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Enterprise systems (ESs) are hard to maintain, since they embed a large fraction of organisational data and tasks, which are often intertwined and highly interdependent. The purpose of this paper is to propose a methodology for enterprise resource planning (ERP) post-implementation change management to support business analysts during perfective maintenance.

The methodology draws a parallel line with engineering change management and considers the steps of mapping the dependencies among ES components, understanding the ripple effects of change, and defining metrics to quantify and assess the impact of change. The methodology is instantiated in the case of ERP systems, for which a tool has also been implemented and evaluated by ERP implementation experts.

Experts positively evaluated the proposed methodology. General design principles to instantiate the methodology in the case of systems other than ERP have been derived.

While existing ESs change management methodologies help to identify the need for change, the proposed methodology help to structure the change process, supporting the task of perfective maintenance in an efficient way.

While it is commonly recognised that Big Data have an immense potential to generate value for business organisations, appropriating value from Big Data and, in particular, Big Data-enabled analytics is still an open issue for many organisations. The purpose of this paper is to develop a maturity model to support organisations in the realisation of the value created by Big Data.

The maturity model is developed following a qualitative approach based on literature analysis and semi-structured interviews with domain experts. The completeness and usefulness of the model is evaluated qualitatively by practitioners, whereas the applicability of the model is evaluated by Big Data maturity assessments in three real-world organisations.

The proposed maturity model is considered exhaustive by domain experts and has helped the three assessed organisations to develop a more critical understanding of the next steps to take.

The maturity model integrates existing industry-developed maturity models into one single coherent Big Data maturity model. The proposed model answers the call for research on Big Data to abstract from technical issues to focus on the business implications of Big Data initiatives.

The purpose of this paper is to present a framework for social business process management (BPM) in which social tagging is used to capture process knowledge emerging during the design and enactment of the processes. Process knowledge concerns both the type of activities chosen to fulfil a certain goal and the skills and experience of users in executing specific tasks. This knowledge is exploited by recommendation tools to support the design and enactment of current and future process instances.

The literature about traditional BPM is analysed to highlight the limitations of traditional BPM regarding management of ad hoc and semi-structured processes. Having identified this gap, an innovative BPM framework based on social tagging is proposed to address these limitations. This model is exemplified in a real case scenario and evaluated through the implementation of a prototype and a case study in real world non-profit organisation.

An overview of the social BPM framework is presented, introducing the concepts of role and task recommendation, which are supported by social tagging. The prototype shows the buildability of the social BPM framework as an extension of a Wiki platform. The case study demonstrates that the social BPM framework improves user collaborativeness in designing and executing process instances.

The applicability of the framework is targeted to ad hoc and possibly semi-structured business processes and it does not extend to highly procedural and codified processes. A single case study limits the generalisability of the evaluation results.

The social BPM framework is the first to introduce task and role recommendation supported by social tagging to overcome the limitations of traditional BPM models.

The purpose of this paper is to present a definition of modern configuration for a medium-altitude long-endurance unmanned aerial vehicle (MALE UAV) and its on-board systems to obtain a suitable basis for future definitions such as a possible logistic support configuration first hypothesis.

Starting from high-level requirements, both the UAV conceptual design and on-board systems preliminary design have been carried out through proprietary tools. Then, some peculiarities from previous studies, such as systems advanced UAV alternative energy, have been maintained and confirmed (diesel propulsion and energy storage system).

The improvement of a component of an aircraft can play a relevant role in the whole system. In the paper, it is considered how a concept of MALE UAV can evolve (this topic is considered by the authors since many years) by incorporating advanced on-board systems concepts.

The numerical results promote and support the use of advanced on-board system solutions and architectures to improve the effectiveness, efficiency and performance of MALE UAVs.

Usually, conceptual and preliminary design phases analyze in-depth the aerodynamic and structural solutions and aircraft performance. In this study, the authors aim to focus on the advanced on-board systems for MALE UAVs. This kind of aircraft is not yet a mature concept, with very few operating machines and many projects in the development phase.

Medical diagnosis and treatment processes exhibit a high degree of variability, as during the process execution, healthcare professionals can decide on additional steps, change the execution order or skip a task. Process models can help to document and to discuss such processes. However, depicting variability in graphical process models using standardized languages, such as Business Process Model and Notation (BPMN), can lead to large and complicated diagrams that medical staff who do not have formal training in modeling languages have difficulty understanding. This study proposes a pattern-based process visualization that medical doctors can understand without extensive training. The process descriptions using this pattern-based visualization can later be transformed into formal business process models in languages such as BPMN.

The authors derived patterns for expressing variability in healthcare processes from the literature and medical guidelines. Then, the authors evaluated and revised these patterns based on interviews with physicians in a Danish hospital.

A set of business process variability patterns was proposed to express situations with variability in hospital treatment and diagnosis processes. The interviewed medical doctors could translate the patterns into their daily work practice, and the patterns were used to model a hospital process.

When communicating with medical personnel, the patterns can be used as building blocks for documenting and discussing variable processes.

