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200 Manufacturing Companies Depend on Cloudera Products and Services

To transform data management strategies

Cloudera, Inc. announces that over 200 of manufacturing companies depend on its products and services to transform their data management strategies.

Komatsu Mining Corp., formerly Joy Global, helps its customers optimize mine production using an industrial IoT analytics platform powered by Cloudera Enterprise and Azure.

According to IDC, there will be an estimated 41.6 billion connected IoT devices generating 79.4ZB of data in 2025.

For manufacturing, this explosion of connected devices is helping the industry step into a new era of innovation and automation. The opportunity for the manufacturing industry lies in the implementation of an automated, real-time, two-way information and control loop that will increase productivity and quality of customer services.

Given the complexity, variety, and volume of IoT data, organizations must rethink the way they approach data usage and analytics to achieve faster results and streamline processes.

Manufacturing companies require data-driven insights to not only meet their bottom line but also to stay competitive and improve efficiency,” said Michael Ger, MD, manufacturing and automotive, Cloudera. “Our technology allows these companies to build new services such as predictive maintenance, operations optimization, and supply chain expansion. We are excited to help usher in the next era of manufacturing.

With automation and data-driven solutions, manufacturing firms like Komatsu, Zoomlion Heavy Industry Science & Technology Co., Ltd., and Faurecia can glean insights from millions of IoT devices to make impactful business decisions in real-time.

Komatsu Mining is a mining equipment and services provider specializing in solutions for the excavation of energy, industrial and hard rock minerals. It partnered with Cloudera and Microsoft to create a cloud-based IoT analytics platform that provides scalability, performance, and flexibility to support service teams. Because its engineering staff can also access and analyze the data, they gain valuable insight to help them improve current products and design the next gen of mining equipment.

Cloudera on Azure freed us from making decisions based in part on what our infrastructure could support and allowed us to make decisions solely based on customer needs,” said Shawn Terry, lead architect, Komatsu. “We can now scale and grow incrementally at a reasonable cost. This allows us to expand our user base, delivering services faster and better.

Zoomlion is China’s largest, and the world’s sixth largest, construction machinery enterprise. The company manufactures high-tech equipment, such as construction and agricultural machinery that creates value for customers in more than 100 countries on six continents. With Cloudera’s big data platform, its service cost has dropped by 30%, and its spare parts turnover rate has increased by 20%. The company has realized about $25 million (¥160 million) annually in new profit as a result of improved efficiency of working sites.

We believe Cloudera’s approach can help Zoomlion more effectively integrate and securely manage data to achieve more in-depth data mining and analysis,” said Dr. Zhou Zhizhong, chief architect, Zoomlion.

Faurecia is a tier-one automotive supplier specializing in automotive seating, interior systems, and emission control technologies. The company has more than 300 sites in 35 countries and serves some of the biggest automotive manufacturers, including Volkswagen, Ford, Renault-Nissan, and the PSA Group. Working with Cloudera, it created an enterprise data hub for IoT that brings together and analyzes data from a variety of sources, including thousands of machines and millions of sensors, to help drive predictive maintenance and improve product quality.

The move to IoT-enabled predictive maintenance has helped Faurecia reduce unplanned production downtime and maintenance costs. It has also been able to improve product quality and move closer to its goal of zero defects.

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