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Enhancing Delivery Efficiency with Custom Mapping Solution: A Case Study on a Top US-Based Food Delivery Company
技术
- 分析与建模 - 机器学习
- 网络与连接 - 网关
适用行业
- 食品与饮料
- 运输
适用功能
- 物流运输
用例
- 楼宇自动化与控制
- 最后一英里交付
服务
- 系统集成
挑战
美国领先的食品配送公司在满足不断增长的需求和客户期望方面面临着挑战。该公司正在努力在不影响服务质量的情况下实现其雄心勃勃的增长目标。现有的地图解决方案十分僵化，不能满足公司的具体需求。该公司需要更量身定制的地图解决方案，帮助他们实现更高效、更准时的交付，提供更准确的预计到达时间 (ETA)，并降低地图 API 的成本。
关于客户
该客户是一家总部位于美国的领先食品配送公司。随着食品配送垂直行业的不断发展，该公司正在努力满足不断增长的需求和客户的高期望。公司拥有雄心勃勃的发展目标，致力于提供优质服务。然而，该公司现有的地图解决方案面临着挑战，这些解决方案僵化且不适合其特定需求。该公司需要更高效的地图解决方案来确保准时交货、提供准确的预计到达时间并降低地图 API 的成本。
解决方案
NextBillion.ai 提出了一种双管齐下的方法来应对公司的挑战。首先，他们强调了构建和拥有一个地图平台对于推动业务成果和竞争差异化的重要性。其次，他们确定了自定义地图、专有的第一方数据、实时数据、交付区域和超本地化细微差别等关键元素，这些元素对于构建有效的自定义解决方案以优化交付时间至关重要。根据这些评估，NextBillion.ai 开发了一个由先进机器学习算法提供支持的独家模块化映射堆栈。该解决方案包括可以刷新、编辑和更新的自定义地图、自定义和可扩展的 ETA 和路由 API，以及完全本地部署和与云无关的部署。该解决方案旨在提供可靠的预计到达时间、无障碍的路由和成本效益。
运营影响
数量效益
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