- Accurate real time data at your fingertips
- Minimal downtime
- High levels of throughput with little to no waste
- Optimised operations
Advance warnings 9 months before potential failures and £150k operational savings per turbine.
Predict 90% failures before they occur saving over £100k per composite part and reducing energy and material waste.
Automated defect detection reduced the man power required and therefore reduced the associated costs substantially. It also allowed staff to focus on other business critical tasks.
An automated product quality inspection system which has increased quality levels and saves £20m per plant annually.
£1M savings in a single quarter, and potentially achieving £6M savings within 18 months.
Computer vision based fault detection, automating the inspection process and increasing coverage by over 3 fold.
Detecting scrap in the single grain crystallisation casting process using regression modelling and advanced analytics.
Robotic fulfilment rig that demonstrates how robotics and machine learning can be used to help support staff.
Switched from the regular cutting tool replacement process at every 200 products to a monitored based system that extended the life of a cutting tool by up to 3 times.
Health Indicator application provides early alerts that equipment is degrading and likely to need maintenance.
Power usage effectiveness (PUE) is an important industry metric for measuring the energy efficiency of a data centre. Optimising this can provide huge cost savings of up to 20% in this case.
Condition-based tool replacement using data and machine learning to inform decisions. Leading to a reduction in cost and extending the life of the tools.
Smartia’s MAIO will enable AI at the edge, supporting privacy-preserving distributed learning. Use of AI for cyber security to detect attacks and improve the security of large scale IIoT systems.