Adaptive AI-based causal control: Toward an autonomous factory in solder paste printing - Herchenbach, Marvin; Weinzierl, Sven; Zilker, Sandra; Schwulera, Erik; Matzner, Martin (Computers in Industry, 2025)
In recent years, there has been a remarkable shift from automated plants to intelligent production in the industrial context, accelerated by technologies such as artificial intelligence (AI). The ultimate goal is an autonomous plant that is capable of self-regulation and self-optimization. In electronics production, the first approaches have been proposed for deriving and adjusting machine parameters for solder paste printing in the surface-mount technology production of printed circuit boards. However, these approaches are often static and perform reactive actions since they are either based on expert systems or data-driven models. To reach a dynamic optimization, this work proposes a methodology, called adaptive AI-based causal control, allowing offline and online optimization. Following the principles of the Design for Six Sigma method, customer-oriented key performance indicators were derived, that aimed at a stable soldering process by focusing on the spread of the solder volume and a dedicated overall spread metric. The offline optimization (open-loop control) is based on a surrogate model approach to find optimal initial printing parameters. The online optimization (closed-loop control) employs a data-driven model predictive control to adjust the printing parameters dynamically. In addition, to consider the causal effects of the control variables in the online optimization, a causal graph is exploited in the predictive controller. Regarding the effectiveness of the open-loop control, our evaluation reveals a reduction in spread by 11.3% in production. Furthermore, in terms of the efficacy of the closed-loop control, we obtain a reduction in volume range by 16.7% in a simulated setting of the predictive controller. Thereby, the integration of a causal inference component based on a generated causal graph, achieving a recall of 76.9% by considering process knowledge identified with domain experts, accounts for about 2.8%
Keywords: manufacturing; electronics production; process optimization; control system; causal machine learning; surrogate model

A machine learning framework for interpretable predictions in patient pathways: The case of predicting ICU admission for patients with symptoms of sepsis - Zilker, Sandra; Weinzierl, Sven; Kraus, Mathias; Zschech, Patrick; Matzner, Martin (Health Care Management Science, 2024)
Proactive analysis of patient pathways helps healthcare providers anticipate treatment-related risks, identify outcomes, and allocate resources. Machine learning (ML) can leverage a patient’s complete health history to make informed decisions about future events. However, previous work has mostly relied on so-called black-box models, which are unintelligible to humans, making it difficult for clinicians to apply such models. Our work introduces PatWay-Net, an ML framework designed for interpretable predictions of admission to the intensive care unit (ICU) for patients with symptoms of sepsis. We propose a novel type of recurrent neural network and combine it with multi-layer perceptrons to process the patient pathways and produce predictive yet interpretable results. We demonstrate its utility through a comprehensive dashboard that visualizes patient health trajectories, predictive outcomes, and associated risks. Our evaluation includes both predictive performance – where PatWay-Net outperforms standard models such as decision trees, random forests, and gradient-boosted decision trees – and clinical utility, validated through structured interviews with clinicians. By providing improved predictive accuracy along with interpretable and actionable insights, PatWay-Net serves as a valuable tool for healthcare decision support in the critical case of patients with symptoms of sepsis.<br
Keywords: patient pathway; process prediction; sepsis; interpretability; interpretable machine learning; interpretation plots; deep learning

A method for predicting workarounds in business processes - Weinzierl, Sven; Bartelheimer, Christian; Zilker, Sandra; Beverungen, Daniel; Matzner, Martin (Proceedings of the 25th Pacific Asia Conference on Information Systems, 2022)
Workarounds are performed intentionally by employees to bypass obstacles constraining their day-to-day work. These obstacles manifest from latent misfits in the interplay of information systems, organizational structure, and human agency. While workarounds are often mandatory for employees to perform their work, they can yield positive and negative effects on an organization’s performance. Process managers are supposed to identify workarounds early, promoting their positive while reducing their negative consequences. While related research has touched upon detecting workarounds in event logs that include data on completed processes, little is known on how to predict workarounds in a running business process. We set out to design a workaround prediction method using a deep learning approach. The IT artifact enables process managers to proactively intervene if workarounds are about to emerge in a business process, reducing their adverse effects while supporting organizational learning and process innovation.<br
Keywords: workarounds; business processes management; process mining; predictive business process monitoring; deep learning; machine learning

