<i>K</i>-means clustering combined with principal component analysis for material profiling in automotive supply chains

At a time where available data is rapidly increasing in both volume and variety, descriptive mining (DM) can be an important tool to support meaningful decision-making processes dynamic supply chain (SC) contexts. Up until now, however, scarce attention has been given the application of DM techniques field inventory management. Here, we take advantage detect grasp patterns among several features that coexist real-world automotive SC. Principal component analysis (PCA) employed analyse understand interrelations between ten quantitative dependent variables multi-item/multi-supplier environment. Afterwards, principal scores are characterised via K-means clustering, allowing us classify samples into four clusters derive different profiles for multiple items. This work provides evidence contributes find interesting feature-patterns, resulting identification risk may effectively leverage management improved SC performance. [Received: 5 April 2019; Revised: 1 December 22 January 2020; Accepted: 21 2020]