Manufacturing Data Engine and Manufacturing Link, two new Google Cloud products, allow manufacturers to connect previously segregated assets, process and standardize data, and increase visibility from the factory floor to the cloud. These solutions would allow three essential AI and analytics-based use cases once data is harmonized: manufacturing analytics and insights, predictive maintenance, and machine-level anomaly detection.
Rising customer expectations, supply chain unpredictability, altering buyer behavior, and other factors are driving manufacturers to embark on a big digital transformation, stated Google. Despite this, Gartner research revealed that just 21 percent of manufacturers have active AI projects in place to help tackle these issues. While data from many systems may be manually prepared for AI and analytics pilots, siloed data sets must be available centrally and in real time in order to enable production scale.
Many contemporary AI and analytics systems are built for data scientists and are difficult to use by factory executives, according to Google.
Manufacturing Data Engine and Manufacturing Connect, both of which are now accessible, assist manufacturers in unifying their data and empowering their employees with simple analytics and AI solutions built on cloud infrastructure:
- Manufacturing Data Engine – is an end-to-end solution that processes, contextualizes, and stores factory data on Google Cloud’s market-leading data platform. It offers a configurable and adaptable blueprint for manufacturing data intake, translation, storage, and access. Cloud Dataflow, PubSub, BigQuery, Cloud Storage, Looker, Vertex AI, Apigee, and other Google Cloud products are integrated into a manufacturing-specific solution.
- Manufacturing Connect – is a factory edge platform co-developed with Litmus Automation that can quickly connect to, and stream data from, nearly any manufacturing asset and industrial system to Google Cloud, based on an extensive library of more than 250 machine protocols. The Manufacturing Data Engine’s deep connectivity enables speedy data input into Google Cloud for processing machine and sensor data. The ability to deploy containerized apps and machine learning models to the edge opens up new use cases.
“The growing amount of sensor data generated on our assembly lines creates an opportunity for smarter analytics around product quality, production efficiency and equipment health monitoring, but it also means new data intake and management challenges,” said Jason Ryska, Director Manufacturing Technology Development, Ford Motor Company. “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. 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.”
Industry-Specific Use Cases
After the Manufacturing Data Engine and Manufacturing Connect have consolidated and standardized data, it may be utilized to meet an increasing number of industry-specific use cases, such as:
- Manufacturing analytics & insight – This enables businesses to easily develop bespoke dashboards to view crucial data ranging from factory KPIs like Overall Equipment Effectiveness (OEE) to data from specific machine sensors. Engineers and plant managers may use the Manufacturing Data Engine to build up new equipment and factories automatically, allowing for standardized dashboards, KPIs, and on-demand drill-downs into the data to reveal new insights possibilities across the facility. These may then be readily shared within the organization and with partners.
- Machine-level anomaly detection – It uses Google Cloud’s Time Series Insights API to assist manufacturers spot anomalies in real-time machine and sensor data like noise, vibration, and temperature and sends out notifications.
- Predictive maintenance – This would allow manufacturers to predict when an item will require servicing, reducing downtime and maintenance costs. Manufacturers can use machine learning models and high-accuracy AI enhancements in weeks.
“Bridging gaps across systems and placing easy-to-use AI directly into the hands of manufacturing engineers leads to better results,” said Hans Thalbauer, Managing Director, Supply Chain and Manufacturing Industries, Google Cloud. “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.”
