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Because of its effectiveness, process mining is rapidly becoming ubiquitous. A recent IBM Institute for Business Value (IBV) survey found that 65 percent of organizations report actively using process mining to improve processes. And in partnership with software-as-a-service (SaaS) providers to add even greater insight into their processes, 69 percent compare their organization’s data with other SaaS customers. And as many as 77 percent of supply chain executives say they are at least at the implementation stage of process and task mining.

The IBM Institute for Business Value and APQC, in cooperation with Oxford Economics, surveyed 2,000 C-level executives in first half of 2022 from 13 countries in all major geographies and across 22 industries. The IBV and APQC implemented an in-depth analysis of how organizations use benchmarking and process mining tools, the benefits they gain from use of these tools and how they anticipate using them in the future.

Big data and digital technologies also creates new possibilities for measuring performance and revealing process improvement opportunities through process mining ? a relatively new discipline that applies data science to discover, validate and improve workflows in real time.

By utilizing data from IT systems to create a process model and then examining the end-to-end process, process mining enables root causes of variations from norms to be identified using specialized algorithms, and these insights enable management to see if processes are functioning as intended and identify new opportunities to optimize them.

More recently, the scope of process mining initiatives has widened to encompass more sophisticated mission-critical functions, notably human capital, cybersecurity and sales. Organizations that embrace process mining outperform others across key business measures, including profitability, innovation, agility, customer satisfaction and technological sophistication. 10;

The purpose of this paper is to present a comparison of a number of business process mining tools currently available in the UK market. An outline of the practice of business process mining is given, along with an analysis of the main techniques developed by academia and commercial entities. This paper also acts as a primer for the acceptance and further use of process mining in industry, suggesting future directions for this practice.

Secondary research has been completed to establish the main commercial business process mining tool vendors for the market. A literature survey has also been undertaken into the latest theoretical techniques being developed in the field of business process mining.

The authors have identified a number of existing commercially available business process mining tools and have listed their capabilities within a comparative analysis table. All commercially available business process mining tools included in this paper are capable of process comparison and at least 40 per cent of the tools claim to deal with noise in process data.

The contribution of this paper is to provide a state‐of‐the‐art review of a number of commercial business process mining tools available within the UK. This paper also presents a summary of the latest research being undertaken in academia in this subject area and future directions for the practice of business process mining.

Digitalization, innovation and changing customer requirements drive the continuous improvement of an organization's business processes. IT demand management (ITDM) as a methodology supports the holistic governance of IT and the corresponding business process change (BPC), by allocating resources to meet a company's requirements and strategic objectives. As ITDM decision-makers are not fully aware of how the as-is business processes operate and interact, making informed decisions that positively impact the to-be process is a key challenge.

In this paper, the authors address this challenge by developing a novel approach that integrates process mining and ITDM. To this end, the authors conduct an action research study where the researchers participated in the design, creation and evaluation of the approach. The proposed approach is illustrated using two sample demands of an insurance claims process. These demands are used to construct the artefact in multiple research circles and to validate the approach in practice. The authors applied learning and reflection methods for incrementally adjusting this study’s approach.

The study shows that the utilization of process mining activities during process changes on an operational level contributes to (1) increasing accuracy and efficiency of ITDM; (2) timely identification of potential risks and dependencies and (3) support of testing and acceptance of IT demands.

The implementation of this study’s approach improved ITDM practice. It appropriately addressed the information needs of decision-makers and unveiled the effects and consequences of process changes. Furthermore, providing a clearer picture of the process dependencies clarified the responsibilities and the interfaces at the intra- and inter-process level.

The purpose of this paper is to design a consortium-blockchain based framework for cross-organizational business process mining complying with privacy requirements.

Business process modeling in a cross-organizational setting is complicated due to privacy concerns. The process mining in this situation occurs through trusted third parties (TTPs). It uses a special class of Petri-nets called workflow nets (WF-nets) to represent the formal specifications of event logs in a blockchain-enabled cross-organization.

Using a smart contract algorithm, the proposed framework discovers the organization-specific business process models (BPM) without a TTP. The discovered BPMs are formally represented using WF-nets with a message factor to support the authors’ claim. Finally, the applicability and suitability of the proposed framework is demonstrated using a case study of multimodal transportation.

The proposed framework complies with privacy requirements. It shows how to represent the formal specifications of event logs in a blockchain using a special class of Petri-nets called WF-nets. It also presents a smart contract algorithm to discover organization-specific business process models (BPM) without a TTP.

The purpose of this paper is to review empirical studies on process mining in order to understand its use by organizations. The paper further aims to outline future research opportunities.

The authors propose a classification model that combines core conceptual elements of process mining with prior models from technology classification from the enterprise resource planning and business intelligence field. The model incorporates an organizational usage, a system-orientation and service nature, adding a focus on physical services. The application is based on a systematic literature review of 144 research papers.

The results show that, thus far, the literature has been chiefly concerned with realization of single business process management systems in single organizations. The authors conclude that cross-system or cross-organizational process mining is underrepresented in the ISR, as is the analysis of physical services.

Process mining researchers have paid little attention to utilizing complex use cases and mining mixed physical-digital services. Practitioners should work closely with academics to overcome these knowledge gaps. Only then will process mining be on the cusp of becoming a technology that allows new insights into customer processes by supplying business operations with valuable and detailed information.

