This paper aims to contribute with a literature review on the use of AI for cleaner production throughout industries in the consideration of AI’s advantage within the environment, economy, and society. The survey report based on the analysis of research papers from the recent literature from leading database sources such as Scopus, the Web of Science, IEEE Xplore, Science Direct, Springer Link, and Google Scholar identifies the strategic strengths of AI in optimizing the resources, minimizing the carbon footprint and eradicating wastage with the help of machined learning, neural networks and predictive analytics. AI integration presents vast aspects of environmental gains, including such enhancements as a marked reduction concerning the energy and materials consumed along with enhanced ways of handling the resulting waste. On the economic aspect, AI enhances the processes that lead to better efficiency and lower costs in the market on the other hand, on the social aspect, the application of any AI influences how people are utilized as workers/clients in the community. The following are some of the limitations towards AI adoption as proposed by the review of related literature; The best things that come with AI are yet accompanied by some disadvantages; there are implementation costs, data privacy, as well as system integration that may be a major disadvantage. The review envisages that with the continuation of the AI development in the following years, the optic is going to be the accentuation on the enhancement of the process of feeding the data in real-time mode, IoT connections, and the implementation of the proper ethical approaches toward the AI launching for all segments of the society. The conclusions provide precise suggestions to the people working in the industry to adopt the AI advancements appropriately and at the same time, encourage the lawmakers to create favorable legal environments to enable the ethical uses of AI. This review therefore calls for more targeted partnerships between the academia, industry, and government to harness the full potential of AI for sustainable industrial practices worldwide.

In this paper, we will provide an extensive analysis of how Generative Artificial Intelligence (GenAI) could be applied when handling Supply Chain Management (SCM). The paper focuses on how GenAI is more relevant in industries, and for instance, SCM where it is employed in tasks such as predicting when machines are due for a check-up, man-robot collaboration, and responsiveness. The study aims to answer two main questions: (1) What prospects can be identified when the tools of GenAI are applied in SCM? Secondly, it aims to examine the following question: (2) what difficulties may be encountered when implementing GenAI in SCM? This paper assesses studies published in academic databases and applies a structured analytical framework to explore GenAI technology in SCM. It looks at how GenAI is deployed within SCM and the challenges that have been encountered, in addition to the ethics. Moreover, this paper also discusses the problems that AI can pose once used in SCM, for instance, the quality of data used, and the ethical concerns that come with, the use of AI in SCM. A grasp of the specifics of how GenAI operates as well as how to implement it successfully in the supply chain is essential in assessing the performance of this relatively new technology as well as prognosticating the future of generation AI in supply chain planning.

We studied the role of industry-academic collaboration (IAC) in the enhancement of educational opportunities and outcomes under the digital driven Industry 4.0 using research and development, the patenting of products/knowledge, curriculum development, and artificial intelligence as proxies for IAC. Relevant conceptual, theoretical, and empirical literature were reviewed to provide a background for this research. The investigator used mainly principal (primary) data from a sample of 230 respondents. The primary statistics were acquired through a questionnaire. The statistics were evaluated using the structural equation model (SEM) and Stata version 13.0 as the statistical software. The findings indicate that the direct total effect of Artificial intelligence (Aint) on educational opportunities (EduOp) is substantial (Coef. 0.2519916) and statistically significant (p < 0.05), implying that changes in Aint have a pronounced influence on EduOp. Additionally, considering the indirect effects through intermediate variables, Research and Development (Res_dev) and Product Patenting (Patenting) play crucial roles, exhibiting significant indirect effects on EduOp. Res_dev exhibits a negative indirect effect (Coef = −0.009969, p = 0.000) suggesting that increased research and development may dampen the impact of Aint on EduOp against a priori expectation while Patenting has a positive indirect effect (Coef = 0.146621, p = 0.000), indicating that innovation, as reflected by patenting, amplifies the effect of Aint on EduOp. Notably, Curriculum development (Curr_dev) demonstrates a remarkable positive indirect effect (Coef = 0.8079605, p = 0.000) underscoring the strong role of current development activities in enhancing the influence of Aint on EduOp. The study contributes to knowledge on the effective deployment of artificial intelligence, which has been shown to enhance educational opportunities and outcomes under the digital driven Industry 4.0 in the study area.

