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U.S. Army Increases Financial Agility with AI by Reclaiming Funds for High Priority Projects $2.2B+ in excess funds identified at a 3x higher yield
技术
- 分析与建模 - 机器学习
适用行业
- 国家安全与国防
适用功能
- 采购
用例
- 减废预测
服务
- 数据科学服务
挑战
美国陆军面临的挑战是确定因合同到期而可能损失的资金。他们需要一种创新的 AI 解决方案，帮助合同官员准确预测最有可能资金不足的合同，以便他们可以快速解除合同并将这些资金重新分配给其他高优先级项目。未清偿债务 (ULO) 项目诞生于陆军总部分析实验室 (HAL) 和 Deep Green OBT 计划。
关于客户
本案例研究中的客户是美国陆军。美国陆军是组成国防部的三个军事部门（陆军、海军和空军）之一。陆军在世界各地执行作战和机构任务，通常进行陆战和持续的陆战行动。陆军还包括空军和海军陆战队等其他军种。在本案例中，美国陆军正在寻找一种解决方案来帮助他们确定可能因合同到期而损失的资金。
解决方案
美国陆军利用 DataRobot 的增强智能平台确保对存在资金过剩风险的合同预测进行适当的管理。该平台用于在仪表板中对优先合同进行分类，以使合同官员能够收回资金。这项工作是在高度可视化、自我管理和可持续的环境中完成的，从而提高了财务灵活性。人工智能解决方案能够发现可能因合同到期而损失的资金，使合同官员能够收回资金并将其重新分配到其他重要领域——提高了解除义务率并节省了数千个员工小时。
运营影响
数量效益
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