Optimisation of Big Data and Artificial Intelligence Driven Digital Intelligence in Manufacturing Budget Management

Xinyue Chang ()
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Xinyue Chang: University of Sussex

A chapter in Proceedings of the 2025 10th International Conference on Social Sciences and Economic Development (ICSSED 2025), 2025, pp 517-530 from Springer

Abstract: Abstract With the advancement of manufacturing industry’s transformation to digital intelligence, budget management, as an important part of corporate financial management, is gradually integrated into big data and artificial intelligence technology, ushering in new opportunities for digital intelligence transformation. This essay discusses the challenges and opportunities faced by manufacturing budget management in the process of digital and intellectual transformation, focusing on how big data and artificial intelligence technology drive the optimisation of budget management. In the budgeting process, dynamic budgeting and rolling budget mechanisms combined with real-time data and AI forecasts are used to achieve flexible budget adjustments and improve the ability to respond to market changes. In budget enforcement, the use of BI systems and visual dashboards as well as real-time deviation analysis, timely detection of budget deviations and provision of adjustment recommendations, significantly improving the efficiency and transparency of execution. In budget evaluation, introduce a multi-dimensional evaluation framework that combines financial, non-financial and external environmental factors, and dynamically adjust evaluation weights through artificial intelligence to make the evaluation more comprehensive and accurate. Through these optimisation strategies, the manufacturing industry can achieve more efficient and accurate budget management, and promote the improvement of overall operational efficiency and sustainable development.

Keywords: Budget management; Digital transformation; Big data (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:spr:advbcp:978-94-6463-734-2_59

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DOI: 10.2991/978-94-6463-734-2_59

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