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

Sott, Michele Kremer; Furstenau, Leonardo Bertolin; Kipper, Liane Mählmann; Rodrigues, Yan Pablo Reckziegel; López-Robles, José Ricardo; Giraldo, Fáber D.; Cobo, Manuel J.

doi:10.1108/bpmj-05-2020-0181

Cited by 31 publications

(19 citation statements)

References 144 publications

(205 reference statements)

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“…In addition, the SciMAT presents a robust preprocessing module, which ensures data quality [ 30 ]. With the support of the SciMAT, we plotted a strategic diagram and the intellectual network structure of the most important themes [ 61 , 62 , 63 ]. In addition, it was possible to generate an evolutionary diagram that presented the most important themes of the field of study distributed over time.…”

Section: Methodsmentioning

confidence: 99%

A Bibliometric Network Analysis of Recent Publications on Digital Agriculture to Depict Strategic Themes and Evolution Structure

Sott

Nascimento

Foguesatto

et al. 2021

Sensors

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The agriculture sector is one of the backbones of many countries’ economies. Its processes have been changing to enable technology adoption to increase productivity, quality, and sustainable development. In this research, we present a scientific mapping of the adoption of precision techniques and breakthrough technologies in agriculture, so-called Digital Agriculture. To do this, we used 4694 documents from the Web of Science database to perform a Bibliometric Performance and Network Analysis of the literature using SciMAT software with the support of the PICOC protocol. Our findings presented 22 strategic themes related to Digital Agriculture, such as Internet of Things (IoT), Unmanned Aerial Vehicles (UAV) and Climate-smart Agriculture (CSA), among others. The thematic network structure of the nine most important clusters (motor themes) was presented and an in-depth discussion was performed. The thematic evolution map provides a broad perspective of how the field has evolved over time from 1994 to 2020. In addition, our results discuss the main challenges and opportunities for research and practice in the field of study. Our findings provide a comprehensive overview of the main themes related to Digital Agriculture. These results show the main subjects analyzed on this topic and provide a basis for insights for future research.

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Section: Methodsmentioning

confidence: 99%

A Bibliometric Network Analysis of Recent Publications on Digital Agriculture to Depict Strategic Themes and Evolution Structure

Sott

Nascimento

Foguesatto

et al. 2021

Sensors

Self Cite

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“…Finally, other fields demand further investigation in the OS field (for instance, Business Process Modelling (BPM) and its links with smart manufacturing). Sott et al (2021aSott et al ( , p. 1391 stated: "…the need to develop new languages or extensions capable of representing the dynamism, interoperability and multiple technologies of smart factories". In this way, other study stated that, "Most of the studies are aimed at developing new frameworks or integrating languages with other techniques for mapping and modelling organizational processes (Sott et al 2021b, p. 545)".…”

Section: Topic Perspectivesmentioning

confidence: 99%

Trends and challenges in operations strategy research: Findings from a systematic literature review

Vivares¹,

Avella²,

Sarache³

2022

cuadernosgestion

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Operations strategy is a mature field and requires novel insights for further research and practitioners in companies. To fill this gap, 280 articles were analyzed, by way of a systematic literature review, considering two approaches: topic perspectives (content, process, and competitive priorities), and research paradigms (analysis perspective, general methodology, research design, research typology, and alternative paradigms). Eight challenges were identified and discussed to make novel contributions to cutting-edge knowledge. Among other things, it was found that people’s perceptions of object reality prevail as the dominant paradigm, regarding the source and type of information used. In this sense, the literature suggests additional investigation through the direct observation of object reality and experimental studies, in which the action research approach could play an important role as an alternative paradigm. Additionally, considering Industry 4.0 advances, new research opportunities have emerged which permit artificial reconstruction of object reality to support decision-making. The main contribution of this study is to discuss eight challenges by which to improve research relevance and make both academic and practical contributions. Moreover, several useful data for researchers are provided, including statistics regarding response rates in survey research (dominant paradigm). Finally, these findings can be used to perform further research with additional functional strategies in the companies.

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“…It is essential to have the definition of methods that assist in the development of research and ensure its effectiveness (López-Robles et al, 2020). This makes it possible to carry out steps that guarantee the quality and reproducibility of the study (da Silva et al, 2020;Sott et al, 2021). In this work, the PICOC protocol was used to support the process of elaborating research questions as well as defining the search string and criteria for including and excluding documents.…”

Section: Picoc Protocolmentioning

confidence: 99%

Data mining and knowledge discovery in databases for urban solid waste management: A scientific literature review

et al. 2021

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The processes related to solid waste management (SWM) are being revised as new technologies emerge and are applied in the area to achieve greater environmental, social and economic sustainability for society. To achieve our goal, two robust review protocols (Population, Intervention, Comparison, Outcome, and Context (PICOC) and Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA)) were used to systematically analyze 62 documents extracted from the Web of Science database to identify the main techniques and tools for Knowledge Discovery in Databases (KDD) and Data Mining (DM) as applied to SWM and explore the technological potential to optimize the stages of collecting and transporting waste. Moreover, it was possible to analyze the main challenges and opportunities of KDD and DM for SWM. The results show that the most used tools for SWM are MATLAB (29.7%) and GIS (13.5%), whereas the most used techniques are Artificial Neural Networks (35.8%), Linear Regression (16.0%) and Support Vector Machine (12.3%). In addition, 15.3% of the studies were conducted with data from China, 11.1% from India and 9.7% of the studies analyzed and compared data from several other countries. Furthermore, the research showed that the main challenges in the field of study are related to the collection and treatment of data, whereas the opportunities appear to be linked mainly to the impact on the pillars of sustainable development. Thus, this study portrays important issues associated with the use of KDD and DM for optimal SWM and has the potential to assist and direct researchers and field professionals in future studies.

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

Cited by 31 publications

References 144 publications

A Bibliometric Network Analysis of Recent Publications on Digital Agriculture to Depict Strategic Themes and Evolution Structure

A Bibliometric Network Analysis of Recent Publications on Digital Agriculture to Depict Strategic Themes and Evolution Structure

Trends and challenges in operations strategy research: Findings from a systematic literature review

Data mining and knowledge discovery in databases for urban solid waste management: A scientific literature review

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