Monitoring
Monitor REMOTE ASSETS and improving service operations
Transparency anywhere at any time
Challenges and Pressures
- Up to date visibility and control over operations
- Ensuring that machines, equipment, and assets are used efficiently
- Access to remotely located equipment, or a fleet of vehicles
- Achieving value for money from machine and resource investments
- Having real data to back up decision making
Solution
- Direct asset connectivity
- Raw data and performance metrics breakdown visualised in detail
- Analytics converting data into insights
- Expert advice to implement changes
Benefits
- Monitor assets in real-time, from anywhere
- Automate processes and reduce manual labour
- Enhance operational efficiency and productivity
- Improve safety and security of assets
- Minimise downtime and maintenance costs
Features
- Connect directly to sensors and PLCs for real time, rich, high frequency data
- Real time visualisation of metrics and KPIs
- Performance breakdown of all the assets and equipment used in the facility
- Out of the box analytics to convert raw data into actionable insights
- Further tools to create your own dashboards and analytics
Remote Monitoring
Remote Monitoring over 7,000 assets in the UK
- Manage over 7,000 pubs in the UK alone
- Produces 348 international, regional, local and speciality beers and ciders
- Using Smartia Monitoring to remotely monitor their smart dispense systems in pubs and bars across the UK
- Early indication of issues occurring and proactive maintenance planning
Our Projects
Helping industrial companies harness the power of AI. Our consultative approach to automating and enhancing industrial operations allows us to deploy customised AI solutions that deliver tangible improvements to production efficiency, machine uptime, maintenance scheduling and quality control applications.
Predictive Maintenance
Advance warnings 9 months before potential failures and £150k operational savings per turbine.
Predictive Maintenance
Predictive Failure Detection
Predict 90% failures before they occur saving over £100k per composite part and reducing energy and material waste.
Predictive Failure Detection
Automated Defect Detection
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.
Automated Defect Detection
Automated Quality Inspection
An automated product quality inspection system which has increased quality levels and saves £20m per plant annually.
Automated Quality Inspection
Inventory Optimisation
£1M savings in a single quarter, and potentially achieving £6M savings within 18 months.
Inventory Optimisation
Fault Detection
Computer vision based fault detection, automating the inspection process and increasing coverage by over 3 fold.
Fault Detection
Scrap Reduction
Detecting scrap in the single grain crystallisation casting process using regression modelling and advanced analytics.
Scrap Reduction
Smart Robotics Object Detection
Robotic fulfilment rig that demonstrates how robotics and machine learning can be used to help support staff.
Smart Robotics Object Detection
Ultra Precision Manufacturing
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.
Ultra Precision Manufacturing
Preventive Maintenance Support
Health Indicator application provides early alerts that equipment is degrading and likely to need maintenance.
Preventive Maintenance Support
Energy Usage Optimisation
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.
Energy Usage Optimisation
Tool Life Optimisation
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.
Tool Life Optimisation
AI-based Security at the Edge
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.
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