Order Allocation via Priority Feasibility Classification
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Solution Overview
Problem
Current production planning systems inadequately address dynamic demand and availability changes by ignoring additional relevant information, leading to inefficient and unreliable allocation of material flows, which can result in disadvantageous outcomes.
Innovation Solution
A computer-implemented method that classifies demand and availability orders into priority and feasibility classes, creating pegging linkages between the highest priority and feasibility orders to determine material flows, considering time criteria and optimizing the allocation process to ensure efficient and reliable results.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If allocating procedure uses only time criteria, then allocation is simple and fast, but additional relevant information is ignored leading to disadvantageous results
Solution Approach 1:
The patent changes the parameters considered in the allocation procedure from only time criteria to multiple parameters including priority classes for requirement orders and feasibility classes for receipt orders. This allows the system to consider additional relevant information while maintaining computational efficiency through structured classification.
Solution Approach 2:
The patent segments the allocation procedure into distinct classification steps: first classifying requirement orders into priority classes, then classifying receipt orders into feasibility classes, and finally matching them. This segmentation makes the complex multi-parameter allocation manageable and computationally efficient.
2Reliability
If allocating procedure uses priority and time criteria, then high priority demands are covered well, but additional information about availabilities is ignored
Solution Approach 1:
The patent adds feasibility class as an additional parameter for receipt orders, complementing the priority class parameter for requirement orders. This ensures that information about both demands and availabilities is utilized in the allocation decision-making process.
Solution Approach 2:
The patent segments the allocation criteria into two independent classification dimensions: priority classes for requirement orders and feasibility classes for receipt orders. This segmented approach allows comprehensive information utilization while maintaining clear matching logic.
3Reliability
If multiple classification criteria are used, then allocation result quality improves, but computation complexity increases
Solution Approach 1:
The patent divides the complex multi-criteria allocation into separate classification steps: first classifying requirement orders by priority, then classifying receipt orders by feasibility, and finally matching them. This segmentation reduces computational complexity compared to evaluating all criteria simultaneously.
Solution Approach 2:
The patent performs classification and matching in stages, completing the allocation process through sequential partial actions rather than attempting to optimize all criteria simultaneously. This approach achieves good results while maintaining computational efficiency.
Data Source
AI summary
A method and device for planning a production includes determining material flows of products that are part of the production. This determination is done by allocating available quantities of a product for covering demanded quantities of the product in time. An allocating procedure classifies orders for the demanded quantities according a priority and orders for the available quantities according to a feasibility to provide the quantity. The classifications of the orders are used to cover the demanded quantities with a higher priority by available quantities with a higher feasibility.