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Importance of Manufacturing
Analytics in Industry

Predictive Maintenance

Monitoring machine performance and analyzing previous data helps predictive maintenance algorithms detect early indications of expected breakdowns. 

Quality Control

Quality control is an essential aspect of production that ensures products meet or exceed client needs and statutory requirements. Manufacturing analytics improves quality control by delivering real-time data into production operations.

Remote Monitoring

Remote monitoring offers complete, real-time visibility into the status of manufacturing processes and equipment. Executives can monitor machinery equipment performance indicators and detect problems without having to be on the shop floor.

Enhances Data-driven Decision Making 

A data-driven strategy is essential for optimizing operations, reducing costs, and boosting innovation production. It also helps to optimize manufacturing operations by finding inefficiencies for development. 

How to Use Smart Factory Metrics for Productivity Improvement

Benefits of Industrial IoT in Manufacturing

Enhanced Quality Control

Using smart sensors and real-time data analytics to constantly track production processes and product quality. assists in identifying trends and patterns in quality issues, which enables proactive enhancements and reductions in waste.

Real-Time Monitoring

Real-time data collection provides full data on production processes, equipment status, and overall factory performance. Real-time information allows for more informed decision-making and helps in the optimization of operations and the proactive resolution of issues.

Predictive Maintenance

By forecasting breakdowns of machinery before they happen, predictive maintenance plays a major role in manufacturing by significantly reducing downtime and maintenance expenses. Predictive maintenance improves the longevity of machines and reduces the need for emergency repairs.

Increased Production Efficiency

IIoT improves operational and production efficiency by streamlining workflows, removing issues, and optimizing processes. You can find inefficiencies and enhance operations with the help of modern analytics.

Energy Efficiency and Sustainability

For modern production, sustainability and energy efficiency are essential goals, and IIoT is vital to reaching these goals. Increased energy efficiency reduces utility expenses, which boosts the manufacturer’s bottom line and results in overall cost reductions.

Our Core
Feature

Customizable Dashboards

Dashboards can be customized by users to show the most relevant indicators and KPIs, enhancing decision-making and visibility. Dashboards may feature live charts, graphs, and plugins that allow users to get further down into data and get more information.

Algorithm Processing

Advanced algorithms analyze huge amounts of data to detect patterns, deviations, and connections that may not be instantly apparent. Algorithms can predict future performance and maintenance needs, allowing for proactive management. 

Real Time Data

Real-time data offers quick visualization of the current status of operations, enabling quick action to identify and address issues, reducing downtime, and ensuring smooth operations.

Document Storage

Extensive reports covering production-related issues, such as quality control statistics, efficiency measures, and wider data insights offered by analytical tools, help identify areas needing improvement.

Machine Alert Notification

Notifications of machine alerts are essential for preserving production continuity and avoiding unexpected downtime. Adjustable alerts can be configured to activate in response to specific conditions or thresholds, ensuring relevant notifications.  

Data Entry

Accurate and reliable data for every relevant detail is ensured by effective data entry modules. Users can enter data faster and more effectively due to intuitive interfaces. 

Predictive Maintenance

Using data analytics, predictive maintenance is a pivotal feature that foresees equipment breakdowns before they occur. 

Lifetime Data storage

The security and availability of all previous data for analysis and compliance are ensured by lifetime data storage. For easier long-term analysis and trend identification, keep a complete historical record of all production data. 

Document Storage

Factory metrics solutions that store documents ensure that every relevant detail is readily available and arranged.

Key Components of an IIoT
Platform

Connectivity and Communication Protocols

To assure effective and dependable data transfer, connectivity and communication protocols make use of a variety of network protocols, including MQTT protocol, CoAP protocol, and OPC UA.

Data Storage and Cloud Integration

Cloud integration and data storage are essential for managing the massive volumes of data produced by IIoT devices. These elements provide effective data storage as well as accessibility and analysis when required.

Advanced Analytics and Machine Learning

IIoT platforms are built on advanced analytics and machine learning, which turn raw data into useful insights that boost productivity and efficiency.

Automated Reports and Live Dashboards

Live dashboards provide immediate analysis of production metrics, machine status, and overall operational efficiency by displaying data in real-time. Automated reporting systems can identify patterns and anomalies, enabling effective management.

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