Process Chain Optimization Method for Industrial Manufacturing Cost Reduction
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Solution Overview
Problem
Current industrial manufacturing processes often resort to sub-optimization by improving specific steps or product redesign without considering the complete production chain and all cost parameters, leading to inefficiencies and increased costs.
Innovation Solution
A method that utilizes predictive models for the entire process chain to optimize product properties and production costs by finding the best combination of raw material sources and processing parameters to meet customer requirements at the lowest possible cost, incorporating 'Through Process Modelling' with cost models and an optimization routine.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If sub-optimization is used to improve specific process steps or product redesign, then local improvements are achieved, but overall production costs increase and inefficiencies occur
Solution Approach 1:
The patent combines multiple process steps, cost models, and property models into a single integrated optimization framework. This merging allows simultaneous optimization of the complete process chain rather than isolated sub-optimization of individual steps, thereby improving overall production efficiency while maintaining manufacturing precision.
Solution Approach 2:
The patent creates a universal optimization method that can handle multiple objectives simultaneously (cost reduction, property optimization, energy efficiency) across different process steps. This multi-functional approach replaces multiple separate optimization processes with a single comprehensive system that improves overall productivity.
2Productivity
If complete process chain optimization is implemented, then true optimisation of product properties and costs is achieved, but model complexity and data requirements increase significantly
Solution Approach 1:
The patent segments the complex optimization problem into distinct components: process step models, cost models, and property models. Each segment can be developed and validated independently, then integrated into the complete optimization framework. This segmentation reduces the perceived complexity while enabling comprehensive process chain optimization.
Solution Approach 2:
The patent transforms complex process models into parameter-based representations that can be optimized systematically. By changing the representation from detailed process simulations to parameter-driven models, the system achieves complete process chain optimization with reduced computational complexity and data requirements.
3Reliability
If all cost parameters and process models are integrated, then lowest possible production cost is achieved, but the methodology becomes difficult to implement and requires specialized competence
Solution Approach 1:
The patent performs preliminary actions by pre-integrating cost models and process models into a unified framework before actual optimization. This preliminary integration creates a ready-to-use system that requires less specialized competence during implementation, while still achieving accurate cost optimization through the pre-established model connections.
Data Source
AI summary
A method for optimisation of product properties and production costs of industrial processes where a product is manufactured in several operations, Including the following steps: —establishing process chain models (1-m) for each process (1-n), altogether (m×n) models for the calculation of product properties and product production costs, —defining model inputs and outputs related to each of the (m×n) models for physical, chemical or biological parameters and costs of the product, —defining limitations or minimum requirements related to each of the (m×n) models of product properties or process capacity or ability, —providing an optimizing tool linked to the input and output steps and model limitations performing iterations and flow of data between said steps to optimize the product based on customer requirements.