The patterns can reduce complexity in process visualization. This study provides the first validation of these patterns in a hospital.

The primary goal of this research is to introduce a data-driven Problem-Solving Approach for Performance Improvement in Healthcare Systems (DPAPIH). This approach combines process mining and data mining techniques to enhance operational efficiency by identifying bottlenecks in Coronary Artery Bypass Grafting (CABG) procedures, particularly focusing on variability in Length of Stay (LOS) in the Intensive Care Unit (ICU). The study, implemented at Tehran Heart Center, aims to optimize patient flow, reduce ICU congestion and improve hospital efficiency by predicting and managing the occurrence of postoperative Atrial Fibrillation (AF), a significant cause of prolonged ICU stays.

The study introduces a data-driven problem-solving approach that integrates process mining and data mining techniques to improve performance in healthcare systems. Focusing on coronary artery bypass grafting (CABG) at Tehran Heart Center, the approach identifies bottlenecks, particularly variability in ICU length of stay (LOS) and predicts postoperative atrial fibrillation (AF). A mixed-methods approach is employed, combining quantitative process mining analyses with qualitative insights from expert consultations. The CHAID decision tree algorithm, alongside other models, is used to predict AF, enabling preemptive interventions, improving patient flow and optimizing resource allocation to reduce hospital congestion and costs.

The study reveals that postoperative Atrial Fibrillation (AF) significantly increases the length of stay (LOS) in the Intensive Care Unit (ICU), creating bottlenecks that delay subsequent surgeries and elevate hospital costs. A predictive model developed using CHAID decision tree algorithms achieved a prediction accuracy of 71.4%, allowing healthcare providers to anticipate AF occurrences. This capability enables proactive measures to reduce ICU congestion, improve patient flow and optimize resource allocation. The findings emphasize the importance of AF management in enhancing operational efficiency and improving patient outcomes in Coronary Artery Bypass Grafting (CABG) procedures.

This study presents an innovative integration of fuzzy process mining and data mining algorithms to address performance bottlenecks in healthcare systems, specifically within the coronary artery bypass surgery process. By identifying atrial fibrillation as a key factor in length of stay fluctuations and developing a robust predictive model, the research offers a novel, data-driven approach to performance improvement. The implementation at Tehran Heart Center validates the model’s practical applicability, demonstrating significant potential for enhancing patient outcomes, optimizing resource allocation and informing decision-making in healthcare management.

This study aims to provide a methodology and tools to design new organizational processes and artificial intelligence (AI)-based scoring to optimize the resources management in healthcare units.

Process design and process data-driven simulation: the processes are designed by the business process modeling and notation and the unified modeling language standards. Data processing is performed by Correlation matrix analysis and by Fuzzy c-Means data clustering. The matching between the two methods provides the most indicated final corrective actions of the “TO BE” organizational model.

This proposed method, experimentally applied in this work merging the lean management model (LMM), process mining (PM) and AI methods, named process mining organization (PMO) model (Rosa et al., 2023 (b)), is able to improve organizational processes of a hospitalization unit (HU) by developing three propaedeutic phases: (1) analysis of the current state of the processes (“AS IS”) by identifying the critical issues as bottlenecks of processes, (2) AI data processing able to provide additional classified and predicted information allowing the “TO BE” workflow process and (3) implementation of corrective actions suggested by the PMO in order to support strategic decision-making processes in the short, medium and long term by classifying an order of priority about the healthcare procedures/protocols to perform.

The main limitation of the proposed case study is in the limited number of available digital data to process. This aspect reduces the capability to interpret result. In any case, the proposed methodology is a “launch” work to define a new approach to integrate organizational processes including workflow design and AI scoring. Future work will be focused on managerial implications due to use of the discussed method: design and development of new human resource (HR) organizational protocols following data analysis to optimize costs and care services and to decrease injury compensation claims.

Main implications are in healthcare managerial scenarios: design and development of new HR organizational protocols following data analysis to optimize costs and care services and to decrease injury compensation claims.

Care services optimization is addressed on HUs.

The design of HR organizational processes integrates AI-driven data decision-making processes. This case study examines AI-based innovation analytics addressed on resource efficiency.

The purpose of this study is to offer a straightforward, cost-effective, and feasible resolution for managers to assess their processes in a live manner using the process mining technique and to identify anomalies in cases that deviate from the standard. Consequently, the findings of this research can be utilized by organizational managers, while process mining vendors can also leverage it as a feature for their solutions.

Our two-step method is designed to initially evaluate the level of standardization within the process, followed by identifying its underlying cause. These two steps are aimed at helping managers effectively evaluate their business processes. The steps are: (1). Start-End Case Diagram: This diagram allows for the evaluation of the lead time trend and identification of cases that deviate from the standard trend line in a service-based process. (2). Happy Path Analysis: Pareto law is suggested to identify the most frequent process variants.

This approach enables organizations to easily identify problematic cases and investigate bottlenecks when deviations from the standards occur.

The novelty of the paper lies in the introduction and utilization of the start-end case diagram, as well as the combination of this diagram with the Pareto law for the identification of happy path and root cause analysis.