Vehicles

Luxoft

By 2025, Transport for London will have to meet strict emission-control regulations. This means buying and operating new fleets of hybrid or fully electric, zero-emission buses. As a consequence, many Original Equipment Manufacturers (OEMs) and operators will have to develop new technologies to help them get-to-market fast enough to meet demand.

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Automotive manufacturer increases productivity for cylinder-head production by 2

IBM

Daimler AG was looking for a way to maximize the number of flawlessly produced cylinder-heads at its Stuttgart factory by making targeted process adjustments. The company also wanted to increase productivity and shorten the ramp-up phase of its complex manufacturing process.

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		Integral Plant Maintenance Siemens Mercedes-Benz and his partner GAZ chose Siemens to be its maintenance partner at a new engine plant in Yaroslavl, Russia. The new plant offers a capacity to manufacture diesel engines for the Russian market, for locally produced Sprinter Classic. In addition to engines for the local market, the Yaroslavl plant will also produce spare parts. Mercedes-Benz Russia and his partner needed a service partner in order to ensure the operation of these lines in a maintenance partnership arrangement. The challenges included coordinating the entire maintenance management operation, in particular inspections, corrective and predictive maintenance activities, and the optimizing spare parts management. Siemens developed a customized maintenance solution that includes all electronic and mechanical maintenance activities (Integral Plant Maintenance).	Download PDF
		Cutting-edge Predictive Analytics for HIROTEC Group ThingWorx Hirotec needed to ensure continuous operations and to minimize unplanned downtime in its manufacturing facilities. Unplanned downtime is costly and compromises Hirotec's ability to deliver its goods to customers on time.	Download PDF
		Porsche Announces Augmented Reality at Scale, Powered by Atheer Atheer The usual practice for car repairs at a Porsche car dealership is to have a factory representative or regional engineer visit to help diagnose the problem, and sometimes a faulty assembly is shipped back to company HQ for damage analysis. All that costs time and money for customers and dealers alike.	Download PDF
		Scalable Predictive Maintenance in Nissan Senseye With an abundance of data and insufficient skilled resources to perform analysis, Nissan were keen to expand the benefits of using data to influence maintenance. It decided to embark on a Condition Based maintenance programme to reduce production downtime by up to 50% across thousands of diverse assets. It was attracted to Senseye by its strong prognostics offering underpinned by machine learning.	Download PDF
		Big Data and Predictive Maintenance Endian Predictive maintenance refers to techniques that help determine the condition of in-service equipment in order to predict and/or optimize when maintenance should be performed. Predictive maintenance is one of the most important benefits of the Industry 4.0 revolution.	Download PDF
		Bilia's New Deal Telia While mechanical service is still needed, incre- asing digitalization of cars has led to the need for rmware and software updates to complement service and repairs. Instead of repairing cars once they have broken down, real-time data from connected cars enables predictive and proactive service. This creates new opportunities for OEMs to increase the direct relationship to the end customer. Conversely, repair shops need to adapt to these new conditions and innovate new business models to ensure future competitiveness.	Download PDF
		Parts Quality Gets Robotic Boost Intel When manufacturers, such as the world's top car makers and automotive parts suppliers, produce components in their factories, traditional QA testing has been limited to verifying the quality of random parts pulled off the line throughout the day.It was time consuming to perform the detailed tests required, and defective parts could get through despite randomized tests.If a defective part caused a recall or accident, manufacturers could face costly litigation or irreparable damage to their reputation.	Download PDF
		Predictive Maintenance For Connected Vehicles Luxoft By 2025, Transport for London will have to meet strict emission-control regulations. This means buying and operating new fleets of hybrid or fully electric, zero-emission buses. As a consequence, many Original Equipment Manufacturers (OEMs) and operators will have to develop new technologies to help them get-to-market fast enough to meet demand.	Download PDF
		Automotive manufacturer increases productivity for cylinder-head production by 2 IBM Daimler AG was looking for a way to maximize the number of flawlessly produced cylinder-heads at its Stuttgart factory by making targeted process adjustments. The company also wanted to increase productivity and shorten the ramp-up phase of its complex manufacturing process.	Download PDF

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