3M Manufacturing Plant Leverages Azure SQL Edge for Efficiency and Cost Savings
Technology Category
- Analytics & Modeling - Machine Learning
- Functional Applications - Manufacturing Execution Systems (MES)
Applicable Industries
- Electronics
- Transportation
Applicable Functions
- Logistics & Transportation
- Maintenance
Use Cases
- Manufacturing System Automation
- Predictive Maintenance
Services
- Cloud Planning, Design & Implementation Services
- Data Science Services
About The Customer
3M is a multinational conglomerate corporation known for its wide range of products, from office supplies like Scotch tape and Post-it notes to healthcare equipment like N95 respirators. The company categorizes its more than 60,000 products into four diverse business groups: safety and industrial, transportation and electronics, healthcare, and consumer. Founded in 1902 as a Minnesota-based mining company, 3M has evolved into a Fortune 500 company that employs 96,000 employees in 87 countries. The company is known for its commitment to innovation and efficiency in its manufacturing processes.
The Challenge
3M, a multinational conglomerate corporation, produces over 60,000 products across various business groups. At one of its US manufacturing plants, the 3M Corporate Research Lab and the local 3M Manufacturing team identified an opportunity to predict anomalies and use these insights to reduce manufacturing downtime. The challenge was to integrate data streams from two production lines, correlate them, and then run analytics and machine learning locally. However, the data from the two systems arrived at different times, and the plant’s network connectivity was limited. The team needed a solution that would provide the necessary performance, management, and security.
The Solution
The team proposed building a custom module with Microsoft Azure SQL Edge deployed through Microsoft Azure IoT Edge. This solution allowed them to process and analyze big data locally, at the edge. They developed an algorithm that predicted problems on the manufacturing line hours before they appeared. The module, which sat on their Azure IoT Edge device, was cloud trained and deployed to Azure SQL Edge, where the manufacturing line data resided. It operated seamlessly to create predictions. By correlating and analyzing two data streams, they were able to apply analytics and machine learning to the data. Using Azure SQL Edge, the team created a solution to sync data from there to the cloud, which was fault-tolerant under almost any network condition. This solution also resolved the plant’s problem of limited network capacity.
Operational Impact
Quantitative Benefit
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