Google Cloud’s newest data platforms bring better insights to factory operators
Google Cloud today launched two industry-specific platforms that will enable manufacturers to benefit from what the company says are superior analytics-based insights, predictive maintenance and machine-level anomaly detection.
The idea with Google Cloud’s Manufacturing Data Engine and Manufacturing Connect platforms is to help factory operators unify and take action on the masses of data that their facilities generate on a daily basis.
Although factory machines create lots of information, much of this data remains siloed, meaning it cannot easily be accessed by analytics and artificial intelligence tools that can make a meaningful difference to their operations. Added to that, most analytics and AI tools are designed for data scientists, meaning they’re difficult for manufacturing business leaders to use.
Google Cloud’s new offerings are meant to change that. Manufacturing Data Engine is a service that’s used to store factory data on Google Cloud that comes complete with easily configurable and customizable blueprints for data ingestion, transformation, storage and access. Most importantly, it integrates with Google Cloud products such as Cloud Dataflow, BigQuery, Looker, Apigee, Vertex AI and others, giving manufacturers a way to put that data to use.
The more important offering though is Manufacturing Connect, which is a factory edge platform co-developed by Litmus Automation Inc. With Manufacturing Connect, factory operators can quickly connect any industrial system or machine to Google Cloud and stream data from it in close to real-time, so that information can be processed. That data is then centralized and harmonized within Manufacturing Data Engine, making it available for a number of critical use cases, Google said.
For instance, factory operators can perform analytics on their data to glean useful insights about their operations. They can visualize key performance indicators for each machine or an entire facility, share these easily and uncover new opportunities to boost operational efficiency, Google said.
Manufacturing Data Engine also enables manufacturers to identity anomalies as they happen, with real-time alerts based on data such as noise, vibration or temperature. Finally, it helps with predictive maintenance so managers can better schedule individual machines for servicing, leading to reduced downtime and lower maintenance costs, Google said.
Holger Mueller of Constellation Research Inc. said Google Cloud is increasingly offering vertical solutions such as Manufacturing Data Engine because they are exactly what its customers need. “Businesses are vertical and they need to practice enterprise acceleration, which means moving faster and becoming more agile,” he said. “Out of the box capabilities for vertical demands can help with that, and as always Google’s offering is centered on data, analytics and AI.”
Ford Motor Co., which famously innovated the assembly lines at its factories over a century ago, is trying to repeat that trick as an early adopter of Google Cloud’s newest offerings. Jason Ryska, the company’s director of manufacturing technology development, said the growing amount of sensor data generated by its production lines has created opportunities for smarter analytics that will not only improve product quality but also production efficiency and equipment health monitoring.
The problem, he said, is that new data intake and management is a challenge for most companies. “We worked with Google Cloud to implement a data platform now operating on more than 100 key machines connected across two plants, streaming and storing over 25 million records per week,” Ryska explained. “We’re gaining strong insights from the data that will help us implement predictive and preventive actions and continue to become even more efficient in our manufacturing plants.”
Hans Thalbauer, managing director of supply chain and manufacturing industries at Google Cloud, said the new offerings are all about bridging systems to put easy-to-use AI into the hands of engineers. “These new solutions can support workforce transformation initiatives by providing engineers with the tools to be self-sufficient, without the need for data scientists or additional integration code,” he said.
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