Deep learning for manufacturing sustainability: Models, applications in Industry 4.0 and implications

Jamwal, Anbesh; Agrawal, Rajeev; Sharma, Monica

doi:10.1016/j.jjimei.2022.100107

Cited by 49 publications

(15 citation statements)

References 99 publications

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“…In fact, the misalignment of the terminology is a relevant problem because it makes it difficult to identify clear strategic guidelines used by the firms to promote the effective implementation of digital technologies aimed at the compliance of standards established by United Nations. Industry 4.0 takes on a crucial role in promoting the transition and the achievement of sustainability in manufacturing [98][99][100]. However, there is still a research gap concerning Industry 4.0's impediments to sustainable digital manufacturing.…”

Section: Discussionmentioning

confidence: 99%

To Align Technological Advancement and Ethical Conduct: An Analysis of the Relationship between Digital Technologies and Sustainable Decision-Making Processes

Riso

Morrone

2023

Sustainability

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Digitalization and sustainability is widely investigated; however, only few studies have analyzed the role of sustainable decision-making processes in the business strategy field for achieving sustainable development goals: The study proposes a structured literature review (SLR), analyzing 318 documents published in the period 2019–2023. The results of the SLR bring to light that the publications on the topic mainly regard seven research areas. Green supply chains, logistics and digital management is the principal one, followed by sustainable goals, green indicators, and digital advancement. Furthermore, the analysis marks future research lines: although this document offers an overview of the main studies in literature, the major limitation is the use of only one database and a time span of 5 years. This study could contribute to generate further research on sustainable decision-making processes, promoting a different organizational approach to value creation and sustainable performance.

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Section: Discussionmentioning

confidence: 99%

To Align Technological Advancement and Ethical Conduct: An Analysis of the Relationship between Digital Technologies and Sustainable Decision-Making Processes

Riso

Morrone

2023

Sustainability

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“…In order to make the most of the potential energy sources available, businesses must consider alternative sources of energy, such as wind and geothermal, and integrate energy storage solutions such as advanced battery systems into their operations. Doing so can help businesses remain competitive in today's ever-changing and increasingly technological world [14]. As solar energy continues to become more viable as a source of renewable energy, more and more manufacturers are turning to solar energy as an alternative to the traditional sources of energy [15].…”

Section: Methodsmentioning

confidence: 99%

Enhancing Power Efficiency in 4IR Solar Plants through AI-Powered Energy Optimization

Boobalan,

Lakshmi,

Ghate

et al. 2023

RAiSE-2023

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“…Redchuk et al (2023) conducted a case study on the adoption of the industrial Internet of Things and ML for improving energy consumption in a process manufacturing firm operating under the Industry 5.0 paradigm. The Industry Revolution 4.0 and 5.0 emphasize intelligent production and real-time monitoring to optimize manufacturing performance by identifying the most effective pathway for process improvement (Jamwal et al , 2022; Redchuk et al , 2023). Gao et al (2023) proposed a digital twin-based approach to optimize operational energy consumption in automated container terminals.…”

Section: Thematic Areasmentioning

confidence: 99%

Machine learning for sustainable development: leveraging technology for a greener future

Kagzi,

Khanra,

Paul

2023

JSIT

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Purpose From a technological determinist perspective, machine learning (ML) may significantly contribute towards sustainable development. The purpose of this study is to synthesize prior literature on the role of ML in promoting sustainability and to encourage future inquiries. Design/methodology/approach This study conducts a systematic review of 110 papers that demonstrate the utilization of ML in the context of sustainable development. Findings ML techniques may play a vital role in enabling sustainable development by leveraging data to uncover patterns and facilitate the prediction of various variables, thereby aiding in decision-making processes. Through the synthesis of findings from prior research, it is evident that ML may help in achieving many of the United Nations’ sustainable development goals. Originality/value This study represents one of the initial investigations that conducted a comprehensive examination of the literature concerning ML’s contribution to sustainability. The analysis revealed that the research domain is still in its early stages, indicating a need for further exploration.

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Deep learning for manufacturing sustainability: Models, applications in Industry 4.0 and implications

Cited by 49 publications

References 99 publications

To Align Technological Advancement and Ethical Conduct: An Analysis of the Relationship between Digital Technologies and Sustainable Decision-Making Processes

To Align Technological Advancement and Ethical Conduct: An Analysis of the Relationship between Digital Technologies and Sustainable Decision-Making Processes

Enhancing Power Efficiency in 4IR Solar Plants through AI-Powered Energy Optimization

Machine learning for sustainable development: leveraging technology for a greener future

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