Machine Optimization
Machine Optimization
Machine Optimization refers to the systematic and data-driven process of enhancing the performance, efficiency, and reliability of industrial machines and processes through the utilization of real-time data collection, analysis, and adaptive tuning. The goal is to improve Overall Equipment Effectiveness (OEE) by addressing challenges in data acquisition, connectivity, and process inefficiencies. It involves the integration of advanced technologies such as Artificial Intelligence (AI) and Machine Learning (ML) to gain insights from rich process and diagnostics data, enabling continuous improvement and proactive maintenance. Machine Optimization aims to achieve optimal production outcomes by providing a unified environment for data collection, visualization, performance tuning, and integration with other applications in both on-premises and cloud environments.
CoreTigo’s TigoLeap is an integrated software platform designed for real-time vendor-agnostic data collection and machine optimization. It addresses challenges in data acquisition by offering a standardized connectivity solution that handles diverse industrial protocols, interfaces, and equipment types. TigoLeap facilitates the seamless setup of data collection without programming, capturing high-resolution synchronized data, and providing tools for visualization, performance tuning, and integration with Business Intelligence (BI), AI, and ML applications. The platform’s key functionalities include user-friendly edge data collection, interactive visualization, adaptive machine process tuning, and integration capabilities for both on-premises and cloud applications. TigoLeap’s focus on performance improvement, machine-centric approach, and user-friendly design makes it a valuable solution for machine builders and manufacturers seeking enhanced machine development processes, efficient commissioning, reduced ramp-up times, and improved remote support and collaboration. The platform’s flexibility and vendor-agnostic nature address the gaps in current solutions tailored for specific machines, making it suitable for a variety of machine types in diverse industrial settings.