Business process simulation is an approach to evaluate business process changes prior to implementation. Existing methods in this field primarily support tactical decision-making, where simulations start from an empty state and aim to estimate the long-term effects of process changes. A complementary usecase is operational decision-making, where the goal is to forecast short-term performance based on ongoing cases and to analyze the impact of temporary disruptions, such as demand spikes and shortfalls in available resources. An approach to tackle this usecase is to run a long-term simulation up to a point where the workload is similar to the current one (warm-up), and measure performance thereon. However, this approach does not consider the current state of ongoing cases and resources in the process. This paper studies an alternative approach that initializes the simulation from a representation of the current state derived from an event log of ongoing cases. The paper addresses two challenges in operationalizing this approach: (1) Given a simulation model, what information is needed so that a simulation run can start from the current state of cases and resources? (2) How can the current state of a process be derived from an event $\log$ ? The resulting short-term simulation approach is embodied in a simulation engine that takes as input a simulation model and a log of ongoing cases, and simulates cases for a given time horizon. An experimental evaluation shows that this approach yields more accurate short-term performance forecasts than long-term simulations with warm-up period, particularly in the presence of concept drift or bursty performance patterns.

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 Waiting is a waste in business processes, adversely affecting performance metrics such as cycle time or on-time delivery. Process mining techniques allow business users to analyze waiting times and their causes based on data extracted from enterprise systems. However, process mining techniques, per se, do not assist users in identifying redesign options to optimize business processes, e.g. to reduce waiting time. Recent studies suggest that Large Language Models (LLMs) may aid business users in various process analysis tasks, particularly in conjunction with process mining techniques. This paper studies how to use LLMs to assist business users in analyzing and redesigning business processes to optimize waiting time. The study compares two methods to prompt an LLM to recommend redesign options to reduce waiting times: (1) a baseline (“zero-shot”) method involving a minimalistic prompt; and (2) an enhanced method where the prompt includes descriptions of redesign patterns that may lead to redesigned processes with lower waiting times. To compare these methods, we conduct a user evaluation that combines semi-structured interviews with a survey involving process analysts. The analysts compare the recommended redesigns in terms of desirable properties of recommendation systems, such as relevance, usefulness, and diversity of the recommended redesigns. The results suggest that the enhanced prompting method yields more relevant and actionable redesign options. In contrast, the baseline produces high-level recommendations more suited for managerial decision-making.

Waiting times in a business process often arise when a case transitions from one activity to another. Accordingly, analyzing the causes of waiting times in activity transitions can help analysts identify opportunities for reducing the cycle time of a process. This paper proposes a process mining approach to decompose observed waiting times in each activity transition into multiple direct causes and to analyze the impact of each identified cause on the process cycle time efficiency. The approach is implemented as a software tool called Kronos that process analysts can use to upload event logs and obtain analysis results of waiting time causes. The proposed approach was empirically evaluated using synthetic event logs to verify its ability to discover different direct causes of waiting times. The applicability of the approach is demonstrated in a real-life process. Interviews with process mining experts confirm that Kronos is useful and easy to use for identifying improvement opportunities related to waiting times.

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 Waiting times in a business process often arise when a case transitions from one activity to another. Accordingly, analyzing the causes of waiting times of activity transitions can help analysts to identify opportunities for reducing the cycle time of a process. This paper proposes a process mining approach to decompose the waiting time observed in each activity transition into multiple direct causes and to analyze the impact of each identified cause on the cycle time efficiency of the process. An empirical evaluation shows that the proposed approach is able to discover different direct causes of waiting times. The applicability of the proposed approach is demonstrated in a real-life process.

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 Overprocessing is a source of waste that occurs when unnecessary work is performed in a process. Overprocessing is often found in application-to-approval processes since a rejected application does not add value, and thus, work that leads to the rejection constitutes overprocessing. Analyzing how the knock-out checks are executed can help analysts to identify opportunities to reduce overprocessing waste and time. This paper proposes an interpretable process mining approach for discovering improvement opportunities in the knock-out checks and recommending redesigns to address them. Experiments on synthetic and real-life event logs show that the approach successfully identifies improvement opportunities while attaining a performance comparable to black-box approaches. Moreover, by leveraging interpretable machine learning techniques, our approach provides further insights on knock-out check executions, explaining to analysts the logic behind the suggested redesigns. The approach is implemented as a software tool and its applicability is demonstrated on a real-life process.

