使用 Alteryx 改进容器供应链流程：案例研究
技术
- 分析与建模 - 机器学习
- 分析与建模 - 预测分析
适用行业
- 水泥
- 运输
适用功能
- 物流运输
用例
- 智能包装
- 车辆到基础设施 (V2I)
服务
- 数据科学服务
关于客户
澳大利亚墨尔本州政府与全球咨询公司 GHD 的机器学习 (ML) 团队签约，通过收集和理解来自行业、政府和运输软件服务提供商的大型数据集来改进集装箱供应链流程。
挑战
每五年，澳大利亚墨尔本港（PoM）就需要跟踪所有进出的集装箱。了解货运流向对于确保正确的基础设施、工业用地、规划控制和政策设置到位以支持高效的供应链至关重要。 PoM 利用超过 57 个独立组来跟踪 60 多种不同格式的数据。此过程通常需要数百小时的手动时间和资源，而且从历史上看，预测率低于 30%。此外，直到最近他们才能够成功完成比赛分析。
解决方案
2019 年，Nikita Atkins 和 GHD 的机器学习团队开发了预测建模流程并支持商品和集装箱调查。他们使用 Alteryx 收集 2019 年 9 月至 10 月（超过 250,000 次集装箱运输）的数据，对其进行标准化、合并，并对所提供的 1 亿条记录进行重复数据删除，并在数据可供使用之前纳入 200 多个业务规则。他们能够将 190 万条记录的最终数据集与 PoM 进行比较，发现使用 Alteryx 进行数据清理后的匹配率为 99.9965%。他们使用 Alteryx Intelligence Suite 构建了 10 个预测模型并比较了它们的有效性。他们缩小了选择范围，并能够估计集装箱的位置，包括其始发地和目的地、所装商品、每个集装箱的容量水平，并深入了解集装箱的回程终点和时间表。
运营影响
数量效益
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