Mapping physical and virtual elements into an asset management structure using existing data sources is step one. This includes: locations, areas, key processes and activities, equipment, maintainable components, attributes, and documentation.
Once the asset management structure is in place, each asset is equipped with specialized hardware, so that IoT data can be communicated to Forensixx.
Now there is a comprehensive set of details for every asset. From asset management: type, age, location, components etc. From IoT: temperature, vibrations, RPMs, telemetry (historic) data etc. This combined data is the starting point for creating a powerful predictive model.
Monitoring is made easy with real-time, condition-based, and predictive alerts. Thresholds can be set, so that alerts are sent whenever equipment exceeds parameters (e.g. optimal temperature). This helps operators pinpoint problems easily and react quickly. It can even prevent issues, minimizing impact on equipment and processes.
Staff across all levels of responsibility can receive relevant alerts via desktop, laptop, mobile, and more.
Once an issue is identified, ticket management takes care of the complete elimination lifecycle, linking all documentation to asset management and providing easy-to-use maintenance wizard steps, and immediate access to the right spare parts and current stock levels.
We apply Machine Learning (ML) and Artificial Intelligence (AI) to all data gathered over time, allowing FX4L to learn from patterns and help predict failures. This means that companies can see what issues to expect, and how soon, planning their budgets and maintenance schedules accordingly.
FX4L empowers your company with intuitive graphical overviews of KPIs. In this way, you can drill down on processes being over or under fed, and optimize performance and efficiency.
FX4L can be integrated with any solution, system, or hardware the customer might already have in place.
We are experts in integration across a vast range of systems, with more than 25 years of experience.