# Anomaly Detection in Automotive Industry Using Clustering Methods--A Case Study
## 1 Introduction  
Efficiency in the cost management at the automotive companies is extremely important to generate competitive products in a global open market. Financial and managerial successes are linked to a fast adaption to current daily challenges, with free competition. Innovative products with reduced launch times are frequently requested, and customers perceive a high quality. Moreover, flexibility and competitive prices are prerequisites to survive in the automotive business. These challenges are increasingly present in the industry and mainly influence a company's responsiveness, versatility, speed and adaptability to changes. Simultaneously, the industry is always looking for cost reduction [1, 2, 3]. This scenario leads to constant launches of new vehicles with new configurations and options. However, low production volumes in specific configurations increase the costs due to manufacturing complexity. This often leads to decentralization in the engineering sector by a product family, which leads to the development of components with minor design changes and possibly triggering a significant increase in manufacturing cost [4; 5; 6; 7]. Many variables in the manufacturing processes and the composition of products in the automotive industry generate several combinations and configurations daily, making it impossible to process them manually. As a large volumes of data are generated, much of the potential knowledge achieved is not directly applied to similar items [3; 8]. Due to the advancement of information and management systems technology in the production chain, there is a significant increase in the amount of data generated, collected, and stored in various applications [9; 10; 11]. Therefore, efficient tools that automatically execute knowledge discoveries are useful in large data sets, especially those with complexity and heterogeneity [12; 13; 14]. However, there is a lack of methodologies that recognize similarities among the many items in the automotive industry and group it adequately to identify pricing and classification anomalies. The work developed by [PERSON] and [PERSON] [15] proposed constructing a model for grouping parts to replace the conventional manufacturing process by additive manufacturing (AM). The current evaluations of (AM) are manually done, which reduces the scope of the application. In this model, the grouping of components is done automatically by clustering algorithms for other manufacturing processes by additive manufacturing (AM). [PERSON], [PERSON] and [PERSON] [16] developed a non-threshold consensus model that combines the minimum cost and maximum consensus-increasing for multi-attribute large-group decision-making (MALGDM). The main differences between traditional clustering techniques is that instead of using a predefined threshold and a maximum number of iterations, a termination index is developed to terminate the consensus reaching process (CRP), considering different consensus metrics. In addition, [PERSON] et al. [17] introduced a two-mode modularity clustering method with new similarity measures for parts and machines using an ordinal part-machine matrix. The proposed method considers both incidences, transition between parts and machines and find optimal clusters. The method produces reasonable cell formation solutions in terms of several performance measures. [PERSON], [PERSON] and [PERSON] [18] make a literature review on intelligent cost estimation methods for parts to be procured in the manufacturing industry is carried out by text mining. Consequently, in this paper, approaches derived from Multitask Learning and Explainable Machine Learning can be found. A combination of methods considered most suitable for predictive analytics to estimate procurement costs is presented. Finally, [PERSON], [PERSON] and [PERSON] [19] developed a new cost estimation framework based on big data analytics tools. The manufacturing cost associated with a new job can be estimated based on similar ones in the past.