Accurately forecast arrival times based on the type of product/package, time of the year, origin, destination, weather, historical delivery times, customer feedback, and other aspects. This could be done with multi-modal deliveries as well. Give customers real-time access to the most accurate ETA, boosting their satisfaction. Decrease Planning Errors, and enable better Logistics Planning.
Understand the Use-case under 5 minutes
Solution Paper (5 minutes)
Product Tour for Oracle’s ML-based Shipment ETA Prediction Solution. Shows capabilities for data ingestion, model training, variable importance, model usage, and relevant information products
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Get to know more Business and Technical details about the use-case (15-30 minutes)
More detailed introduction covering business and technical aspects
Article (8 minutes)
PMEDD (Shipments Predicted to Miss Their Estimated Delivery Date) uses machine learning models to assign each in-transit shipment a risk score, indicating how likely the package will miss its EDD.
Read
Article (13 minutes)
Discover how Aramex uses Machine Learning to accurately predict Delivery Times, increasing the accuracy of delivery predictions by 74%, optimizing transport and delivery costs, and reducing call center volumes by 40%
Visit
Video (10 minutes)
How ML is used to predict 10+ time points for each delivery, resulting into higher accuracy ETA Prediction for their orders (watch starting 11:00)
WatchCase studies, Organizational Aspects, Return on Investment examples
White Paper (20 minutes)
Extensive Research by Accenture in partnership with companies who’ve implemented warehouse automation, & automation solution providers. Insights on common challenges and best implementation practices
VisitWhite Paper (18 minutes)
An Outlook on the global warehouse automation market, what’s working vs not, major trends, key players, and emerging technologies
ReadMore details on the technical aspects of the use-case
Article (15 minutes)
Uber Eats Machine Learning Pipeline for Predicting Order Delivery Times, empowering their their Dispatch System
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Video (20 minutes)
How ML is used to predict 10+ time points for each delivery, resulting into higher accuracy ETA Prediction for their orders (watch starting 11:00)
Watch
Article (34 minutes)
Explore the challenges that face Dell while estimating the delivery date of an order using machine learning and how they encountered the difficulties
Visit
Technical resources that will help you implement the use-case (notebooks, tutorials..)
Github Repo (Code + Data)
Using DoorDash Delivery data to predict delivery times. The dataset includes variables like store category, total items, max item price, and more
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Article (13 minutes)
A broad overview of how Walmart predicts the customer delivery time using different machine learning algorithms, linear regression, random forest, neural network, etc. this intelligent approach gives them the opportunity to save costs and the additional revenue by more than $4-5 million per year.
Visit
Article (20 minutes)
building a (real-world) delivery time prediction model for a food delivery startup and how it came to give better predictions than a trained operations team
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Paper
Provide different end-to-end deep neural network architectures to predict parcel delivery time using a real-world difficult large-scale dataset of parcels delivered in Toronto
Read
Data Sets you can use to build Demos, POCs, or test Algorithms
The dataset corresponds to thousands of deliveries and the data consists of detailed weather data, distance, start and end time to package, and vehicle type.
Off-the-Shelf Products using AI for Delivery Time Prediction
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