Machine Learning Revised Delivery Date Prediction
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Solution Overview
Problem
Conventional information processing systems use outdated linear models to calculate revised delivery dates (RDDs) based on static and delayed data, leading to inaccurate delivery commitments, resulting in low compliance rates and adverse impacts on order experiences.
Innovation Solution
A predictive services platform utilizing machine learning algorithms, such as boosted decision trees and neural networks, to automatically generate RDDs by incorporating expanded variables and real-time data, supported by a cloud-based data science platform for improved accuracy and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional linear models with static data are used to calculate revised delivery dates, then the calculation process is simple, but the accuracy of delivery commitments is poor
Solution Approach 1:
The patent transforms the calculation parameters from static historical data to dynamic real-time data, and from linear relationships to non-linear machine learning patterns. This allows the system to capture complex interactions between multiple order attributes, supply chain factors, and delivery conditions, significantly improving delivery date prediction accuracy while managing complexity through automated model training.
Solution Approach 2:
The patent replaces conventional statistical linear models with advanced machine learning algorithms including gradient boosting machines and neural networks. This substitution enables the system to automatically learn complex non-linear relationships from data without requiring manual specification of interaction terms, thereby improving predictive accuracy while reducing the need for domain expertise in model formulation.
2Measurement precision
If real-time data and expanded variables are incorporated into machine learning models, then the accuracy of RDD calculation is improved, but the computational complexity increases
Solution Approach 1:
The patent performs preliminary data preprocessing, feature engineering, and model training in advance before actual delivery date predictions are needed. Historical data is processed to create training sets, and machine learning models are trained offline on expanded variables and real-time data patterns. This preliminary action reduces the computational burden during live operations, allowing accurate predictions to be generated quickly when delivery dates are actually needed.
3Measurement precision
If manual input of lead times is used in conventional systems, then the system is easy to operate, but the delivery commitments are inaccurate
Solution Approach 1:
The patent implements self-service automation where the machine learning system automatically ingests data from multiple sources, performs feature engineering, trains models, and generates delivery date predictions without manual intervention. The system autonomously handles lead time calculations by learning from historical patterns in the data, eliminating the need for manual lead time input while significantly improving prediction accuracy through automated data processing and model inference.
Data Source
AI summary
An apparatus in one embodiment comprises at least one processing platform including a plurality of processing devices. The processing platform is configured to receive a request to execute one or more predictive models for generating a delivery date, to initiate execution of the one or more predictive models responsive to the request, and to invoke a plurality of machine learning algorithms using data from a plurality of data sources when executing the one or more predictive models. The processing platform is further configured to capture the data from the plurality of data sources and organize the data into a sparse matrix, to automatically generate the delivery date, and to automatically transmit the delivery date to one or more user devices.