MAINTENANCE AND
PREDICTIVE QUALITY
TYRIS AI IS LEADER IN PREDICTIVE MAINTENANCE SYSTEMS WITH MACHINE LEARNING TECHNOLOGY.
The system creates predictive models to identify future breakdowns/failures in critical machine components before they occur, through automatic learning of production parameters. The main objective of the system is to increase machine availability, increase production and reduce uncontrolled downtime.
Metrics and Improvements
The system integrates the process data through access to the different information sources of the factory (Sensors, Databases, Automatons/PLCs, Maintenance Parts and Excel, CMMS Systems, MES, ERP, SCADA, etc.).
55%
REDUCTION OF CRITICAL MACHINE FAULTS
The anticipated knowledge of the breakdowns allows, knowing the type of failure, the workpiece model and the corrective to apply in planned stoppages.
35%
INCREASED MACHINE AVAILABILITY
The main objective is to increase the machines availability, increase production and reduce uncontrolled downtime.
30%
INCREASE THE AVERAGE TIME BETWEEN FAULTS (MTBF)
Predictive analytics allows you to improve corrective operations, optimizes plannings and the management of spare parts helping to increase MTBF
45%
REDUCED MAINTENANCE COSTS
The predictive system allows optimizing the main factors for the reduction of maintenance costs (Times, Resources, Components, etc.).
process quality
predictive system
(NOK vs. OK Production )
System that integrates different sources of process information to generate predictive models that allow to identify when the facilities are going to produce elements with quality defects before this occurs.
The objective is to make a small so that the machine continues its process within the quality standards.
Metrics and enhancements
The system processes the information 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, to report on energy expenditure and inefficient processes or to reorganize the production planning. All this while guaranteeing the production requirements and minimizing the associated energy consumption.
Energy efficiency
with Machine Learning
Machine learning system developed to optimize and minimize energy consumption associated with distributed processes or production facilities.
The integrated system monitors multiple data sources such as process information, consumption, demand forecasts and external data such as energy prices.
Metrics and enhancements
The system integrates the process data by accessing the different information sources of the factory (Sensorics, Databases, PLCs, Maintenance and Excel reports, CMMS systems, MES, ERP, SCADA, etc.).
PRODUCTION
PLANNING SYSTEM
Tailor-made system for automatic production planning and maximization of resources.
Customized for each specific production process taking into account the different parts of the process (orders, raw materials, stocks, production and human resources, work orders, deliveries, etc.).
Metrics and enhancements
The system can greatly reduce incidents related to process quality. It can reduce up to 35% the generation of NOK products with any quality defect, or the optimization of inefficient processes that produce a higher cost of raw materials, energy consumption or process times.
FORECASTING THE DEMAND FOR SUPPLY IN THE SHORT AND LONG TERM
OPTIMIZATION AND INCREASE IN PRODUCTION AVAILABILITY
REAL-TIME ROUTING REORGANIZATION
STOCK REDUCTION AND MAINTENANCE
EXPERTS IN INDUSTRIAL DATA ANALYSIS AND INTEGRATION
COVERING THE ENTIRE PROCESS
Industrial Data Analytics is an industrial analytics solution that includes 3 main modules:
Data Integration + Big Data Module + Predictive Analytics.
PRODUCTION DATA
INTEGRATION
SPECIALIZED PARTNERS IN PREDICTIVE SENSORS
The system integrates and processes the different information sources of the production line on a Big Data architecture in real time (Automaton Network / Sensors / Databases / IoT Disp. / CMMS/MES/ERP Systems).
BIG DATA
CORE
INDUSTRIAL IOT PLATFORM
Executes vertical predictive analytics and process optimization based on process needs (Predictive Maintenance / Predictive Quality / Energy Efficiency / Supply Chain Optimization).
PRODUCTIVE ANALYTICS
WITH MACHINE LEARNING
PREDICTION ENGINE. MACHINE LEARNING
It also allows the execution of predictive analytics verticals on IoT Platforms or Corporate Management Systems such as: PI Osisoft, Nexus Integra or SAP HANA among others.
