Anomaly Detection in Automotive Industry Using Clustering Methods—A Case Study

In automotive industries, pricing anomalies may occur for components of different products, despite their similar physical characteristics, which raises the total production cost of the company. However, detecting such discrepancies is often neglected since it is necessary to find the problems consi...

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Autores principales:	Marcio Trindade Guerreiro, Eliana Maria Andriani Guerreiro, Tathiana Mikamura Barchi, Juliana Biluca, Thiago Antonini Alves, Yara de Souza Tadano, Flávio Trojan, Hugo Valadares Siqueira
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	clustering cost anomaly detection classification anomaly detection automotive industry Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
Acceso en línea:	https://doaj.org/article/30d640b41c84415d99418377441764ad
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spelling	oai:doaj.org-article:30d640b41c84415d99418377441764ad2021-11-11T14:59:34ZAnomaly Detection in Automotive Industry Using Clustering Methods—A Case Study10.3390/app112198682076-3417https://doaj.org/article/30d640b41c84415d99418377441764ad2021-10-01T00:00:00Zhttps://www.mdpi.com/2076-3417/11/21/9868https://doaj.org/toc/2076-3417In automotive industries, pricing anomalies may occur for components of different products, despite their similar physical characteristics, which raises the total production cost of the company. However, detecting such discrepancies is often neglected since it is necessary to find the problems considering the observation of thousands of pieces, which often present inconsistencies when specified by the product engineering team. In this investigation, we propose a solution for a real case study. We use as strategy a set of clustering algorithms to group components by similarity: K-Means, K-Medoids, Fuzzy C-Means (FCM), Hierarchical, Density-Based Spatial Clustering of Applications with Noise (DBSCAN), Self-Organizing Maps (SOM), Particle Swarm Optimization (PSO), Genetic Algorithm (GA) and Differential Evolution (DE). We observed that the methods could automatically perform the grouping of parts considering physical characteristics present in the material master data, allowing anomaly detection and identification, which can consequently lead to cost reduction. The computational results indicate that the Hierarchical approach presented the best performance on 1 of 6 evaluation metrics and was the second place on four others indexes, considering the Borda count method. The K-Medoids win for most metrics, but it was the second best positioned due to its bad performance regarding SI-index. By the end, this proposal allowed identify mistakes in the specification and pricing of some items in the company.Marcio Trindade GuerreiroEliana Maria Andriani GuerreiroTathiana Mikamura BarchiJuliana BilucaThiago Antonini AlvesYara de Souza TadanoFlávio TrojanHugo Valadares SiqueiraMDPI AGarticleclusteringcost anomaly detectionclassification anomaly detectionautomotive industryTechnologyTEngineering (General). Civil engineering (General)TA1-2040Biology (General)QH301-705.5PhysicsQC1-999ChemistryQD1-999ENApplied Sciences, Vol 11, Iss 9868, p 9868 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	clustering cost anomaly detection classification anomaly detection automotive industry Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
spellingShingle	clustering cost anomaly detection classification anomaly detection automotive industry Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999 Marcio Trindade Guerreiro Eliana Maria Andriani Guerreiro Tathiana Mikamura Barchi Juliana Biluca Thiago Antonini Alves Yara de Souza Tadano Flávio Trojan Hugo Valadares Siqueira Anomaly Detection in Automotive Industry Using Clustering Methods—A Case Study
description	In automotive industries, pricing anomalies may occur for components of different products, despite their similar physical characteristics, which raises the total production cost of the company. However, detecting such discrepancies is often neglected since it is necessary to find the problems considering the observation of thousands of pieces, which often present inconsistencies when specified by the product engineering team. In this investigation, we propose a solution for a real case study. We use as strategy a set of clustering algorithms to group components by similarity: K-Means, K-Medoids, Fuzzy C-Means (FCM), Hierarchical, Density-Based Spatial Clustering of Applications with Noise (DBSCAN), Self-Organizing Maps (SOM), Particle Swarm Optimization (PSO), Genetic Algorithm (GA) and Differential Evolution (DE). We observed that the methods could automatically perform the grouping of parts considering physical characteristics present in the material master data, allowing anomaly detection and identification, which can consequently lead to cost reduction. The computational results indicate that the Hierarchical approach presented the best performance on 1 of 6 evaluation metrics and was the second place on four others indexes, considering the Borda count method. The K-Medoids win for most metrics, but it was the second best positioned due to its bad performance regarding SI-index. By the end, this proposal allowed identify mistakes in the specification and pricing of some items in the company.
format	article
author	Marcio Trindade Guerreiro Eliana Maria Andriani Guerreiro Tathiana Mikamura Barchi Juliana Biluca Thiago Antonini Alves Yara de Souza Tadano Flávio Trojan Hugo Valadares Siqueira
author_facet	Marcio Trindade Guerreiro Eliana Maria Andriani Guerreiro Tathiana Mikamura Barchi Juliana Biluca Thiago Antonini Alves Yara de Souza Tadano Flávio Trojan Hugo Valadares Siqueira
author_sort	Marcio Trindade Guerreiro
title	Anomaly Detection in Automotive Industry Using Clustering Methods—A Case Study
title_short	Anomaly Detection in Automotive Industry Using Clustering Methods—A Case Study
title_full	Anomaly Detection in Automotive Industry Using Clustering Methods—A Case Study
title_fullStr	Anomaly Detection in Automotive Industry Using Clustering Methods—A Case Study
title_full_unstemmed	Anomaly Detection in Automotive Industry Using Clustering Methods—A Case Study
title_sort	anomaly detection in automotive industry using clustering methods—a case study
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/30d640b41c84415d99418377441764ad
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Anomaly Detection in Automotive Industry Using Clustering Methods—A Case Study

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