Predictive Analytics for Manufacturing Equipment Speed Optimization

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Solution Overview

Problem

Existing manufacturing systems fail to efficiently leverage large quantities of data to optimally set manufacturing equipment parameters in real-time environments, leading to suboptimal performance in terms of time and cost per piece.

Innovation Solution

A method that utilizes data from an in-memory computing module and an enterprise resource planning database to predict target time and cost per piece, allowing for the setting of equipment speed based on these predictions to achieve minimum acceptable levels.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If traditional data collection systems are used to store manufacturing data, then data can be archived for future reference, but the data cannot be efficiently leveraged for real-time equipment parameter optimization

Engineering Contradiction:

Improvedata availabilityVSAvoidreal-time optimization capability

Core Design Contradiction:

ReliabilityVSProductivity

Solution Approach 1:

The system segments data storage and processing into two distinct components: traditional ERP databases for historical data archiving and in-memory computing modules for real-time data processing. This segmentation allows each component to fulfill its specific function optimally without compromising the other.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The patent introduces in-memory computing modules as intermediaries between the traditional ERP database and the equipment control system. These modules act as a bridge that retrieves historical data from the database, processes it in real-time using high-speed memory, and generates optimized equipment parameters, thereby enabling real-time optimization while preserving reliable data archiving.

Inventive Principle:

Principle #24Intermediary (Mediator)

2Productivity

If equipment speed is increased to improve productivity, then output per unit time increases, but time per piece and cost per piece may worsen due to suboptimal parameter settings

Engineering Contradiction:

Improveoutput per unit timeVSAvoidtime per piece

Core Design Contradiction:

ProductivityVSLoss of time

Solution Approach 1:

The system dynamically adjusts equipment parameters including speed based on real-time predictions generated by in-memory computing. Rather than operating at fixed or statically optimized speeds, the equipment parameters are continuously adapted to current production conditions, allowing the system to maximize productivity while minimizing time per piece through real-time responsiveness.

Inventive Principle:

Principle #15Dynamics

Solution Approach 2:

The patent implements a feedback mechanism where performance indicators are continuously monitored, fed into in-memory computing modules for analysis, and used to generate updated equipment parameters. This closed-loop feedback system ensures that equipment speed and other parameters are continuously optimized based on actual performance data, preventing deterioration of time per piece even as productivity increases.

Inventive Principle:

Principle #23Feedback

3Ease of operation

If equipment parameters are set based on historical data only, then parameter setting is simple, but the system cannot adapt to changing real-time manufacturing conditions

Engineering Contradiction:

Improveparameter setting simplicityVSAvoidreal-time condition adaptation

Core Design Contradiction:

Ease of operationVSAdaptability or versatility

Solution Approach 1:

The system performs preliminary actions by pre-processing and analyzing historical data in in-memory computing modules to establish baseline performance models. These pre-computed models enable rapid real-time adjustments without requiring complex manual parameter setting, thus maintaining ease of operation while dramatically improving adaptability to changing conditions.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The patent enables the system to self-adjust equipment parameters by automatically analyzing performance indicators through in-memory computing and generating optimized settings without human intervention. This self-service capability maintains operational simplicity while providing sophisticated real-time adaptation, as the system autonomously handles the complexity of parameter optimization.

Inventive Principle:

Principle #25Self-service

Data Source

PatentUS9189760B2System, method and computer readable medium for using performance indicators and predictive analysis for setting manufacturing equipment parameters

Publication Date: 2015.11.17 SAP SE

AI summary

The method includes receiving first data from an in-memory computing module, the data including performance indicators, receiving second data from a enterprise resource planning database, predicting a target time per piece based on the first data and the second data, predicting a target cost per piece based on the first data and the second data, and setting an equipment speed based on the target time per piece and/or the target cost per piece.