An automotive Tier 1 manufacturer had deployed an MES system to automate end-to-end manufacturing processes. The machine settings, such as speed, temperature, and pressure, were preloaded into the MES and passed on to the production equipment. However, these preset parameters needed frequent adjustments based on the quality characteristics of raw materials (such as Zinc) and other real-time operational conditions. To address these dynamic conditions, the manufacturer developed a Machine Learning (ML) model that could calculate optimised parameters based on the quality of raw materials and machine performance data. However, the process was disconnected, requiring manual intervention to adjust the settings at the SCADA layer. This manual input led to data mismatches between the MES and actual machine performance, affecting traceability reports and introducing inefficiencies.
The company needed to integrate their ML model seamlessly with its existing MES and SCADA layers to ensure real-time adjustments to machine settings without manual intervention. This integration would allow for real-time feedback loops and eliminate the possibility of human error, ensuring consistency between machine parameters and the MES system's data.
IndustryApps implemented a Digital Twin solution that integrated the ML model directly into the digital twin of the machine and product. Key features of the solution included :
By leveraging IndustryApps' Digital Twin platform, the automotive Tier 1 manufacturer was able to achieve real-time feedback loops, seamlessly integrate their ML model, and ensure precise control over their production processes. This improved operational efficiency, higher product quality, and accurate traceability across the production line.
Join the future of industrial digital transformation with IndustryApps. Experience seamless integration, unparalleled data control, and cutting-edge technology tailored to your needs.
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Industrial data management involves collecting, integrating, and analysing data from various sources within an industrial environment. It is crucial for enhancing operational efficiency, making informed decisions, and driving innovation through real-time insights and predictive analytics.
Industrial data management involves collecting, integrating, and analysing data from various sources within an industrial environment. It is crucial for enhancing operational efficiency, making informed decisions, and driving innovation through real-time insights and predictive analytics.
An AI co-pilot provides real-time insights and recommendations, helping operators optimize processes, reduce downtime, and improve productivity by leveraging advanced analytics and machine learning algorithms.
An AI co-pilot provides real-time insights and recommendations, helping operators optimize processes, reduce downtime, and improve productivity by leveraging advanced analytics and machine learning algorithms.
Cloud solutions are hosted online, offering flexibility and remote access. On-premise solutions are installed locally on your hardware, providing greater control and security.
Cloud solutions are hosted online, offering flexibility and remote access. On-premise solutions are installed locally on your hardware, providing greater control and security.
Our software includes robust security measures such as data encryption, regular updates, and compliance with industry standards to protect your data.
Our software includes robust security measures such as data encryption, regular updates, and compliance with industry standards to protect your data.
Yes, our solutions are designed for seamless integration with ERP, MES, IIOT, and other IT systems, ensuring smooth operation across all platforms.
Yes, our solutions are designed for seamless integration with ERP, MES, IIOT, and other IT systems, ensuring smooth operation across all platforms.
Implementation times vary based on the project’s complexity, but our streamlined process ensures you’re up and running quickly, minimizing downtime and disruption.
Implementation times vary based on the project’s complexity, but our streamlined process ensures you’re up and running quickly, minimizing downtime and disruption.
AI-supported data mapping ensures consistency and accuracy across all data sources, enhancing data quality and facilitating advanced analytics and AI applications.
AI-supported data mapping ensures consistency and accuracy across all data sources, enhancing data quality and facilitating advanced analytics and AI applications.
