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When improving business processes, process analysts can use data-driven methods, such as process mining, to identify improvement opportunities. However, despite being supported by data, process analysts decide which changes to implement. Analysts often use process visualisations to assess and determine which changes to pursue. This paper helps explore how process mining visualisations can aid process analysts in their work to identify, prioritise and communicate business process improvement opportunities.

The study follows the design science methodology to create and evaluate an artefact for visualising identified improvement opportunities (IRVIN).

A set of principles to facilitate the visualisation of process mining outputs for analysts to work with improvement opportunities was suggested. Particularly, insights into identifying, prioritising and communicating process improvement opportunities from visual representation are outlined.

Prior work focuses on visualisation from the perspectives – among others – of process exploration, process comparison and performance analysis. This study, however, considers process mining visualisation that aids in analysing process improvement opportunities.

Lean Six Sigma in public and private healthcare organisations has received considerable attention over the last decade. Nevertheless, such process improvement methodologies are not generalizable, and their effective implementation relies on contextual variables. The purpose of this study is to explore the readiness of Italian hospitals for Lean Six Sigma and Quality Performance Improvement (LSS&QPI), with a focus on gender differences.

A survey comprising 441 healthcare professionals from public and private hospitals was conducted. Multivariate analysis of variance was used to determine the mean scores on the LSS&QPI dimensions based on hospital type, gender and their interaction.

The results showed that public healthcare professional are more aware of quality performance improvement initiatives than private healthcare professionals. Moreover, gender differences emerged according to the type of hospital, with higher awareness for men than women in public hospitals, whereas for private hospitals the opposite was true.

This study contributes to the Lean Six Sigma literature by focusing on the holistic assessment of LSS&QPI implementation.

This study informs healthcare managers about the revolution within healthcare organisations, especially public ones. Healthcare managers should spend time understanding Lean Six Sigma as a strategic orientation to promote the “lean hospital”, improving processes and fostering patient-centredness.

This is a preliminary study focussing on analysing inter-relationship between perceived importance of soft readiness factors such as gender dynamics as a missing jigsaw in the current literature. In addition, the research advances a holistic assessment of LSS&QPI, which sets it apart from the studies on single initiatives that have been documented to date.

This study specifically seeks to investigate the strategic implementation of machine learning (ML) algorithms and techniques in healthcare institutions to enhance innovation management in healthcare settings.

The papers from 2011 to 2021 were considered following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. First, relevant keywords were identified, and screening was performed. Bibliometric analysis was performed. One hundred twenty-three relevant documents that passed the eligibility criteria were finalized.

Overall, the annual scientific production section results reveal that ML in the healthcare sector is growing significantly. Performing bibliometric analysis has helped find unexplored areas; understand the trend of scientific publication; and categorize topics based on emerging, trending and essential. The paper discovers the influential authors, sources, countries and ML and healthcare management keywords.

The study helps understand various applications of ML in healthcare institutions, such as the use of Internet of Things in healthcare, the prediction of disease, finding the seriousness of a case, natural language processing, speech and language-based classification, etc. This analysis would help future researchers and developers target the healthcare sector areas that are likely to grow in the coming future.

The study highlights the potential for ML to enhance medical support within healthcare institutions. It suggests that regression algorithms are particularly promising for this purpose. Hospital management can leverage time series ML algorithms to estimate the number of incoming patients, thus increasing hospital availability and optimizing resource allocation. ML has been instrumental in the development of these systems. By embracing telemedicine and remote monitoring, healthcare management can facilitate the creation of online patient surveillance and monitoring systems, allowing for early medical intervention and ultimately improving the efficiency and effectiveness of medical services.

By offering a comprehensive panorama of ML's integration within healthcare institutions, this study underscores the pivotal role of innovation management in healthcare. The findings contribute to a holistic understanding of ML's applications in healthcare and emphasize their potential to transform and optimize healthcare delivery.

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.

This paper explores Lean Six Sigma principles and the DMAIC (define, measure, analyze, improve, control) methodology to propose a new Lean Six Sigma 4.0 (LSS 4.0) framework for employee occupational exams and address the real-world issue of high-variability exams that may arise.

This study uses mixed methods, combining qualitative and quantitative data collection. A detailed case study assesses the impact of LSS interventions on the exam management process and tests the applicability of the proposed LSS 4.0 framework for employee occupational exams.

The results reveal that changing the health service supplier in the explored organization caused a substantial raise in occupational exams, leading to increased costs. By using syntactic interoperability, lean, six sigma and DMAIC approaches, improvements were identified, addressing process deviations and information requirements. Implementing corrective actions improved the exam process, reducing the number of exams and associated expenses.

It is important to acknowledge certain limitations, such as the specific context of the case study and the exclusion of certain exam categories.

The practical implications of this research are substantial, providing organizations with valuable managerial insights into improving efficiency, reducing costs and ensuring regulatory compliance while managing occupational exams.

This study fills a research gap by applying LSS 4.0 to occupational exam management, offering a practical framework for organizations. It contributes to the existing knowledge base by addressing a relatively novel context and providing a detailed roadmap for process optimization.

Output from the Irish Dairy Industry has grown rapidly since the abolition of quotas in 2015, with processors investing heavily in capacity expansion to deal with the extra milk volumes. Further capacity gains may be achieved by extending the processing season into the winter, a key enabler for which being the reduction of duration of the winter maintenance overhaul period. This paper aims to investigate if Lean Six Sigma tools and techniques can be used to enhance operational maintenance performance, thereby releasing additional processing capacity.

