2022
DOI: 10.1186/s40537-022-00644-w
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Machine learning approach for predicting production delays: a quarry company case study

Abstract: Predictive maintenance employing machine learning techniques and big data analytics is a benefit to the industrial business in the Industry 4.0 era. Companies, on the other hand, have difficulties as they move from reactive to predictive manufacturing processes. The purpose of this paper is to demonstrate how data analytics and machine learning approaches may be utilized to predict production delays in a quarry firm as a case study. The dataset contains production records for six months, with a total of 20 col… Show more

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Cited by 7 publications
(2 citation statements)
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“…The study employed a learning rate weight value of 0.1 of four hidden layers with error of 0.01. Several studies use various methods to predict product sales [11]- [13], compare forecasting techniques for financial prediction [14], supply chain [15]- [17], and manufacturing processes [18].…”
Section: Related Workmentioning
confidence: 99%
“…The study employed a learning rate weight value of 0.1 of four hidden layers with error of 0.01. Several studies use various methods to predict product sales [11]- [13], compare forecasting techniques for financial prediction [14], supply chain [15]- [17], and manufacturing processes [18].…”
Section: Related Workmentioning
confidence: 99%
“…To overcome most of these challenges in the correlation of oil viscosity, soft-computing and machine learning-based computational models were adopted in estimating the viscosity of crude oil. Linear regression, artificial neural networks, support vector machines, decision tree and so many other machine learning methods (see [35][36][37][38][39][40][41][42][43][44]) have been reported to have performed more efficiently than conventional empirical correlations. A study that involved predicting the viscosity of crude oil samples from Nigeria using a neural network with 0.99 as the coefficient of correlation presented an improved performance when compared to already developed empirical correlations.…”
Section: Introductionmentioning
confidence: 99%