In today’s fast-moving, disrupted business environment, supply chain risk management is crucial. More critically, Industry 4.0 has conferred competitive advantages on supply chains through the integration of digital technologies into manufacturing and logistics, but it also implies several challenges and opportunities regarding the management of these risks. This paper looks at some ways emerging technologies, especially Artificial Intelligence (AI), help address pressing concerns about the management of risk and sustainability in logistics and supply chains. The study, using a systemic literature review (SLR) backed by a mapping study based on the Scopus database, reveals the main themes and gaps of prior studies. The findings indicate that AI can substantially enhance resilience through early risk identification, optimizing operations, enriching decision-making, and ensuring transparency throughout the value chain. The key message from the study is to bring out what technology contributes to rendering supply chains resilient against today’s uncertainties.

Indonesia’s tourism industry has emerged as a strategic sector, contributing to the country’s foreign exchange earnings. Given the prominence of this sector, there is significant potential for further development. Indeed, a mapping study to assess the dissemination of the trend and the potential for further issues to emerge would be highly beneficial. It is encouraging to note that academics have produced substantial literature on the subject, offering insights into its many facets. However, there is still a need for more in-depth analysis to understand the trends and issues currently facing the sector entirely. Consequently, this article examines the core themes in Indonesia’s tourism studies and maps the potential for future research on tourism issues and regulations. To this end, it employs a qualitative, four-year data set (2020–2023) and a SWOT analysis to identify critical aspects of Indonesian tourism issues. The data was collected in three forms: government reports, statistical data, and research articles (n = 252 samples) from the Scopus database. The results demonstrate that the predominant trend in Indonesia’s tourism industry is the widespread embrace of ecotourism at both the local and regional levels. Instead of identifying a limited number of leading destinations, the focus has shifted towards developing tourism villages and multi-stakeholder tourism. The primary concerns are the Indonesian tourism industry’s growth potential and sustainability. The development potential of Indonesian destinations based on SWOT objectives is a crucial aspect, and its score shows that Indonesia’s tourism sector is strategically positioned to take advantage of strengths and opportunities.

Heat transfer fluids (HTFs) are critical in numerous industrial processes (e.g., the chemical industry, oil and gas, and renewable energy), enabling efficient heat exchange and precise temperature control. HTF degradation, primarily due to thermal cracking and oxidation, negatively impacts system performance, reduces fluid lifespan, and increases operational costs associated with correcting resulting issues. Regular monitoring and testing of fluid properties can help mitigate these effects and provide insights into the health of both the fluid and the system. To date, there is no extensive literature published on this topic, and the current narrative review was designed to address this gap. This review outlines the typical operating temperature ranges for industrial heat transfer fluids (i.e., steam, organic, synthetic, and molten salts) and then focuses specifically on organic and synthetic fluids used in industrial applications. It also outlines the mechanisms of fluid degradation and the impact of fluid type and condition. Other topics covered include the importance of fluid sampling and analysis, the parameters used to assess the extent of thermal degradation, and the management strategies that can be considered to help sustain fluid and system health. Operating temperature, system design, and fluid health play a significant role in the extent of thermal degradation, and regular monitoring of fluid properties, such as viscosity, acidity, and flash point, is crucial in detecting changes in condition (both early and ongoing) and providing a basis for decisions and interventions needed to mitigate or even reverse these effects. This includes, for example, selecting the right HTF for the specific application and operating temperature. This article concludes that by understanding the mechanisms of thermal degradation and implementing appropriate management strategies, it is possible to sustain the lifespan of thermal fluids and systems, ensure safe operation, and help minimise operational expenditure.