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17 pages, 1706 KiB

Open AccessArticle

Intelligent Rework Process Management System under Smart Factory Environment

by Da-Seol Jo, Tae-Woong Kim and Jun-Woo Kim

Sustainability 2020, 12(23), 9883; https://doi.org/10.3390/su12239883 - 26 Nov 2020

Cited by 7 | Viewed by 3487

Abstract

Rework for defective items is very common in practical shopfloors; however, it generally causes unnecessary energy consumptions and operational costs. In order to address this problem, we propose a novel approach called the intelligent rework process management (i-RPM) system. The proposed system is [...] Read more.

Rework for defective items is very common in practical shopfloors; however, it generally causes unnecessary energy consumptions and operational costs. In order to address this problem, we propose a novel approach called the intelligent rework process management (i-RPM) system. The proposed system is based on intelligent rework policy, which provides a preventive rework procedure for items with latent defects. Such items can be detected before quality tests by applying conventional classification techniques. Moreover, training sets for the classification algorithms can be collected by using modern information and communications technology (ICT) infrastructures. Items with latent defects are not allowed to proceed to the following processes under intelligent rework policy. Instead, they are returned to the preceding processes for rework in order to avoid unnecessary losses on the shopfloor. Consequently, the proposed system helps to achieve a sustainable manufacturing system. Nevertheless, misclassification by the classification model can degrade the performance of intelligent rework policy. Therefore, the i-RPM system is designed to compare rework policies based on classification accuracy and choose the best one of them. For illustration, we applied the i-RPM system to the rework procedure of a steel manufacturer located in Busan, South Korea, and our experiment results revealed that the cost reduction effect of the intelligent rework policy is affected by several input parameters. Full article

(This article belongs to the Special Issue Industry 4.0 and Industrial Sustainability)

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39 pages, 2969 KiB

Open AccessArticle

Sustainable Industry 4.0 in Production and Operations Management: A Systematic Literature Review

by Andreas Felsberger and Gerald Reiner

Sustainability 2020, 12(19), 7982; https://doi.org/10.3390/su12197982 - 26 Sep 2020

Cited by 69 | Viewed by 11068

Abstract

This study draws attention to the upcoming changes within sustainable value chains and manufacturing environments caused by the digital transformation. With a special focus on Industry 4.0 (I4.0), the presented study explores the scientific progress within this research field. A systematic literature review [...] Read more.

This study draws attention to the upcoming changes within sustainable value chains and manufacturing environments caused by the digital transformation. With a special focus on Industry 4.0 (I4.0), the presented study explores the scientific progress within this research field. A systematic literature review approach using a set of predefined keywords and with several exclusion criteria was adopted in order to identify the literature that is related to sustainability in I4.0 and its impact in the area of production and operations management (P&OM). A total of 89 papers from the period 2010–2020 were identified, which were then examined along the lines of the most influential journals, key topics of the selected literature, geographical distribution, and sustainability dimensions. The analysis was executed via bibliometric and text mapping tools, namely NVivo and BibExcel. Furthermore, a focus group discussion with experts from European semiconductor manufacturing companies and researchers from several academic institutions was conducted to derive practical insights. The results of this study will contribute to the evaluation of sustainable I4.0 innovations from the past 10 years. The key issues and research gaps identified in this article will provide a reference point to encourage and guide interested researchers for future studies, thus supporting both theoretical and practical progress in this research area. Full article

(This article belongs to the Special Issue Industry 4.0 and Industrial Sustainability)

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24 pages, 2591 KiB

Open AccessArticle

The Use of Big Data for Sustainable Development in Motor Production Line Issues

by Yao-Chin Lin, Ching-Chuan Yeh, Wei-Hung Chen, Wei-Chun Liu and Jyun-Jie Wang

Sustainability 2020, 12(13), 5323; https://doi.org/10.3390/su12135323 - 1 Jul 2020

Cited by 10 | Viewed by 2911

Abstract

This study explores big data gathered from motor production lines to gain a better understanding of production line issues. Motor products from Solen Electric Company’s motor production lines were used to predict failure points based on big data analytics, where 3606 datapoints from [...] Read more.