A next click recommender system for web-based service analytics with context-aware LSTMs - Weinzierl, Sven; Stierle, Matthias; Zilker, Sandra; Matzner, Martin (Proceedings of the 53rd Hawaii International Conference on System Sciences, 2020)

A technology-specific process mining maturity grid for manufacturing and logistics - Dunzer, Sebastian; Zilker, Sandra; Weinzierl, Sven; Tang, Willi; Dieckmann, Felix; Stenglein, Simon; Rist, Johannes; Matzner, Martin (International Journal of Production Research, 2025)
Process mining applies data mining to the data produced in the slipstream of the executing process instances. Hence, it enables organisations gain transparency about how exactly the processes are executed. Organisations can use this transparency to find opportunities to improve their business processes, such as manufacturing and logistics. Previous research provides methodological guidance on how process mining projects can be conducted and how process mining capabilities can be assessed from an organisational perspective. However, organisations tend to lack experience with novel technologies, which is why they often miss out on opportunities to improve their operations using the insights gained through process mining. This study is the first to contribute to the existing literature by examining process mining capabilities from a technological perspective, focussing on the manufacturing and logistics domain. We conducted a systematic case study review and 12 semi-structured interviews with different manufacturing organisations to develop and validate our technology-specific process mining maturity grid for manufacturing and logistics. The resulting maturity grid is a tool for demonstrating development paths for the further application of process mining in organisations. The grid details maturity using three focus areas (i.e. data input, process mining utilisation, and integration) structured into five maturity level
Keywords: process mining; maturity grid; process improvement; manufacturing; logistics

Best of both worlds: Combining predictive power with interpretable and explainable results for patient pathway prediction - Zilker, Sandra; Weinzierl, Sven; Zschech, Patrick; Kraus, Mathias; Matzner, Martin (Proceedings of the 31st European Conference on Information Systems, 2023)
Proactively analyzing patient pathways can help healthcare providers to anticipate treatment-related risks, detect undesired outcomes, and allocate resources quickly. For this purpose, modern methods from the field of predictive business process monitoring can be applied to create data-driven models that capture patterns from past behavior to provide predictions about running process instances. Recent methods increasingly focus on deep neural networks (DNN) due to their superior prediction performances and their independence from process knowledge. However, DNNs generally have the disadvantage of showing black-box characteristics, which hampers dissemination in critical environments such as healthcare. To this end, we propose the design of HIXPred, a novel artifact combining predictive power with explainable results for patient pathway predictions. We instantiate HIXPred and apply it to a real-life healthcare use case for evaluation and demonstration purposes and conduct interviews with medical experts. Our results confirm high predictive performance while ensuring sufficient interpretability and explainability to provide comprehensible decision suppor
Keywords: Patient pathway prediction; predictive business process monitoring; explainable artificial intelligence; interpretable artificial intelligence; deep neural networks; business process management; Diskriminierung

Bringing light into the darkness - A systematic literature review on explainable predictive business process monitoring techniques - Stierle, Matthias; Brunk, Jens; Weinzierl, Sven; Zilker, Sandra; Matzner, Martin; Becker, Jörg (Proceedings of the 29th European Conference on Information Systems, 2021)
Predictive business process monitoring (PBPM) provides a set of techniques to perform different prediction tasks in running business processes, such as the next activity, the process outcome, or the remaining time. Nowadays, deep-learning-based techniques provide more accurate predictive models. However, the explainability of these models has long been neglected. The predictive quality is essential for PBPM-based decision support systems, but also its explainability for human stakeholders needs to be considered. Explainable artificial intelligence (XAI) describes different approaches to make machine-learning-based techniques explainable. To examine the current state of explainable PBPM techniques, we perform a structured and descriptive literature review. We identify explainable PBPM techniques of the domain and classify them along with different XAI-related concepts: prediction purpose, intrinsically interpretable or post-hoc, evaluation objective, and evaluation method. Based on our classification, we identify trends in the domain and remaining research gaps.<br
Keywords: business process; prediction; interpretability; explainable artificial intelligence

