Regression Formula Predicts KPIs for Manufacturing Optimization
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Manufacturing environments face challenges in efficiently leveraging large datasets to optimally set manufacturing equipment parameters and predict their effects, as existing systems fail to effectively utilize the collected data for performance optimization.
Innovation Solution
A method utilizing regression formulas to derive relationships between reason indicators and performance parameters, predicting key performance indicators (KPIs), and varying parameters based on these predictions and associated costs, implemented within a high-performance analytic appliance (HANA) that processes data in real-time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If large quantities of data are collected in manufacturing environments, then the potential for optimization increases, but the ability to efficiently leverage this data deteriorates
Solution Approach 1:
The patent extracts only the most relevant features and patterns from the large manufacturing dataset using regression formulas, rather than processing the entire dataset. This selective extraction of critical information enables efficient prediction of key performance indicators while maintaining optimization potential.
Solution Approach 2:
The patent transforms raw manufacturing data into meaningful predictions by changing parameters through regression analysis. The system converts large volumes of raw data into condensed predictive models that relate manufacturing parameters to key performance indicators, enabling efficient data utilization.
2Measurement precision
If regression formulas are used to predict key performance indicators, then prediction accuracy improves, but computational complexity increases
Solution Approach 1:
The patent applies regression formulas selectively to predict only the most critical key performance indicators rather than analyzing all possible outcomes. This partial action approach maintains prediction accuracy for essential metrics while reducing overall computational complexity.
3Productivity
If manufacturing equipment parameters are adjusted based on predictions, then operational efficiency improves, but system complexity increases
Solution Approach 1:
The patent implements a feedback mechanism where predicted key performance indicators are used to adjust manufacturing equipment parameters, which in turn generate new data for further predictions. This closed-loop feedback system improves operational efficiency while managing complexity through iterative optimization.
Solution Approach 2:
The system automatically adjusts manufacturing parameters based on regression predictions without requiring extensive manual intervention. The predictive model serves itself by continuously learning from new data and autonomously optimizing parameters, reducing the operational complexity burden.
Data Source
AI summary
Disclosed are a system, computer readable medium and method for predicting key performance indicators. The method includes receiving one or more data pairs, the one or more data pairs indicating a performance parameter and reason indicator associated with the performance parameter, deriving a formulaic relationship, utilizing a regression formula, between the reason indicator and the performance parameter, predicting at least one key performance indicator (KPI), utilizing a regression formula, for each of the one or more data pairs, associating a cost with each of the one or more data pairs, and varying a parameter based on the KPI and the associated cost.