Fill
Predicting delivery volumes to cut fleet idle time and fuel costs.
Customer Background
Fill provides last-mile logistics for urban hubs. Their margins depend entirely on predicting the exact driver headcount needed on any given day. Over-staffing burns cash; under-staffing ruins SLAs.
The Challenge
Their legacy routing logic couldn't handle dynamic variables like weather or localized events. They needed a predictive model to staff efficiently, but their data was siloed across different systems and they lacked a dedicated data science team to build it.
Our Solution
Data Pipeline Unification
We ripped out their fragmented reporting scripts and built a clean, centralized data pipeline. This gave the predictive model a reliable, single source of truth for historical delivery volumes.
Predictive Routing Model
We trained and deployed a custom machine learning model tailored to their specific urban hubs. It outputs a daily forecast that automatically integrates directly into their existing fleet management dashboard.
The Results
Achieved 92% accuracy in daily delivery volume forecasting.
Cut fleet idle time and fuel consumption by 24%, directly boosting margins.
Reduced average delivery SLA times by 18%.
Services Used
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