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Process mining is a research discipline that applies data analysis and computational intelligence techniques to extract knowledge from event logs of information systems. It aims to provide new means to discover, monitor, and improve processes. Process mining has gained particular attention over recent years and new process mining software tools, bo...
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Several decision points exist in business processes (e.g., whether a purchase order needs a manager’s approval or not), and different decisions are made for different process instances based on their characteristics (e.g., a purchase order higher than €500 needs a manager approval). Decision mining in process mining aims to describe/predict the rou...
Several decision points exist in business processes (e.g., whether a purchase order needs a manager's approval or not), and different decisions are made for different process instances based on their characteristics (e.g., a purchase order higher than $500 needs a manager approval). Decision mining in process mining aims to describe/predict the rou...
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... Lastly, action A-G5 is about the selection of the right process mining tool. Various factors, such as licensing and techniques offered, influence the decision on a process mining tool (Drakoulogkonas and Apostolou 2021). Practitioners should not hesitate to re-evaluate choices and try out different vendors before making a decision. ...
Process mining is advancing as a powerful tool for revealing valuable insights about process dynamics. Nevertheless, the imperative to employ process mining to enhance process transparency is a prevailing concern for organizations. Despite the widespread desire to integrate process mining as a pivotal catalyst for fostering a more agile and flexible Business Process Management (BPM) environment, many organizations face challenges in achieving widespread implementation and adoption due to deficiencies in various dimensions of process mining readiness. The current Information Systems (IS) knowledge base lacks a comprehensive framework to aid organizations in augmenting their process mining readiness and bridging this intention-action gap. The paper presents a Process Mining Maturity Model (P3M), refined through multiple iterations, which outlines five factors and 23 elements that organizations must address to increase their process mining readiness. The maturity model advances the understanding of how to close the intention-action gap of process mining initiatives in multiple dimensions. Furthermore, insights from a comprehensive analysis of data gathered in eleven qualitative interviews are drawn, elucidating 30 possible actions that organizations can implement to establish a more responsive and dynamic BPM environment by means of process mining.
... RapidProM, an extension of ProM, allows for the modeling and executing complex process mining workflows [29]. A methodology for comparing process mining tools is proposed, enabling the assessment of their suitability based on various criteria [30], as indicated in Table 1. A website for comparing commercial process mining tools is also available [31]. ...
This study focuses on the application of process mining in the healthcare sector. Despite its potential to enhance efficiency, reduce costs, and improve patient satisfaction, the selection of process-mining software poses significant challenges due to the diverse nature of healthcare processes and the lack of comprehensive evaluation methods. To bridge this gap, this study employed a hybrid Multi-Criteria Decision-Making (MCDM) approach, integrating the Neural Network-Augmented Analytical Hierarchy Process (NNA-AHP) and Grey Relational Analysis—a technique for Order Preference by Similarity to Ideal Solution (GRA-TOPSIS). The study evaluated process mining software on functionalities, ease of use, cost, technical support, scalability, and security with their respective sub-criteria. The principal results indicate that Disco is the top-performing alternative, followed by Celonis and ProM. Sensitivity analysis was conducted to understand the influence of variations in criteria weights on evaluating alternatives. In the NNA-AHP, Celonis consistently scored the highest. The GRA-TOPSIS method provided performance scores, indicating that higher scores yield better performance. The new hybrid method consolidates evaluations from all methods and offers the most comprehensive and dependable alternative assessment. Disco and its alternatives, Celonis and ProM, are recommended for optimizing healthcare processes. Further research is needed to investigate the integration of NNA-AHP and GRA-TOPSIS in healthcare management, especially in areas beyond business process analysis. This study provides valuable insights for professionals and researchers in the field and contributes to understanding the effectiveness of process mining.
... Due to the growing number of PM software on the market, Layola-Gonzales [23] conducts a thorough analysis of the 16 leading PM software and provides a comprehensive taxonomy that includes 55 features, such as "data management, process graphing, process analysis and analytics, compliance auditing, operational support, advanced extensibility, views, monitoring and reporting, and security and compliance". Similarly, Drakoulogkonas and Apostolou [24] developed a framework that allows users to compare different PM software based on a comprehensive list of comparative analysis criteria [24]. ...
... Due to the growing number of PM software on the market, Layola-Gonzales [23] conducts a thorough analysis of the 16 leading PM software and provides a comprehensive taxonomy that includes 55 features, such as "data management, process graphing, process analysis and analytics, compliance auditing, operational support, advanced extensibility, views, monitoring and reporting, and security and compliance". Similarly, Drakoulogkonas and Apostolou [24] developed a framework that allows users to compare different PM software based on a comprehensive list of comparative analysis criteria [24]. ...
