Business Process Technology
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Item Extracting Decision Models from Digitally Drawn or Hand-drawn DMN Images(2025-01-07) Leribaux, Aurélie; Heijmans, Caroline; Goossens, Alexandre; Vanthienen, JanDecision Model and Notation (DMN) models are used to model and automate operational decisions. Frequently, these DMN models are distributed as images within documents, either as screenshots or as pictures of hand-drawn models. This distribution method can results in the loss of the original source format. Re-using these images then entails the manual process of remodelling or redrawing them, a task that is both time-consuming and complex. In this study, deep learning techniques are employed to extract DMN models from both digitally drawn and hand-drawn DMN images. A substantial dataset was collected and annotated to train and test the diverse range of models. Subsequently, the work's outcome has been integrated into a DMN Computer Vision Tool application which can be used to reconstruct DMN source files based on hand-drawn sketches and digital images.Item When Technology Assets become Liabilities: Evolution of the Business Process Management Systems Market(2025-01-07) Behnam, Maryam; Zur Muehlen, MichaelTracing market entries and exits, we present a longitudinal analysis of the marketplace for Business Process Management Systems and identify four successive phases of development that match the theory of industry life cycles: Introduction, Growth, Maturity, and Decline. Looking more closely at the transitions between these phases, we show that external technology developments can be seen as environmental shocks that signal the transition from one life cycle phase to the next. Our findings provide a novel mechanism for analysts to forecast possible technology market shifts.Item Structuring Intelligent Automation: A Hyperautomation Taxonomy(2025-01-07) Neis, Nicolas; Kathol, Elise; Winkelmann, AxelHyperautomation is an emerging technology stack that combines various automation technologies to streamline and optimize business processes. Companies increasingly adopt these technologies to reduce costs, increase efficiency, and gain a competitive advantage. This study focuses on combining the different technologies under the hyperautomation approach. From a management perspective, we examine the benefits and challenges of hyperautomation that focus on increasing company productivity. Based on a literature review, we develop a taxonomy that enables a profound understanding of the complex hyperautomation landscape. Findings underscore the substantial potential of hyperautomation to improve productivity, particularly through cost and time savings and enhanced employee efficiency. This research addresses the lack of systematization in previous studies, presenting a nuanced understanding of hyperautomation's implications for managerial strategies.Item Introduction to the Minitrack on Business Process Technology(2025-01-07) Alexander Reijers, Hajo; Leopold, Henrik; Van Der Aa, Han; Corea, CarlItem Tell Me What to Do: Automatically Generating Process Improvement Suggestions(2025-01-07) Rochlitzer, Alexander; Leopold, HenrikProcess mining techniques play an important role for understanding, analyzing, and improving business processes. Despite their value, deriving actionable improvement measures from process mining insights remains challenging, requiring manual analysis by process analysts. Existing approaches and frameworks provide abstract suggestions, necessitating translation into actionable solutions. Recent efforts focus on generating alternative execution paths rather than explainable improvement suggestions based on specific identified weaknesses, leaving process improvement a labor-intensive task. Addressing this gap, we propose a natural language-driven technique leveraging Large Language Models (LLMs) and social media posts as a rich information source for business-to-consumer (B2C) processes. Our technique identifies process weaknesses from social media posts and generates improvement suggestions using multiple knowledge resources. An evaluation against manually annotated posts demonstrates the effectiveness of our approach, producing suggestions perceived as more useful than human-generated ones. Each suggestion is traceable to its source, enhancing explainability and validity. Furthermore, our technique allows to adapt its knowledge base, allowing seamless integration of additional knowledge resources. Thus, it offers a promising avenue to automate and streamline process redesign efforts across diverse contexts, reducing manual effort in the business process management lifecycle.