<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>The current research is dedicated to harnessing cutting-edge technologies within the paradigm of Industry 5.0. The objective is to capitalize on advancements in Machine and Deep Learning techniques. This research endeavors to construct robust predictive models, utilizing historical data, for precise real-time predictions in estimating material quantities within a cement workshop. Machine Learning regressors evaluated based on several metrics, SVR (R-squared 0.9739, MAE 0.0403), Random Forest (R-squared 0.9990, MAE 0.0026), MLP (R-squared 0.9890, MAE 0.0255), Gradient Boosting (R-squared 0.9989, MAE 0.0042). The time series models LSTM and GRU yielded R-squared 0.9978, MAE 0.0100, and R-squared 0.9980, MAE 0.0099, respectively. The ultimate outcomes include improved and efficient production, optimization of production processes, streamlined operations, reduced downtime, mitigation of potential disruptions, and the facilitation of the factory’s evolution towards intelligent manufacturing processes embedded within the framework of Industry 5.0. These achievements underscore the potential impact of leveraging advanced machine learning techniques for enhancing the operational dynamics and overall efficiency of manufacturing facilities</p>
</abstract>

The current research is dedicated to harnessing cutting-edge technologies within the paradigm of Industry 5.0. The objective is to capitalize on advancements in Machine and Deep Learning techniques. This research endeavors to construct robust predictive models, utilizing historical data, for precise real-time predictions in estimating material quantities within a cement workshop. Machine Learning regressors evaluated based on several metrics, SVR (R-squared 0.9739, MAE 0.0403), Random Forest (R-squared 0.9990, MAE 0.0026), MLP (R-squared 0.9890, MAE 0.0255), Gradient Boosting (R-squared 0.9989, MAE 0.0042). The time series models LSTM and GRU yielded R-squared 0.9978, MAE 0.0100, and R-squared 0.9980, MAE 0.0099, respectively. The ultimate outcomes include improved and efficient production, optimization of production processes, streamlined operations, reduced downtime, mitigation of potential disruptions, and the facilitation of the factory’s evolution towards intelligent manufacturing processes embedded within the framework of Industry 5.0. These achievements underscore the potential impact of leveraging advanced machine learning techniques for enhancing the operational dynamics and overall efficiency of manufacturing facilities

Forecasting material quantity using machine learning and times series techniques

Laboratory of Automation and Manufacturing

Industrial Engineering Department, Batna 2 University

Safety Engineering Interest Group, Department of Chemical and Environmental Engineering, Faculty of Engineering

Journal of Electrical Engineering

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

{"article-title":"Forecasting material quantity using machine learning and times series techniques"}

The current research is dedicated to harnessing cutting-edge technologies within the paradigm of Industry 5.0. The objective is to capitalize on advancements...

Forecasting material quantity using machine learning and times series techniques

Publicado en línea: 08 jun 2024

Páginas: 237 - 248

Recibido: 12 abr 2024

DOI: https://doi.org/10.2478/jee-2024-0029

Palabras clave
advanced technologies, intelligent manufacturing, smart manufacturing, forecasting, machine learning, time series

© 2024 Hanane Zermane et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Forecasting material quantity using machine learning and times series techniques

Publicado en línea: 08 jun 2024

Páginas: 237 - 248

Recibido: 12 abr 2024

DOI: https://doi.org/10.2478/jee-2024-0029

Palabras claveadvanced technologies, intelligent manufacturing, smart manufacturing, forecasting, machine learning, time series

© 2024 Hanane Zermane et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Palabras clave
advanced technologies, intelligent manufacturing, smart manufacturing, forecasting, machine learning, time series