This study explores big data gathered from motor production lines to gain a better understanding of production line issues. Motor products from Solen Electric Company’s motor production lines were used to predict failure points based on big data analytics, where 3606 datapoints from the company’s testing equipment were statistically analyzed. The current study focused on secondary data and expert interview results to further define the relevant statistical dimensions. Only 14 of the original 88 detection parameters were required for monitoring the production line. The relationships between these parameters and the relevant motor components were established to indicate how an abnormal reading may be interpreted to quickly resolve an issue. Thus, a theoretical model for the monitoring of the motor production line was proposed. Further implications and practical suggestions are also offered to improve the production lines. This study explores big data analysis and smart manufacturing and demonstrates the promise of these technologies in improving production line efficiency and reducing waste to promote sustainable production goals. Big data thus constitute the core technology for advancing production lines into Industry 4.0 and promoting industry sustainability. Full article

(This article belongs to the Special Issue Industry 4.0 and Industrial Sustainability)

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15 pages, 2796 KiB

Open AccessArticle

Development of an Assessment Method for Evaluation of Sustainable Factories

by Behrouz Pirouz, Natale Arcuri, Behzad Pirouz, Stefania Anna Palermo, Michele Turco and Mario Maiolo

Sustainability 2020, 12(5), 1841; https://doi.org/10.3390/su12051841 - 29 Feb 2020

Cited by 22 | Viewed by 3750

Abstract

The role of the industrial sector in total greenhouse gas (GHG) emissions and resource consumption is well-known, and many industrial activities may have a negative environmental impact. The solution to decreasing the negative effects cannot be effective without the consideration of sustainable development. [...] Read more.

The role of the industrial sector in total greenhouse gas (GHG) emissions and resource consumption is well-known, and many industrial activities may have a negative environmental impact. The solution to decreasing the negative effects cannot be effective without the consideration of sustainable development. There are several methods for sustainability evaluation, such as tools based on products, processes, or plants besides supply chain or life cycle analysis, and there are different rating systems suggesting 80, 140, or more indicators for assessment. The critical point is the limits such as required techniques and budget in using all indicators for all factories in the beginning. Moreover, the weight of each indicator might change based on the selected alternative that it is not a fixed value and could change in a new case study. In this regard, to determine the impact and weight of different indicators in sustainable factories, a multi-layer Triangular Fuzzy Analytic Hierarchy Process (TFAHP) approach was developed, and the application of the method was described and verified. The defined layers are six; for each layer, the pairwise comparison matrix was developed, and the total aggregated score concerning the sustainability goal for each alternative was calculated that shows the Relative Importance Coefficient (RIC). The method is formulated in a way that allows adding the new indicators in all layers as the verification shows, and thus, there are no limits for using any green rating systems. Therefore, the presented approach by TFAHP would provide an additional tool toward the sustainable development of factories. Full article

(This article belongs to the Special Issue Industry 4.0 and Industrial Sustainability)

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15 pages, 2429 KiB

Open AccessArticle

An Innovative Industry 4.0 Cloud Data Transfer Method for an Automated Waste Collection System

by Costel Emil Cotet, Gicu Calin Deac, Crina Narcisa Deac and Cicerone Laurentiu Popa

Sustainability 2020, 12(5), 1839; https://doi.org/10.3390/su12051839 - 29 Feb 2020

Cited by 22 | Viewed by 3615

Abstract

Moving to Industry 4.0 involves the collection of massive amounts of data and the development of big data applications that can ensure a quick data flow between different systems, including massive amounts of data and information collection from smart sensors, and sending them [...] Read more.

Moving to Industry 4.0 involves the collection of massive amounts of data and the development of big data applications that can ensure a quick data flow between different systems, including massive amounts of data and information collection from smart sensors, and sending them to cloud applications that allow real-time data monitoring and processing. Securing and protecting the transmitted data represents a big issue to be discussed and resolved. In the paper, we propose a new method of data encoding and encryption for cloud applications using PNG format images. The proposed method is described in comparison with one of the classical methods of data encoding and transmission used currently. The paper includes a case study in which the proposed method was used to collect and transmit data from an automated waste collection system. The results show that the proposed method represents a secure, fast and efficient way to send and store the data in the cloud compared to the methods currently used. The proposed method is not limited to being used only in waste management but can be used successfully for any type of manufacturing system from smart factories. Full article

(This article belongs to the Special Issue Industry 4.0 and Industrial Sustainability)

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18 pages, 1246 KiB

Open AccessArticle

A Study on the Relationship between Paradox Cognition, Green Industrial Production, and Corporate Performance

by Yi Gao, Zhiguo Li and Kashif Khan

Sustainability 2019, 11(23), 6588; https://doi.org/10.3390/su11236588 - 21 Nov 2019

Cited by 12 | Viewed by 3285

Abstract

Based on the theory of paradox cognition, a relationship model among paradox cognition, industrial green production, and enterprise performance has been constructed, which mainly focuses on a study on whether the paradox cognition can have positive influences on the green production behavior of [...] Read more.

