Data Management: Aggregation and Analytics
Xentara is the ideal solution for data aggregation, processing and analytics, or – as we call it for short – data management. Being able to connect to all kinds of devices and protocols, Xentara is the perfect intermediary between both greenfield or brownfield shopfloors and the enterprise level office floor.
Typical Data Magagment Uses
Raw Data to Actionable Insights
Preprocess and refining data sets to make them suitable for analysis or integration into various applications, streamlining decision-making processes and improving overall data quality.
Constant Automatic OEE Calculation
Maximize manufacturing equipment efficiency through real-time monitoring and analysis, aiming to minimize production downtime and increase overall equipment effectiveness.
Process Optimization
Analyze production processes to identify areas for improvement, optimize workflows, and increase overall operational efficiency using advanced analytics techniques such as process mining.
Detect and Act on Anomalies
Leverage machine learning to identify unusual patterns or deviations in data and trigger automated responses or alerts to address potential issues in real time, enhancing operational efficiency and reducing risks.
Quality Control Optimization
Detect quality issues early in the manufacturing process, allowing for immediate corrective actions to be taken to improve product quality and reduce waste by analyzing production data in real-time.
Predictive Maintenance
Utilize historical equipment data and machine learning algorithms to predict equipment failures before they occur, enable proactive maintenance and minimize unplanned downtime.
Safety and Compliance Monitoring
Analyze safety data and regulatory compliance requirements to identify potential safety risks and ensure adherence to industry standards and regulations.
Energy Management
Identify opportunities for energy savings, optimize energy usage, and reduce operational costs by analyzing energy consumption data from manufacturing processes.
Supply Chain Optimization
Analyze supply chain data to optimize inventory levels, reduce lead times, and identify potential bottlenecks or inefficiencies in the supply chain network.
Asset Performance Management
Analyze data from industrial assets such as machinery, equipment, and infrastructure to monitor performance, identify maintenance needs, and optimize asset utilization.
Demand Forecasting
Generate accurate demand forecasts, helping manufacturers optimize production schedules, minimize inventory costs, and meet customer demand more effectively by analyzing historical sales data and market trends.
Edge Pre-Processing
Perform data preprocessing tasks including ML directly at the edge of the network, enabling faster data analysis, reducing latency and cloud storage, and optimizing bandwidth usage.
AI OT Connnector
Bridging operational technology (OT) systems with artificial intelligence (AI) algorithms, enabling the analysis of real-time data from industrial equipment to optimize processes, predict failures, and improve overall efficiency.
Semantic Data Formatting
Structuring and organizing data in a standardized format using semantic technologies such as RDF and OWL, enabling enhanced data interoperability, integration, and knowledge representation across diverse applications and domains.
Feed Real-Time Dashboards
Continuously updated visualizations with live data streams, providing immediate insights into key performance metrics and enabling timely decision-making.
OPC UA Server
Providing a standardized platform for secure and reliable data exchange between industrial devices and systems, facilitating interoperability and seamless integration in industrial automation environments.
