Inventory Reallocation Under Distribution Center Capacity Violations
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Solution Overview
Problem
Online retailers face challenges in managing inventory across distribution centers due to limited capacity, making it unfeasible to stock every SKU at every center, and existing methods struggle to adapt to changes in demand or capacity issues.
Innovation Solution
A method and system that determine and adjust inventory mirroring plans by clustering distribution centers, allocating items based on feasible allocation plans, and reallocating SKUs to centers with available capacity, considering existing inventory and demand zones to minimize shipping costs and ensure service levels.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If SKUs are placed in distribution centers to minimize outbound shipping costs and guarantee service levels, then shipping costs are reduced and service levels are maintained, but distribution centers may become overloaded and capacity constraints are violated
Solution Approach 1:
The patent implements dynamic allocation plans that can be executed partially rather than requiring full execution. This allows the system to adapt to changing capacity conditions at distribution centers, executing only those allocations that fit within available capacity while maintaining service levels for high-priority items. The dynamic nature enables the system to respond to real-time capacity violations without completely failing to meet service level agreements.
Solution Approach 2:
The system changes the parameter of allocation plan execution from binary (all-or-nothing) to continuous (partial execution percentage). By adjusting the execution percentage parameter, the system can optimize between meeting service level requirements and respecting capacity constraints, allowing flexible adaptation to different capacity scenarios at various distribution centers.
2Reliability
If every SKU is stocked at every distribution center, then service levels are maximized and customer demand is fully met, but distribution center capacity is exceeded and operational costs increase
Solution Approach 1:
The patent applies local quality by differentiating allocation strategies for different SKUs and distribution centers based on their specific characteristics. High-velocity items receive priority allocation to ensure service levels, while low-velocity items are allocated only when capacity is available. Each distribution center receives customized allocation plans based on its capacity constraints and local demand patterns, rather than a uniform approach.
Solution Approach 2:
The system implements partial action by executing only the portion of the allocation plan that fits within capacity constraints. Rather than failing to execute the entire plan when capacity is tight, the system executes a calculated percentage of allocations, prioritizing high-velocity items and critical service level requirements, thereby partially meeting demand while respecting capacity limits.
3Ease of manufacture
If a fixed allocation plan is used for SKU distribution, then planning simplicity is maintained and implementation is straightforward, but the system cannot adapt to changes in demand or capacity issues
Solution Approach 1:
The patent transforms the static allocation plan into a dynamic system that automatically adjusts based on real-time capacity conditions. The allocation plan includes execution percentages that are dynamically modified based on capacity violations detected at distribution centers, enabling automatic adaptation to demand changes and capacity issues without requiring complete plan redesign or complex manual intervention.
Solution Approach 2:
The system implements feedback mechanisms by monitoring capacity utilization at distribution centers and using this information to adjust allocation plan execution percentages. When capacity violations are detected, the system receives feedback and automatically modifies the allocation plan to reduce or eliminate violations while maintaining service levels, creating a closed-loop adaptive system.
Data Source
AI summary
A method of reallocating inventory in a fulfillment network is disclosed herein. The fulfillment network can include a plurality of distribution centers. An allocation plan can be created in a one of a variety of different manners, where the allocation plan involves allocating an item to one or more distribution centers in the fulfillment network. Thereafter, the allocation plan can be analyzed for feasibility. If the allocation plan is not feasible, each distribution center in the allocation plan can be analyzed to determine if using the distribution center is feasible. If the distribution center cannot be used, another distribution in the same cluster of distribution centers is examined for feasibility. This process is repeated for each distribution center in the allocation plan. Once an alternative allocation plan has been developed in this manner, items can be allocated. Existing inventory can be taken into account in the allocation plan.