Machine Learning and Process Mining applied to Process Optimization: Bibliometric and Systemic Analysis

Fernandes, Ederson Carvalhar; Fitzgerald, Barry; Brown, Liam; Borsato, Milton

doi:10.1016/j.promfg.2020.01.012

Cited by 9 publications

(5 citation statements)

References 29 publications

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“…Within the research subject, the ProKnow-C process (knowledge development process – constructivist) has been used as the intervention tool for the selection of the BP, bibliometric and systemic analyses (Ensslin et al , 2015). This process has been used by many authors (De Oliveira Lacerda et al , 2018; Fernandes et al , 2019; Vieira, 2019) and provides a structured framework for the literature review as illustrated in Figure 1. This work focused on the first three steps (aforementioned) of the chosen intervention tool.…”

Section: Methodsmentioning

confidence: 99%

Data management within new product development and collaborative engineering: a bibliometric and systemic analysis

Larocca

Borsato

Kubo

et al. 2021

VJIKMS

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Purpose Although organizations have more data than ever at their disposal, actually deriving meaningful insights and actions from them is easier said than done. In this concern, the main objective of this study is to identify trends and research opportunities regarding data management within new product development (NPD) and collaborative engineering. Design/methodology/approach Bibliometric and systemic analyses have been carried out using the methodological procedure ProKnow-C, which provides a structured framework for the literature review. A bibliographic portfolio (BP) was consolidated with 33 papers that represent the state of art in the subject. Findings Most recent researches within the BP indicate new trends and paradigm shifts in this area of research, tackling subjects such as the internet of things, cloud computing, big data analytics and digital twin. Research gaps include the lack of data automation and the absence of a common architecture for systems integration. However, from a general perspective of the BP, the management of experimental data is suggested as a research opportunity for future works. Although many studies have tackled data and collaboration based on computer-aided technologies environments, no study examined the management of the measured data collected during the verification and validation stages of a product. Originality/value This work provides a fresh and relevant source of authors, journals and studies for researchers and practitioners interested in the domain of data management applied to NPD and collaborative engineering.

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Section: Methodsmentioning

confidence: 99%

Data management within new product development and collaborative engineering: a bibliometric and systemic analysis

Larocca

Borsato

Kubo

et al. 2021

VJIKMS

View full text Add to dashboard Cite

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“…This vast research area called predictive business process monitoring, attracted several literature reviews (e.g., Neu et al (2021); Harane and Rathi (2020)). ML can also be used to optimise existing processes Fernandes et al (2019) or to get a compact representation of traces Bui et al (2019Bui et al ( , 2020. Recently, there has been interest in the interpretability of RNNs models, specifically in a process mining context Hanga et al (2020).…”

Section: Machine Learning For Process Monitoring and Miningmentioning

confidence: 99%

VaryMinions: leveraging RNNs to identify variants in variability-intensive systems’ logs

Fortz,

Temple,

Devroey

et al. 2024

Empir Software Eng

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From business processes to course management, variability-intensive software systems (VIS) are now ubiquitous. One can configure these systems’ behaviour by activating options, e.g., to derive variants handling building permits across municipalities or implementing different functionalities (quizzes, forums) for a given course. These customisation facilities allow VIS to support distinct relevant customer requirements while taking advantage of reuse for common parts. Customisation thus allows realising both scope and scale economies. Behavioural differences amongst variants manifest themselves in event logs. To re-engineer this kind of system, one must know which variant(s) have produced which behaviour. Since variant information is barely present in logs, this paper supports this task by employing machine learning techniques to classify behaviours (event sequences) among variants. Specifically, we train Long Short Term Memory (LSTMs) and Gated Recurrent Units (GRUs) recurrent neural networks to relate event sequences with the variants they belong to on six different datasets issued from the configurable process and VIS domains. After having evaluated 20 different architectures of LSTM/GRU, our results demonstrate that it is possible to effectively learn the trace-to-variant mapping with high accuracy (at least $$80\%$$ 80 % and up to $$99\%$$ 99 % ) and at scale, i.e., identifying 50 variants using 5000+ traces for each variant.

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“…Ref. [87] used the Proknow-C technique for systematic bibliometric analysis in the context of smart factories and Industry 4.0.…”

Section: Literature Review Related To Bibliometric Analysismentioning

confidence: 99%

“…However, a bibliometric analysis of relevant fields, such as fault diagnosis and predictive maintenance [86], RUL [86], and process optimization, has emerged recently, from 2015 onwards. Additionally, research work on digital twins used for smart manufacturing in the Fourth Industrial Revolution attracted significant attention from the group of researchers from the year 2017 onwards [47,55,87,89].…”

Section: Literature Review Related To Bibliometric Analysismentioning

confidence: 99%

“…Regarding the method, Table 1 shows that a single article [87] used the Proknow-C method for the bibliometric analysis, which involves executing procedures serially until a final portfolio of articles is obtained, which has a strong correlation with the research theme and contains 3562 articles. As mentioned in Table 1, other studies use the traditional approach to bibliometric analysis, which mainly considers the statistical data.…”

Section: Literature Review Related To Bibliometric Analysismentioning

confidence: 99%

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Sustainable Development of Smart Manufacturing Driven by the Digital Twin Framework: A Statistical Analysis

et al. 2021

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The Fourth Industrial Revolution drives industries from traditional manufacturing to the smart manufacturing approach. In this transformation, existing equipment, processes, or devices are retrofitted with some sensors and other cyber-physical systems (CPS), and adapted towards digital production, which is a blend of critical enabling technologies. In the current scenario of Industry 4.0, industries are shaping themselves towards the development of customized and cost-effective processes to satisfy customer needs with the aid of a digital twin framework, which enables the user to monitor, simulate, control, optimize, and identify defects and trends within, ongoing process, and reduces the chances of human prone errors. This paper intends to make an appraisal of the literature on the digital twin (DT) framework in the domain of smart manufacturing with the aid of critical enabling technologies such as data-driven systems, machine learning and artificial intelligence, and deep learning. This paper also focuses on the concept, evolution, and background of digital twin and the benefits and challenges involved in its implementation. The Scopus and Web of Science databases from 2016 to 2021 were considered for the bibliometric analysis and used to study and analyze the articles that fall within the research theme. For the systematic bibliometric analysis, a novel approach known as Proknow-C was employed, including a series of procedures for article selection and filtration from the existing databases to get the most appropriate articles aligned with the research theme. Additionally, the authors performed statistical and network analyses on the articles within the research theme to identify the most prominent research areas, journal/conference, and authors in the field of a digital twin. This study identifies the current scenarios, possible research gaps, challenges in implementing DT, case studies and future research goals within the research theme.

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Machine Learning and Process Mining applied to Process Optimization: Bibliometric and Systemic Analysis

Cited by 9 publications

References 29 publications

Data management within new product development and collaborative engineering: a bibliometric and systemic analysis

Data management within new product development and collaborative engineering: a bibliometric and systemic analysis

VaryMinions: leveraging RNNs to identify variants in variability-intensive systems’ logs

Sustainable Development of Smart Manufacturing Driven by the Digital Twin Framework: A Statistical Analysis

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