Challenging the performance-interpretability trade-off: An evaluation of interpretable machine learning models - Kruschel, Sven; Hambauer, Nico; Weinzierl, Sven; Zilker, Sandra; Kraus, Mathias; Zschech, Patrick (Business & Information Systems Engineering, 2025)
Machine learning is permeating every conceivable domain to promote data-driven decision support. The focus is often on advanced black-box models due to their assumed performance advantages, whereas interpretable models are often associated with inferior predictive qualities. More recently, however, a new generation of generalized additive models (GAMs) has been proposed that offer promising properties for capturing complex, non-linear patterns while remaining fully interpretable. To uncover the merits and limitations of these models, this study examines the predictive performance of seven different GAMs in comparison to seven commonly used machine learning models based on a collection of twenty tabular benchmark datasets. To ensure a fair and robust model comparison, an extensive hyperparameter search combined with cross-validation was performed, resulting in 68,500 model runs. In addition, this study qualitatively examines the visual output of the models to assess their level of interpretability. Based on these results, the paper dispels the misconception that only black-box models can achieve high accuracy by demonstrating that there is no strict trade-off between predictive performance and model interpretability for tabular data. Furthermore, the paper discusses the importance of GAMs as powerful interpretable models for the field of information systems and derives implications for future work from a socio-technical perspective.<br
Keywords: Decision support; Predictive analytics; Interpretable machine learning; Generalized additive models; Explainable artificial intelligence

Context-aware explanations of accurate predictions in service processes - Weinzierl, Sven; Zilker, Sandra; Brunk, Jens; Revoredo, Kate; Matzner, Martin; Becker, Jörg (Proceedings of the 57th Hawaii International Conference on System Sciences, 2024)
The performance of a service process can be improved by the early anticipation of future behavior, such as predicting the next activity using predictive business process monitoring (PBPM). Recent PBPM techniques are based on deep neural networks (DNNs) and consider the process context to create accurate predictions. To provide explainability of these predictions, model-agnostic explainable AI (XAI) methods, for example, SHAP, can be used. However, creating these explanations is time-consuming and, therefore, not applicable to service processes where customers are involved. In this paper, we propose a context-aware DNN-based technique to efficiently create meaningful explanations of next activity predictions using layer-wise relevance propagation. We evaluate the predictive quality and the explanation creation time, using three real-life service event logs. Further, we demonstrate its visual output, highlighting its utility for end-users.<br
Keywords: service analytics; predictive process monitoring; machine learning; deep learning; XAI

Designing a Method for Resource-specific Next Activity Prediction - Zilker, Sandra (Proceedings of the 25th Pacific Asia Conference on Information Systems, 2022)

Design Principles for Comprehensible Process Discovery in Process Mining - Stierle, Matthias; Zilker, Sandra; Dunzer, Sebastian; Tenschert, Johannes; Karagegova, Gergana (28th European Conference on Information Systems - Liberty, Equality, and Fraternity in a Digitizing World, ECIS 2020, Marrakech, Morocco, June 15-17, 2020, 2020)

Design Principles for Using Business Process Management Systems - Dunzer, Sebastian; Tang, Willi; Höchstädter, Nico; Zilker, Sandra; Matzner, Martin (Business Process Management Workshops, 2023)

