Manufacturing Execution System Dynamic Line Metrics
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Solution Overview
Problem
Conventional manufacturing execution systems (MES) fail to accurately model the logical relationships between physical assets in industrial processes, leading to suboptimal production metrics and performance reporting.
Innovation Solution
An enhanced MES that models both physical assets and their logical relationships, using entity and line models to generate production metrics dynamically based on real-time data, identifies bottleneck entities, and provides performance indicators such as line production metrics, availability, quality, and realization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional MES models physical assets as discrete entities without logical relationships, then the system structure is simple, but the production metrics accuracy deteriorates
Solution Approach 1:
The patent implements a hierarchical modeling structure where entity models (discrete physical assets) are nested within line models (logical groupings). This allows the system to maintain simple discrete entity representations while simultaneously enabling complex logical relationship modeling through the line model layer, thereby improving production metrics accuracy without fundamentally complicating the base entity structure
Solution Approach 2:
The patent adds a logical relationship dimension to the existing physical asset models by introducing line models that operate alongside entity models. This dimensional addition enables the system to capture both individual asset performance and interconnected line-level performance, improving metrics accuracy by considering multiple levels of organization simultaneously
2Adaptability or versatility
If MES uses static production metric determination, then the system operation is simple, but the adaptability to changing production conditions deteriorates
Solution Approach 1:
The patent implements dynamic production metric determination by continuously monitoring entity performance data and automatically adjusting which entities are included in line-level metric calculations. The system transitions from static, pre-configured metric determination to a dynamic process that adapts to changing production conditions, entity availability, and performance patterns, thereby improving adaptability while maintaining automated operation
Solution Approach 2:
The patent incorporates feedback mechanisms where production metric results are continuously monitored and used to adjust future metric determination. The system uses real-time entity performance data to feed back into the line model, automatically refining production metric calculations based on actual production conditions, entity bottlenecks, and performance variations, thus achieving dynamic adaptation through closed-loop control
3Loss of information
If MES monitors all entities individually without logical grouping, then the data collection is comprehensive, but the performance reporting effectiveness deteriorates
Solution Approach 1:
The patent merges individual entity performance data with line-level logical relationships to create integrated production metrics. By combining comprehensive entity-level monitoring with logical line groupings, the system produces unified performance reports that capture both detailed entity performance and overall line performance, thereby improving reporting effectiveness without losing individual entity information
Solution Approach 2:
The patent creates line models that serve multiple functions: they aggregate entity performance data, identify bottlenecks, calculate line-level metrics, and provide contextual interpretation of entity performance. This multi-functional approach allows the system to process comprehensive entity data through a universal line model framework, improving reporting efficiency by handling multiple analysis tasks through a single integrated structure
Data Source
AI summary
A manufacturing execution system (MES) for providing an indication of the performance of the line. The MES includes a configuration module for modeling entities and lines containing the entities. The MES also includes a runtime module configured to determine the entities on the line whose production can be extrapolated to evaluate the performance of the line. In some cases, the MES determines which of the entities on the line limits the performance of the line; in other cases, the MES determines which of the entities has a production amount that best represents that of the line. The MES is operatively connected to field inputs associated with the entities that provide production data for the entities. Using the production data for the entities chosen to represent the line, the MES generates production metrics representative of the performance of the line and displays them to MES users.