Industry 4.0: A Classification Scheme

Perales, David Pérez; Valero, Faustino Alarcón; García, Andrés Boza

doi:10.1007/978-3-319-58409-6_38

Cited by 36 publications

(17 citation statements)

References 9 publications

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“…The smart machines, storage systems, and production facilities integrated with the cyber-physical system enable the process of sharing data and information, initiating actions, and controlling each other [43]. With increasing cyber-physical systems in the manufacturing industry, organizations can achieve interoperability in which systems, people, and information can intercommunicate [44][45], create intra-organization cross-linking and cross-organization integration into interconnected industrial value creation networks [46][47] in real-time [2]. This industrial value creation generates technological leaps in machine-to-machine and human-to-machine communication and collaborations [48].…”

Section: E Technology Readiness and Industry 40 Implementationmentioning

confidence: 99%

Key Enablers of Industry 4.0 Development at Firm Level: Findings From an Emerging Economy

Adebanjo

Laosirihongthong

Samaranayake

et al. 2023

IEEE Trans. Eng. Manage.

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Organizations in both developed and developing economies are paying great attention to the Industry 4.0 revolution and associated uses of technologies due to its potential benefits to the manufacturing industry. However, there are a limited number of empirical studies due to its early stage of adoption around the world, especially regarding the key technological factors that are necessary. This study addresses this research gap by identifying the factors that enable successful Industry 4.0 technologies adoption in an emerging economy country, grouping them, and ranking the groups based on priorities for adoption. The study adopts a mixed-method research methodology. Q-Sort technique and Analytic Hierarchy Process (AHP) respectively were used to group enabling factors and prioritize the groups for Industry 4.0 technologies adoption. Thereafter, semi-structured interviews of key stakeholders in the manufacturing sector in Thailand were carried out to validate and support findings from the quantitative analysis. Five industry experts from automotive and electronic parts/components manufacturers were interviewed. The results show that human capital is the most important readiness dimension for Industry 4.0 technologies implementation.Interoperability and data handling were found to be the next in importance. On the contrary, hardware and technology systems such as data security and technological infrastructure were identified as the least important of the technology readiness dimensions. These findings provide a different perspective to extant studies that posited that technology-based factors as the most important for Industry 4.0 success. Managerial Relevance StatementThe rapid development of Industry 4.0 has significantly influenced manufacturing organizations' operations and decisions. As manufacturers increasingly seek to adopt Industry 4.0, it is important that they know where to direct their efforts and resources. This study emphasizes that human development issues are critical to the success of Industry 4.0. The implication for managers is that human capital must be developed in tandem with technology. The study also highlights the importance of interoperability and therefore, managers must choose Industry 4.0 systems and technology with the ability to be interoperable with other systems and/or organizations in their ecosystems. Finally, the research encourages managers to consider production flexibility and process stability as the most important performance measures for Industry 4.0 implementation.

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Section: E Technology Readiness and Industry 40 Implementationmentioning

confidence: 99%

Key Enablers of Industry 4.0 Development at Firm Level: Findings From an Emerging Economy

Adebanjo

Laosirihongthong

Samaranayake

et al. 2023

IEEE Trans. Eng. Manage.

View full text Add to dashboard Cite

show abstract

“…However, there is a lack of a backbone for initiating radical digitalization strategies [76]. On top of that, most industries are still unsure of and unfamiliar with Industry 4.0 because of their ignorance of its potential advantages; thus, many industries have shown resistance to the adoption of Industry 4.0 technologies [56,77].…”

Section: Resistance To Change (C 10 )mentioning

confidence: 99%

Fermatean Fuzzy CRITIC-COPRAS Method for Evaluating the Challenges to Industry 4.0 Adoption for a Sustainable Digital Transformation

2021

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Decision and policymakers are looking at the potential of Industry 4.0 smart technologies to create a green economy as the European Commission aims to deliver the European Green Deal by rethinking policies for clean energy supply. Industry 4.0 will eventually be applied to all aspects of life; however, it is necessary to identify the challenges to the adoption of Industry 4.0 for a sustainable digital transformation. In this vein, the present study aims to identify the challenges to the adoption of Industry 4.0 in fintech companies and to develop a novel Fermatean fuzzy CRITIC-COPRAS method to rank the identified challenges and evaluate the performance of companies concerning the weighted challenges based on three decision experts’ support. The results indicated that “difficulty in coordination and collaboration” is the most significant challenge to the adoption of Industry 4.0 out of the fourteen identified challenges, followed by “resistance to change” and “governmental support.” In addition, the superiority and efficiency of the proposed method were investigated through comparative analyses.

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“…The vast majority of the manufacturing industry is up to this point, unknown and unsure about the area of Industry 4.0. Due to unawareness of potential benefits, the dominant part of the industry is doubtful to implement the innovations and technologies of Industry 4.0 (Theorin et al 2017;Perales, Valero, and García 2018;). Türkeș et al (Türkeș et al 2019) consider a superficial understanding of the Industry 4.0 concept as a barrier to its adoption.…”

Section: Low Knowledge Of Industry 40 Conceptmentioning

confidence: 99%

Prioritization of barriers for Industry 4.0 adoption in the context of Indian manufacturing industries using AHP and ANP analysis

Nimawat

Gidwani

2021

International Journal of Computer Integrated Manufacturing

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Industry 4.0 has gained enormous attention from the academic and industrial community in recent years and lacks empirical research. This makes it imperative to reveal and perceive barriers for Industry 4.0 adoption. The article explores the prioritization of industry 4.0 adoption barriers in the context of Indian manufacturing industries utilizing the Analytical hierarchy process (AHP) and Analytic network process (ANP) analysis and validation through sensitivity analysis state-of-the-art in the area of Industry 4.0. According to the literature review and validated by specialists from industries and academicians, 15 barriers have been considered and categorized into four criteria. Both models provide enterprises with a direct and trouble-free approach to prioritize the barriers for Industry 4.0 adoption. The two approaches illustrated the impression of being influential in selecting industry 4.0 adoption barriers with high and low priority. The two methods achieved almost similar findings. Adopting such AHP and ANP models on Industry 4.0 barriers in manufacturing industries will enable executives, specialists, and decision-makers of Industry 4.0 to effectively categorize these barriers and concentrate on critical barriers when implementing Industry 4.0 in their respective industries.

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Industry 4.0: A Classification Scheme

Cited by 36 publications

References 9 publications

Key Enablers of Industry 4.0 Development at Firm Level: Findings From an Emerging Economy

Key Enablers of Industry 4.0 Development at Firm Level: Findings From an Emerging Economy

Fermatean Fuzzy CRITIC-COPRAS Method for Evaluating the Challenges to Industry 4.0 Adoption for a Sustainable Digital Transformation

Prioritization of barriers for Industry 4.0 adoption in the context of Indian manufacturing industries using AHP and ANP analysis

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