Visual hierarchical clustering of supply chain using growing hierarchical self-organising map algorithm

Abstract

The study identifies a need for efficient and robust visual clustering approach that can potentially deal with complex supply chain clustering problems. Based on the underlying philosophy of group technology, a growing hierarchical self-organising map algorithm (GHSOM) is proposed to identify a lower two-dimension visual clustering map that can effectively address supply chain clustering problems. The proposed approach provides optimal solutions by decomposing a large-sized supply chain problem into independent, small, manageable problems. It facilitates simple decision-making by exploring similar clusters that are represented by the neighbouring branches in the GHSOM map structure. Unlike other approaches in literature, the proposed approach can further attain good topological ordered representations of the various work order families, to be processed by clusters of supply units along with information on hierarchical sub-cell formation as identifiable from the visually navigable map. The proposed approach has been successfully applied on 16 benchmarked problems. The performance of GHSOM based on grouping efficacy measure outperformed the best results in literature.

Keywords:

• supply chain clustering
• visual clustering
• cellular manufacturing
• growing hierarchical self-organising map
• grouping efficacy

Acknowledgement

The authors are indebted and convey their heartfelt thanks to the Area Editor and anonymous reviewers for their valuable comments and constructive suggestions on this work which have improved the quality of the paper substantially.

Disclosure statement

No potential conflict of interest was reported by the authors.