Principal Component Analysis Method Application for Inventory Related Decisions-Making.

This research aims to analyze the importance of inventory control and management, especially regarding the components used in industrial machinery maintenance. The methodology used was based on a quantitative approach, descriptive scope, and non-experimental cross-sectional design. Principal Component Analysis (PCA) and Clustering techniques were employed to evaluate both the quantities consumed and the frequency of usage, to efficiently categorize the components according to their distinctive attributes. This approach is proposed as an objective guidance that will significantly contribute to improving effectiveness and sustainability in the overall management of spare parts inventories. The developed methodology allowed for a detailed description of spare parts behavior; 1,753 references of spare parts used in the maintenance of industrial equipment in a food factory during the period from the second half of 2021 to the first half of 2022. The highest production peaks were recorded during this time, highlighting consumed quantities and usage frequencies, guiding the material management teams in making decisions about spare parts storage. [ABSTRACT FROM AUTHOR]