Production Equipment Sizing Using Polynomial Demand Models
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Solution Overview
Problem
Existing methods for sizing production equipment at manufacturing locations are subjective, computationally intensive, and inaccurate, leading to inefficiencies in meeting demand and optimizing output, as they rely on human expertise or complex computer-based approaches that consume significant resources and exclude key variables.
Innovation Solution
The development of optimization software that uses computationally efficient models, such as third-order polynomials, to accurately select and deploy production equipment based on consumption patterns, facilitating the installation and operation of equipment to match demand while minimizing waste and energy consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If complex computer-based approaches are used to size production equipment, then measurement precision may improve, but device complexity and computational resource consumption increase significantly
Solution Approach 1:
The patent replaces complex, resource-intensive computational models with simple, lightweight polynomial models (e.g., third-order polynomials) that require minimal computational resources. These simplified models provide sufficient accuracy for equipment sizing decisions without consuming significant processing power or time, effectively using 'cheap' computational approaches to solve the sizing problem.
Solution Approach 2:
The patent transforms the equipment sizing problem from a complex multi-variable optimization into a simplified parameter-based calculation using polynomial expressions. By changing the mathematical approach from complex simulations to polynomial parameter fitting, the system achieves adequate precision with dramatically reduced computational complexity.
2Adaptability or versatility
If human expertise is used to size production equipment, then adaptability may improve, but productivity and objective accuracy decrease due to subjectivity
Solution Approach 1:
The patent implements a self-service automated system where the polynomial-based model independently determines equipment sizing without requiring human expert intervention. The system automatically processes consumption data, applies the polynomial model, and generates equipment deployment recommendations, eliminating subjective human judgment while maintaining objective accuracy and significantly improving deployment speed.
Solution Approach 2:
The patent replaces the mechanical process of human expert analysis with an automated computational system using polynomial models. This substitution eliminates the limitations of human expertise (subjectivity, time constraints) while providing consistent, objective, and rapidly executable equipment sizing decisions.
3Reliability
If production equipment is oversized to meet peak demand, then reliability improves, but energy consumption and waste increase
Solution Approach 1:
The patent employs dynamic polynomial models that capture time-varying consumption patterns at production locations. By modeling consumption as a function of time and operational parameters, the system can size equipment to match actual demand dynamics rather than static peak values, ensuring reliability during high demand while avoiding unnecessary energy consumption during low demand periods.
Solution Approach 2:
The patent uses historical consumption data and polynomial modeling to predict future demand patterns before equipment deployment. This preliminary analysis allows for optimized equipment sizing that anticipates demand variations, preventing both overproduction (waste) and underproduction (unmet demand) by pre-calculating the optimal equipment capacity based on modeled consumption trends.
Data Source
AI summary
A manufacturing process for physical components can be optimized using some techniques described herein. For example, a system can receive, via a graphical user interface, a user selection of a particular type of production location. The system can determine a cumulative consumption of a component at the particular type of production location over a particular time window. The system can analyze a group of candidate types of production equipment to identify a particular type of production equipment that can accommodate the cumulative consumption of the component during the particular time window. And the system can execute one or more computing operations configured to facilitate deployment of the particular type of production equipment at the particular type of production location.