Reducing data requirements when selecting key performance indicators for supply chain management: The case of a multinational automotive component manufacturer

The recent trend towards collecting large amounts of data potentially allows organisations to identify previously unknown patterns that can lead significant improvements in their performance. However, carrying on this over time and across numerous locations is expensive. Consequently, when monitoring performance, be faced with a dichotomy between continuing collect or whether use much reduced set data. This particular problem Key Performance Indicators (KPIs). Additionally, too many indicators difficulty interpretation overlaps the indicators, making understanding managing changes performance more difficult. In paper, novel statistical approach introduced based Principal Component Analysis (PCA) reduce number KPIs, followed by TOPSIS (Technique for Order Similarity Ideal Solution) validating results. It applied case multinational automotive component manufacturer where 28 KPIs were 8. original was compared 8 KPIs. peaks two time-series coincided, there high correlation them. Therefore, having extra 20 provided little precision considered interval. Hence, valuable tool helping down practical useable number.