Industry 4.0 Maturity Models to Support Smart Manufacturing
Transformation: A Systematic Literature Review
Dublin Core
Title
Industry 4.0 Maturity Models to Support Smart Manufacturing
Transformation: A Systematic Literature Review
Transformation: A Systematic Literature Review
Subject
maturity model; intelligent systems; smart manufacturing; industry 4.0
Description
With increasing pressure to revitalize manufacturing industries with Smart Manufacturing capability within the Industry 4.0
(I4.0) context, companies have uneven readiness reflecting their gaps and barriers for transforming to the I4.0 state.
Understanding factors and measuring a company’s maturity in addressing the I4.0 transformation is crucial to diagnose the
company’s current condition and provide corresponding prescriptive action plan effectively. Despite the positive trend of
maturity models for the industries, companies still face challenges with low I4.0 adoption rate. Designing a corresponding
diagnostic framework into an intelligent maturity model will ultimately lead the company’s pathways toward the desired
capabilities. In response, we systematically review and select the state-of-the-art research through a Systematic Literature
Review (SLR) conduct to scrutinize the main characteristics of 14.0 Maturity Models. Subsequently, 35 exceptional articles
published between 1980-2020 were selected for in-depth analysis of their structure, dimensions, and analytical features. Our
analysis revealed the descriptive method have been widely used in many maturity models while few more-advanced prescriptive
models design adopt fuzzy rule-base analytical hierarchy, knowledge based, Monte-Carlo methods, and even expert-system
approaches. Furthermore, people, culture, organization, resources, information system, business processes, and smart
technology, products and services have been treated as the popular evaluation dimensions which will define the state of an
industry’s maturity level
(I4.0) context, companies have uneven readiness reflecting their gaps and barriers for transforming to the I4.0 state.
Understanding factors and measuring a company’s maturity in addressing the I4.0 transformation is crucial to diagnose the
company’s current condition and provide corresponding prescriptive action plan effectively. Despite the positive trend of
maturity models for the industries, companies still face challenges with low I4.0 adoption rate. Designing a corresponding
diagnostic framework into an intelligent maturity model will ultimately lead the company’s pathways toward the desired
capabilities. In response, we systematically review and select the state-of-the-art research through a Systematic Literature
Review (SLR) conduct to scrutinize the main characteristics of 14.0 Maturity Models. Subsequently, 35 exceptional articles
published between 1980-2020 were selected for in-depth analysis of their structure, dimensions, and analytical features. Our
analysis revealed the descriptive method have been widely used in many maturity models while few more-advanced prescriptive
models design adopt fuzzy rule-base analytical hierarchy, knowledge based, Monte-Carlo methods, and even expert-system
approaches. Furthermore, people, culture, organization, resources, information system, business processes, and smart
technology, products and services have been treated as the popular evaluation dimensions which will define the state of an
industry’s maturity level
Creator
Akhmad Hadi Susanto1
, Togar M. Simatupang2
, Meditya Wasesa3
, Togar M. Simatupang2
, Meditya Wasesa3
Publisher
Institut Teknologi Bandung
Date
26-03-2023
Contributor
Fajar bagus W
Format
PDF
Language
Indonesia
Type
Text
Files
Collection
Citation
Akhmad Hadi Susanto1
, Togar M. Simatupang2
, Meditya Wasesa3, “Industry 4.0 Maturity Models to Support Smart Manufacturing
Transformation: A Systematic Literature Review,” Repository Horizon University Indonesia, accessed June 6, 2025, https://repository.horizon.ac.id/items/show/9362.
Transformation: A Systematic Literature Review,” Repository Horizon University Indonesia, accessed June 6, 2025, https://repository.horizon.ac.id/items/show/9362.