Manufacturing Prediction Models for Time and Cost Optimization
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Solution Overview
Problem
Manufacturing processes often rely on human experience, leading to inaccurate predictions and inconsistencies, and lack reliable objective tools for optimizing production times and costs, especially when dealing with digital models that vary over time or depend on exogenous variables.
Innovation Solution
A predictive system using regression machine learning models and multi-objective optimization to generate accurate and consistent predictions for manufacturing processes, incorporating features from digital models and supply chain data to optimize production attributes such as time and cost.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If human experience is used for predictions, then subjective judgment is applied, but accuracy and consistency deteriorate
Solution Approach 1:
The patent replaces the mechanical system of human judgment with an automated machine learning system that processes digital models and manufacturing data. The system uses trained models to generate predictions for manufacturing time, cost, and other attributes, eliminating human subjectivity while maintaining consistency through standardized algorithms and objective data processing.
2Reliability
If standardized methodologies are implemented, then objectivity improves, but adaptability to varying conditions deteriorates
Solution Approach 1:
The patent implements a dynamic system where machine learning models are continuously trained and updated with new manufacturing data. The system adapts to changing conditions by incorporating updated digital models, revised manufacturing processes, and new transaction data from supply chain entities, maintaining objectivity through standardized methodologies while achieving adaptability through continuous learning and model retraining.
3Ease of operation
If manual estimation processes are used, then flexibility is maintained, but time consumption and overhead increase
Solution Approach 1:
The patent implements a self-service system where the machine learning model automatically processes incoming digital models and manufacturing requests without requiring manual intervention. The system independently retrieves relevant data from databases, executes predictions for multiple attributes simultaneously, and generates results in near real-time, maintaining flexibility through automated decision-making while dramatically reducing time consumption compared to manual estimation processes.
Data Source
AI summary
The subject technology is related to methods and apparatus for training a set of regression machine learning models with a training set to produce a set of predictive values for a pending manufacturing request, the training set including data extracted from a set of manufacturing transactions submitted by a set of entities of a supply chain. A multi-objective optimization model is implemented to (1) receive an input including the set of predictive values and a set of features of a physical object, and (2) generate an output with a set of attributes associated with a manufacture of the physical object in response to receiving the input, the output complying with a multi-objective condition satisfied in the multi-objective optimization model.