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The purpose of this paper is to demonstrate that process mining techniques can help to discover process models from event logs, using conventional high-level process modeling languages, such as Business Process Model and Notation (BPMN), leveraging their representational bias.

The integrated discovery approach presented in this work is aimed to mine: control, data and resource perspectives within one process diagram, and, if possible, construct a hierarchy of subprocesses improving the model readability. The proposed approach is defined as a sequence of steps, performed to discover a model, containing various perspectives and presenting a holistic view of a process. This approach was implemented within an open-source process mining framework called ProM and proved its applicability for the analysis of real-life event logs.

This paper shows that the proposed integrated approach can be applied to real-life event logs of information systems from different domains. The multi-perspective process diagrams obtained within the approach are of good quality and better than models discovered using a technique that does not consider hierarchy. Moreover, due to the decomposition methods applied, the proposed approach can deal with large event logs, which cannot be handled by methods that do not use decomposition.

The paper consolidates various process mining techniques, which were never integrated before and presents a novel approach for the discovery of multi-perspective hierarchical BPMN models. This approach bridges the gap between well-known process mining techniques and a wide range of BPMN-complaint tools.

Process mining provides a generic collection of techniques to turn event data into valuable insights, improvement ideas, predictions, and recommendations. This paper uses spreadsheets as a metaphor to introduce process mining as an essential tool for data scientists and business analysts. The purpose of this paper is to illustrate that process mining can do with events what spreadsheets can do with numbers.

The paper discusses the main concepts in both spreadsheets and process mining. Using a concrete data set as a running example, the different types of process mining are explained. Where spreadsheets work with numbers, process mining starts from event data with the aim to analyze processes.

Differences and commonalities between spreadsheets and process mining are described. Unlike process mining tools like ProM, spreadsheets programs cannot be used to discover processes, check compliance, analyze bottlenecks, animate event data, and provide operational process support. Pointers to existing process mining tools and their functionality are given.

Event logs and operational processes can be found everywhere and process mining techniques are not limited to specific application domains. Comparable to spreadsheet software widely used in finance, production, sales, education, and sports, process mining software can be used in a broad range of organizations.

The paper provides an original view on process mining by relating it to the spreadsheets. The value of spreadsheet-like technology tailored toward the analysis of behavior rather than numbers is illustrated by the over 20 commercial process mining tools available today and the growing adoption in a variety of application domains.

The increase in business process management projects in the past decade has seen an increase in demand for business process modelling (BPM) techniques. A rapidly growing aspect of BPM is the use of workflow management systems to automate routine and sequential processes. Workflows tend to move away from traditional definitions of business processes that can often be forced to fit a model that does not suit its nature. Existing process modelling tools tend to be biased to either the informational, behavioural or object‐oriented aspect of the workflow. Because of this, models can often miss important aspects of a workflow. As well as managing the relationship between the types of model it is important to consider who will be using it, as process models are useful in various ways. The paper aims to address these issues.

This paper reports on a case study in a manufacturing company, where users were surveyed to see which are the notations that are most common in modelling based on two main categories (i.e. behavioural and informational).

The research outcomes showed that there is no prevailing set of standards used for either of these categories, while most users feel the need to use more than one approach to model their system at any given time. Many companies face problems when trying to model the behaviour of human workers in the business processes. Existing techniques are mostly designed at modelling information systems or business processes, and rarely attempt to integrate the two.

This paper proposes the use of a new hybrid modelling methodology, which is an original idea, on existing tools and methodologies. Key authors in the literature recommend against the trend of developing a brand new methodology, so existing tools from each end of the scale were combined to provide a solution which is capable of modelling IS and BP.

Increasing flexibility and complexity of modern process‐aware information systems (PAIS) usually leads to less guidance for its users and consequently requires more experienced users. A flexible PAIS allows users to freely choose a specific execution sequence of activities. However, there are no guarantees that the chosen sequence conforms to established business rules. This context proposes a supervisory control service (SCS), which can be used to support end‐users of flexible PAIS during process execution by giving a list of disabled (or enabled) events (activities), i.e. at any point in time a list of possible next steps is given.

