A Method for Predicting Production Costs Based on Data Fusion from Multiple Sources for Industry 4.0: Trends and Applications of Machine Learning Methods

Abstract

There is a growing need for manufacturing processes that improve product quality and production rates while reducing costs. With the advent of multisensory information fusion technology, individuals can acquire a broader range of information. Several data fusion and machine learning methods have been discussed in this article within the context of the Industry 4.0 paradigm. Depending on its purpose, a prognostic method can be categorized as descriptive, predictive, or prescriptive. ANN and CNN models are applied to predicting production costs using neural networks based on multisource information fusion, and multisource information fusion theory is examined and applied to ANNs and CNNs. In this study, ANN and CNN predictions have been compared. CNN has demonstrated more remarkable skill in predicting the six cost categories than ANN. When predicting the true value of each cost category, CNN is superior to ANN. As a result, CNN’s forecast error for the current month’s total income is 0.0234. Because of its improved prediction accuracy and more straightforward training technique, CNN is better suited to incorporating information from several sources. Furthermore, both neural networks overestimate indirect costs, including direct material costs and item consumption prices.