Quantifying and Explaining Machine Learning Uncertainty in Predictive Process Monitoring: An Operations Research Perspective

In today's highly competitive and complex business environment, organizations are under constant pressure to optimize their performance and decision-making processes. According to Herbert Simon, enhancing organizational performance relies on effectively channeling finite human attention towards critical data for decision-making, necessitating the integration of information systems (IS), artificial intelligence (AI) and operations research (OR) insights [1]. Recent OR research provides evidence in support of this proposition, as the discipline has witnessed a transformation due to the abundant availability of rich and voluminous data from various sources coupled with advances in machine learning [2]. As of late, heightened academic attention has been devoted to prescriptive analytics, a discipline that suggests combining the results of predictive analytics with optimization techniques in a probabilistic framework to generate responsive, automated, restricted, time-sensitive, and ideal decisions [3]. The confluence of AI and OR is evident due to their interdependent and complementary nature, as both disciplines strive to augment decision-making processes through computational and mathematical methodologies [4].