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).

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.

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. 

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.

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. 

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.

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.

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.

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.