Tyris AI as a leader in Predictive Maintenance Systems with machine learning technologies is developing and installing several predictive analytics models that identify future failures and out-of-service states in the critical components of the machines before they occur, by automatically learning the production parameters.
The main objective of the system is to increase machine availability, increase the global production and reduce downtime.
We are currently working with big industrial companies as Aguas de Valencia, SEAT or SACYR in Predictive Analytics Systems with very good results.

Improvements obtained and metrics:

– Reduction of critical machine failures: 55%
– Reduction of maintenance costs: 45%
– Increase in mean time between failures (MTBF): 30%
– Increase in production availability: 35%
The system performs an integration of the process data by accessing the different sources of information of the factory (Sensors, Databases, PLCs, Maintenance and Excel parts, CMMS systems, MES, ERP, SCADA, etc.)

Improvements obtained and metrics:

The system realizes information processing in real time and generates an analytical model that allows, depending on the process to be optimized, to act directly on the production equipment to minimize costs, report sources of energy costs and inefficient processes or reorganize production planning All this guaranteeing the production requirements and minimizing the associated energy consumption.


Machine Learning module developed to optimize and minimize energy consumption associated with machinery or production processes in industry.
The solution has a data integration layer that connects with the systems to be optimized to integrate and monitor internal data processes such as manufacturing status, the associated consumption or forecast of demand, and also external data such as the price of the electric pool.
It allows communicating with the sensors deployed in the facilities of
production, PLCs, and databases.


Machine learning allows discovering patterns in supply chain data by applying algorithms that quickly identify several logistic factors that guarantee the success of a supply network, while constantly learning in the process.
The system allows forecasting the demand with warnings in time real to warehouse anticipating future supplies and logistics problems. In addition to an optimization and reorganization of routes according to the daily production planning.


  • Reorganization of Routes in real time
  • Forecast of short and long-term supply demand
  • Optimization and increase of production availability
  • Stock reduction and maintenance

Industrial data analytics

Industrial Data Analytics

Industrial Data Analytics  is a solution that includes 3 main modules: Integration + Big Data Module + Predictive Analytics. The system integrates and processes the different information sources of the production line on a Big Data architecture in real time (Automation & PLCs / Sensors / Databases / IoT Devices / GMAO Systems / MES / ERP) and executes predictive analytics and process optimization systems according to the needs of the production process (Predictive Maintenance / Predictive Quality / Energy Efficiency / Supply Chain Optimization).

The system allows executing predictive analytics verticals on Industrial IoT Platforms or Corporate Management Systems such as PI Osisoft, Nexus Integra or SAP HANA among others.


Reduction of critical machine failures
Increase in machine availability
Increase in mean time between failures (MTBF)
Reduction of maintenance costs