Using different neural networks to predict production costs based on multisource information fusion

What is it about?

This paper presents a new method for predicting production costs in Industry 4.0 using data fusion from multiple sources. The method uses artificial neural networks (ANNs) and convolutional neural networks (CNNs) to predict production costs based on data from multiple sources, such as sensors, machines, and enterprise resource planning (ERP) systems. The results show that CNNs have better prediction accuracy than ANNs. This method can help manufacturers to improve their production planning and cost control.

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Photo by Natalie Parham on Unsplash

Photo by Natalie Parham on Unsplash

Why is it important?

The key innovation of this work lies in the application of CNNs for production cost prediction, which has not been explored in previous studies. The experimental results demonstrate that CNNs outperform ANNs in terms of prediction accuracy. This method offers several advantages, including improved production planning, enhanced cost control, and broader applicability across diverse manufacturing industries.