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AI-Driven Transformation in Romanian Farming: A Case Study
技术
- 分析与建模 - 机器学习
- 分析与建模 - 预测分析
适用行业
- 城市与自治市
- 教育
用例
- 需求计划与预测
- 运动预测
服务
- 数据科学服务
挑战
农村投资融资机构 (AFIR) 是罗马尼亚政府机构，负责支持农民和公司获得赠款以及每年平均 20 亿欧元的欧盟 (EU) 农村发展项目资金。然而，管理这些资助项目的一项重要的内部预测任务需要 AFIR 员工的大量手动干预。这个过程不仅缓慢而且容易出现错误。该组织热衷于提高支出和融资预测的准确性，因为这不仅可以帮助其用户，还可以提高其自身的运营效率。该流程需要大量纸张且复杂，据 AFIR IT 总监 Daniel Ifrim 称，该流程“远未达到优化”。该团队需要一个解决方案，将这一流程转变为完全无纸化的在线系统，并减少生成支出和融资预测报告所需的时间。
关于客户
农村投资融资机构 (AFIR) 是一家位于布加勒斯特的罗马尼亚政府机构。它是支持罗马尼亚农民和公司获得赠款以及每年平均 20 亿欧元欧盟 (EU) 农村发展项目资金的主要政府机构。 AFIR 是罗马尼亚政府的重要组成部分，其主要作用是将资金和财政支持送到罗马尼亚农民和其他需要资源来适当发展和照顾农村的主要利益相关者手中。该组织规模庞大，拥有 1,000 至 9,999 名员工。
解决方案
在当地微软合作伙伴 Genisoft 对招标过程做出回应后，AFIR 决定尝试人工智能 (AI)。 Genisoft 提出了一种全新的支付预测机制，该机制结合了 Microsoft Azure Synapse Analytics 和 Azure 机器学习、Azure SQL 和 Microsoft Power BI。这种基于人工智能的支付预测机制提供了许多改进，特别是在更准确的预测方面。人工智能驱动的预测消除了可能影响报告准确性的错误风险。转型过程花了几个月的时间，但最终形成了一个完全无纸化的在线系统。新系统不仅提高了支出和融资需求预测的准确性，还将生成报告的时间从十天缩短到了十分钟。
运营影响
数量效益
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