Achieving sustainable development goal 9: A study of enterprise resource optimization based on artificial intelligence algorithms

Wang, Zeyu; Deng, Yulin; Zhou, Shouan; Wu, Zhongbang

doi:10.1016/j.resourpol.2022.103212

Cited by 60 publications

(24 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Wang [ 39 ] employed a recommendation system to aid decision-making and resource optimization, focusing on complex entrepreneurial projects in the cultural and creative industries. Their research introduced an entrepreneurial project recommendation and resource optimization model, enhancing identification accuracy and reducing prediction errors, thereby providing experimental references and contributions for subsequent sustainable development and entrepreneurial resource optimization in the socio-economic sphere.…”

Section: Literature Reviewmentioning

confidence: 99%

Optimizing green supply chain circular economy in smart cities with integrated machine learning technology

Liu,

Guan,

Wang

et al. 2024

Heliyon

Self Cite

View full text Add to dashboard Cite

Section: Literature Reviewmentioning

confidence: 99%

Optimizing green supply chain circular economy in smart cities with integrated machine learning technology

Liu,

Guan,

Wang

et al. 2024

Heliyon

Self Cite

View full text Add to dashboard Cite

“…At the industrial level, existing studies have explored the proposition that big data, as a pivotal component of smart manufacturing, can foster green development through technological innovation, the transformation of traditional industries 40 , and enhancing green total factor productivity. The theory of discontinuous innovation posits that advancements in AI technology lead to discontinuous technological innovations, propelling traditional industries towards intelligent development and giving rise to new formats and industries 41 , thereby fostering green performance.…”

Section: Theoretical Analysismentioning

confidence: 99%

Intelligentization helps the green and energy-saving transformation of power industry-evidence from substation engineering in China

Liang,

Liu,

Liang

et al. 2024

Sci Rep

View full text Add to dashboard Cite

The coordinated development of intelligence and greening is an intrinsic demand for high-quality economic and social development. Intelligentization and greening are the leading directions of sustainable development of the power industry. This paper directs of sustainable development of the power industry. This paper empirically analyzes the effect and mechanism of intelligence on the green environmental friendliness of electric power substations by using a panel fixed-effects model and instrumental variable regression, using substation engineering data from China southern power grid during 2013–2022. It is found that the level of intelligence significantly promotes the green performance of substation projects, and this conclusion still holds after a series of robustness tests. Intelligence can reduce material waste and pollutant emissions by improving the engineering environmental monitoring capability and the refinement of engineering resource control, thus improving the environmental friendliness of the project. The research in this paper helps to promote the integrated development of intelligent and green power engineering, to better achieve economic and green goals.

show abstract

“…Human resource management at the corporate level extensively uses big data to enhance management capabilities. Effective data required to complete a business task is predicted by BD [ 10 ]. BD advocates for a wide variety of high-quality HRM solutions in businesses.…”

Section: Introductionmentioning

confidence: 99%

Development of big data assisted effective enterprise resource planning framework for smart human resource management

Zhao

2024

PLoS ONE

View full text Add to dashboard Cite

The planning of human resources and the management of enterprises consider the organization’s size, the amount of effort put into operations, and the level of productivity. Inefficient allocation of resources in organizations due to skill-task misalignment lowers production and operational efficiency. This study addresses organizations’ poor resource allocation and use, which reduces productivity and the efficiency of operations, and inefficiency may adversely impact company production and finances. This research aims to develop and assess a Placement-Assisted Resource Management Scheme (PRMS) to improve resource allocation and usage and businesses’ operational efficiency and productivity. PRMS uses expertise, business requirements, and processes that are driven by data to match resources with activities that align with their capabilities and require them to perform promptly. The proposed system PRMS outperforms existing approaches on various performance metrics at two distinct levels of operations and operating levels, with a success rate of 0.9328% and 0.9302%, minimal swapping ratios of 12.052% and 11.658%, smaller resource mitigation ratios of 4.098% and 4.815%, mean decision times of 5.414s and 4.976s, and data analysis counts of 6387 and 6335 Success and data analysis increase by 9.98% and 8.2%, respectively, with the proposed strategy. This technique cuts the switching ratio, resource mitigation, and decision time by 6.52%, 13.84%, and 8.49%. The study concluded that PRMS is a solid, productivity-focused corporate improvement method that optimizes the allocation of resources and meets business needs.

show abstract

Achieving sustainable development goal 9: A study of enterprise resource optimization based on artificial intelligence algorithms

Cited by 60 publications

References 33 publications

Optimizing green supply chain circular economy in smart cities with integrated machine learning technology

Optimizing green supply chain circular economy in smart cities with integrated machine learning technology

Intelligentization helps the green and energy-saving transformation of power industry-evidence from substation engineering in China

Development of big data assisted effective enterprise resource planning framework for smart human resource management

Contact Info

Product

Resources

About