Smart Factories Grow on Data

 2022-01-26 By: Elroy Yao

Today a growing number of factories implement IIoT devices to collect data, perform analysis, and other functions with the main goals of improving efficiency & productivity. Predictive maintenance scheduling then becomes a standard procedure as it generally costs less and can finish faster than a repair operation. However this is not the end goal of IIoT, but in fact the starting steps. As competition between businesses grows, so too does the need for organizations to define their unique selling propositions (USP). This can be quite the challenge for factories and industries as they often compete on price rather than innovation.

At the same time, creating new innovations is not an easy feat when processes and materials are clearly defined and have little room for deviation. So rather than creating something new, perhaps using an existing technology creatively will be a more feasible option that can deliver concrete results sooner. This edition of the InnoVEX Editorial will discuss a few startups exhibiting in InnoVEX Online that aim to provide industrial application solutions to fulfill multiple purposes including live monitoring, sensing, and visualization.

Factories have often implemented sensors even before the proliferation of IoT; collecting data to enable automation, quality control, documentation, etc. The general principle is that the higher the degree of automation, the more sensors are implemented and the more variation of sensors are used. While conventional factories will review the data once in a certain period of time for review and improvements; especially for repairs or process adjustments. This review will often need a certain trigger before it is conducted, oftentimes errors of damages; the more catastrophic, the more details are scrutinized.

This human reliant cycle will not do for smart factories that ideally have as little unexpected downtime as possible. Maintenance will have to be conducted preventively and minimize preventable errors as well as downtimes. To achieve this, data collection will be the key as it needs to be accurate, varied, and numerous to ensure optimal results as the changes and improvements will need to be done where needed rather than specialized in a certain aspect.

Data collection is the focus of Brazil's I-Sensi that offers data collection and analysis solutions for shop floor optimization, process visualization, predictive maintenance, and environment monitoring with cost reduction and performance maximization. Their services include end-to-end IoT Packages combined with a Hardware + Software as a Service to empower stakeholders with smart dashboards, real-time data, online alerts, and machine learning analyses. The data collected includes operation/ idle information, process validation, machine status, etc. These can then be used to generate actionable reports for production supervisors, maintenance workers, and the process improvement team.

NADI SYSTEM of Taiwan focuses on a different aspect of industrial application, namely data visualization. Their 3D OCMS (3D Operation Central Management System) creates 3D digital twins to visualize IoT data in real-time. By correlating IoT data from multiple sensors/devices on a 3D model, 3D OCMS enables managers to make critical decisions through data visualization, real-time monitoring and AR maintenance systems.Their solutions include include tools for 2D and 3D visualization that can be integrated with other applications providing a seamless ability to analyze, plan and react in real time to event.

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