Maturity Models in Industrial Internet: a Review

Bertolini, Massimo; Esposito, Giovanni; Neroni, Mattia; Romagnoli, Giovanni

doi:10.1016/j.promfg.2020.01.253

Cited by 18 publications

(17 citation statements)

References 25 publications

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“…Identifying the scope is the most critical step during the design process (Mettler, 2011). Two criteria for the scoping of the model: the focus of the model, which will determine the specificity and extensibility of the model within a particular domain (Bertolini et al, 2019). The second is development stakeholders, which define the source of knowledge and the possible audience (Bertolini et al, 2019).…”

Section: Discussionmentioning

confidence: 99%

“…Two criteria for the scoping of the model: the focus of the model, which will determine the specificity and extensibility of the model within a particular domain (Bertolini et al, 2019). The second is development stakeholders, which define the source of knowledge and the possible audience (Bertolini et al, 2019). The scope of the maturity model should be linked with the demands of stakeholders (e.g., practitioners, academics, non-profit organizations) (Mettler, 2011).…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Artificial intelligence maturity model: a systematic literature review

Sadiq

Safie

Rahman

et al. 2021

PeerJ Computer Science

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Organizations in various industries have widely developed the artificial intelligence (AI) maturity model as a systematic approach. This study aims to review state-of-the-art studies related to AI maturity models systematically. It allows a deeper understanding of the methodological issues relevant to maturity models, especially in terms of the objectives, methods employed to develop and validate the models, and the scope and characteristics of maturity model development. Our analysis reveals that most works concentrate on developing maturity models with or without their empirical validation. It shows that the most significant proportion of models were designed for specific domains and purposes. Maturity model development typically uses a bottom-up design approach, and most of the models have a descriptive characteristic. Besides that, maturity grid and continuous representation with five levels are currently trending in maturity model development. Six out of 13 studies (46%) on AI maturity pertain to assess the technology aspect, even in specific domains. It confirms that organizations still require an improvement in their AI capability and in strengthening AI maturity. This review provides an essential contribution to the evolution of organizations using AI to explain the concepts, approaches, and elements of maturity models.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Artificial intelligence maturity model: a systematic literature review

Sadiq

Safie

Rahman

et al. 2021

PeerJ Computer Science

View full text Add to dashboard Cite

show abstract

“…They conduct the review with limited search terms and the quantitative analyzes process is not clearly defined. Bertolini et al (2019) made a very detailed systematic literature review and a qualitative review of the models. They conducted their qualitative review according to the main rules of Curry et al (2009).…”

Section: Findings From the Review Articlesmentioning

confidence: 99%

A systematic literature review for industry 4.0 maturity modeling: state-of-the-art and future challenges

Elibal¹,

Özceylan

2020

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Purpose The purpose of this paper is to conduct a systematic literature review for industry 4.0 maturity modeling research studies to obtain a clear view of the current state-of-the-art. Identifying characteristics of the studies; gaps, limitations and highlighted features has been aimed to guide future research studies. Design/methodology/approach The study includes a systematic literature review conducted on Scopus, IEEE Xplore and Web of Science databases and 90 publications have been reviewed. A novel qualitative taxonomy has been constructed which aims to reduce the cognitive load of the readers. Findings While industry 4.0 maturity modeling is an emerging concept and taking researchers’ attraction, review studies are still in infancy. Current review papers are inadequate in getting a clear idea about the concept, especially from the perspective of guiding future researchers. By the conducted approach of classification conducted in this paper, it has been seen that there are some challenges for improving the industry 4.0 maturity modeling. Research limitations/implications Findings represented in this study can serve academicians and practitioners to develop and/or improve industry 4.0 maturity models. Originality/value The study includes a novel classification for the reviewed papers. Constructed taxonomy is among the first and tabular representations instead of prose analogy that aims to simplify the review of papers.

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“…Especially in the area of Industry 4.0 and IoT, MMs have been recognized as a topic of great interest with increasing numbers of approaches from academia and industry [10]. However, with a share of only 6%, a very limited number of IoT-related MM publications also incorporates the topic of business processes into their model design [11]. Moreover, the aspect of designing a MM to evaluate the capabilities to effectively exploit IoT for digital transformation and BPI has not been considered at all.…”

Section: Introductionmentioning

confidence: 99%

Digital Transformation and Improvement of Business Processes with Internet of Things: A Maturity Model for Assessing Readiness

Stoiber¹,

Schönig²

2022

Proceedings of the Annual Hawaii International Conference on System Sciences

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Companies of all industrial sectors are increasingly integrating Internet of Things (IoT) technology into their processes to realize a data-driven transformation of their businesses. The generation and use of comprehensive process data in real-time and the connection of process entities enables an improvement and beneficial redesign of business processes of all kinds. However, a goal-oriented exploitation of IoT technology for digital transformation and Business Process Improvements (BPI) is challenging due to the complexity of integrating IoT into existing processes. Companies require appropriate guidance to evaluate and scope their initiatives regarding IoT-based BPI. We therefore propose a holistic IoT-based BPI Maturity Model that assists organizations to determine their current state and get assistance to optimize or develop specific capabilities. This paper provides an overview about the structured development process of the maturity model comprising an extensive literature review and a six-round Delphi study.

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Maturity Models in Industrial Internet: a Review

Cited by 18 publications

References 25 publications

Artificial intelligence maturity model: a systematic literature review

Artificial intelligence maturity model: a systematic literature review

A systematic literature review for industry 4.0 maturity modeling: state-of-the-art and future challenges

Digital Transformation and Improvement of Business Processes with Internet of Things: A Maturity Model for Assessing Readiness

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