Improving merger and acquisition decision-making using fuzzy logic and simulation

Abstract

Mergers and acquisitions (M&A) are an internationally adopted expansion strategy, but not every case can be successfully executed nor achieved the intended post-expansion results. Most studies focus on M&A wave or post-M&A integration, yet this article focuses on pre-M&A analysis and planning. An M&A evaluation and prioritization model (MAEPM) is proposed to assist decision makers in a more objective and effective manner so that to implement and execute each M&A deal with the aim of maximizing the success rate rather than any other irrelevant reasons. Risk analysis, fuzzy critical path analysis, cost–benefit evaluation, as well as decision rule and prioritization were developed and integrated, which provide insight into M&A evaluation and serve as indicators. Eleven case studies were conducted to verify the MAEPM and the results generated from the MAEPM were compared with the actual results that confirmed the MAEPM is significant and plausible. The novel MAEPM is confirmed to be reliable that enables firms to select the most winning M&A deal(s) to be made according to the availability of resources and capital and thus enhance the success rate of M&A.