Organizations are in ever changing environments which results in the need for constant adaptation of business processes and structures. Continuous business process improvements can result in cost savings as well as higher efficiency and effectiveness. In some cases business process improvements can be realized through experience and competent management. However, in more complex processes, decision makers may require some form of decision support. A popular decision support method is business process simulation (BPS) One of the most commonly used applications of BPS is Discrete Event Simulation (DES). This application is also used at ING for business process management purposes. DES can be a very powerful method in case much data is available in the target system and the process is transparent. However, if a system contains deep uncertainties, a different approach is required. Deep uncertainty exists in business processes in case process analysts and other stakeholders do not know or cannot agree upon the structure of a process, the value of key variables in a process, and the valuation of desired outcomes. A possible approach in dealing with deep uncertainty is Exploratory Modeling and Analysis (EMA). EMA can be used to explore possible futures based on simulation models. Existing methods for dealing with uncertainty in discrete event simulations are largely limited to variations in input variables. Hence, it seems undesirable to use DES in highly uncertain environments. However, based on promising applications of EMA in other modeling fields (system dynamics and agent based modeling), the questions arises whether or not it is possible and if so, desirable to apply an EMA approach on DES studies. So far, no attempts have been made to apply an EMA approach on DES studies, resulting in the central research question for this thesis: How can an Exploratory Modeling and Analysis (EMA) approach be applied on Discrete Event Simulation (DES) in order to help decision makers design business processes and develop adaptive polices under uncertainty? To answer this research question an approach is proposed based on traditional DES modeling from an EMA point of view. This approach is tested in a case study at ING Arrears Management where there is a need for decision support during the development of new processes in an uncertain environment. Hence, the main objective of this thesis is to experiment with applying EMA on DES in an uncertain business process environment and to elicit the basic methodological principles for doing so. Uncertainties are identified at ING Arrears Management, aggregate simulation models are used to produce large databases with thousands of scenarios depicting a solution space full of plausible future scenarios in terms of business process performance at ING Arrears Management. This solution space is explored through an EMA methodology called scenario discovery. In scenario discovery, the Patient Rule Induction Method (PRIM) is applied to find danger zones in the solution space. PRIM is essentially a bump hunting algorithm that identifies areas in the solution space that contain a high density of cases of interest. These high density areas are interpreted as danger zones that could jeopardize the achievement of business objectives at ING Arrears Management. The application of the proposed approach towards applying EMA on DES resulted in the identification of several danger zones that form a starting point for the development of adaptive policies at ING Arrears Management for the purpose of avoiding the identified danger zones. Furthermore, bottlenecks were identified as well potential capacity issues in various sub-processes. However, numerous potentially dangerous scenarios remain unexplained through the application of PRIM analysis. Therefore, based on the case study, it can be concluded ING Arrears Management was partly helped in designing efficient new business processes in an uncertain business process environment. Even though the case study at ING Arrears Management was not completely solved through the application of the proposed approach, it can be concluded that the approach shows great potential compared to a traditional DES approach. Not only in the appropriate use of tools and techniques for EMA, but also in the application of an iterative approach in practice that resulted in helping decision makers at ING Arrears Management in identifying gaps, risks and weak spots in their proposed business processes. Considering the added value of an application of EMA at ING Arrears Management, it can be concluded that a partial proof of concept for the proposed approach has been acquired. However, the (partial) proof of concept is based on a single case study. For this reason, extrapolation of conclusions towards business processes under uncertainty in general must be done with great care. Considering the proof of (partial) proof of concept provided in this research is only valid for the case study presented in this report, the most important recommendation is to apply an EMA approach on DES on other cases where business processes under (deep) uncertainty can be identified. When choosing case studies for future research, it is recommended to select case studies in which an attempt can be made to study identified methodological obstacles including probabilistic information in DES models, application of other data mining and machine learning techniques, and further development of integrated technical tools for applying EMA on DES.