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

• Published in: Applied sciences 2021-11, Vol.11 (21), p.9868
• Main Authors: Guerreiro, Marcio Trindade, Guerreiro, Eliana Maria Andriani, Barchi, Tathiana Mikamura, Biluca, Juliana, Alves, Thiago Antonini, de Souza Tadano, Yara, Trojan, Flávio, Siqueira, Hugo Valadares
• Format: Article
• Language: English
• Subjects: Additive manufacturing; Algorithms; Anomalies; Automobile industry; automotive industry; Case studies; classification anomaly detection; Clustering; Computer applications; cost anomaly detection; Costs; Evolutionary algorithms; Evolutionary computation; Genetic algorithms; Knowledge; Learning algorithms; Machine learning; Methods; Optimization; Physical characteristics; Physical properties; Self organizing maps; Similarity
• ISSN: 2076-3417; 2076-3417
• DOI: 10.3390/app11219868

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.