Performance measurement of legacy equipment through its connection to the cloud
DOI:
https://doi.org/10.14488/BJOPM.1962.2024Keywords:
Connected Manufacturing, Intelligent Manufacturing, Cloud System, Performance Measurement, IIoTAbstract
Goal: Objective focuses on development and application of an architecture for measuring the performance of legacy equipment through its connection to the cloud.
Methodology: The research was approached by the Design Science Research (DSR) method, which focuses on the development and evaluation of the application of the artifact (legacy equipment performance measurement system) through its technological base.
Results: Both in the development and in the evaluation of the application of the system architecture in a real manufacturing system, the results were satisfactory through the accuracy obtained of 86% in the measurement carried out. Regarding equipment performance measurement, the measured index achieved an OEE of 27% efficiency, which is considered low compared to the average of companies with world-class manufacturing. It is concluded that the results made it possible to verify the reliability of the information generated by the architecture of the application system and to measure the performance of the equipment through its applicability in a real manufacturing scenario with a focus on manufacturing management.
Search limitations: The development of this research is limited to the application architecture for measuring performance for bread production line.
Practical Implications: The cloud architecture contextualizes the use of IIoT technologies (sensors, devices, among others) through cloud application architecture and how this has been transforming the industry and helping in the management of manufacturing operations.
Originality / Value: This research underscores the efficacy of a customized integration approach for leveraging existing technologies. While cloud-based Industrial Internet of Things (IIoT) solutions are readily available, this work transcends the limitations of off-the-shelf options by tailoring the architecture to the specific requirements of a real-world production line. This focus on customization enables the architecture to be adapted to a broad spectrum of legacy equipment and production lines across diverse industries, thereby unlocking the full potential of IIoT technologies. Consequently, this research demonstrates the replicability of the proposed methodology for various manufacturing scenarios.
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