Cleanaway uses machine learning to optimize waste collection fleet routes
CleanawayDelta Lake · MLflow · Power BICleanaway, Australia's leading waste and recycling services provider, operationalized machine learning models using MLflow and Delta Lake on the Databricks Data + AI Platform to assess and optimize the most efficient daily routes for its fleet of over 2,800 solid waste collection vehicles, as part of demand forecasting, ESG and predictive maintenance use cases. The transformative results encouraged Cleanaway to expand route optimization across its entire 5,000-vehicle fleet.
Overview
Cleanaway, Australia's leading waste and recycling services provider, operationalized machine learning models using MLflow and Delta Lake on the Databricks Data + AI Platform to assess and optimize the most efficient daily routes for its fleet of over 2,800 solid waste collection vehicles, as part of demand forecasting, ESG and predictive maintenance use cases. The transformative results encouraged Cleanaway to expand route optimization across its entire 5,000-vehicle fleet.
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Inspect the highlighted sourceThe challenge
Cleanaway's diverse waste collection, sorting and logistics operations generated large volumes of operational and service data scattered across multiple disjointed systems (GPS, connected fleet, transactions, sales, marketing), and aggregating it was an inefficient, manual process compounded by inconsistent data quality and unreliable insights.
The solution
Cleanaway implemented a lakehouse architecture on the Databricks Data + AI Platform, operationalizing its first advanced analytics and machine learning models using MLflow and Delta Lake together with Power BI to assess and optimize the most efficient daily routes for its fleet of over 2,800 solid waste collection vehicles, as part of demand forecasting, ESG and predictive maintenance use cases.
Reported business value
Cleanaway rolled out modern, automated analytics and ML use cases in less than a year at lower cost, optimizing routes for a fleet of 2,800+ vehicles, and the results encouraged the company to expand route optimization across its entire 5,000-vehicle fleet.
Sources
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