The SCS has been implemented in process mining framework (ProM – www.processmining.org) and the experiment that has been performed shows its feasibility. The computational infrastructure already implemented in ProM allows communication with external applications and allows the SCS to be integrated with PAIS that records events. This paper demonstrated that the SCS in ProM can cooperate with PAIS system in order to support users.

The SCS proposed in this paper monitors and restricts execution sequences of activities such that business rules are always obeyed. The system offers support based on business rules but does not limit the user by imposing rigid control‐flow structures. Users can adopt this service as a guide to execute activities with a guarantee that business rules are followed and goals are met. The SCS also offers flexibility to users to choose execution sequences.

The paper argues that the proposed approach has many advantages on controlling/supporting flexible processes. Supervisory control theory allows an automatic control synthesis instead of the usual manual and heuristic procedures. Thus, new control actions may be rapidly and automatically designed when modifications, such as redefinition of business rules or activities arrangements, are necessary. The flexible processes can be made to behave optimally with respect to a variety of criteria, where optimal means in minimally restrictive way.

This paper aims to depict the practical execution of the problem-solving structure provided by the define, measure, analyze, improve and control (DMAIC) framework in combination with the analytical power provided by process mining capabilities, to improve the supply chain quality of a health-care provider.

Prior to the study, a literature review was conducted to identify existing frameworks combining six sigma with process mining. The authors use a descriptive case study approach to explain how the two methodologies blend across the different phases of DMAIC in a health-care setting.

This case study describes how analyzing data extracted from core information systems has significant value to improvement initiatives when complemented by traditional quality methods. By intersecting process mining techniques with lean six sigma tools, the researchers found 65% of orders not complying with the target ordering time and 200 redundant purchases with high operational costs.

By depicting how the two methodologies can be intertwined, this paper complements existing research by presenting it as a viable quality improvement approach.

This paper provides insights for six sigma and process mining practitioners on the benefits of combining both methodologies within the DMAIC structure. Implementing this blended approach can bring visibility to operations and accelerate process improvement initiatives.

The prime value of this paper lies in the integration of traditional six sigma methods with process mining as a technological approach in a health-care context, going beyond existing research, which, to the best of the knowledge, lacks descriptive case studies.

The purpose of this research is to develop a better understanding of the hurdles in implementing Lean Six Sigma (LSS) for operational excellence in digital emerging technology companies.

We have conducted case studies of LSS implementations in six US-based companies in the digital emerging technology industry.

Critical success factors (CSF) for LSS implementations in digital emerging technology companies are: (1) organizational leadership that is engaged to the implementation, (2) LSS methodology that is rebranded to fit existing shared values in the organization, (3) restructuring of the traditional LSS training program to include a more incremental, prioritized, on-the-job training approach and (4) a modified LSS project execution methodology that includes (a) condensing the phases and tools applied in LSS projects and (b) adopting more iterative project management methods compared to the standard phased LSS project approach.

The qualitative nature of our analysis and the geographic coverage of our sample limit the generalizability of our findings.

Implications comprise the awareness and knowledge of critical success factors and LSS methodology modifications specifically relevant for digital emerging technology companies or companies that share similarities in terms of focus on product development, innovation and growth, such as R&D departments in high-tech manufacturing companies.

Research on industry-specific enablers for successful LSS implementation in the digital emerging technology industry is virtually absent. Our research informs practitioners on how to implement LSS in this and alike industries, and points to aspects of such implementations that are worthy of further attention from the academic community.

Declarative process modelling is a constraint-centric approach that treats business rules as first-class citizens in business process models. Augmenting the declarative process modelling technique with capability to detect the constraint violations during business process execution is of crucial importance. The purpose of this paper is to contribute to the modelling of business rules through a repository of pattern-oriented templates.

