In the dynamic and changing realm of technology and business operations, staying abreast of recent trends is paramount. This review evaluates the progress in the development of the integration of machine learning (ML) with enterprise resource planning (ERP) systems, revealing the impact of these trends on the ERP optimization. In recent years, there has been a significant advancement in the integration of ML technology within ERP environments. ML algorithms characterized by their ability to extract intricate patterns from vast datasets are being harnessed to enable ERP systems to make more accurate predictions and data-driven decisions. Therefore, ML enables ERP systems to adapt dynamically based on real-time insights, resulting in enhanced efficiency and adaptability. Furthermore, organizations are increasingly looking for artificial intelligence (AI) solutions as they actually try to make ML models within ERP clear and comprehensible for stakeholders. These solutions enable ERP systems to process and act on data as it flows in, due to the utilization of ML models, which enables enterprises to react effectively to changing circumstances. The rapid insights and useful intelligence offered by this trend have had a significant impact across industries. IoT (Internet of Things) and ML integration with ERP are continuously gaining significance. These algorithms allow for the creation of adaptable strategies supported by ongoing learning and data-driven optimization, which has a number of benefits for ERP system optimization. In addition, the Industrial Internet of Things (IIoT) was investigated in this review to provide the state-of-the-art and emerging challenges due to ML integration. This review provides a comprehensive analysis of the integration of machine learning algorithms across several ERP applications by conducting an extensive literature assessment of recent publications. By synthesizing the latest research findings, this comprehensive review provides an in-depth analysis of the cutting-edge techniques and recent advancements in the context of machine learning (ML)-driven optimization of enterprise resource planning (ERP) systems. It not only provides an insight into the methodology and impact of the state-of-the-art but also offers valuable insights into where the future of ML in ERP may lead, propelling ERP systems into a new era of intelligence, efficiency, and innovation.

中文翻译：

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机器学习驱动的企业资源规划 (ERP) 系统优化：全面回顾

在动态和变化的技术和业务运营领域，紧跟最新趋势至关重要。本综述评估了机器学习 (ML) 与企业资源规划 (ERP) 系统集成的开发进展，揭示了这些趋势对 ERP 优化的影响。近年来，机器学习技术在 ERP 环境中的集成取得了重大进展。机器学习算法的特点是能够从庞大的数据集中提取复杂的模式，它被用来使 ERP 系统能够做出更准确的预测和数据驱动的决策。因此，机器学习使 ERP 系统能够根据实时洞察进行动态调整，从而提高效率和适应性。此外，组织越来越多地寻求人工智能 (AI) 解决方案，因为他们实际上试图使 ERP 中的 ML 模型对利益相关者来说清晰易懂。由于使用机器学习模型，这些解决方案使 ERP 系统能够在数据流入时对其进行处理和操作，从而使企业能够对不断变化的情况做出有效反应。这一趋势提供的快速洞察和有用情报对整个行业产生了重大影响。IoT（物联网）和 ML 与 ERP 的集成不断变得越来越重要。这些算法允许创建由持续学习和数据驱动优化支持的适应性策略，这对 ERP 系统优化有很多好处。此外，本次审查还对工业物联网 (IIoT) 进行了研究，以提供由于 ML 集成而带来的最先进的和新兴的挑战。本综述通过对最近出版物进行广泛的文献评估，对跨多个 ERP 应用程序的机器学习算法集成进行了全面分析。通过综合最新的研究成果，这篇全面的综述深入分析了机器学习 (ML) 驱动的企业资源规划 (ERP) 系统优化的前沿技术和最新进展。它不仅提供了对最先进技术的方法和影响的见解，还提供了有关 ERP 中机器学习的未来可能走向何方的宝贵见解，推动 ERP 系统进入智能、效率和创新的新时代。