Successful digital transformations happen when you can connect assets, systems, and strategies in whole new ways that dramatically accelerate learning. Teams that learn the fastest are the teams that win in any market and in any economy.
Operations and Maintenance have changed forever.
The world is becoming more complicated - more data, more connectivity, more real-time, more competitive... while at the same time there are fewer people who can solve these tough challenges. Strong experience remains, no doubt, but how do companies leverage those remaining experts and all that data in the best way?
By leveraging existing data and people in a way that allows them to learn fast and act faster.
Teams that operationalize predictive technologies in a learning manner will transform their businesses.
TwinThread is here to partner with you on the journey
We were able to move our first machine insight to all like machines in seconds! No other product can do that!
Empower Your Experts
Realize the true benefits of AI and machine learning technologies, person by person.
- Real-time 'future' alerts - avert production downtimes
- Interactive recommendations - grow close to the 'perfect process'
- Enabled write backs to SCADA, PLC, MES systems - automated
- Prebuilt model templates/Model Factory enable rapid deployment
- No need for complex data cleansing - TwinThread handles it
- Model factory, prebuilt industrial templates for rapid analysis & iteration
- Algorithms run simultaneously to optimize for best prediction
- Models learn and iterate, supporting lifelong operational improvement
- Cost drivers included for energy optimization
- Proactive alerts let you avoid costly shutdowns
- Integrated with applications you use
- Recommended settings for optimum operations
- Optimize repair shop visits through holistic review of all PM requirements, existing and predicted
- Prompt crew dispatch via proactive intelligent alerts
- Identify root causes and correct them in-line
- Less replacement inventory necessary, due to improved uptime