Design Principles for Using Business Process Management Systems - Dunzer, Sebastian; Tang, Willi; Höchstädter, Nico; Zilker, Sandra; Matzner, Martin (Business Process Management Workshops, 2023)
Organizations aim to achieve operational excellence to reduce costs and improve the quality of their business processes. Business process management (BPM) enables continuous improvement of business processes. Business process management systems (BPMS) serve as an entry point to BPM activities and afford firms to manage, execute, and automate business processes. This study follows an action design research approach to design a BPMS in use together with a medium-sized German fashion company. We concurrently evaluated the artifact-in-use by tracking performance indicators that are aligned with the company’s objective. As a result of our formalization of learning, we propose seven design principles for using BPMSs to achieve continuous improvement of business processes. These design principles comprise user management, process modeling, automation, logging, monitoring, integration, and case handling.<br
Keywords: Business process management systems; action design research; design principles

From predictive to prescriptive process monitoring: Recommending the next best actions instead of calculating the next most likely events - Weinzierl, Sven; Zilker, Sandra; Stierle, Matthias; Park, Gyunam; Matzner, Martin (Proceedings of the 15th International Conference on Wirtschaftsinformatik, 2020)
Predictive business process monitoring (PBPM) deals with predicting a process’s future behavior based on historical event logs to support a process’s execution. Many of the recent techniques utilize a machine-learned model to predict which event type is the next most likely. Beyond PBPM, prescriptive BPM aims at finding optimal actions based on considering relevant key performance indicators. Existing techniques are geared towards the outcome prediction and deal with alarms for interventions or interventions that do not represent process events. In this paper, we argue that the next event prediction is insufficient for practitioners. Accordingly, this research-in-progress paper proposes a technique for determining next best actions that represent process events. We conducted an intermediate evaluation to test the usefulness and the quality of our technique compared to the most frequently cited technique for predicting next events. The results show a higher usefulness for process participants than a next most likely event.<br
Keywords: Prescriptive business process monitoring; predictive business process monitoring; business process management; design science research

GAM(e) changer or not? An evaluation of interpretable machine learning models based on additive model constraints - Zschech, Patrick; Weinzierl, Sven; Hambauer, Nico; Zilker, Sandra; Kraus, Mathias (Proceedings of the 30th European Conference on Information Systems, 2022)
The number of information systems (IS) studies dealing with explainable artificial intelligence (XAI) is currently exploding as the field demands more transparency about the internal decision logic of machine learning (ML) models. However, most techniques subsumed under XAI provide post-hoc-analytical explanations, which have to be considered with caution as they only use approximations of the underlying ML model. Therefore, our paper investigates a series of intrinsically interpretable ML models and discusses their suitability for the IS community. More specifically, our focus is on advanced extensions of generalized additive models (GAM) in which predictors are modeled independently in a non-linear way to generate shape functions that can capture arbitrary patterns but remain fully interpretable. In our study, we evaluate the prediction qualities of five GAMs as compared to six traditional ML models and assess their visual outputs for model interpretability. On this basis, we investigate their merits and limitations and derive design implications for further improvements.<br
Keywords: Predictive Analytics; Interpretable Machine Learning; Generalized Additive Models; Chancengleichheit

How risky is my AI system? A method for transparent classification of AI system descriptions by regulated AI risk categories - Weinzierl, Sven; Zilker, Sandra; Zschech, Patrick; Kraus, Mathias; Leibelt, Tobias; Matzner, Martin (Proceedings of the 45th International Conference on Information Systems, 2024)
Risk-based artificial intelligence (AI) regulations define risk categories for AI-enabled systems. The operators of such systems must determine the risk category applicable to their AI systems. This requires detailed knowledge of the classification rules defined in the regulations. Only a few supporting tools have been developed to facilitate the task of risk classification. This paper presents a novel method that describes all the necessary steps to develop such a tool. To demonstrate and evaluate the method, it is instantiated for the European Union’s AI Act. The evaluation shows i) that the classification model achieves promising performance in predicting the risk categories for AI systems, ii) that users can effectively use the web application to carry out a risk classification, and iii) that users find SHAP text plots integrated into the web application helpful for understanding the reasons of a classification prediction.<br
Keywords: artificial intelligence regularization; EU AI Act; large language model; explainable AI; risk assessment

