This study examines the integration of Markovian and Data Envelopment Analysis (DEA) models to effectively manage the progression of cohorts within organizations and operations settings. By tracking the transitions of these cohorts through various states, the research aims to steer the system towards desired objectives through strategic interventions. We present a modeling framework that combines the stochastic nature of Markov Chains with the deterministic approach of DEA, facilitating the evaluation of policies as Decision Making Units (DMUs) in achieving anticipated outcomes. The models, which operate in both single or two-stage configurations across single and multiple targeting environments, offer insights into the dynamics of and the effectiveness of policy interventions. The findings underscore the benefits and constraints of employing these models to guide operational systems, such as human resource settings or even circular economy entities, towards optimal future structures.

The research project was supported by the Hellenic Foundation for Research and Innovation (H.F.R.I) under the "2nd Call for H.F.R.I. Research Projects to support Faculty Members & Researchers". (Project Number: 3154).