A Similarity-Based Hierarchical Clustering Method for Manufacturing Process Models

Ahn, Hyun; Chang, Tai‐Woo

doi:10.3390/su11092560

Cited by 6 publications

(5 citation statements)

References 23 publications

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“…An extension of the BPMN was used to manage service processes dynamically and holistically (Mazzola et al , 2018). Other authors used standard BPMN to develop the similarity-based hierarchical clustering technique for modeling of manufacturing processes (Ahn and Chang, 2019), and Mazzola et al (2017) used the BPMN 2.0 standard and extension to map the ideal solution for implementing semantic services in cloud manufacturing. In this context, the BPMN-related clusters present issues related to workflow, process changeability and business development for integrating smart processes and emerging technologies of the Fourth Industrial Revolution.…”

Section: Resultsmentioning

confidence: 99%

Process modeling for smart factories: using science mapping to understand the strategic themes, main challenges and future trends

Sott

Furstenau

Kipper

et al. 2021

BPMJ

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PurposeThe purpose of this paper is to identify the relationships between process modeling and Industry 4.0, the strategic themes and the most used process modeling language in smart factories. The study also presents the growth of the field of study worldwide, the perspectives, main challenges, trends and suggestions for future works.Design/methodology/approachTo do this, a science mapping was performed using the software SciMAT, supported by VOS viewer.FindingsThe results show that the Business Process Model and Notation (BPMN), Unified Modelling Language (UML) and Petri Net are the most relevant languages to smart manufacturing. The authors also highlighted the need to develop new languages or extensions capable of representing the dynamism, interoperability and multiple technologies of smart factories.Originality/valueIt was possible to identify the most used process modeling languages in smart environments and understand how these languages assist control and manage smart processes. Besides, the authors highlighted challenges, new perspectives and the need for future works in the field.

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Section: Resultsmentioning

confidence: 99%

Process modeling for smart factories: using science mapping to understand the strategic themes, main challenges and future trends

Sott

Furstenau

Kipper

et al. 2021

BPMJ

View full text Add to dashboard Cite

show abstract

“…Subramaniyan et al ( 2020 ) clustered time-series bottle-neck data using dynamic time wrapping and complete-linkage agglomerative hierarchical clustering technique for determining bottlenecks in manufacturing systems. Ahn and Chang ( 2019 ) discussed business process management for manufacturing models. They used agglomerative hierarchical clustering in the design and management of manufacturing processes.…”

Section: Applications Of Clusteringmentioning

confidence: 99%

Data clustering: application and trends

Oyewole

Thopil

2022

Artif Intell Rev

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Clustering has primarily been used as an analytical technique to group unlabeled data for extracting meaningful information. The fact that no clustering algorithm can solve all clustering problems has resulted in the development of several clustering algorithms with diverse applications. We review data clustering, intending to underscore recent applications in selected industrial sectors and other notable concepts. In this paper, we begin by highlighting clustering components and discussing classification terminologies. Furthermore, specific, and general applications of clustering are discussed. Notable concepts on clustering algorithms, emerging variants, measures of similarities/dissimilarities, issues surrounding clustering optimization, validation and data types are outlined. Suggestions are made to emphasize the continued interest in clustering techniques both by scholars and Industry practitioners. Key findings in this review show the size of data as a classification criterion and as data sizes for clustering become larger and varied, the determination of the optimal number of clusters will require new feature extracting methods, validation indices and clustering techniques. In addition, clustering techniques have found growing use in key industry sectors linked to the sustainable development goals such as manufacturing, transportation and logistics, energy, and healthcare, where the use of clustering is more integrated with other analytical techniques than a stand-alone clustering technique.

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“…In the context of adoption of information technologies in the manufacturing industry, the manufacturing companies should strive to manage their data and manufacturing processes in a way to enhance their manufacturing competency (Ahn and Chang, 2019).…”

Section: Management By Competenciesmentioning

confidence: 99%

Human factor in business process management: modeling competencies of BPM roles

Hrabal

Tuček

Molnár³

et al. 2020

BPMJ

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PurposeThe study proposes competence models for the roles of process owners, process analysts and industrial engineers based on qualitative research.Design/methodology/approachThe research methodology is a combination of a questionnaire survey and interviewing in Czech companies, which develop the process approach. The proposed competence models can be utilized during business process management (BPM) implementation while appointing process owners, analysts and industrial engineers and their further development.FindingsThis paper emphasizes the role of human factor and presents research results concerning most important BPM roles and their competencies.Research limitations/implicationsThere is lack of research (a research gap) in the field of BPM roles, what they do and what they should do.Practical implicationsA system of competence models is thus a tool for human resource management and should increase the success rate of BPM projects. Another possible utilization is in higher education in business administration.Social implicationsAnother possible utilization is in higher education in business administration.Originality/valueIt proposes competence models for the roles of process owners, process analysts and industrial engineers based on qualitative research.

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A Similarity-Based Hierarchical Clustering Method for Manufacturing Process Models

Cited by 6 publications

References 23 publications

Process modeling for smart factories: using science mapping to understand the strategic themes, main challenges and future trends

Process modeling for smart factories: using science mapping to understand the strategic themes, main challenges and future trends

Data clustering: application and trends

Human factor in business process management: modeling competencies of BPM roles

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