AWS for Industries
Category: Best Practices
Reducing manufacturing machine downtime using intelligent document processing on AWS
In the rapidly evolving automotive industry, manufacturers and suppliers must proactively address the challenges of managing vast amounts of data and documentation to streamline operations. These challenges include paper documents, disparate document versions and folder structures, collaboration with other plants, and finding important information when needed, especially when it comes to mitigating the disruptive impact […]
Industrial Data Fabric solution best practices – Part 2, build, launch, and scale
This blog is the second in a two-part series that distills some of the most important best practices to help organizations accelerate the implementation of an Industrial Data Fabric (IDF) solution on Amazon Web Services (AWS) and increase the value it brings enterprise-wide. The best practices are organized by stage, as shown in figure 1 […]
Industrial Data Fabric solution best practices – Part 1, organizational readiness and alignment
Industrial Data Fabric (IDF) solutions on Amazon Web Services (AWS) enable manufacturing and industrial companies to build the foundation for digital transformation to help optimize operations. IDF helps align different data types in the time domain with context as it combines many disparate data sources like time series data from machines, maintenance data from EAM […]
Empowering predictive maintenance with Amazon Bedrock
In the competitive field of industrial operations, minimizing downtime is crucial for financial success. The challenge lies in managing complex data spread across various systems within industrial environments. Quickly identifying and solving issues without advanced analytics can be difficult. Operators often spend time consulting manuals to diagnose problems and follow protocols. Automation can streamline this […]
The Transformative Impact of Generative AI in Manufacturing at Hannover Messe 2024
Generative artificial intelligence (AI) is rapidly becoming a cornerstone technology, driving significant advancements in manufacturing through the creation of synthetic data and images, optimized designs, process simulations, insights from operational data, and more. Recent research from Capgemini indicates that a substantial majority of manufacturers are not just curious about generative AI; 55% are actively exploring […]
Generative AI for Semiconductor Design and Verification
The emergence of generative AI presents tremendous opportunities for advancing technical and business processes in high tech and semiconductor industries. From optimizing complex system design processes to accelerating time-to-market for new products, generative AI has unlimited potential to improve engineering and manufacturing methodologies and processes. Generative design methodologies powered by AI can automatically design chips […]
Accelerate chip-design verification process by running Siemens EDA Calibre on AWS
Amazon is also a fabless semiconductor company. Several tapeouts are completed every year that result in products like Kindle, FireTV, Echo, etc. Amazon Web Services (AWS) does its inhouse semiconductor design for its data center operations through its internal team at Annapurna Labs. AWS Graviton, an ARM based CPU as well as AWS Trainium and […]
Edge2Web no-code tools suite for AWS IoT SiteWise
Powered by AWS IoT SiteWise, Edge2Web Factory Insights is a ready-made SaaS smart manufacturing solution tailored to shop floor operators, supervisors, and managers. Factory Insights gives organizations instant visibility into key performance metrics such as Overall Equipment Effectiveness (OEE), Overall Operations Effectiveness (OOE), Total Effective Equipment Performance (TEEP), and MTBF/MTTR. Manufacturers use Factory Insights to […]
Improve tire manufacturing effectiveness with a process digital twin
Introduction to the concept of a process twin for tires | What problem does it solve? The process digital twin for tire manufacturing aims to improve the Golden Batch mixing process and, as a result, reduce the number of overall noncompliant products (NCPs) as well as the associated offgrade percent. Through standardized, pre-modeled controls and […]
Cut time to results without changing your EDA flows
Using advanced node technology to successfully manufacture a chip is getting harder as chip geometries continue to shrink. Electronic Design Automation (EDA) consumes more compute, storage, and time. Giving your engineers more time to iterate and find bugs in the design and verification phases will result in saving millions in re-spins and lost revenue. Further […]