In 2015, the Object Management Group published the Decision Model and Notation with the goal to structure and connect business processes, decisions and underlying business logic. Practice shows that several vendors adopted the DMN standard and (started to) integrate the standard with their tooling. However, practice also shows that there are vendors who (consciously) deviate from the DMN standard while still trying to achieve the goal DMN is set out to reach. This research aims to 1) analyze and benchmark available tooling and their accompanied languages according to the DMN-standard and 2) understand the different approaches to modeling decisions and underlying business logic of these vendor specific languages. We achieved the above by analyzing secondary data. In total, 22 decision modelling tools together with their languages were analyzed. The results of this study reveal six propositions with regards to the adoption of DMN with regards to the sample of tools. These results could be utilized to improve the tools as well as the DMN standard itself to improve adoption. Possible future research directions comprise the improvement of the generalizability of the results by including more tools available and utilizing different methods for the data collection and analysis as well as deeper analysis into the generation of DMN directly from tool-native languages.
Process Mining can roughly be defined as a data-driven approach to process management. The basic idea of process mining is to automatically distill and to visualize business processes using event logs from company IT-systems (e.g. ERP, WMS, CRM etc.) to identify specific areas for improvement at an operational level. An event log can be described as a database entry that signifies a specific action in a software application at a specific time. Simple examples of these actions are customer order entries, scanning an item in a warehouse, and registration of a patient for a hospital check-up.Process mining has gained popularity in the logistics domain in recent years because of three main reasons. Firstly, the logistics IT-systems' large and exponentially growing amounts of event data are being stored and provide detailed information on the history of logistics processes. Secondly, to outperform competitors, most organizations are searching for (new) ways to improve their logistics processes such as reducing costs and lead time. Thirdly, since the 1970s, the power of computers has grown at an astonishing rate. As such, the use of advance algorithms for business purposes, which requires a certain amount of computational power, have become more accessible.Before diving into Process Mining, this course will first discuss some basic concepts, theories, and methods regarding the visualization and improvement of business processes.
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Organizations are struggling to choose from or combine the different business process management paradigms offered in today's BPM landscape, such as workflow management, dynamic case management and straight through processing. The field of declarative processes seems to be able to address this challenge by offering a unified approach to business process modeling, providing variable amounts of flow at execution time and different levels of autonomy to the actors based on models using a single formalism. The notion of declarativity in business processes seems to be ill defined and is often treated as a black and white distinction. However, a number of quite different formalisms have been developed that are broadly agreed to be declarative. This paper proposes a number of qualitative characteristics to characterize the declarative nature of process modeling formalisms. The characteristics are evaluated by applying them to a number of relevant process modeling formalisms, both imperative and declarative, and we discuss how these characteristics can be utilized to create business processes that offer activity flows that are known up front where needed, and allow ad hoc approaches to offer experts freedom and to support impediment driven approaches in an STP context.
