M2DDM – A Maturity Model for Data-Driven Manufacturing

Weber, Christian; Königsberger, Jan; Kassner, Laura; Mitschang, Bernhard

doi:10.1016/j.procir.2017.03.309

Cited by 124 publications

(54 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Digital Twin applications, such as real-time monitoring, prediction, and control impose a stringent latency requirement for the data processing architecture [52]. A 'designed for latency' data processing architecture becomes a critical criterion.…”

Section: Time-sensitive Data Processingmentioning

confidence: 99%

Digital Twin-driven smart manufacturing: Connotation, reference model, applications and research issues

Liu

Wang

et al. 2020

Robotics and Computer-Integrated Manufacturing

888

316

View full text Add to dashboard Cite

This paper reviews the recent development of Digital Twin technologies in manufacturing systems and processes, to analyze the connotation, application scenarios, and research issues of Digital Twin-driven smart manufacturing in the context of Industry 4.0. To understand Digital Twin and its future potential in manufacturing, we summarized the definition and state-of-theart development outcomes of Digital Twin. Existing technologies for developing a Digital Twin for smart manufacturing are reviewed under a Digital Twin reference model to systematize the development methodology for Digital Twin. Representative applications are reviewed with a focus on the alignment with the proposed reference model. Outstanding research issues of developing Digital Twins for smart manufacturing are identified at the end of the paper.

show abstract

Section: Time-sensitive Data Processingmentioning

confidence: 99%

Digital Twin-driven smart manufacturing: Connotation, reference model, applications and research issues

Liu

Wang

et al. 2020

Robotics and Computer-Integrated Manufacturing

888

316

View full text Add to dashboard Cite

show abstract

“…There are many maturity models in the literature on the subject, which differ in measurement criteria, the number of maturity levels, methodology, etc. [29][30][31][32][33]. However, there are two common approaches for maturity assessment.…”

Section: Figure 1 the Number Of Publications On Maturity Models In The Scopus Damentioning

confidence: 99%

From Fragility through Agility to Resilience: The Role of Sustainable Improvement in Increasing Organizational Maturity

Stachowiak

Pawłyszyn

2021

Sustainability

View full text Add to dashboard Cite

Nowadays, due to a large number of changes in the external environment, as well as increased competition, organizations must actively support business processes. Internal management processes must be constantly improved. To understand whether a company is developing in the right direction, it is necessary to conduct a maturity assessment. The directions of changes in enterprises should be well designed and based on analysis of the gap between the present and expected state definition. The diagnosis for the present state definition should be holistic, reliable and for the expected state-based strategy and goal recognition. Hence, the goal of the paper is to present the methodology of a company’s self-assessment and definition of an individualized improvement strategy to strengthen the company’s ability to thrive and prosper. The paper includes a presentation of the diagnosis methodology based on maturity models and the resilience concept and its validation based on experts’ opinions and a case study. Companies need a diagnosis to know where they are, and guidance to move in the right direction, which makes the results of the research utilitarian. The methodology can be used for self-assessment, benchmarking and designing an improvement strategy.

show abstract

“…The holistic models aim to assess and utilize elements of I4.0 from all perspectives and hence derive encompassing success factors. The specific MMs focus on the specific areas of I4.0 application or a limited number of aspects related to I4.0 such as logistics (Facchini et al, 2020), supply chain (Asdecker and Felch, 2018), information technologies (Weber et al, 2017). There aren't any modifications of generic I4.0 MMs that take into account the specifics of certain industry sectors in the literature not even specific approaches focusing on the field of Q4.0.…”

Section: Existing Industry 40 Maturity Modelsmentioning

confidence: 99%

Learning Needs Determination for Industry 4.0 Maturity Development in Automotive Organisations in Slovakia

Sütőová

Šooš

Kóča³

2020

QIP Journal

View full text Add to dashboard Cite

Purpose: This paper aims to present the results of the study focused on the assessment of Industry 4.0 (I4.0) maturity level and adoption level of Quality 4.0 (Q4.0) intelligent technologies in organisations operating in the automotive industry in Slovakia (OEMs, Tier 1 and Tier 2 suppliers). The results serves as inputs for identification of learning and development needs.Methodology/Approach: The background of the study was a literature review and quantitative research. The I4.0 maturity model published by PwC (2016) was used in the study, while dimension elements were adjusted to the specifics of the automotive industry.Findings: Tier 1 and Tier 2 automotive suppliers are in the early stages of I4.0 maturity and adoption of Q4.0 intelligent technologies. OEMs achieve the level of horizontal collaborators in most of the dimensions. Q4.0 intelligent technologies are mostly adopted at an average level. Further development of OEMs to achieve the level of digital champions requires new disruptive business models and a fully integrated partner ecosystem.Research Limitation/Implication: The research is limited by the sample size and target levels of particular dimensions, related elements and Q4.0 intelligent technologies, which were not examined.Originality/Value of paper: The results bring more in-depth insight into the current state of I4.0 maturity and Q4.0 technology adoption level of the automotive organisations in Slovakia. There is no evidence of the study examining holistically the I4.0 maturity and Q4.0 technologies in the automotive.

show abstract

M2DDM – A Maturity Model for Data-Driven Manufacturing

Cited by 124 publications

References 6 publications

Digital Twin-driven smart manufacturing: Connotation, reference model, applications and research issues

Digital Twin-driven smart manufacturing: Connotation, reference model, applications and research issues

From Fragility through Agility to Resilience: The Role of Sustainable Improvement in Increasing Organizational Maturity

Learning Needs Determination for Industry 4.0 Maturity Development in Automotive Organisations in Slovakia

Contact Info

Product

Resources

About