Order Preparation Time Prediction Using Item-Level Embeddings
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Solution Overview
Problem
Existing methods for predicting order preparation times in restaurants are inaccurate and fail to account for dynamic changes in internal and external conditions, leading to frustrated diners and delivery service providers due to inconsistent and often incorrect pickup and delivery time estimates.
Innovation Solution
A deep learning system that utilizes two neural networks to generate accurate item-level and order-level preparation time predictions by leveraging historical data and metadata, including kitchen conditions and external factors, and provides real-time updates through a backend server system.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If deep learning neural networks are used to generate item-level and order-level embeddings, then prediction accuracy is improved, but device complexity increases
Solution Approach 1:
The system segments the prediction task into two distinct neural networks: one for item-level preparation time prediction and another for order-level prediction. This segmentation allows each network to specialize in specific aspects of the prediction problem, improving overall accuracy while maintaining manageable complexity through modular architecture.
Solution Approach 2:
The system transforms the prediction problem by creating embeddings that add a new dimensional representation to menu items and orders. These embeddings capture complex relationships and features in a compressed vector space, enabling more accurate predictions without proportionally increasing system complexity.
2Measurement precision
If historical data and metadata are processed through multiple neural networks, then prediction precision is improved, but processing time increases
Solution Approach 1:
The system performs preliminary processing by generating item-level embeddings and predictions before final order-level prediction. This preliminary action allows the second neural network to work with pre-processed features rather than raw data, reducing its computational burden and processing time while maintaining high precision.
Solution Approach 2:
By dividing the data processing into separate item-level and order-level stages with dedicated neural networks, the system processes different aspects of the data in parallel rather than sequentially through a single complex model, reducing overall processing time while improving precision through specialized processing.
Data Source
AI summary
A method for predicting preparation times includes: retrieving set of item-level records from a database for preparation menu items for all subscriber restaurants; training and executing a first neural network to generate embeddings for each of the menu items; for a first subset of the set, calculating actual item-level preparation time vectors; for a second subset of the historical set, generating estimated item-level preparation time vectors; retrieving a set of order-level records for preparation of orders from the database; training a second neural network to predict the order-level preparation times, wherein inputs to the second neural network comprise one or more of the item-level preparation time vectors and metadata taken from the order-level records; and following training, executing the second neural network to generate predicted order-level preparation times for current orders within a restaurant.