A classification scheme for representing the variation in business and supply chain performance in Indian manufacturing industry. Issue 4 (2nd May 2017)

• Record Type: Journal Article
• Authors: Soni, Gunjan; Kodali, Rambabu
• Is Part Of: Benchmarking. Volume 24:Issue 4(2017)
• Page Start: 1013
• Page End: 1036
• Publication Date: 2017-05-02
• Subjects: Cluster analysis -- Taxonomy -- Supply chain management -- K-means -- Ward's agglomerative hierarchical clustering; Benchmarking (Management) -- Periodicals; Total quality management -- Periodicals; 658.562
• DOI: 10.1108/BIJ-04-2016-0049
• Languages: English
• ISSNs: 1463-5771

Abstract

Abstract : Purpose: The purpose of this paper is to identify a classification scheme which represents the variation in business and supply chain performance of supply chains in Indian manufacturing industry. Classification is done by presenting an empirical taxonomy of clusters representing supply chains in Indian manufacturing industry based on variation in supply chain excellence index (SCEI) and business performance index (BPI). Design/methodology/approach: The clustering of supply chains in Indian manufacturing industry is done by considering BPI and SCEI as clustering variables, which were found by using survey responses and results of a prior empirical study which was carried out in Indian manufacturing industry. The cluster analysis is performed by using Ward's agglomerative hierarchical clustering followed by using K -means clustering algorithm to establish final set of clusters. Findings: It was found that supply chains in Indian manufacturing industries can be clustered in four major clusters which are named as strategic, celebrity, capable and undeveloped cluster. The characteristics of these clusters reveal some major characteristics of supply chains in Indian manufacturing industry. Originality/value: The research work presented in this paper takes a novel way to introduce the clusters of supply chains in Indian manufacturing industry. The researchers who are seeking patterns in large data sets of manufacturing companies of Indian industry will be benefitted by using the proposed clusters. While practitioners who are seeking to move their supply chain one step ahead will also reap the benefits of the paper by seeking the characteristics of particular cluster.