Printed Material Workload Prediction for Specification Changes
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Solution Overview
Problem
Existing systems struggle to accurately predict the workload involved in the creation process of printed materials due to frequent changes in specifications or design, leading to operational inefficiencies in large factories with complex manufacturing flows.
Innovation Solution
An information processing system that utilizes a pre-learned model to predict the workload of a creation process by inputting new order information, including details such as text, voice inputs, and images, to estimate the workload involved in determining specifications or design of printed materials.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a manufacturing plan is made for complex printed materials with multiple components, then the manufacturing process can be organized, but the workload prediction becomes difficult due to frequent specification changes
Solution Approach 1:
The system performs preliminary learning by collecting historical order information and actual workload data to build a prediction model before actual manufacturing planning occurs. This pre-computed model enables rapid workload estimation for new orders without requiring time-consuming manual analysis of complex specifications
Solution Approach 2:
The system creates a computational model that copies the essential patterns and relationships from historical manufacturing data. This model replicates the complexity of manual workload assessment while eliminating the need for repeated manual analysis of similar order patterns
2Measurement precision
If manual workload assessment is performed for each new order, then accurate planning is possible, but the operator becomes heavily loaded due to frequent change requests
Solution Approach 1:
The system enables self-service by allowing the workload assessment to be performed automatically by the prediction model without requiring operator intervention. The model independently analyzes new order information against historical data to generate workload estimates, freeing operators from repetitive assessment tasks while maintaining consistent accuracy
Solution Approach 2:
The system replaces the mechanical process of manual workload assessment with an automated information processing system. The prediction model computationally analyzes order specifications and historical patterns, substituting human cognitive effort with algorithmic processing that maintains accuracy while eliminating operator burden
3Adaptability or versatility
If the creation process determines specifications or design, then customization is achieved, but the workload becomes unpredictable due to uncertain specification changes
Solution Approach 1:
The system incorporates feedback mechanisms by continuously collecting actual workload data from completed orders and using it to refine the prediction model. This feedback loop enables the system to adapt to new specification patterns and change requests, improving predictability while maintaining the ability to handle customized orders
Solution Approach 2:
The prediction model is designed to be dynamic, continuously learning from new data and adapting to changing specification patterns. This dynamic nature allows the system to maintain reliable workload predictions even as customization requirements evolve, rather than relying on static assessment criteria
Data Source
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AI summary
An information processing system includes a processor configured to: output information on a workload of a creation process corresponding to new order information by inputting to a model the new order information on a printed material, the model being pre-learned such that, when order information on a printed material is input, information on the workload involved in the creation process that determines specifications or design of the printed material is output.