A “genomic” classification scheme for supply chain management information systems

Abstract

Purpose

This paper has the objective of demonstrating a more structured and useful method for evaluating functionality of enterprise software packages such as supply chain management information systems (SCM IS). Existing taxonomies have limited utility for software selection and analysis due to the variation and overlap in functionality found in modern enterprise systems.

Design/methodology/approach

A qualitative analysis of over 1,800 pages of SCM IS documentation and independent analyst reports is used to identify relevant SCM IS functional attributes in the seven most widespread SCM IS packages. Pattern matching and coding of constructs is used to iteratively build a hierarchical taxonomy of SCM IS functionality.

Findings

The taxonomy developed describes 83 major functional attributes that form five top‐level categories: primary supply chain processes, data management, decision support, relationship management, and performance improvement. The codes representing supply chain processes agree with the widely used Supply Chain Operations Reference (SCOR) process model, although the terminology was not used consistently in vendor and analyst documents.

Research limitations/implications

The approach described enables richer classification schemes to be built that will better distinguish between the wide‐ranging functionality found in modern enterprise information systems.

Practical implications

Selection and analysis of SCM IS is difficult due to the functional overlaps in different systems. The approach described enables a more structured, detailed, and useful analysis of an organization's current or proposed information systems.

Originality/value

This paper contributes a novel approach for conceptualizing and analyzing complex information systems using hierarchical rather than traditional flat taxonomies.

Citation

McLaren, T.S. and Vuong, D.C.H. (2008), "A “genomic” classification scheme for supply chain management information systems", Journal of Enterprise Information Management, Vol. 21 No. 4, pp. 409-423. https://doi.org/10.1108/17410390810888688