Despite the scientific interest in process mining, particularly scant attention has been given by researchers to investigating its use in relatively complex scenarios, e.g., cross-system and cross-organizational process mining. Furthermore, coverage on the use of process mining from a service perspective is limited, which fails to reflect the marketing and business context of most contemporary organizations, wherein the importance of such scenarios is widely acknowledged. The small number of studies encountered may be due to a lack of knowledge about the potential of such scenarios as well as successful examples, a situation the authors seek to remedy with this study.

This paper seeks to examine the area of business process mining, providing an overview of state‐of‐the‐art techniques. An outline of the main problems experienced in the practice of process mining is given along with reference to work that addresses the most challenging issues experienced in this field. This paper also aims to examine the application of soft computing techniques to process‐mining problems.

This paper is based on a comprehensive review of literature covering more than 50 research papers. These papers are analysed to identify current trends and future research directions in the field.

Process‐mining techniques are now becoming available as graphical interface‐driven software tools, where flow diagram representations of processes may be manipulated as part of the mining task. A significant number of papers employ mining heuristics to aid in the task of process discovery. Soft computing algorithms are increasingly being investigated to aid the accuracy and speed of mining algorithms. Many papers exist that address common mining problems such as noise and mining loops. However, problems such as duplicate tasks, mining perspectives and delta analysis require further research.

The contribution of this paper is to provide a summary of the current trends in process‐mining practice and point out future research directions. A review of the work in this new and expanding area has been provided in the form of illustrative graphs and tables that identify the trends in this area. This is the most comprehensive and up‐to‐date review of business process‐mining literature.

The purpose of this study is to examine whether the compliance management activities in the risk management environment of financial institutions can be enhanced using a Process Mining application.

In this research, an implementation procedure for a selected Process Mining application is developed and evaluated at a financial institution in Germany.

The evaluation of the process data with the Process Mining application Disco shows that the compliance of the real-life execution of business processes can be monitored in real-time. Moreover, potential non-compliant activities and durations can be analysed in a detailed manner.

When the research results are regarded, it must be considered that a general condition for the usage of a Process Mining application is that the process data is available and exportable in the required format and that data privacy regulations are fulfilled.

This research presents a practical use case for the implementation of a Process Mining application at the risk management department of financial institutions. It shows the value of using a technical application to carry out tedious tasks that are usually executed manually. This value is discussed and compared with the aim to help financial institutions in determining how the effectiveness and efficiencies of compliance management activities can be improved. Therefore, this research can be taken as a foundation for the practical implementation of a Process Mining application at financial institutions.

The purpose of this paper is to propose a solution to the problem of a lack of machine processable semantics in business process management.

The paper introduces a methodology that combines domain and company‐specific ontologies and databases to obtain multiple levels of abstraction for process mining and analysis. The authors valuated this approach with a real case study from the apparel domain, using a prototype system and techniques developed in the Process Mining Framework (ProM). The results of this approach are compared with similar research.

Semantically enriching process execution data can successfully raise analysis from the syntactic to the semantic level, and enable multiple perspectives of analysis on business processes. Combining this approach with complementary research in semantic business process management (SBPM) can provide results comparable to multidimensional analysis in data warehouse and on line analytical processing (OLAP) technologies.

The approach and prototype described in this paper improve the richness of semantics available for open‐source process mining and analysis tools like ProM, and the richness and detail of the resulting analysis.

This study explores how process managers perceive the adoption, use and management of process mining in practice. While research in process mining predominantly focuses on the technical aspects, our work highlights organizational and managerial implications.

We report on a focus group study conducted with process managers from various industries in Central Europe. This setting allowed us to gain diverse and in-depth insights about the needs and expectations of practitioners in relation to the adoption, use and management of process mining.

We find that process managers face four central challenges. These challenges are largely related to four stages; (1) planning and business case calculation, (2) process selection, (3) implementation, and (4) process mining use.

We point to research opportunities in relation to the adoption, use and management of process mining. We suggest that future research should apply interdisciplinary study designs to better understand the managerial and organizational implications of process mining.

The reported challenges have various practical implications at the organizational and managerial level. We explore how existing BPM frameworks can be extended to meet these challenges.

This study is among the first attempts to explore process mining from the perspective of process managers. It clarifies important challenges and points to avenues for future research.

Process mining is a research area used to discover, monitor and improve real business processes by extracting knowledge from event logs available in process-aware information systems. The purpose of this paper is to evaluate the application of artificial neural networks (ANNs) and support vector machines (SVMs) in data mining tasks in the process mining context. The goal was to understand how these computational intelligence techniques are currently being applied in process mining.

The authors conducted a systematic literature review with three research questions formulated to evaluate the use of ANNs and SVMs in process mining.

The authors identified 11 papers as primary studies according to the criteria established in the review protocol. Most of them deal with process mining enhancement, mainly using ANNs. Regarding the data mining task, the authors identified three types of tasks used: categorical prediction (or classification); numeric prediction, considering the “regression” type, and clustering analysis.

Although there is scientific interest in process mining, little attention has been specifically given to ANNs and SVM. This scenario does not reflect the general context of data mining, where these two techniques are widely used. This low use may be possibly due to a relative lack of knowledge about their potential for this type of problem, which the authors seek to reverse with the completion of this study.