The semantics of the business rule templates is underpinned by linear temporal logic (LTL). Automated temporal reasoning is then conducted for determining whether process executions adhere to the business rules through the utilisation of the Logics Workbench (LWB). An application of the methodological framework is illustrated by a realistic case study on degree requirements verification.

To access the practicality of the approach, the case study of this paper is based on the verification of degree requirements, which is different from the domain area of the case study in the author’s prior work. The findings indicated that the temporal framework could be applied to the declarative process modelling in a consistent and efficient manner.

This paper is an extended version of the author’s earlier study. More details on the LTL and LWB are provided in the current study. The author introduces 17 new business rule templates and illustrates the utilisation of the new templates via a case study that belongs to a different domain area.

Organizations rely on their business processes to achieve their business objectives and ensure compliance with relevant laws and regulations. Hence, conformance to process specifications is essential to remain compliant. Various factors influence an organization’s ability to operate in conformance to its process specifications. This study investigates the influence of business process management (BPM)-supportive culture and individual process orientation on process conformance.

A construct was created for perceived process conformance and two constructs were selected from literature to represent BPM-supportive culture and individual process orientation. A survey was conducted with 178 employees of a global enterprise, hypotheses were formulated, and a statistical model was constructed and validated.

Results pinpoint the key role of the BPM-supportive culture in influencing both individual process orientation and conformance. Individual process orientation is also found to have a significant influence on process conformance. The findings provide additional evidence for the significance of human-related aspects of BPM in achieving BPM success.

The contributions of this paper help better understand how soft factors of BPM contribute to employees’ process conformance drawing on and relating concepts of BPM and organizational routines.

Process redesign refers to the intentional change of business processes. While process redesign methods provide structure to redesign projects, they provide limited support during the actual creation of to-be processes. More specifically, existing approaches hardly develop an ontological perspective on what can be changed from a process design point of view, and they provide limited procedural guidance on how to derive possible process design alternatives. This paper aims to provide structured guidance during the to-be process creation.

Using design space exploration as a theoretical lens, the authors develop a conceptual model of the design space for business processes, which facilitates the systematic exploration of design alternatives along different dimensions. The authors utilized an established method for taxonomy development for constructing the conceptual model. First, the authors derived design dimensions for business processes and underlying characteristics through a literature review. Second, the authors conducted semi-structured interviews with professional process experts. Third, the authors evaluated their artifact through three real-world applications.

The authors identified 19 business process design dimensions that are grouped into different layers and specified by underlying characteristics. Guiding questions and illustrative real-world examples help to deploy these design dimensions in practice. Taken together, the design dimensions form the “Business Process Design Space” (BPD-Space).

Practitioners can use the BPD-Space to explore, question and rethink business processes in various respects.

The BPD-Space complements existing approaches by explicating process design dimensions. It abstracts from specific process flows and representations of processes and supports an unconstrained exploration of various alternative process designs.

Increased fraudulent practices have heightened the need for innovation in anti-fraud programs, necessitating the development of analytics techniques for detecting and preventing fraud. The subject of fraud analytics will continue to expand in the future for public-sector organizations; therefore, this research examined the progress of fraud analytics in public-sector transactions and offers suggestions for its future development.

This study systematically reviewed research on fraud analytics development in public-sector transactions. The review was conducted from June 2021 to June 2023 by identifying research objectives and questions, performing literature quality assessment and extraction, data synthesis and research reporting. The research mainly identified 43 relevant articles that were used as references.

This research examined fraud analytics development related to public-sector financial transactions. The results revealed that fraud analytics expansion has not spread equally, as most programs have been implemented by governments and healthcare organizations in developed countries. This research also exposed that the analytics optimization in fraud prevention is higher than for fraud detection. Such analytics help organizations detect fraud, improve business effectiveness and efficiency, and refine administrative systems and work standards.

This research offers comprehensive insights for researchers and public-sector professionals regarding current fraud analytics development in public-sector financial transactions and future trends.

This study presents the first systematic literature review to investigate the development of fraud analytics in public-sector transactions. The findings can aid scholars' and practitioners' future fraud analytics development.