Data Lake / Data Warehouse Modernization · Supply Chain Optimization
Cleanaway had operational, fleet and customer data spread across disparate systems, making aggregation manual, data quality inconsistent and insight delivery too slow for responsive operational decisions and sustainability goals.
Reported impact
Cleanaway rolled out automated business-intelligence use cases in less than a year at lower cost, optimized daily routes for more than 2,800 waste-collection vehicles, expanded the approach toward a 5,000-vehicle fleet and uncovered gains in operational efficiency, profitability and customer experience.
Cleanaway built an enterprise lakehouse on the Databricks Data + AI Platform on Azure to unify operational data from waste collection, sorting, transport and customer systems that had been fragmented across multiple sources. GPS and connected fleet feeds, customer transactions, sales and marketing data are consolidated as a single source of truth, then standardized so business intelligence, analytics and machine learning teams work from the same foundation. Delta Lake underpins the shared lakehouse, MLflow is used to operationalize the first advanced analytics and machine learning models, and Power BI surfaces changes in business performance metrics. One of the first production models optimizes daily routes for more than 2,800 collection vehicles, while aggregated customer views help teams respond faster to demand and identify growth opportunities.
GPS and connected fleet systems, customer transactions, sales and marketing data flow into the Databricks lakehouse, where Delta Lake stores the unified datasets and MLflow-managed models analyze performance and optimize routes. Power BI then serves aggregated customer and business metrics back to operational teams.
Aggregating data sets from several systems was an inefficient and manual process. This was compounded by inconsistent data quality, which resulted in unreliable insights.
Cleanaway has operationalized its first advanced analytics and machine learning models using MLflow and Delta Lake together with Power BI.
Cleanaway looked to Databricks to implement a lakehouse architecture that could function as a single source of truth by unifying disparate and siloed data sources.
One of its first models in production was used to assess and optimize the most efficient daily routes possible for Cleanaway's fleet of over 2,800 solid waste services collection vehicles.
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.
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Scoring methodology
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Traceability
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