... Presented in Fig. 1 is the algorithm for artificial intelligence, which consists of five levels of administrative loops [35][36][37]. The target criteria is considered to be the percentage of vacant positions in the workforce. ...
The nuclear reactor control unit employs human factor engineering to ensure efficient operations and prevent any catastrophic incidents. This sector is of utmost importance for public safety. This study focuses on simulated analysis of specific areas of nuclear reactor control, specifically administration, operation, and maintenance, using artificial intelligence software. The investigation yields effective artificial intelligence algorithms that capture the essential and non-essential components of numerous parameters to be monitored in nuclear reactor control. The investigation further examines the interdependencies between various parameters and validates the statistical outputs of the model through attribution analysis. Furthermore, a Multivariant ANOVA analysis is conducted to identify the interactive plots and mean plots of crucial parameters interactions. The artificial intelligence algorithms demonstrate the correlation between the number of vacant staff jobs and both the frequency of license event reports each year and the ratio of contract employees to regular employees in the administrative domain. An AI method uncovers the relationships between the operator failing rate (OFR), operator processed errors (OEE), and operations at limited time frames (OLC). The AI algorithm reveals the interdependence between equipment in the out of service (EOS), progressive maintenance schedule (PRMR), and preventive maintenance schedules (PMRC). Effective machine learning neural network models are derived from generative adversarial network (GAN) algorithms and proposed for administrative, operational and maintenance loops of nuclear reactor control unit.
... A process refers to a field of research that involves the application of data analysis and computational intelligence techniques to extract insights from event logs of information systems. Its objective is to discover, monitor, and enhance processes [15,17]. PM is a technique that can help improve business process management. ...
... Vom Brocke et al. [35] highlight that non-technical factors are also essential for the implementation and administration of PM, in addition to the creation and enhancement of algorithms. Drakoulogkonas & Apostolou [17] gave an overview of various software used as PM tool for its selection. Drawing upon a multi-criteria framework, three techniques were used, namely, ontology, decision trees, and AHP, to list and explain the parameters that can help compare instruments to choose which software product best meets a company's needs according to the challenges that organizations meet. ...
Family small and medium enterprises (FSMEs) differ from non-family SMEs regarding leadership type, human resource management practices, innovation orientation, change management, information and communication technology deployment, process maturity, and resource availability. These differences present challenges when leading any change. Process mining (PM) tools can optimize process value and eliminate non-added-value activities in FSMEs based on “Event Logs”. The present study investigates how a PM project is implemented in an FSME operating in the agri-food sector, focusing on challenges faced in every project phase to extract the most appropriate process that eliminates all sources of waste and bottleneck cases. Drawing upon the L*Lifecycle methodology combined with quality and lean management tools such as the fishbone diagram, Pareto diagram, and overall equipment efficiency (OEE), this study applied a PM project to a manufacturing process for an FSME operating in the agri-food sector. To achieve theoretical production capacity (TPC) and customer satisfaction, the method was analyzed and optimized using Disco and ProM toolkits. The results analysis using Disco and ProM toolkits gave clues about the organizational and technical causes behind the manufacturing process’s inefficiency. First, OEE showed that the studied FSME is struggling with equipment availability. Then, the implementation of the L*Lifecycle methodology allowed for the identification of five critical causes. An action plan to eliminate causes was proposed to the FSME managers.
... Further, the use of process mining software tools is important; in [20], a comparative analysis methodology was employed for a supply chain SME in order to evaluate five Process Mining software tools (Apromore Community Edition, ProM, Celonis, myInvenio, and Disco) through eleven specific criteria to identify the tool that best suits the needs of the SME. The methodology offers three different approaches for comparison: ontology, decision tree, and Analytic Hierarchy Process (AHP). ...
One of the challenges the organizations confront is to extract data from the information systems to know the reality of their processes to improve their efficiency. In this study, the application of Process Mining is addressed as an opportunity in the specific context of an SME dedicated to software development, implementing the L* life cycle model methodology from a layered Software Engineering approach. This research is carried out based on process improvement in an initial SME project. Subsequently, it is compared with a second project, using different Process Mining perspectives such as control flow, case, organization, and time, with the aim of extending the process model. This holistic view allows not only to better understand the processes involved, but also to identify and analyze the similarities and differences between the two projects. As a result, the Process Mining analysis shows crucial aspects such as the representation of integrated models, traces on sequences of actions, and the interaction of activities with specific roles and deviations in the flow of activities that compromise the quality of the process and the product. At the same time, the challenges that emerged during the improvement cycle are highlighted. These challenges cover issues such as data extraction, fluid communication between those involved, and the documentation associated with the processes. This study contributes to the body of knowledge of Process Mining. Likewise, the case study results offer a vision for other SMEs seeking to incorporate Process Mining as part of their improvement strategies.
