Self-Learning Freight Estimation Advisor for Invoice Validation
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Solution Overview
Problem
Existing systems face challenges in accurately estimating future transportation costs and auditing freight invoices, which are often incomplete or contain incorrect data, leading to uncertainty and increased costs for shippers and carriers.
Innovation Solution
A freight invoice estimation advisor system utilizing an artificial neural network model to analyze historical data, determine variables, and estimate costs based on a knowledge base, with the ability to validate and enrich data for improved accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual estimation and auditing methods are used, then simplicity and ease of operation are maintained, but measurement precision and reliability of cost estimates deteriorate
Solution Approach 1:
An artificial neural network model serves as an intermediary between historical freight data and cost estimation, automatically processing and analyzing data to generate accurate predictions. The system intermediates between raw document data and meaningful cost insights, resolving the contradiction by providing high precision through automated intelligent processing rather than manual methods.
Solution Approach 2:
The system performs self-learning by automatically training the neural network model on historical data without requiring manual intervention for each estimation. The model continuously improves its accuracy by learning from past freight invoices and documents, enabling self-service operation that maintains high precision while reducing operational complexity.
2Measurement precision
If comprehensive data analysis is performed to improve estimation accuracy, then measurement precision improves, but loss of time in processing increases
Solution Approach 1:
The system performs preliminary actions by pre-processing and storing historical freight data in structured formats before actual estimation is needed. The neural network model is pre-trained on comprehensive historical datasets, so when a new document requires analysis, the system can quickly retrieve and apply learned patterns without performing exhaustive analysis from scratch, thus maintaining high accuracy while reducing processing time.
Solution Approach 2:
Manual mechanical analysis of freight documents is replaced with an automated neural network system that processes data electronically. The system substitutes human analysts with intelligent algorithms that can analyze comprehensive datasets instantaneously, achieving high measurement precision without the time loss associated with manual review processes.
3Adaptability or versatility
If multiple carriers are contracted to provide flexibility, then adaptability improves, but loss of information about optimal carrier selection increases
Solution Approach 1:
The system implements feedback mechanisms by continuously analyzing actual freight invoices and comparing them with estimated costs. This feedback loop identifies which carriers provide optimal value for different shipment types, routes, and commodities. The neural network learns from this feedback and continuously refines its recommendations, ensuring that flexibility in carrier selection does not lead to loss of optimization information.
Solution Approach 2:
The estimation system serves multiple functions: it estimates costs for different carriers, validates invoices, provides recommendations, and continuously learns from diverse data sources. This multi-functional approach consolidates carrier selection intelligence in a single universal system, preventing loss of optimization data even as the organization contracts with multiple carriers for flexibility.
Data Source
AI summary
Systems and methods for estimating freight costs are disclosed herein. The system receives, from a user interacting with the system, a first document and a second document, where an actual value is associated with the second document. Further, the system determines a set of variables for each of the first document and the second document based on a statistical analysis of historical data. Furthermore, the system estimates, using a trained artificial neural network model, a cost associated with each of the first document and the second document based on the determined set of variables. The cost associated with the first document includes an estimated freight cost for the first document, and the cost associated with the second document includes an estimated true value for the second document.