Machine learning for KPIs prediction: a case study of the overall equipment effectiveness within the automotive industry

Mazgualdi, Choumicha El; Masrour, Tawfik; Hassani, Ibtissam El; Khdoudi, Abdelmoula

doi:10.1007/s00500-020-05348-y

Cited by 22 publications

(9 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…• Support Vector Regression Generic Algorithm [23,26,27]; • Extreme Gradient Boost [15,23,26]; • Artificial Neural Network [19,24,28,29];…”

Section: Oee Prediction With Machine Learningmentioning

confidence: 99%

Overall Equipment Effectiveness Prediction with Multiple Linear Regression for Semi-automatic Automotive Assembly Lines

Dobra,

Jósvai

2023

Period. Polytech. Mech. Eng.

View full text Add to dashboard Cite

In the field of industry, especially in the production areas, it is particularly important that the monitoring of assembly efficiency takes place in real-time mode, and that the related data-based estimation also works quickly and reliably. The Manufacturing Execution System (MES), Enterprise Resource Planning (ERP) and Customer Relationship Management (CRM) systems used by companies provide excellent support in data recording, processes, and storing. For Overall Equipment Effectiveness (OEE) data showing the efficiency of assembly lines, there is a regular need to determine expected values. This paper focuses on OEE values prediction with Multiple Linear Regression (MLR) as supervised machine learning. Many factors affecting OEE (e.g., downtimes, cycle time) are examined and analyzed in order to make a more accurate estimation. Based on real industrial data, we used four different methods to perform prediction with various machine learning algorithms, these were the cumulative, fix rolling horizon, optimal rolling horizon and combined techniques. Each method is evaluated based on similar mathematical formulas.

show abstract

“…• Support Vector Regression Generic Algorithm [23,26,27]; • Extreme Gradient Boost [15,23,26]; • Artificial Neural Network [19,24,28,29];…”

Section: Oee Prediction With Machine Learningmentioning

confidence: 99%

Overall Equipment Effectiveness Prediction with Multiple Linear Regression for Semi-automatic Automotive Assembly Lines

Dobra,

Jósvai

2023

Period. Polytech. Mech. Eng.

View full text Add to dashboard Cite

show abstract

“…n -quantity of produced products, d -quantity of incorrect products (defects). (6) ways of interpreting the OEE model, taking into account the context of the object and the production/exploitation process, and the effects of which may be an element of the decision-making process [7,8,15,25].…”

Section: Analysis Of the Possibility And Need Of Using Oeementioning

confidence: 99%

Geometric approach to machine exploitation efficiency : modelling and assessment

Loska¹,

Paszkowski²

2022

Eksploatacja i Niezawodność – Maintenance and Reliability

View full text Add to dashboard Cite

This article presents a new approach to the exploitation assessment of machines and devices. A key aspect of this approach is the construction of the assessment model based on the geometric representation of measures associated with each other, which covers the full specifics of the exploitation process. This approach is successfully implemented by the Overall Equipment Effectiveness (OEE) model, which is fully susceptible to the geometric modelling process due to the three-way system of assessed exploitation aspects. The result of this approach is the vectored OEE model and its interpretation in terms of time series of changes in values of components. Methods of determining vector calculus measures were developed, including the second-order tensor and gradient. This is the subject of the variability of the reliability conditions of machines or production processes. It allows for the realisation of an exploitation assessment based on dynamic changes in the values of their components in the time domain. This is a significant difference to the classical static approach to such an assessment. The developed new geometric OEE model was confirmed by verification tests using the LabView software, based on two parallel data sets obtained with analytical and simulation methods using the FlexSim software.

show abstract

“…Machine learning is an algorithm to construct empirical models from the dataset and is categorized as data-driven modeling requiring a sufficient quantity of historical data to predict future demand reliably [8]. Machine learning algorithms extract essential information presented in large amounts of the recorded data, thereby achieving better performance and accuracy [7,20]. Systematic literature reviews of artificial intelligence and machine learning algorithms are provided by Duan et al [21], Borges et al [22], and Dwivedi et al [23].…”

Section: Literature Reviewmentioning

confidence: 99%

“…e nal value is evaluated by aggregating the results from all leaves of each tree [35,42]. e RF is also one of the best algorithms for estimating the importance of variables and is applied in various elds [20,[43][44][45]. Furthermore, the RF is an excellent prediction algorithm and has the advantages of its generalization and a good balance of error [11,46,47].…”

Section: Literature Reviewmentioning

confidence: 99%

Hybrid Machine Learning Model for Electricity Consumption Prediction Using Random Forest and Artificial Neural Networks

Kesornsit

Sirisathitkul

2022

Applied Computational Intelligence and Soft Computing

View full text Add to dashboard Cite

Predicting electricity consumption is notably essential to provide a better management decision and company strategy. This study presents a hybrid machine learning model by integrating dimensionality reduction and feature selection algorithms with a backpropagation neural network (BPNN) to predict electricity consumption in Thailand. The predictive models are developed and tested using an actual dataset with related predictor variables from public sources. An open geospatial data gathered from a real service as well as geographical, climatic, industrial, household information are used to train, evaluate, and validate these models. Machine learning methods such as principal component analysis (PCA), stepwise regression (SWR), and random forest (RF) are used to determine the significant predictor variables. The predictive models are constructed using the BPNN with all available variables as baseline for comparison and selected variables from dimensionality reduction and feature selection methods. Along with creating a predictive model, the most related predictors of energy consumption are also selected. From the comparison, the hybrid model of RF with BPNN consistently outperforms the other models. Thus, the proposed hybrid machine learning model presented from this study can predict electricity consumption for planning and managing the energy demand.

show abstract

Machine learning for KPIs prediction: a case study of the overall equipment effectiveness within the automotive industry

Cited by 22 publications

References 27 publications

Overall Equipment Effectiveness Prediction with Multiple Linear Regression for Semi-automatic Automotive Assembly Lines

Overall Equipment Effectiveness Prediction with Multiple Linear Regression for Semi-automatic Automotive Assembly Lines

Geometric approach to machine exploitation efficiency : modelling and assessment

Hybrid Machine Learning Model for Electricity Consumption Prediction Using Random Forest and Artificial Neural Networks

Contact Info

Product

Resources

About