Intelligent Prediction for Equipment Manufacturing Management
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Original equipment manufacturers (OEMs) face challenges in accurately estimating manufacturing costs for new products or enhancements due to a lack of knowledge about the main contributing factors, which hampers their negotiation capabilities with original design manufacturers/contract manufacturers (ODMs/CMs).
Innovation Solution
An automated equipment manufacturing management system with intelligent prediction functionality that uses structured descriptions, manufacturing-related data, and prediction models to compute predicted attributes, including costs, enabling better negotiation with ODMs/CMs by generating sequenced clean sheets and forecasting costs through collaboration with suppliers and ODMs/CMs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If OEMs manually estimate manufacturing costs without sufficient knowledge of contributing factors, then negotiation capabilities with ODMs/CMs deteriorate, but implementing an intelligent prediction system increases system complexity and data processing requirements
Solution Approach 1:
The patent introduces an intelligent prediction system as an intermediary between the OEM and ODM/CM negotiation process. This system acts as a mediator that objectively analyzes manufacturing cost data, components, and processes to generate predicted costs, thereby enhancing the OEM's negotiation capability without requiring the OEM team to have deep expertise in cost breakdown analysis.
Solution Approach 2:
The system performs preliminary cost analysis and prediction before the actual negotiation takes place. By pre-computing manufacturing costs based on historical data, components, and processes, the OEM is equipped with accurate cost information in advance, enabling more effective negotiation positioning without needing to perform complex calculations during the negotiation itself.
2Measurement precision
If OEMs implement intelligent prediction systems to accurately estimate manufacturing costs, then negotiation position improves, but the system requires extensive manufacturing data and processing resources
Solution Approach 1:
The system collects and processes manufacturing data, component information, and process details in advance before prediction is needed. By performing preliminary data aggregation and validation, the system ensures high measurement precision for cost estimation without requiring intensive data processing during the actual prediction or negotiation phase.
Solution Approach 2:
The system uses historical manufacturing data from previous products and processes as copies or references for current cost predictions. By leveraging existing data patterns and relationships from similar manufacturing scenarios, the system achieves accurate cost estimation without needing to process entirely new data sets from scratch for each prediction.
3Measurement precision
If OEMs lack knowledge of main contributing factors in manufacturing cost determination, then cost estimation accuracy deteriorates, but acquiring and analyzing detailed manufacturing data increases time and resource consumption
Solution Approach 1:
The system incorporates feedback mechanisms that learn from historical manufacturing data and previous cost predictions. By continuously analyzing outcomes and refining prediction models based on actual manufacturing costs and contributing factors, the system improves cost estimation accuracy over time without requiring increasing amounts of data analysis time for each new prediction.
Solution Approach 2:
The system performs preliminary identification and categorization of main contributing factors to manufacturing costs before detailed analysis is required. By pre-processing and structuring manufacturing data, component information, and process parameters in advance, the system enables rapid accurate cost estimation without consuming excessive time during the actual prediction process.
Data Source
AI summary
Intelligent prediction techniques for equipment manufacturing management are disclosed. For example, a method comprises obtaining: (i) a structured description of at least one of components and processes associated with manufacturing of equipment in accordance with a given design; (ii) first manufacturing-related data from one or more potential manufacturing entities for the equipment; and (iii) second manufacturing-related data representing at least one of current attributes and historical attributes associated with manufacturing equipment at least similar to the equipment of the given design. The method then applies one or more prediction models based on at least portions of the obtained structured description, the first manufacturing-related data, and the second manufacturing-related data to compute a predicted attribute associated with manufacturing the equipment.