Industry 4.0 and micro and small enterprises: systematic literature review and analysis

Silva, Núbia Adriane da; Abreu, Jaqueline Lilge; Klingenberg, Cristina Orsolin; Antunes, José Antônio Valle; Lacerda, Daniel Pacheco

doi:10.1080/21693277.2022.2124466

Cited by 10 publications

(7 citation statements)

References 80 publications

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“…In table 5, the calibrated pixel value on the slip gauge image was calculated for four different cases (see table 4) using equation (1) and was further multiplied by the desired diameter millimeter value, where the millimeter value of the desired diameter was converted into a pixel value. The absolute percentage pixel errors (irrespective of signs) of the measured pixel values of the image diameter from the pixel value of the desired diameter were calculated by using equation (5). For each case, the minimum value among the calculated percentage pixel errors was chosen for analysis, which is shown in table 5.…”

Section: Resultsmentioning

confidence: 99%

“…In Industry 4.0, systems and machines become smart due to the involvement of CPS and IoT with the ability to make smart decisions [4]. The Internet of Things is the major technology used in Industry 4.0, which enhances processes and efficiency and overall improves the value chain of the system [5]. In recent years, due to the rapid development of the internet, most digital systems have become interconnected and are available for communicating data with each other.…”

Section: Introductionmentioning

confidence: 99%

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A novel IoT based machine vision system for on-machine diameter measurement and optimization

Zende,

Pawade

2023

Eng. Res. Express

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The use of machine vision systems has been made user-friendly, cost-effective, and flawless by the rapid development in the fields of advanced electro-optical and camera systems, electronics systems, and software systems. One such application of machine vision systems in the field of manufacturing is the inspection of a semi-finished or finished component during an ongoing manufacturing process. In this study, the camera's intrinsic and extrinsic parameters were maintained constant, while red, green, and blue light sources were employed to measure the component diameter using pixel analysis. A novel approach was used in an IoT-based machine vision system where, on the same image, the smartphone camera was calibrated and the image diameter of the component under study was measured, which was found to be quite accurate. Four different cases were used in the error analysis of image diameter, in which experimental results show that under blue light, the percentage pixel error span is the largest at 0.2624 % followed by 0.1422 % under green light and 0.0903 % under red light. Further, the use of four different cases was followed by the 'Weighted Sum Model', which optimized the percentage errors in estimated actual diameter precisely and effectively, where outcome results showed that the approximate percentage errors were determined within 0.8 % for blue light, 0.5 % for a red light, and 0.1 % for a green light. The proposed IoT-based machine vision system was found to be robust and effective for on-machine measurement.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A novel IoT based machine vision system for on-machine diameter measurement and optimization

Zende,

Pawade

2023

Eng. Res. Express

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“…This feature allows for the removal of barriers separating the workspace of robots from that of operators, making the industrial layout more flexible and enabling new forms of interaction [2]. In addition, this perspective allows to promote greater adaptability and reconfigurability of production processes [3,4]. However, working closely with a robot can induce stressful situations for operators, impacting the performance and quality of production processes.…”

Section: Introductionmentioning

confidence: 99%

An experimental focus on learning effect and interaction quality in human–robot collaboration

Gervasi

Mastrogiacomo

Franceschini

2023

Prod. Eng. Res. Devel.

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In the landscape of the emerging Industry 5.0, human–robot collaboration (HRC) represents a solution to increase the flexibility and reconfigurability of production processes. Unlike classical industrial automation, in HRC it is possible to have direct interaction between humans and robots. Consequently, in order to effectively implement HRC it is necessary to consider not only technical aspects related to the robot but also human aspects. The focus of this paper is to expand on previous results investigating how the learning process (i.e., the experience gained through the interaction) affects the user experience in the HRC in conjunction with different configuration factors (i.e., robot speed, task execution control, and proximity to robot workspace). Participants performed an assembly task in 12 different configurations and provided feedback on their experience. In addition to perceived interaction quality, self-reported affective state and stress-related physiological indicators (i.e., average skin conductance response and heart rate variability) were collected. A deep quantitative analysis of the response variables revealed a significant influence of the learning process in the user experience. In addition, the perception of some configuration factors changed during the experiment. Finally, a significant influence of participant characteristics also emerged, auguring the necessity of promoting a human-centered HRC.

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“…Furthermore, there is an exploration of the adaptability of Industry 4.0 maturity models for SMEs within the production and logistics sector, as elucidated by Elhusseiny and Crispim [14]. Additionally, da Silva et al [15] explored the practical application of Industry 4.0 technologies in SMEs, examining challenges and performance outcomes. Lastly, Chavez et al [16] focused on digitization levels in SME deviation management.…”

Section: Introductionmentioning

confidence: 99%

Industry 4.0 Implementation in Small- and Medium-Sized Enterprises: Recommendations Extracted from a Systematic Literature Review with a Focus on Maturity Models

Häring,

Pimentel,

Teixeira

2023

Logistics

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Background: Industry 4.0 signifies a profound global transformation in production and service activities through its novel organizational principles, including digital connectivity, information transparency, technical assistance, and decentralized decision making. This transformation poses significant challenges for businesses, particularly small- and medium-sized enterprises (SMEs). In response, maturity models have been developed and adapted to facilitate a transparent and supportive entry into this transformative domain. Method/Aim: This study is dedicated to the comprehensive analysis of Industry 4.0 maturity models through a systematic literature review to identify and evaluate previously published recommendations for the adoption and utilization of Industry 4.0. The aim is to provide valuable insights in this context, with a particular focus on easing entry into this domain for SMEs. Results: Quantitative findings reveal a growing demand for fundamental support when entering this domain, with maturity models capable of meeting the demand for structured guidance. However, these models are currently under-validated, lacking transparency, and are often unsuitable for SMEs. Qualitative results categorize numerous insights and recommendations into ten distinct categories related to Industry 4.0. Conclusions: This paper provides a structured summary to support newcomers, research institutions, and businesses in effectively initiating and optimizing their Industrsy 4.0 activities.

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Industry 4.0 and micro and small enterprises: systematic literature review and analysis

Cited by 10 publications

References 80 publications

A novel IoT based machine vision system for on-machine diameter measurement and optimization

A novel IoT based machine vision system for on-machine diameter measurement and optimization

An experimental focus on learning effect and interaction quality in human–robot collaboration

Industry 4.0 Implementation in Small- and Medium-Sized Enterprises: Recommendations Extracted from a Systematic Literature Review with a Focus on Maturity Models

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