Prescriptive Process Monitoring (PrPM) systems recommend interventions in ongoing business process cases to improve performance. However, performance gains only materialize if users follow the recommendations. Prior research has shown that users are more likely to follow recommendations when they understand them. In this paper, we explore the use of Large Language Models (LLMs) to generate explanations for PrPM recommendations. We developed a prompting method based on typical user questions and integrated it into an existing PrPM system. Our evaluation indicates that LLMs can help users of PrPM systems to better understand the recommendations. The results indicate that LLMs can help users of PrPM systems to better understand the recommendations, and to produce recommendations that have sufficient detail and fulfill their expectations. However, the explanations fall short in addressing the underlying "why" and do not always support users in assessing the trustworthiness of the recommendations.

Abstract Prescriptive process monitoring (PrPM) systems analyze ongoing business process instances to recommend real-time interventions that optimize performance. The usefulness of these systems hinges on users applying the generated recommendations. Thus, users need to understand the rationale behind these recommendations. One way to build this understanding is to enhance each recommendation with explanations. Existing approaches generate explanations consisting of static text or plots, which users often struggle to understand. Previous work has shown that dialogue systems enhance the effectiveness of explanations in recommender systems. Large Language Models (LLMs) are an emerging technology that facilitates the construction of dialogue systems. In this paper, we investigate the applicability of LLMs for generating explanations in PrPM systems. Following a design science approach, we elicit explainability questions that users may have for PrPM outputs, we design a prompting method on this basis, and we conduct an evaluation with potential users to assess their perception of the explanations and their approach to interact with the system. The results indicate that LLMs can help users of PrPM systems to better understand the origin of the recommendations, and to produce recommendations that have sufficient detail and fulfill their expectations. On the other hand, users find that the explanations do not always address the “why” of a recommendation and do not let them judge if they can trust the recommendation.

Abstract Prescriptive process monitoring methods recommend interventions during the execution of a process to maximize its success rate. Current research in this field focuses on algorithms to learn intervention policies that maximize the expected payoff of the interventions under certain statistical assumptions. In contrast, there has been limited attention on how to aid process stakeholders in understanding the outputs of these algorithms. In this research, we set to develop an interface to provide end users with relevant information to guide the decision on where and when to trigger interventions in a process. We draw upon an analysis of existing solutions and a review of the literature to elicit information items for a user interface for prescriptive process monitoring. Thereon, we develop a user interface concept and evaluate it with experts. The evaluation confirms the informational needs covered by the user interface concept. In addition, the evaluation shows that different end-user groups (operational users, tactical managers, and process analysts) can benefit from the information items included in the interface.

Abstract Organizations constantly seek ways to improve their business processes. This often involves using digital technologies to enable process improvements. However, simply substituting existing technology with newer technology has limited value as compared to using the capabilities of digital technologies to introduce changes to business processes. Therefore, process analysts need to understand how the capabilities of digital technologies can be used to redesign business processes. In this paper, we conducted a systematic literature review and examined 40 case studies where digital technologies were used to redesign business processes. We identified that, within the context of business process improvement, capabilities of digitalization, communication, analytics, digital representation, and connectivity can enable business process redesign. Furthermore, we note that these capabilities enable applying nine redesign heuristics. Based on our review, we map how each capability can facilitate the implementation of specific redesign heuristics to improve a business process. Thus, our mapping can aid analysts in identifying candidate redesigns that capitalize on the capabilities of digital technologies.

Abstract Prescriptive process monitoring methods recommend interventions during the execution of a case that, if followed, can improve performance. Research on prescriptive process monitoring so far has focused mainly on improving the underlying algorithms and providing suitable explanations for recommendations. Empirical works indicate, though, that process workers often do not follow recommendations even if they understand them. Drawing inspiration from the field of persuasive technology, we developed and evaluated a visualization that nudges process workers towards accepting a recommendation, following a design science approach. Our evaluation points towards the feasibility of the visualization and provides insights into how users perceive different persuasive elements, thus providing a basis for the design of future systems.

Abstract Organizations have a vital interest in continuously improving their business processes. Process analysts can use process mining tools that provide data-driven discovery and analysis of business processes to achieve this. Current research has mainly focused on creating and evaluating new tools or reporting process mining case studies from different domains. Although usage of process mining has increased in industry, insights into how analysts work with such methods to identify improvement opportunities have consequently been limited. To reduce this gap, we conducted an exploratory interview study of seven process analysts from different domains. Our findings indicate that process analysts assess improvement opportunities by their impact, the feasibility of required implementation, and stakeholders’ input. Furthermore, our results indicate that process mining tools, when used to identify improvement opportunities, do not provide sufficient support for analysis, requiring process analysts to use additional tools. Lastly, analysts use storytelling to frame and communicate their findings to various stakeholders.