A Taxonomy of Industry 4.0 and Related Technologies

Öztemel, Ercan; Gürsev, Samet

doi:10.5772/intechopen.90122

Cited by 17 publications

(15 citation statements)

References 19 publications

(17 reference statements)

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“…it is observed that aspects such as strategic, managerial, technical, and human intervention points of view permeate. Oztemel and Gursev (2020) mention that the integration of the elements of Industry 4.0 is done horizontally (through all participants in the entire value chain) and vertically (in all automation levels) of a given company as a whole. The dimensions shown in Table 1 contextualize vertical integration which aspects of the Organization, personnel, machines, processes, and products are taken.…”

Section: Development Of the Modelmentioning

confidence: 99%

Proposal and validation of an industry 4.0 maturity model for SMEs

Bohórquez

Herrera

2022

JIEM

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Purpose: This paper seeks to establish an Industry 4.0 maturity model for manufacturing SMEs. This research presents the characteristics of the proposed model, which takes the elements and the scope of the fourth industrial revolution, as well as the dimensions and assessment scales of some maturity models already applied. Likewise, this document shows the modeling process and the model’s validation in SMEs in the city of Bogotá-Colombia.Design/methodology/approach: To determine the criteria of the maturity model, 6 major stages have been established: Literature Review, Development of the model; Validation of the model; Application of the model; Data analysis; and Conclusion and Recommendations.Findings: Considering the validation of some maturity models shown in the literature review, and aligned with the purpose of this article, 8 dimensions have been established to measure the maturity level of SMEs: Service; Operations; Quality; Products; Documented information- Big Data; Leadership and strategy; Communication; and Culture and people. A model has been generated that allows evaluating the degree of compliance in each dimension for manufacturing SMEs. The model can be applied to companies in any industry. Also, it can determine the degree of implementation compliance of companies in the same sector.Research limitations/implications: According to the literature reviewed, SMEs, especially those in Latin America, still do not have a culture of applying the elements of Industry 4.0. Therefore, in the research, it was not easy to understand the intrinsic variables of Industry 4.0 that SMEs have applied in different areas, which does not allow us to have the current context of SMEs and from that perspective to have a better simulation of the business model maturity.Practical implications: The model presented in this document serves as a basis for SMEs in Latin America to establish a baseline measurement in relation to the application of Industry 4.0 elements in companies.Social implications: What is intended with this work is to frame a baseline so that companies can understand their current maturity level in terms that industry 4.0 could cover. Likewise, they can generate actions for the appropriation of new technologies that allow them to be more competitive. This document can be taken and applied by those entrepreneurs companies who wish to measure their operations.Originality/value: The essential point for the generation of the maturity level measurement model is focused on determining the necessary dimensions on which the evaluation is based. In the literature found, most models focus their dimensions on measuring the digital in their processes and tangentially evaluate the organizational structure and the relationship between them. Additionally, the authors who address the organization as a whole do not reveal the details for SMEs to self-evaluate. The models found have only been implemented to evaluate one company alone or individually. This model presents the core dimensions holistically and explicitly, taking important criteria such as quality, service, communication, and the culture of all employees. Additionally, it shows in detail the model that allows SMEs of the manufacturing sector to self-assess themselves in each dimension and in turn the degree of the business sector in which they are or belong.

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Section: Development Of the Modelmentioning

confidence: 99%

Proposal and validation of an industry 4.0 maturity model for SMEs

Bohórquez

Herrera

2022

JIEM

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“…While browsing the available publications, we notice that some researchers had focused on creating taxonomies for Industry 4.0 technologies that supported the digital transformation and industrial needs [ 10 , 11 ]. Some researchers studied business use cases or consulted company reports and white papers [ 12 ].…”

Section: Introductionmentioning

confidence: 99%

Possible Applications of Edge Computing in the Manufacturing Industry—Systematic Literature Review

Kubiak

Dec

Stadnicka

2022

Sensors

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This article presents the results of research with the main goal of identifying possible applications of edge computing (EC) in industry. This study used the methodology of systematic literature review and text mining analysis. The main findings showed that the primary goal of EC is to reduce the time required to transfer large amounts of data. With the ability to analyze data at the edge, it is possible to obtain immediate feedback and use it in the decision-making process. However, the implementation of EC requires investments not only in infrastructure, but also in the development of employee knowledge related to modern computing methods based on artificial intelligence. As the results of the analyses showed, great importance is also attached to energy consumption, both in ongoing production processes and for the purposes of data transmission and analysis. This paper also highlights problems related to quality management. Based on the analyses, we indicate further research directions for the application of edge computing and associated technologies that are required in the area of intelligent resource scheduling (for flexible production systems and autonomous systems), anomaly detection and resulting decision making, data analysis and transfer, knowledge management (for smart designing), and simulations (for autonomous systems).

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“…This is because by using advanced Artificial Intelligent (AI) methods to predict disruptions in the functioning of technical systems, predictive maintenance enables the minimization of downtime, prolonging machine life, increasing production efficiency, resource utilization, and reducing costs [3][4][5]. There is no common definition of Maintenance 4.0 or Industry 4.0, however, a number of studies undertaking these issues are growing rapidly and are also witnessed by many taxonomies of problems identified in both these areas, which presented, inter alia, in the works [2, [6][7][8].…”

Section: Introductionmentioning

confidence: 99%

Dynamic Planning of Mobile Service Teams’ Mission Subject to Orders Uncertainty Constraints

Bocewicz

Nielsen

Jasiulewicz-Kaczmarek

et al. 2020

Applied Sciences

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This paper considers the dynamic vehicle routing problem where a fleet of vehicles deals with periodic deliveries of goods or services to spatially dispersed customers over a given time horizon. Individual customers may only be served by predefined (dedicated) suppliers. Each vehicle follows a pre-planned separate route linking points defined by the customer location and service periods when ordered deliveries are carried out. Customer order specifications and their services time windows as well as vehicle travel times are dynamically recognized over time. The objective is to maximize a number of newly introduced or modified requests, being submitted dynamically throughout the assumed time horizon, but not compromising already considered orders. Therefore, the main question is whether a newly reported delivery request or currently modified/corrected one can be accepted or not. The considered problem arises, for example, in systems in which garbage collection or DHL parcel deliveries as well as preventive maintenance requests are scheduled and implemented according to a cyclically repeating sequence. It is formulated as a constraint satisfaction problem implementing the ordered fuzzy number formalism enabling to handle the fuzzy nature of variables through an algebraic approach. Computational results show that the proposed solution outperforms commonly used computer simulation methods.

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A Taxonomy of Industry 4.0 and Related Technologies

Cited by 17 publications

References 19 publications

Proposal and validation of an industry 4.0 maturity model for SMEs

Proposal and validation of an industry 4.0 maturity model for SMEs

Possible Applications of Edge Computing in the Manufacturing Industry—Systematic Literature Review

Dynamic Planning of Mobile Service Teams’ Mission Subject to Orders Uncertainty Constraints

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