Predictive Operational Analytics
Smart use of manufacturing data, and data from ERP, IoT devices, and supply chain systems in order to streamline and boost performance.
The future is here!
A slew of solutions for manufacturing and operating environments,
using advanced machine learning technology
based on IBM’s SPSS Modeler analytics platform
developed by our team of data scientists
Improve productivity and product quality
by using analytics to reduce malfunctions and improve production.
Identify exceptions and predict malfunctions
by using defined approaches to track, predict, and understand failures
Boost performance in minimum time
By making smart use of data. Unique offering developed by Genius Data Science
The information revolution brings a slew of solutions to manufacturing and operating environments.
In plants and anufacturing, the production environment is critical to the organization’s profitability. The COO and heads of QA and manufacturing aim to produce more, at better quality and lower cost.
Overstocked or missing inventory items, an nexpected malfunction disabling a machine and the production line or defective (פחת) or obsolete goods (נפל), as well as poor design of the products (תו”ע) and lines, can cause significant damage.
In response to these challenges, we developed Predictive Operational Analytics (POA), an ntelligent AI & machine learning-based system that performs:
Identifying the root causes of failure in the machines or finished goods.
Early detection of faults in production lines or machines, based on analysis of sensor data measured on a regular basis and identification of the patterns and changes that always precede a malfunction. Such early alerts, which significantly precede the control and command system alerts, enable to prepare in advance rather than make haphazard decisions.
Early prediction of impending malfunctions and repair alerts allow for proactive maintenance rather than breakage maintenance.
Sensitive and accurate detection of any behavior change or anomaly, without needing to know the rules of normal behavior. The system learns what constitutes normal and desirable behavior by studying data from all the machine’s sensors and the relationship between them in different situations, and knows how to alert of any anomaly.
Ongoing monitoring of sensors that are significant for critical phenomena and sending alerts, including with smart graphic display.
Analyzing the reliability of digital or analog sensors and alerting to suspicions of defective sensors that do not transmit reliable data.
Six Sigma quality control. The system is able to connect to the measuring instruments and ERP system and has a user-friendly interface adapted to the production line’s QA stations. It serves both the machine operators and R&D engineers, enabling them to investigate the causes of failure or process and control anomalies.
Early detection of energy over-consumption given all criteria and conditions.
Taking into consideration the production floor resources, constraints, and productivity requirements. The system enables optimal work outcomes (תו”ע) and placement while meeting complex constraints.
The information revolution brings a slew of solutions to manufacturing and operating environments
In plants and manufacturing, the production environment is critical to the organization’s profitability. The COO and heads of QA and manufacturing aim to produce more, at better quality and lower cost.
Overstocked or missing inventory items, an unexpected malfunction disabling a machine and the production line or defective (פחת) or obsolete goods (נפל), as well as poor design of the products (תו”ע) and lines, can cause significant damage.
In response to these challenges, we developed Predictive Operational Analytics (POA), an intelligent AI & machine learning-based system that performs:
Identifying the root causes of failure in the machines or finished goods.
Early detection of faults in production lines or machines, based on analysis of sensor data measured on a regular basis and identification of the patterns and changes that always precede a malfunction. Such early alerts, which significantly precede the control and command system alerts, enable to prepare in advance rather than make haphazard decisions.
Early prediction of impending malfunctions and repair alerts allow for proactive maintenance rather than breakage maintenance.
Sensitive and accurate detection of any behavior change or anomaly, without needing to know the rules of normal behavior. The system learns what constitutes normal and desirable behavior by studying data from all the machine’s sensors and the relationship between them in different situations, and knows how to alert of any anomaly.
Ongoing monitoring of sensors that are significant for critical phenomena and sending alerts, including with smart graphic display.
Analyzing the reliability of digital or analog sensors and alerting to suspicions of defective sensors that do not transmit reliable data.
Six Sigma quality control. The system is able to connect to the measuring instruments and ERP system and has a user-friendly interface adapted to the production line’s QA stations. It serves both the machine operators and R&D engineers, enabling them to investigate the causes of failure or process and control anomalies.
Early detection of energy over-consumption given all criteria and conditions.
Taking into consideration the production floor resources, constraints, and productivity requirements. The system enables optimal work outcomes (תו”ע) and placement while meeting complex constraints.