... This perspective thus seeks to establish what happened in a specific transaction; thus, it permits the scrutiny of a specific part of a business process. The point of focus of the time perspective is on the timing and frequency of events, this perspective uses the timestamps in the event log in process discovery and also to discover bottlenecks in the process (Drakoulogkonas & Apostolou, 2021). Typology is required to identify the data quality issues; thus, we identified the various data quality dimensions linked to timestamp from literature review and the various data quality issues that affect timestamp. ...
Timestamps play a key role in process mining because it determines the chronology of which events occurred and subsequently how they are ordered in process modelling. The timestamp in process mining gives an insight on process performance, conformance, and modelling. This therefore means problems with the timestamp will result in misrepresentations of the mined process. A few articles have been published on the quantification of data quality problems but just one of the articles at the time of this paper is based on the quantification of timestamp quality problems. This article evaluates the quality of timestamps in event log across two axes using eleven quality dimensions and four levels of potential data quality problems. The eleven data quality dimensions were obtained by doing a thorough literature review of more than fifty process mining articles which focus on quality dimensions. This evaluation resulted in twelve data quality quantification metrics and the metrics were applied to the MIMIC-III dataset as an illustration. The outcome of the timestamp quality quantification using the proposed typology enabled the user to appreciate the quality of the event log and thus makes it possible to evaluate the risk of carrying out specific data cleaning measures to improve the process mining outcome.
... The need for a common methodology or approach to compare a set of PM tools as a guideline for practitioners to select the right PM tools for their business is stated later in this paper. Drakoulogkonas and Apostolou [20] introduce a multi-criteria methodology for practitioners to compare PM tools. The methodology consists of three different selection methods such as ontology, Analytic Hierarchy Process (AHP), and decision tree and it aims to help users to determine the right tool in terms of their needs. ...
Process Mining (PM) and PM tool abilities play a significant role in meeting the needs of organizations in terms of getting benefits from their processes and event data, especially in this digital era. The success of PM initiatives in producing effective and efficient outputs and outcomes that organizations desire is largely dependent on the capabilities of the PM tools. This importance of the tools makes the selection of them for a specific context critical. In the selection process of appropriate tools, a comparison of them can lead organizations to an effective result. In order to meet this need and to give insight to both practitioners and researchers, in our study, we systematically reviewed the literature and elicited the papers that compare PM tools, yielding comprehensive results through a comparison of available PM tools. It specifically delivers tools' comparison frequency, methods and criteria used to compare them, strengths and weaknesses of the compared tools for the selection of appropriate PM tools, and findings related to the identified papers' trends and demographics. Although some articles conduct a comparison for the PM tools, there is a lack of literature reviews on the studies that compare PM tools in the market. As far as we know, this paper presents the first example of a review in literature in this regard.
... Several authors have already done similar works, through which they also made a comparison between different Process Mining tools. We can consider as examples the works proposed in [9] and [10], which, as expected, introduce different comparative criteria and different tools for comparison. ...
... In the academic literature, two main research streams regarding the use of PM in organizations can be distinguished: (1) finer granularity: targeted PM case studies and (2) coarser granularity: dedicated research on the general use of PM in organizations. Other works, focusing on PM tool comparison and selection (e.g., Agostinelli et al. 2019;Drakoulogkonas and Apostolou 2021;Turner et al. 2012), are relevant to support the uptake of PM, but are dedicated to the technical aspects of certain techniques or tools. As a consequence, they will not be elaborated upon. ...
Process Mining is an active research domain and has been applied to understand and improve business processes. While significant research has been conducted on the development and improvement of algorithms, evidence on the application of Process Mining in organisations has been far more limited. In particular, there is limited understanding of the opportunities and challenges of using Process Mining in organisations. Such an understanding has the potential to guide research by highlighting barriers for Process Mining adoption and, thus, can contribute to successful Process Mining initiatives in practice. In this respect, this paper provides a holistic view of opportunities and challenges for Process Mining in organisations identified in a Delphi study with 40 international experts from academia and industry. Besides proposing a set of 30 opportunities and 32 challenges, the paper conveys insights into the comparative relevance of individual items, as well as differences in the perceived relevance between academics and practitioners. Therefore, the study contributes to the future development of Process Mining, both as a research field and regarding its application in organisations.
