Problem

The Last Mile Hub was facing a major challenge in accurately predicting the order volume and allocating labor for each facility hub. The manual process of forecasting and planning was time-consuming, leading to inefficiencies in operations. The team needed a solution that could automate the process and improve accuracy.

Additionally, the extraction of invoice details from scanned and non-scanned invoices was also a manual process, leading to errors and delays. The team needed an innovative approach to automate this process as well.

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Solution

The Data Science team spearheaded the development and deployment of a Time Series Forecasting Model using the "Middle out" approach. This resulted in an impressive 11% enhancement in order volume prediction accuracy at each Last Mile Hub. Additionally, a labor forecasting model was engineered with an exceptional 86% accuracy rate for predicting ideal labor allocation at each Facility hub.

To further streamline operations, the team pioneered the implementation of a Simulation Dashboard that enabled real-time adjustments to forecasts based on additional insights. Finally, they innovatively automated the extraction of Invoice Details from both scanned and non-scanned invoices, effectively reducing manual labor by an impressive 40%.

Impact

11% enhancement in order volume prediction accuracy
86% accuracy rate for predicting ideal labor allocation
28% reduction in manual labor planning

"This project had an incredible impact on our operations, streamlining processes and improving accuracy across the board."

Team

Team Member #1 - Data Scientist
- Spearheaded the development and deployment of the Time Series Forecasting Model
- Demonstrated strong leadership by effectively managing and leading the Data Science team
Team Member #2 - Software Engineer
- Pioneered the implementation of a Simulation Dashboard
- Innovatively automated the extraction of Invoice Details from both scanned and non-scanned invoices

Last Mile Hub Boosts Accuracy, Reduces Labor