Based on the theory of paradox cognition, a relationship model among paradox cognition, industrial green production, and enterprise performance has been constructed, which mainly focuses on a study on whether the paradox cognition can have positive influences on the green production behavior of industrial enterprises, and then further promote the improvement of enterprises’ economic benefits. The author wrote this thesis on the basis of results obtained from 305 sample surveys and verified the direct and indirect influence relationships among variables in the model with structural equation path coefficient and mediation effect. The empirical results show that: firstly, paradox cognition has a positive and significant impact on the industrial green production behavior. The higher the level of paradox cognition, the more likely the enterprises are to implement the industrial green production behavior. Secondly, paradox cognition can improve the potential performance of enterprises by affecting “green product provision”, “green production management”, and “green production technology”, and then indirectly improve the financial performance of enterprises. Full article

(This article belongs to the Special Issue Industry 4.0 and Industrial Sustainability)

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Review

Jump to: Research

15 pages, 15916 KiB

Open AccessReview

Industry 4.0 and Sustainability: A Bibliometric Literature Review

by Ana Teresa Tavares-Lehmann and Celeste Varum

Sustainability 2021, 13(6), 3493; https://doi.org/10.3390/su13063493 - 22 Mar 2021

Cited by 30 | Viewed by 4165

Abstract

Industry 4.0 (I4.0), Sustainability, and the Circular Economy are recently popularized concepts likely to redefine how economies and industries work. This paper, as the opening piece of this Special Issue, consists of a bibliometric study of 393 articles linking the Issue’s key themes: [...] Read more.

Industry 4.0 (I4.0), Sustainability, and the Circular Economy are recently popularized concepts likely to redefine how economies and industries work. This paper, as the opening piece of this Special Issue, consists of a bibliometric study of 393 articles linking the Issue’s key themes: Industry 4.0, Sustainability and the Circular Economy. Given that this is still a recent area of the literature, and the fact that it already commands a fast-growing number of publications, the provision of an updated overview of the relevant scientific production in the field is a relevant contribution. Full article

(This article belongs to the Special Issue Industry 4.0 and Industrial Sustainability)

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21 pages, 6181 KiB

Open AccessReview

Investigating the Impact of Energy Source Level on the Self-Guided Vehicle System Performances, in the Industry 4.0 Context

by Massinissa Graba, Sousso Kelouwani, Lotfi Zeghmi, Ali Amamou, Kodjo Agbossou and Mohammad Mohammadpour

Sustainability 2020, 12(20), 8541; https://doi.org/10.3390/su12208541 - 15 Oct 2020

Cited by 13 | Viewed by 2917

Abstract

Automated industrial vehicles are taking an imposing place by transforming the industrial operations, and contributing to an efficient in-house transportation of goods. They are expected to bring a variety of benefits towards the Industry 4.0 transition. However, Self-Guided Vehicles (SGVs) are battery-powered, unmanned [...] Read more.

Automated industrial vehicles are taking an imposing place by transforming the industrial operations, and contributing to an efficient in-house transportation of goods. They are expected to bring a variety of benefits towards the Industry 4.0 transition. However, Self-Guided Vehicles (SGVs) are battery-powered, unmanned autonomous vehicles. While the operating durability depends on self-path design, planning energy-efficient paths become crucial. Thus, this paper has no concrete contribution but highlights the lack of energy consideration of SGV-system design in literature by presenting a review of energy-constrained global path planning. Then, an experimental investigation explores the long-term effect of battery level on navigation performance of a single vehicle. This experiment was conducted for several hours, a deviation between the global trajectory and the ground-true path executed by the SGV was observed as the battery depleted. The results show that the mean square error (MSE) increases significantly as the battery’s state-of-charge decreases below a certain value. Full article

(This article belongs to the Special Issue Industry 4.0 and Industrial Sustainability)

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