Abstract
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.
Highlights
We present the results separated by an evaluation metric
It is widespread to develop new products with similar characteristics designed by different development teams, which increases the complexity of parts communization abroad for all products
Due to the high volume of data generated by multiple teams, that do not use data mining tools, this investigation can press the production cost by the supply chain increase of complexity
Summary
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). The new framework is implemented and demonstrated for additive manufacturing, where the similarities of the 3D geometry of parts and printing processes are established by identifying relevant features In this sense, a challenge for identifying pricing anomalies is to monitor whether the classifications (labels) available in the registration data are adequate for the correct grouping of parts, which should have similar manufacturing costs. There is an immediate need to expand the company’s focus to include analyzing large sample sets, which can be multidimensional and complex for manual analysis This way, clustering techniques are used to identify pricing anomalies in similar parts, using their specifications as a basis.
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