MIT researchers have developed an algorithm that designs optimized machine-learning models up to 200 times faster than traditional methods.
Companies with a manufacturing execution system (MES) often generate a good initial return on investment (ROI), but there is a great deal of potential companies don't realize.
Demystify the need for Big Data and five related challenges: data structure, scalability, integration, storage, and upgrades.
Cyber-physical environments will change what managers do.
Companies need to be open to new technologies to see fundamental paradigm shifts in productivity, beyond incremental improvements. Rapid increases in productivity, visibility, flexibility, and agility are needed.
Some applications require data acquisition strategies without cloud connectivity, but they can still benefit from advanced analytics technology.
Software uses real-time data to draw real-world conclusions.
Centralized data architectures are adapting to new opportunities for data collection and analytics.
SKF Pulse combines a handheld sensor with a mobile app to allow users to monitor rotating equipment and machine health to predict issues and improve reliability before operations are impacted.
TrendMiner 2019.R1 self-service analytics software release for process manufacturing includes a visual representation of time-series data, making it easier for users to analyze, monitor and predict process and asset performance.