Key Factor Extraction of Supply Chain Performance Based on Heterogeneous Selective Ensemble PCA
It is valuable for supply chain management to analyze and evaluate the operation performance of supply chain, and use timely and accurate operation state feedback to adjust the system to keep smooth running. Aiming at the complexity and uncertainty of supply chain, this thesis put forward a heterogeneous selective ensemble principle component analysis (PCA) algorithm based on fuzzy integral via Bagging ensemble learning. Besides, by using dimension reduction on high-dimensional data set, it realizes to extract the key factors of supply chain performance and breaks the bottleneck that problems in supply chain management cannot be recognized rapidly or analyzed by traditional performance evaluation method. According to the empirical research on survey data of supply chain performance at C Group, the algorithm is proved be effective.
Hongyang Zhao, Kun Liu and Xiaoguang Yu
C. H. Zhang et al., "Key Factor Extraction of Supply Chain Performance Based on Heterogeneous Selective Ensemble PCA", Applied Mechanics and Materials, Vols. 397-400, pp. 2626-2630, 2013