Job Seekers' Artificial Intelligence-related Black Box Concerns - Ochmann, Jessica; Zilker, Sandra; Laumer, Sven (Proceedings of the 2020 on Computers and People Research Conference, 2020)

Machine learning in business process management: A systematic literature review - Weinzierl, Sven; Zilker, Sandra; Dunzer, Sebastian; Matzner, Martin (Expert Systems With Applications, 2024)
Machine learning (ML) provides algorithms to create computer programs based on data without explicitly programming them. In business process management (BPM), ML applications are used to analyse and improve processes efficiently. Three frequent examples of using ML are providing decision support through predictions, discovering accurate process models, and improving resource allocation. This paper organises the body of knowledge on ML in BPM. We extract BPM tasks from different literature streams, summarise them under the phases of a process's lifecycle, explain how ML helps perform these tasks and identify technical commonalities in ML implementations across tasks. This study is the first exhaustive review of how ML has been used in BPM. We hope that it can open the door for a new era of cumulative research by helping researchers to identify relevant preliminary work and then combine and further develop existing approaches in a focused fashion. Our paper helps managers and consultants to find ML applications that are relevant in the current project phase of a BPM initiative, like redesigning a business process. We also offer – as a synthesis of our review – a research agenda that spreads ten avenues for future research, including applying novel ML concepts like federated learning, addressing less regarded BPM lifecycle phases like process identification, and delivering ML applications with a focus on end-users.
CRIS-Team Scopus Importer:2024-06-14
Keywords: BPM lifecycle; Business process management; Deep learning; Literature review; Machine learning

Predicting Customer Satisfaction in Service Processes Using Multilingual Large Language Models - Ließmann, Annina; Zilker, Sandra; Weinzierl, Sven; Sukhareva, Maria; Matzner, Martin (Proceedings of the 57th Hawaii International Conference on System Sciences, 2024)
The huge amount of data recorded during business process executions in today’s organizations creates the need to leverage this data. While most existing business process monitoring methods are capable of including structured context information, the incorporation of unstructured information, for example, text, has rarely been researched. Recent advances in natural language processing offer tools to generate contextualized sentence embeddings, which capture more information in human language than ever before. Especially in service processes, such as the incident management process, a variety of unstructured information is created throughout the execution, representing a relevant use case for incorporating (multilingual) text. To close this research gap, a method exploiting multilingual text for predicting the outcome of a service process is presented. Multilingual large language models are used to generate sentence embeddings for unstructured text created during process execution. After instantiating the method, an evaluation was performed using a real-life event log to predict customer satisfaction in the incident management process at a German multinational corporation. We show that text incorporation improves predictive performanc
Keywords: predictive process monitoring; natural language processing; machine learning; service analytics; customer satisfaction

Predictive business process deviation monitoring - Weinzierl, Sven; Dunzer, Sebastian; Tenschert, Johannes; Zilker, Sandra; Matzner, Martin (Proceedings of the 29th European Conference on Information Systems, 2021)
Business processes run at the core of an organisation’s value creation and are often the target of optimisation efforts. Organisations aim at adhering to their optimised processes. However, deviations from the optimised process still occur and may potentially impede efficiency in process executions. Conformance checking can provide valuable insights regarding past process deviations, but it cannot identify deviations before they occur. Outcome-oriented predictive business process monitoring (PBPM) provides a set of methods to predict process outcomes, e. g. key performance indicators. We propose an outcome-oriented PBPM method for predictive deviation monitoring using conformance checking and deep learning to draw the most out of the two domains. By leveraging early intervention, the method supports the proactive handling of deviations, i.e., inserted and missing events in process instances, to reduce their potential harm. Our evaluation shows that the method can predict business process deviations with high predictive quality, particularly for processes with fewer variants. <br
Keywords: predictive business process monitoring; conformance checking; deep learning; process mining

