Target Network Buffer Allocation for Demand-Specific Stockout Prevention
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Solution Overview
Problem
Current large-scale networks, such as supply chain networks, utilize generic resource targets and buffer values, leading to sub-optimal distribution of resources due to network and process limitations, resulting in stockout conditions.
Innovation Solution
A system and method that determines demand node-specific buffer resource allocations by analyzing demand probability distributions, ranking marginal stockout events, and applying a constraint cutoff threshold to generate a resource buffer data structure, optimizing buffer allocations based on historical and predicted demand patterns.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If generic resource targets and buffer values are used for all demand nodes, then network complexity is reduced and ease of operation is improved, but resource distribution efficiency deteriorates and stockout conditions increase
Solution Approach 1:
The patent segments the network into target networks with specific resource targets for different demand nodes. Instead of applying a single generic buffer value to all nodes, the system divides the network into manageable segments (target networks) where each segment has customized resource allocation parameters. This allows tailored buffer values to be assigned to specific demand nodes based on their individual characteristics, resolving the contradiction between operational simplicity and distribution efficiency.
Solution Approach 2:
The patent implements local quality by determining demand node-specific buffer values based on individual demand characteristics, service levels, and resource consumption patterns. Each demand node receives a customized buffer allocation rather than a uniform generic value. This localized approach optimizes resource distribution for each specific node while maintaining overall network manageability through the target network framework.
2Productivity
If demand node-specific buffer allocations are implemented, then resource distribution efficiency is improved and stockouts are reduced, but system complexity and computational requirements increase
Solution Approach 1:
The patent applies preliminary action by pre-determining buffer values for each demand node based on historical data, demand patterns, and service level requirements. The system performs computations in advance to establish target networks and allocate buffers before actual resource distribution occurs. This upfront planning reduces real-time complexity while maintaining optimized distribution efficiency during operation.
Solution Approach 2:
The patent introduces target networks as intermediary structures that mediate between the complex individual demand node requirements and the overall resource allocation system. These target networks serve as intermediate layers that aggregate and manage buffer allocations, simplifying the computational burden while still enabling demand node-specific optimization. The target network framework acts as a mediator that handles the complexity of customized allocations.
3Ease of operation
If generic buffer values are used across the network, then buffer resource allocations are simplified and easier to manage, but the likelihood of stockout conditions increases
Solution Approach 1:
The patent implements local quality by assigning customized buffer values to specific demand nodes within target networks based on their individual service level requirements, demand variability, and resource consumption patterns. This localized buffer allocation ensures that each node receives adequate protection against stockouts tailored to its specific risks, thereby improving overall network reliability while maintaining manageable complexity through the target network structure.
4Reliability
If customized demand node-specific buffer allocations are implemented, then stockout prevention is improved and resource reliability increases, but computational complexity and processing requirements increase
Solution Approach 1:
The patent applies preliminary action by performing comprehensive computational analysis in advance to determine optimal buffer values for each demand node. The system calculates service levels, demand distributions, and buffer requirements before implementing resource allocation. This upfront computational effort establishes reliable, customized buffer allocations that prevent stockouts during operation without requiring continuous complex computations, thus achieving high reliability while managing computational complexity.
Data Source
AI summary
Systems and methods of generating resource allocation data structures for a target network are disclosed. A resource buffer optimization request is received for at least one distribution node and a plurality of demand nodes associated with the at least one distribution node for a selected resource, a demand probability distribution including one or more marginal stockout events for each of the plurality of demand nodes is determined, the one or more marginal stockout events for each of the plurality of demand nodes is ranked in a combined ranking, a constraint cutoff threshold is determined, and a resource buffer data structure including demand node resource buffer allocations for each of the plurality of demand nodes is generated. Each of the demand node resource buffer allocations include a marginal stockout event having a probability greater than the constraint cutoff threshold. The resource buffer data structure is stored in a data store.