Industry 4.0 Technologies for Manufacturing Sustainability: A Systematic Review and Future Research Directions

Jamwal, Anbesh; Agrawal, Rajeev; Sharma, Monica; Giallanza, Antonio

doi:10.3390/app11125725

Cited by 218 publications

(108 citation statements)

References 147 publications

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“…It is generally agreed that innovations relevant to Industry 4.0 would significantly affect current industries and future sector development. Although several companies look forward to introducing new technologies to improve their services’ quality, productivity, and efficiency, they reduce risks and sustain market competitiveness [ 53 , 116 , 117 , 118 ].…”

Section: Research Challenges Opportunities Scope Of Future Work and Implication For Practitionersmentioning

confidence: 99%

“…Industrial standards are used to measure the performance and manage the quality of the product and services. In order to fill this gap, our study focuses on how the manufacturing industries are using different industrial standards to measure the performance and manage the quality of the product and services [ 48 , 51 , 52 , 53 , 54 ].…”

Section: Introductionmentioning

confidence: 99%

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Performance Measurement System and Quality Management in Data-Driven Industry 4.0: A Review

Tambare¹,

Meshram²,

Lee

et al. 2021

Sensors

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The birth of mass production started in the early 1900s. The manufacturing industries were transformed from mechanization to digitalization with the help of Information and Communication Technology (ICT). Now, the advancement of ICT and the Internet of Things has enabled smart manufacturing or Industry 4.0. Industry 4.0 refers to the various technologies that are transforming the way we work in manufacturing industries such as Internet of Things, cloud, big data, AI, robotics, blockchain, autonomous vehicles, enterprise software, etc. Additionally, the Industry 4.0 concept refers to new production patterns involving new technologies, manufacturing factors, and workforce organization. It changes the production process and creates a highly efficient production system that reduces production costs and improves product quality. The concept of Industry 4.0 is relatively new; there is high uncertainty, lack of knowledge and limited publication about the performance measurement and quality management with respect to Industry 4.0. Conversely, manufacturing companies are still struggling to understand the variety of Industry 4.0 technologies. Industrial standards are used to measure performance and manage the quality of the product and services. In order to fill this gap, our study focuses on how the manufacturing industries use different industrial standards to measure performance and manage the quality of the product and services. This paper reviews the current methods, industrial standards, key performance indicators (KPIs) used for performance measurement systems in data-driven Industry 4.0, and the case studies to understand how smart manufacturing companies are taking advantage of Industry 4.0. Furthermore, this article discusses the digitalization of quality called Quality 4.0, research challenges and opportunities in data-driven Industry 4.0 are discussed.

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Section: Research Challenges Opportunities Scope Of Future Work and Implication For Practitionersmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Performance Measurement System and Quality Management in Data-Driven Industry 4.0: A Review

Tambare¹,

Meshram²,

Lee

et al. 2021

Sensors

View full text Add to dashboard Cite

show abstract

“…In addition, it should integrate intelligence throughout the network and include AI logic in the network design, allowing us to control and connect all internal components using AI dynamically. Industry 4.0 was discussed by Jamwal et al [149] to minimize human intervention via using automatic control systems in industrial applications. Recently, a plant has been created to autonomously manage a whole system's communication, calculations, storage, and resources control.…”

Section: Connected Autonomous Vehiclesmentioning

confidence: 99%

Security Requirements and Challenges of 6G Technologies and Applications

Hakeem

Hussein

Kim

2022

Sensors

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After implementing 5G technology, academia and industry started researching 6th generation wireless network technology (6G). 6G is expected to be implemented around the year 2030. It will offer a significant experience for everyone by enabling hyper-connectivity between people and everything. In addition, it is expected to extend mobile communication possibilities where earlier generations could not have developed. Several potential technologies are predicted to serve as the foundation of 6G networks. These include upcoming and current technologies such as post-quantum cryptography, artificial intelligence (AI), machine learning (ML), enhanced edge computing, molecular communication, THz, visible light communication (VLC), and distributed ledger (DL) technologies such as blockchain. From a security and privacy perspective, these developments need a reconsideration of prior security traditional methods. New novel authentication, encryption, access control, communication, and malicious activity detection must satisfy the higher significant requirements of future networks. In addition, new security approaches are necessary to ensure trustworthiness and privacy. This paper provides insights into the critical problems and difficulties related to the security, privacy, and trust issues of 6G networks. Moreover, the standard technologies and security challenges per each technology are clarified. This paper introduces the 6G security architecture and improvements over the 5G architecture. We also introduce the security issues and challenges of the 6G physical layer. In addition, the AI/ML layers and the proposed security solution in each layer are studied. The paper summarizes the security evolution in legacy mobile networks and concludes with their security problems and the most essential 6G application services and their security requirements. Finally, this paper provides a complete discussion of 6G networks’ trustworthiness and solutions.

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“…Their work filled potential gaps in the literature concerning dependencies between the evaluations of the economic, environmental, and social sustainability based on adopting various emerging technologies. Jamwal et al [44] systematically reviewed the literature to identify the current research progress and future research potential of Industry 4.0 technologies to achieve manufacturing sustainability. Industry 4.0 implementations support the triple bottom line (TBL) of sustainability through productivity and product quality improvement (economic benefit), continuous monitoring of energy consumption (environmental impact), and safer working environment, less intense workload, and job enrichment (social needs) and the improvement in economic sustainability directly affects environmental sustainability by improving the efficiency of resource utilization and social sustainability by higher taxes [21].…”

Section: Iot Adoption and Sustainabilitymentioning

confidence: 99%

Prioritizing Enabling Factors of IoT Adoption for Sustainability in Supply Chain Management

2021

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Several studies have reported on enabling factors of IoT adoption, emphasizing the importance of key factors for successful IoT adoption. However, only a few studies have investigated enabling factors with consideration of a sustainability perspective and no similar study has focused on manufacturing from an emerging economy perspective. The main purpose is to investigate enabling factors of IoT adoption from a sustainability perspective. This study aims to (i) identify and select key enabling factors from a comprehensive literature review, and (ii) prioritize them using a multiple criteria decision-making approach, validated through industry experts’ opinions. The results showed that system integration and IoT infrastructure are the top enabling factors in increasing the overall success of IoT adoption. Furthermore, enabling factors of IoT adoption are directly connected with organizational resources/technological capabilities that support the resource-based view theory. Supply chain managers can use the findings of this study to guide and prioritize IoT adoption, and develop strategies for going forward with IoT settings, using the relative importance of enabling factors and interdependencies among them from the technological and organizational perspectives. To generalize these findings through benchmarking of enabling factors in manufacturing, a broader range of industries within the manufacturing sector should be considered in future studies.

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Industry 4.0 Technologies for Manufacturing Sustainability: A Systematic Review and Future Research Directions

Cited by 218 publications

References 147 publications

Performance Measurement System and Quality Management in Data-Driven Industry 4.0: A Review

Performance Measurement System and Quality Management in Data-Driven Industry 4.0: A Review

Security Requirements and Challenges of 6G Technologies and Applications

Prioritizing Enabling Factors of IoT Adoption for Sustainability in Supply Chain Management

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