Combining the Six-Sigma Define, Measure, Analyse, Improve, Control (DMAIC) methodology and the structured approach of Turnaround Maintenance (TAM) widely used in process industries creates a novel hybrid model that promises substantial improvement in maintenance overhaul execution. This paper presents a case study applying the DMAIC/TAM model to Ireland’s largest dairy processing site to optimise the annual maintenance shutdown. The objective was to deliver a 30% reduction in the duration of the overhaul, enabling an extension of the processing season.

Application of the DMAIC/TAM hybrid resulted in process enhancements, employee engagement and a clear roadmap for the operations team. Project goals were delivered, and original objectives exceeded, resulting in €8.9m additional value to the business and a reduction of 36% in the duration of the overhaul.

The results demonstrate that the model provides a structure that promotes systematic working and a continuous improvement focus that can have substantial benefits for wider industry. Opportunities for further model refinement were identified and will enhance performance in subsequent overhauls.

To the best of the authors’ knowledge, this is the first time that the structure and tools of DMAIC and TAM have been combined into a hybrid methodology and applied in an Irish industrial setting.

This study aimed to bridge the gap between the financial measurement of process improvement ideas and Lean Six Sigma measurements. It was required to increase employee engagement in process improvement initiatives.

Through both a practical and theoretical application of the Design for Lean Six Sigma methodology, the researcher was able to design a process and a benefit measuring methodology that was acceptable by finance and aligns with the benefits expected from the elimination of the Lean wastes.

The project found that benefit measurement methodology is not understood by most employees, which leads to a lack of engagement in working on improvements. The result of the study was a model for employees to identify and quantify these benefits. This has resulted in a model for cost-benefit analysis aligning financial costs with non-value add waste costs and cost of poor-quality costs resulting in increased process improvement ideas and activity.

While this study was limited to one company, applying this methodology could benefit any company experiencing the same difficulties.

This is one of the first studies to try and cost the benefits of LSS projects both from an organisational and generic viewpoint.

Using a large-scale school improvement program in Sweden as a case, this article aims to explore the state governance of a large-scale school improvement program in Sweden and how officials at the state agency level made sense of the reform ideas and operationalized them in policy actions.

Data were integrated from Swedish Government Official Reports and formal directives from the Ministry of Education. Officials of the Swedish National Agency for Education (SNAE) were also interviewed. Data were analyzed to identify how regulatory rules, professional norms and cultural–cognitive beliefs shaped SNAE's design of the program.

The article shows how different types of governance (i.e. regulatory rules, professional norms and cultural–cognitive beliefs) set the direction for managing large-scale school improvement. In particular, in the studied case, the lack of clear regulatory directives enabled sensemaking processes clearly influenced by normative ideas and cultural–cognitive beliefs.

The findings are mostly presented from the perspective of managers, so further study is required to attain a broader understanding of the state agency level's role and function.

By illustrating the strengths of understanding various dimensions of educational governance, the findings are highly relevant to both policymakers and educational managers at different levels of school systems.

The article offers a valuable perspective on large-scale school improvement and educational governance by focusing on a level that has hitherto received little attention.

Insufficient productivity development in the global and Finnish infrastructure sectors indicates that there are challenges in genuinely achieving the goals of resource efficiency and digitalization. This study adapts the approach of capability maturity model integration (CMMI) for examining the capabilities for productivity development that reveal the enablers of improving productivity in the infrastructure sector.

Civil engineering in Finland was selected as the study area, and a qualitative research approach was adopted. A novel maturity model was constructed deductively through a three-step analytical process. Previous research literature was adapted to form a framework with maturity levels and key process areas (KPAs). KPA attributes and their maturity criteria were formed through a thematic analysis of interview data from 12 semi-structured group interviews. Finally, validation and refinement of the model were performed with an expert panel.

This paper provides a novel maturity model for examining and enhancing the infrastructure sector’s maturity in productivity development. The model brings into discussion the current business logics, relevance of lifecycle-thinking, binding targets and outcomes of limited activities in the surrounding infrastructure system.

This paper provides a new approach for pursuing productivity development in the infrastructure sector by constructing a maturity model that adapts the concepts of CMMI and change management. The model and findings benefit all actors in the sector and provide an understanding of the required elements and means to achieve a more sustainable built environment and effective operations.

This paper presents Quick Changeover Design (QCD), which is a structured methodological approach for Original Equipment Manufacturers to drive and support the design of machines in terms of rapid changeover capability.

To improve the performance in terms of set up time, QCD addresses machine design from a single-minute digit exchange of die (SMED). Although conceived to aid the design of completely new machines, QCD can be adapted to support for simple design upgrades on pre-existing machines. The QCD is structured in three consecutive steps, each supported by specific tools and analysis forms to facilitate and better structure the designers' activities.

QCD helps equipment manufacturers to understand the current and future needs of the manufacturers' customers to: (1) anticipate the requirements for new and different set-up process; (2) prioritize the possible technical solutions; (3) build machines and equipment that are easy and fast to set-up under variable contexts. When applied to a production system consisting of machines subject to frequent or time-consuming set-up processes, QCD enhances both responsiveness to external market demands and internal control of factory operations.

The QCD approach is a support system for the development of completely new machines and is also particularly effective in upgrading existing ones. QCD's practical application is demonstrated using a case study concerning a vertical spindle machine.