A robust MCDM approach for ERP system selection under uncertain environment based on worst case scenario

Abstract

Purpose

ERP selection is a multi-faceted process and needs to be successful in dealing with high uncertainty. The purpose of this paper is to propose a novel multi-criteria decision making (MCDM) approach, titled as cloud-based design optimization (CBDO), for ERP selection problem to handle high uncertainty with a computationally effective way.

Design/methodology/approach

CBDO has been utilized as an alternative method to fuzzy set theory and stochastic programming, and proposes robust findings for worst case scenario. In order to assess the proposed methodology, a numerical study is conducted by taking into account existing state-of-the-art study on the ERP selection problem for the small medium enterprises. The outputs of the existing state-of-the-art study are assumed as uncertain and varying across time as it is expected in real life; therefore, different scenarios are created in order to reveal the effect of uncertainty on decisions.

Findings

In the methodology, the results given under uncertain conditions are compared with the results obtained under stable conditions. It is clearly seen that ERP system selection problem area has high sensitivity to the uncertain environment, and decision makers should not undervalue the unsteadiness of criteria during the ERP system selection process, especially within volatile economies.

Originality/value

This study contributes to the relevant literature by utilizing CBDO as a MCDM tool in the selection of the ERP software as a first time, and validating the impact of unsteadiness on the ERP selection procedure. It is the first CBDO-based study that validates the effect of distributional differences on uncertainties in the ERP selection processes.

Citation

Temur, G.T. and Bolat, B. (2018), "A robust MCDM approach for ERP system selection under uncertain environment based on worst case scenario", Journal of Enterprise Information Management, Vol. 31 No. 3, pp. 405-425. https://doi.org/10.1108/JEIM-12-2017-0175