Soto Osornio, J.E.; González Lorence, A.; Navarrete Damian, J.; Morales Morales, C.; Ayala Landeros, J.G. Optimizing Productivity Indicators through the Implementation of Industry 4.0 Technologies. Preprints2024, 2024020131. https://doi.org/10.20944/preprints202402.0131.v1
APA Style
Soto Osornio, J.E., González Lorence, A., Navarrete Damian, J., Morales Morales, C., & Ayala Landeros, J.G. (2024). Optimizing Productivity Indicators through the Implementation of Industry 4.0 Technologies. Preprints. https://doi.org/10.20944/preprints202402.0131.v1
Chicago/Turabian Style
Soto Osornio, J.E., Cornelio Morales Morales and José Gabriel Ayala Landeros. 2024 "Optimizing Productivity Indicators through the Implementation of Industry 4.0 Technologies" Preprints. https://doi.org/10.20944/preprints202402.0131.v1
Abstract
Key performance indicators are essential for any company to enhance its competitiveness. How-ever, a common issue is the manual collection of production data using record sheets, which are later digitized on desktop computers and processed in Excel sheets. Shockingly, 41% of companies in Mexico have no plans for digitalization. This work aims to develop a Real-time Productivity Indicator Monitoring System using Industry 4.0 technologies for a medium-sized manufacturing firm specializing in die-cutting automotive parts. The Advanced Quality Planning methodology was applied to develop the Conceptual, Product, and Process Engineering and its validation. The experimental tests demonstrated a reliability level of 99.98%, which exceeded initial expectations. The primary contributions presented in this work are related to the development of algorithms, communication protocols, data analysis, and general optimization of the calculation process. The study's only limitation was the short experimentation time.
Keywords
Industry 4.0; Internet of Things; Programming Algorithms; Data Analisys; Digital Twin; Productivity Indicators
Subject
Engineering, Industrial and Manufacturing Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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