Selection and Assessment of Enterprise Resource Planning System by Using Bipolar Complex Fuzzy MCDM Approach Based on Power Muirhead Mean Operators

Abstract

The selection of enterprise resource planning (ERP) systems is one of the most strategic and profound decisions for organizations since it impacts their performance and competitive position. Due to the complexity and variety of the ERP systems that have evolved over the years, the decision-making (DM) process has become more difficult. These complexities have raised several major research gaps in the literature and the selection methodologies of ERP systems. One significant weakness is that the current strategies pay attention only to the positive aspects of the attributes of ERP systems and ignore the negative aspects of the attributes of ERP systems. As a result, the decision maker may end up with an ERP evaluation that is either unbalanced or skewed, and therefore not entirely accurate. Besides, most of the existing methods do not consider extra fuzzy information concerning the attributes of ERP, which leads to the loss of some important information needed for making a wise decision. Thus, in this manuscript, we propose a new MADM method, namely bipolar complex fuzzy MADM (BCF-MADM), which not only covers positive and negative aspects of ERP attributes but also includes additional fuzzy information and reduces the influence of the extreme values of the decision-makers’ opinions. To overcome the problem of outlier assessments, the power average (PA) operator is used, and the Muirhead mean (MM) operator is used to consider the interrelation between attribute arguments, which provides more flexibility in the aggregation process, and new power MM operators and power dual MM operators are proposed for bipolar complex fuzzy set (BCFS). These operators are used in the aggregation process of the developed MADM problem. After that, we analyze a case study related to the selection of an ERP system and then compare the anticipated theory with certain existing ones to demonstrate the supremacy of the anticipated theory.