Prescriptive business process monitoring for recommending next best actions - Weinzierl, Sven; Dunzer, Sebastian; Zilker, Sandra; Matzner, Martin (Proceedings of the 18th International Conference on Business Process Management Forum, 2020)
Predictive business process monitoring (PBPM) techniques predict future process behaviour based on historical event log data to improve operational business processes. Concerning the next activity prediction, recent PBPM techniques use state-of-the-art deep neural networks (DNNs) to learn predictive models for producing more accurate predictions in running process instances. Even though organisations measure process performance by key performance indicators (KPIs), the DNN’s learning procedure is not directly affected by them. Therefore, the resulting next most likely activity predictions can be less beneficial in practice. Prescriptive business process monitoring (PrBPM) approaches assess predictions regarding their impact on the process performance (typically measured by KPIs) to prevent undesired process activities by raising alarms or recommending actions. However, none of these approaches recommends actual process activities as actions that are optimised according to a given KPI. We present a PrBPM technique that transforms the next most likely activities into the next best actions regarding a given KPI. Thereby, our technique uses business process simulation to ensure the control-flow conformance of the recommended actions. Based on our evaluation with two real-life event logs, we show that our technique’s next best actions can outperform next activity predictions regarding the optimisation of a KPI and the distance to the actual process instances. <br
Keywords: Prescriptive business process monitoring; predictive business process monitoring; business process management

Process Mining for Advanced Service Analytics – From Process Efficiency to Customer Encounter and Experience - Zilker, Sandra; Marx, Emanuel; Stierle, Matthias; Matzner, Martin (Proceedings of the 55th Hawaii International Conference on System Sciences, 2022)
With the ongoing trend of servitization nurtured through digital technologies, the analysis of services as a starting point for improvement is gaining more and more importance. Service analytics has been defined as a concept to analyze the data generated during service execution to create value for providers and customers. To create more useful insights from the data, there is a continuous need for more advanced solutions for service analytics. One promising technology is process mining which has its origins in business process management. Our work provides insights into how process mining is currently used to analyze service processes and how it could be used along the service process. We find that process mining is increasingly applied for the analysis of the providers' internal operations, but more emphasis should be put on analyzing the customer interaction and experienc

Text-aware predictive process monitoring with contextualized word embeddings - Cabrera Pérez, Lena; Weinzierl, Sven; Zilker, Sandra; Matzner, Martin (Proceedings of the BPM 2022 International Workshops, 2022)
Predictive process monitoring (PPM) is the discipline of exploiting event logs of business processes to construct predictive models for anticipating different properties of running business processes. The event logs used contain control flow information of past process executions and, often, additional information about the context in which a process ran. As the process context can add valuable information to a predictive model, recent PPM techniques often incorporate it to improve process predictions. While most techniques incorporate context information as well-structured numerical and categorical context features, only a few utilize unstructured text from process-related comment fields, emails, or documents. The few existing text-aware PPM approaches are limited in capturing semantic information, as different meanings of the same word occurring in different contexts, i.e., sentences, are ignored. This paper addresses this limitation by proposing a text-aware PPM technique using contextualized word embeddings to predict the next activity and the next timestamp of running process instances. An experimental evaluation with a text-enriched real-life event log shows that our technique can outperform text-aware PPM approaches relying on non-contextualized word embeddings in terms of predictive performanc
Keywords: business process management; predictive process monitoring; machine learning; deep learning; natural language processing

The influence of algorithm aversion and anthropomorphic agent design on the acceptance of AI-based job recommendations - Ochmann, Jessica; Michels, Leonard; Zilker, Sandra; Tiefenbeck, Verena; Laumer, Sven (Proceedings of the 41st International Conference on Information Systems (ICIS), 2020)

