RAMSYS is a single modular software platform that uses decision support engines and prediction modelling for short to long-term planning of railway infrastructure maintenance & renewal (M&R) works.
Railway-specific algorithms and configurable rules allow accurately assess the current asset conditions and predict their future behaviour. RAMSYS enables scenarios comparison to ensure safety and quality at minimum life cycle cost.
In fact, RAMSYS allows infrastructure managers and train operators to better manage, monitor and control maintenance operations, resources and equipment while optimising service level outcomes and capital and maintenance expenditure.
RAMSYS validate, analyze and correlate volumes of current and historic data (measurements data, maintenance activities data, asset type’s data...) to effectively plan maintenance, maximize infrastructure availability, ensure on-time reliability, and guarantee the highest level of safety.
RAMSYS can be used as a stand-alone solution or can be integrated with other enterprise systems, such as EAM (Enterprise Asset Management), ERP (Enterprise Resource Management), GIS, Dashboards, etc. to achieve a seamless workflow and enable users to:
RAMSYS has been designed to get full value from all the railway asset data and enable better maintenance decisions. With data validation, data analysis and maintenance planning being even nowadays hosted on different platforms, RAMSYS revolutionizes the way data integration and analysis can contribute to the entire maintenance process by offering a single software platform able to load data from any measuring and inspection system and flexibly implement a diverse set of operational activities.
RAMSYS is logically organized in functional units, named modules (analysis, planning, trending & forecasting…) that can be bundled together into a larger application to flexibly accommodate the specific customer needs. Its architecture allows deployment of a large number of users over local and wide area networks. Every user’s client can be configured to run specific modules’ functions over specific subset of data.