Neural Network Print Cost Estimation for Reprint Orders
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Solution Overview
Problem
Inaccurate cost estimation in printing factories leads to potential profit loss or order cancellation due to unclear resource costs, particularly in reprint calculations relying on craftsmen's estimates.
Innovation Solution
An information processing system utilizing a trained neural network model to predict cost information based on order reception data, including print job and printed matter type, to provide accurate cost estimates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If cost information is estimated based on craftsmen's sense, then the estimation process is simple and quick, but the accuracy and reproducibility are not guaranteed
Solution Approach 1:
The patent replaces the mechanical system of human craftsmanship with an automated neural network model. The neural network processes order reception information (print job information, printed matter type information) to automatically generate cost information, eliminating the need for manual estimation while ensuring consistency and reproducibility through data-driven predictions.
Solution Approach 2:
The patent creates a virtual copy of the estimation process through the neural network model. Instead of relying on individual craftsmen's judgments, the system uses a trained model that replicates accurate cost estimation across multiple orders, ensuring reproducibility and eliminating human variability in the estimation process.
2Productivity
If rough estimate based on craftsman's sense is used, then the calculation process is simple, but accuracy and reproducibility are not guaranteed
Solution Approach 1:
The patent substitutes manual calculation methods with an automated neural network that processes order information and generates cost estimates instantly. This replacement maintains high calculation speed while dramatically improving accuracy through data-driven predictions rather than rough estimates.
Solution Approach 2:
The neural network model performs self-service by automatically processing order reception information and generating cost information without human intervention. The system feeds order data (print job information, printed matter type information) into the trained model, which autonomously produces accurate cost estimates, eliminating the need for manual calculation while maintaining speed.
3Reliability
If cost information is presented with too much margin, then the printing factory protects itself from losses, but the order may be lost
Solution Approach 1:
The patent implements feedback through the neural network's training process, where the model learns from actual cost data and adjusts its predictions to minimize margins while ensuring profitability. This feedback mechanism allows the system to present accurate cost information that reflects true costs, building customer trust and increasing order acquisition while maintaining reliable profit protection.
Solution Approach 2:
The patent changes the parameter of cost estimation from subjective margin-based values to objective data-driven predictions. By using the neural network to calculate precise cost information based on actual order reception data, the system eliminates the need for excessive margins while maintaining profit protection, thereby increasing order acquisition without compromising reliability.
Data Source
AI summary
An information processing system includes a processor configured to, in a case where order reception information related to printing, that is, the order reception information including print job information and printed matter type information is input, input new order reception information related to new printing to a trained model which has been trained in advance in order to output cost information indicating costs regarding the printing, thereby outputting new cost information regarding printing corresponding to the new order reception information.