This book introduces a novel data cube approach for analyzing business process simulation data to support decision-making. The author, Apostolos Benisis, presents a framework designed to extract meaningful insights from complex simulation outputs, enabling more informed strategic choices. The methodology focuses on structuring simulation data in a way that facilitates multidimensional analysis, addressing the challenges of interpreting large datasets generated by process simulations.

This work is likely to be of interest to academics and practitioners in the field of Business Process Management and Operations Research. Its focus on a specific analytical technique suggests a potentially dense, technical read aimed at those with a background in data analysis and simulation. The approach presented aims to provide a structured method for deriving actionable intelligence from simulation data, which is a critical step in optimizing business processes.