The Status Quo of Process Mining in the Industrial Sector - Dunzer, Sebastian; Zilker, Sandra; Marx, Emanuel; Grundler, Valentin; Matzner, Martin (Proceedings of the 16th International Conference on Wirtschaftsinformatik (WI), 2021)
Since process mining started to reveal the potential of event logs, it has been applied in various process settings ranging from healthcare to production. Every single setting poses its challenges to process analysts who want to apply process mining. The present paper aims at minimizing such challenges for enterprises in the industrial sector by providing a coherent overview of existing cases. Our systematic literature review relates each production form and layout from existing case studies to the applied process mining type. Further, we use Porter's value chain to distinguish operations from other primary and support activities in production. We present the application of process mining, particularly for the production process and the primary activities other than the operations. The results indicate that process mining fits best with cellular production layouts with batch or line production processes.<br
Keywords: process mining; production; production form; production layout

Towards automated business process redesign in runtime using generative machine learning - Harl, Maximilian; Zilker, Sandra; Weinzierl, Sven (Proceedings of the 32nd European Conference on Information Systems, 2024)
In business process management, business process redesign (BPR) aims to improve business processes. In the past, BPR was mainly a manual task, with little computational power and typically high labor and time intensity. The increasing amount of stored process data and great advancements in generative machine learning (GML) and other analytical approaches have paved the way for automated BPR. However, existing BPR approaches are designed for offline applications and therefore restricted to computing historical data samples of business processes. In this paper, we argue performing BPR in runtime and leveraging prediction capabilities via GML achieves a higher degree of BPR automation, allowing organizations to improve their processes proactively. Accordingly, this research-in-progress paper outlines a design-science research process for designing a GML-based technique for automated BPR in runtime. In our preliminary evaluation, we present promising results for the proposed technique’s first online task, namely process model prediction, based on real-life event data. <br
Keywords: Business process redesign; business process management; generative artificial intelligence

Transfer Learning for Predictive Process Monitoring - Ließmann, Annina; Wang, Weixin; Weinzierl, Sven; Zilker, Sandra; Matzner, Martin (Proceedings of the 32nd European Conference on Information Systems, 2024)
Event log data reflects the behavior of business processes that are mapped in organizational information systems. Predictive process monitoring (PPM) transforms this data into value; it creates process-related predictions to provide insights required for proactive interventions at process runtime. Existing PPM techniques require sufficient amounts of event data or other relevant resources that might not be readily available, preventing some organizations from taking advantage of PPM. In this paper, we present a transfer-learning-based technique for PM, allowing organizations without suitable data or other relevant resources to implement PPM for effective decision support. We instantiate our artifact, and apply and evaluate it on a real-life use case. The use case includes two event logs for purchase-to-pay processes of two organizations. Our results provide evidence that knowledge of a process of one organization can be transferred to a similar process of another organization to enable PPM in the target organizatio
Keywords: predictive process monitoring; process mining; transfer learning; language model

XNAP: Making LSTM-based next activity predictions explainable by using LRP - Weinzierl, Sven; Zilker, Sandra; Brunk, Jens; Revoredo, Kate; Matzner, Martin; Becker, Jörg (Proceedings of the BPM 2020 International Workshops., 2020)
Predictive business process monitoring (PBPM) is a class of techniques designed to predict behaviour, such as next activities, in running traces. PBPM techniques aim to improve process performance by providing predictions to process analysts, supporting them in their decision making. However, the PBPM techniques' limited predictive quality was considered as the essential obstacle for establishing such techniques in practice. With the use of deep neural networks (DNNs), the techniques' predictive quality could be improved for tasks like the next activity prediction. While DNNs achieve a promising predictive quality, they still lack comprehensibility due to their hierarchical approach of learning representations. Nevertheless, process analysts need to comprehend the cause of a prediction to identify intervention mechanisms that might affect the decision making to secure process performance. In this paper, we propose XNAP, the first explainable, DNN-based PBPM technique for the next activity prediction. XNAP integrates a layer-wise relevance propagation method from the field of explainable artificial intelligence to make predictions of a long short-term memory DNN explainable by providing relevance values for activities. We show the benefit of our approach through two real-life event logs.
Keywords: predictive business process monitoring; explainable artificial intelligence; layer-wise relevance propagation; deep neural networks.