Manufacturing data cloud enables industry to embrace data and artificial intelligence

The manufacturing industry is leveraging new data and artificial intelligence technologies to improve efficiency. As artificial intelligence expands its reach into manufacturing, companies like Nvidia and Databricks have recently released several purpose-built products to help manufacturing companies collect and process large amounts of data from everything from physical operations to supply chains.

Snowflake is also getting in on the action, debuting its manufacturing data cloud. The new offering will enable companies in the automotive, technology, energy and industrial sectors to unlock the value of siled industrial data by leveraging Snowflake's data platform, partner solutions and industry-specific data sets, the company said.

Snowflake Data Cloud provides a platform for data warehouse (Data Warehouse), SQL analysis, machine learning, data engineering and third-party data monetization. The Manufacturing Data Cloud builds on these capabilities to provide industry solutions that help manufacturers lay the foundation for their business, improve supply chain performance, and drive smart manufacturing initiatives. Data Cloud is a fully manageable, secure platform with unified governance and multi-cloud data integration that the company claims can support storage, compute and users of virtually any size.

Tim Long, global head of manufacturing at Snowflake, said: “We are very excited about the Snowflak platform and the coming together of our partner solutions and data in the Manufacturing Data Cloud because we know it will have a big impact on manufacturers. Help."

Long leads the team that goes to market in this industry and worked with more than 50 partners on this launch. Meet hundreds of global manufacturers to understand the challenges they face while drawing on his 20 years of experience in semiconductor manufacturing. Long previously led the data and analytics practice at semiconductor manufacturer Micron, which adopted Snowflake and found it to be the best platform for unifying company data and improving factory performance. “We moved our entire manufacturing data footprint from on-site to Snowflake in the cloud in just four months,” he said. “Through this experience, I understand first-hand the opportunities and opportunities manufacturers face when it comes to data. Difficult.”

Optimizing Supply Chain Performance with Visibility

Supply chain effectiveness has a huge impact on a successful manufacturing operation, look beyond the four walls of the factory to see what is happening throughout the supply chain is key: “Our thesis is that the way to improve the performance of a business is through better visibility. The way to improve visibility is to have better data that goes beyond first-party data , beyond the immediate view of the enterprise.”

Snowflake’s Manufacturing Data Cloud enables data sharing and data sharing across an organization’s entire supply chain by combining its proprietary data with data from partners and the Snowflake Marketplace. Collaborate to improve downstream and upstream visibility. Companies can then leverage this data using SQL and Snowflake, a development framework for Python, Java and Scala. The platform allows different teams to work with shared data to build AI and ML models for use cases such as forecasting demand, raw material prices and energy prices.

The solution is built on Snowflake, leveraging Snowflake’s data collaboration to provide data connectivity and insights into supplier performance. One of the partners offered by Snowflake Marketplace is a solution from freight tracking specialist company FourKites. The company provides near real-time tracking insights for products shipped by land or sea, and manufacturers can access this FourKites data directly from the Snowflake Manufacturing Data Cloud. Long explained how they combine these insights with internal data to better schedule and ensure customer shipments arrive on time at a manageable cost, while mentioning that 3M is a current customer for this capability.

Other partners offering industrial applications include cloud-based supply chain risk management and business marketplace platform provider Avetta, as well as supply chain optimization software specialist Blue Yonder and cloud-native supply chain automation platform Elementum.

AWS is one of many technology partners included in this launch, with solutions that enable manufacturers to mobilize data sets located in disparate locations for comprehensive analysis. Another is Fivetran, a solution that automates various aspects of the ELT process when moving data from databases such as SAP systems and SaaS applications to the new manufacturing data cloud. Dataiku is also a batch performance optimizer partner that brings sensor, IoT and historical batch data into Dataiku to evaluate and predict batch results.

Improving Factory Efficiency for Industry 4.0

In addition to supply chain optimization, Snowflake’s manufacturing data cloud is also dedicated to improving factory operations.

“If we turn inside factories, we see manufacturers trying to increase efficiency using smart manufacturing or what is sometimes called Industry 4.0 technology.” Long said: “The next industrial revolution will actually be due to The possibilities of data and artificial intelligence.”

Artificial intelligence technology has greatly expanded data ingestion capabilities, and the Manufacturing Data Cloud provides native support for semi-structured, structured and unstructured data, including high-volume IoT data from shop floor sensors and equipment. Unifying this data in Snowflake helps manufacturers streamline operations across multiple plants with the ability to predict maintenance needs, analyze cycle times, and improve product yield and quality.

Until recently, technological advancements on the shop floor were less advanced than in other aspects of manufacturing. Operational technology (OT) involves the systems that run the shop floor and are at the heart of core manufacturing operations. Long said these systems are overseen by engineers on the operations floor and are typically outside the purview of IT. OT data is generated by sensors and legacy equipment that can sometimes be quite old.

Long noted: “Manufacturers often can’t use this data because it’s difficult to extract it and bring it to a place where they can mine it to understand product yields and factory efficiency.”

The relevant partner of Snowflake for this release is Riveron, an OT expert who has packaged a set of technologies that Long said are the best of their kind and can move data from data to data in a scalable and efficient way. A workshop or other fringe location as he calls it brought to Snowflak.

One of Riveron's products comes from Opto 22, an industrial automation company that makes a specialized physical hardware device capable of connecting to many types of machinery using any available network interface. The device runs software from another specialist company, Inductive Automation, which translates across hundreds of communication protocols, bringing them together in a standard message format and transmitting them into Snowflake with the help of Cirrus Link.

"(The solution) is completely edge-driven, meaning the assets on the shop floor can be defined there," Long said. "The definitions are things like 'What is the asset itself, what measurements are being collected, the units of measurement. What?' Such information will flow directly into Snowflake where it is dynamically materialized for analysis and defining these assets in the Snowflake cloud requires no additional configuration settings, supporting all the different data types in the Cirrus Link messaging standard, which is another key differentiator that sets Snowflake apart from its competitors.”

Drive industries with data and artificial intelligence

Several large global manufacturers are already using Snowflake to build data clouds, including computing interconnects. Even supplier Molex, which is using the platform to drive its digital transformation efforts.

Another customer is Scania, a manufacturer of trucks, buses and industrial engines, which uses Snowflake to continuously stream data and support machine learning initiatives for monitoring vehicle performance.

“With the shift to electric vehicles, they realize how important data is to the success of their next-generation product,” Long said of Scania. “They are using Snowflake to capture connected vehicles from 600,000 trucks on the road. data and use this data to provide high-value services to truck operators, such as optimized maintenance schedules, recommendations for adjustments to the way these vehicles operate, etc., in order to get the maximum value and performance from the vehicles."

Scania's “Snowflake’s Manufacturing Data Cloud gives us the data foundation we need to gain insights from the 150 million streaming messages we receive from 600,000 vehicles,” Peter Alåsen, head of product, said in a press release. ." "With Snowflake, we are able to reduce downtime by recommending maintenance based on vehicle operations and workshop availability, while increasing revenue-generating activities for servicing and other digital or physical services."

Long on the new release for global manufacturers Enthusiastic about the global prospects and opportunities it brings: "We have unlocked many opportunities with Snowflake in the manufacturing data cloud. We are excited to share these opportunities with the world."

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