Supply Chain Management
Estimated Time of Completion (ETC) Prediction for Last-Mile Logistics
A machine learning model that provides a more accurate estimation of delivery time compared to the conventional method of the Estimated Time of Arrival model.
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Technology overview
The explosion of e-commerce and ride-hailing/food-delivery applications has fueled the need for a more accurate and reliable estimation of delivery times. The current common estimation of delivery time is based on Estimated Time of Arrival (ETA), which relies on the route distance that is calculated between the origin and the desired destination. It only considers the duration from the pickup to drop off and does not consider the additional time needed for preparing and offloading the goods.
RP has developed a Machine Learning (ML) model that is able to calculate the stop duration, which, together with ETA, provides the Estimated Time of Completion (ETC).
Technology features
ML model enables prediction on ETC using a small set of input parameters such as building name/block number, road, postal codes, and time.
Model can be integrated with existing web/mobile-based solutions.
Potential applications
Customer Services / Call Centres
Fleet Management
Loading Bay Assignment
Benefits
Low cost and easy to implement.
Simple to use as only a small set of input parameters is required.
Predict the time taken at each delivery stop with reasonable accuracy.
This model can be used to improve the existing route planning systems as it provides additional job completion prediction on top of ETA.
Commercialisation
This technology is available for industry partners for collaborations and licensing.
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