How can a group of procurement experts select suppliers? An approach for group decision support

Abstract

Purpose

The application of theories on group decision support is yet to be explored extensively in supplier selection literature, although the literature in both domains is extremely rich, in isolation. The purpose of this paper is to explore the application of group decision support theories for supplier selection.

Design/methodology/approach

The row geometric mean method (RGMM) of the analytic hierarchy process (AHP) has been used in this study for the prioritization of group preferences under consensus. A case study was conducted to test the theories of consensual group decision making and compare it with other approaches based on AHP.

Findings

The study establishes that the application of decision support theories for group decision making can improve the supplier selection process. Findings further imply that RGMM is more effective than eigen value method, for group decision making under consensus.

Research limitations/implications

Methodologically, the study highlights the greater regularity in outcome of group decision making, vis-à-vis individual decision making, for the same decision-making context. Also, it highlights how RGMM is more effective since it preserves reciprocal properties and diversity in preferences better.

Practical implications

The study establishes that firms can improve supplier selection processes by leveraging on the collective expertise of a group rather than depending on individual decision-making expertise.

Originality/value

This study explores the application of different theories based on AHP for consensual group decision making. It compares different approaches based on AHP and establishes that RGMM is a superior approach for supplier selection.

Citation

Kumar Kar, A. and Kumar Pani, A. (2014), "How can a group of procurement experts select suppliers? An approach for group decision support", Journal of Enterprise Information Management, Vol. 27 No. 4, pp. 337-357. https://doi.org/10.1108/JEIM-10-2012-0076