Win on all fronts: Streamline production for more uptime and capacity while preventing throughput, energy, and emissions losses.
Unplanned downtime
Reduce downtime and MRO costs
Trapped capacity
Increase capacity
Costly repairs
Increase asset lifecycle
Improve plant safety and ESG outcomes
Improve plant safety and ESG outcomes
Workforce skills gap
Upskill teams
Conflicting business and process objectives
Alignment between production and business objectives
Quality and product waste
Consistent quality with lower material loss
Inefficient capacity management
Improved yield and throughput
High energy consumption and low alternative fuel rate
Lower energy usage and higher use of sustainable energy sources
Sensors capture and transmit data to Augury’s platform 24/7
AI detects issues, explains what caused them, and prescribes courses of action
Augury’s platform, AI insights, and expert support enables users to prioritize and plan
Users collaborate and act on guidance to improve the health and performance of their machines and processes
Sensors capture and transmit data to Augury’s platform 24/7
AI detects issues, explains what caused them, and prescribes courses of action
Augury’s platform, AI insights, and expert support enables users to prioritize and plan
Users collaborate and act on guidance to improve the health and performance of their machines and processes
We have a lot of data at our fingertips, and we don’t always know what to do with it.
Augury can take that data, use it to get smarter over time with their AI algorithms, and then continue developing solutions that make good use of the data we have. This is what we need going forward.
AI can provide crucial insights into the optimum process settings to reach all key KPIs-
from emissions to clinker quality, kiln throughput, energy efficiency and overall production capacity.
Receiving quick and descriptive feedback from the Augury platform and the vibration analysts
has helped push us towards a more proactive role and addressing potential failures earlier on in the failure-mode life cycle.
