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Databricks > 实例探究 > Cleanaway:利用 Databricks Lakehouse 转变废物管理
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Cleanaway's Transformation: Leveraging IoT for a Smarter and Cleaner Australia

技术
  • 分析与建模 - 机器学习
  • 传感器 - 全球定位系统
适用行业
  • 回收与废物管理
  • 公用事业
适用功能
  • 维护
  • 销售与市场营销
用例
  • 车队管理
  • 预测性维护
服务
  • 数据科学服务
挑战
减少废物对环境的影响并改善来自多个来源的数据洞察
关于客户
Cleanaway,澳大利亚废物和回收服务领域的领导者
解决方案
实施 Databricks Lakehouse 来简化和集成数据,并利用高级分析和机器学习模型
运营影响
  • The implementation of the Databricks Lakehouse has transformed Cleanaway's operations, eliminating the silos that traditionally separated and complicated data engineering, analytics, and machine learning. This shift has not only accelerated its time to insights and ability to innovate but also boosted efficiencies and profitability in less than a year after it implemented analytical and machine learning capabilities built on Databricks Lakehouse. The transformative impact of operationalizing advanced AI models for its fleet management systems has also encouraged Cleanaway to expand its route optimization abilities across its entire fleet of 5,000 vehicles. Looking ahead, Cleanaway plans to extend the implementation of Databricks Lakehouse in every aspect of the business, transforming the company toward being truly data and AI driven by 2030.
数量效益
  • Cleanaway was able to roll out modern, automated use cases for business intelligence in less than a year at lower costs.
  • One of the first models in production optimized the most efficient daily routes for Cleanaway’s fleet of over 2,800 solid waste services collection vehicles.
  • Cleanaway was able to achieve an aggregated view of each customer from across multiple systems, enhancing its ability to respond to customer demands and highlight new growth opportunities.

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