Production Planning with Semantic Capability Matching
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Solution Overview
Problem
Current production planning methods are inflexible and time-consuming, particularly in the context of Industry 4.0, as they rely on manual processes and either centralized or decentralized approaches, which struggle with complex machines and granular capability descriptions, leading to suboptimal solutions and increased communication efforts.
Innovation Solution
A computer-implemented method that combines central and decentralized production planning dynamically, using semantic and multi-attribute comparisons to match task descriptions with production capabilities, allowing for situation-dependent decision-making and bridging the gap between abstract task descriptions and machine-level capability descriptions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If manual production planning is used for each production facility, then production flexibility can be maintained, but the time investment required is significant and time to market is long
Solution Approach 1:
The system dynamically adjusts the level of centralization based on situation. The central production planning system can operate at different levels of automation and detail, switching between centralized automated planning and decentralized manual planning depending on the specific production context, task complexity, and available information.
Solution Approach 2:
The production planning process is segmented into different levels: central planning for high-level coordination and decentralized facility-level planning for detailed execution. This segmentation allows each level to operate independently within its scope, reducing overall planning time while maintaining flexibility at the execution level.
2Productivity
If centralized planning based on capability descriptions is used, then production planning efficiency is improved, but the capabilities of machines cannot be fully expressed as declarative description
Solution Approach 1:
A semantic comparison module acts as an intermediary between the central planning system and machine-level capability descriptions. This intermediary translates and reconciles different levels of capability description granularity, enabling the central system to understand and utilize detailed machine capabilities without requiring complete declarative descriptions at the central level.
Solution Approach 2:
Different levels of the planning system operate with different levels of description granularity. The central planning system uses high-level capability descriptions for efficiency, while local facility systems maintain detailed capability descriptions for accuracy. Each level operates with the appropriate level of detail for its function.
3Adaptability or versatility
If decentralized approaches with intelligent machines are used, then production flexibility is improved, but coordinating functions are necessary which lead to suboptimal solutions and require additional communication efforts
Solution Approach 1:
The system merges centralized and decentralized planning approaches into a unified hierarchical structure. The central planning system provides coordination and optimization at the enterprise level, while facility-level systems maintain autonomy for local decision-making. This combination reduces the coordination complexity of purely decentralized systems while preserving their flexibility advantages.
Data Source
Figure 1~2
AI summary
The invention relates to a production planning method using a plurality of manufacturing devices (INTMA) according to which tasks (TD) of a work plan (BOP) are compared (MA) with manufacturing capabilities (SD) of the manufacturing devices (INTMA) and, depending on the one or more results (MAQ) of said comparison (MA), at least one or more manufacturing devices (INTMA) are commissioned to match their manufacturing capabilities (SD) with the task(s) (TD).