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.

Abstract Batch processing reduces processing time in a business process at the expense of increasing waiting time. If this trade-off between processing and waiting time is not analyzed, batch processing can, over time, evolve into a source of waste in a business process. Therefore, it is valuable to analyze batch processing activities to identify waiting time wastes. Identifying and analyzing such wastes present the analyst with improvement opportunities that, if addressed, can improve the cycle time efficiency (CTE) of a business process. In this paper, we propose an approach that, given a process execution event log, (1) identifies batch processing activities, (2) analyzes their inefficiencies caused by different types of waiting times to provide analysts with information on how to improve batch processing activities. More specifically, we conceptualize different waiting times caused by batch processing patterns and identify improvement opportunities based on the impact of each waiting time type on the CTE. Finally, we demonstrate the applicability of our approach to a real-life event log.

Abstract In the last years, process mining has significantly matured and has increasingly been applied by companies in industrial contexts. However, with the growing number of process mining methods, practitioners might find it difficult to identify which ones to apply in specific contexts and to understand the specific business value of each process mining technique. This paper’s main objective is to develop a business-oriented framework capturing the main process mining use cases and the business-oriented questions they can answer. We conducted a Systematic Literature Review (SLR) and we used the review and the extracted data to develop a framework that (1) classifies existing process mining use cases connecting them to specific methods implementing them, and (2) identifies business-oriented questions that process mining use cases can answer. Practitioners can use the framework to navigate through the available process mining use cases and to identify the process mining methods suitable for their needs.

Abstract FinTech companies are challenging established financial institutions' dominance by offering the same products with a superior customer experience and delivering new features faster. The adoption of agile software development partially enables this competitive advantage. In response to this challenge, banks explore how they can improve their agile processes. LHV, a mid‐sized bank, uses agile practices but faces the challenge of further improvement to stay competitive with FinTech companies. In this article, we explore how LHV can improve its agile software development process. We conduct a case study at LHV where we first derive eight change proposals based on a literature review and interviews. Then, we report on how LHV implemented the change proposals and their perceived impact. Our results stress the importance of taking a coherent approach to improving agile processes by considering both business units and operations involved in the product life‐cycle. It is also necessary to align organizational structures to enable team autonomy by, for instance, decentralizing decision authority. Finally, it is beneficial to adapt agile practices to their context and have an IT architecture and technology supporting the agile approach.

Blockchain has emerged as one of the most promising and revolutionary technologies in the past years. Companies are exploring use cases in the hope of reaping benefits from this technology. However, to achieve the desired impact, it is not sufficient to merely replace existing technologies. Current business processes must be redesigned to realize benefits. This paper shows how a number of best practices for Business Process Redesign can be applied to leverage blockchain technology. Specifically, we adapt these best practices for redesigning processes intended to execute on blockchain. We further explore their applicability using a case study. The major elements of process redesign for blockchain are: (1) changing the view from intra- to inter-organizational processes by using the shared data ledger of blockchain, (2) using smart contracts performers of tasks and connectors of processes, (3) using blockchain as means for data communication, and (4) using tokens to manage digitally represented assets.

Process mining methods allow analysts to use logs of historical executions of business processes in order to gain knowledge about the actual behavior of these processes. One of the most widely studied process mining operations is automated process discovery. An event log is taken as input by an automated process discovery method and produces a business process model as output that captures the control-flow relations between tasks that are described by the event log. In this setting, this paper provides a systematic comparative evaluation of existing implementations of automated process discovery methods with domain experts by using a real life event log extracted from an international software engineering company and four quality metrics: understandability, correctness, precision, and usefulness. The evaluation results highlight gaps and unexplored trade-offs in the field and allow researchers to improve the lacks in the automated process discovery methods in terms of usability of process discovery techniques in industry.