Ship-from-store demand assignment optimization
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Solution Overview
Problem
Optimizing supply chain node configurations to efficiently meet demand while minimizing costs and shipping distances is computationally complex, especially in large networks, due to the vast number of permutations and interdependencies between nodes.
Innovation Solution
A method and system that utilize a decision model on a computing system to optimize supply chain node configurations by receiving forecasted demand, node data, and shipping cost data, segmenting the problem space, and generating an optimal configuration that balances throughput and cost constraints, thereby determining an optimal assignment of demand to nodes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If all possible node configurations are computed to achieve optimal configuration, then the optimal configuration can be determined, but the computational complexity becomes unwieldy
Solution Approach 1:
The patent segments the supply chain network into multiple regions, with each region containing a subset of nodes. The optimization process is divided into two stages: first optimizing within each region independently, then performing global optimization across regions. This segmentation reduces the computational complexity from evaluating all possible node configurations globally to evaluating configurations within smaller regional subsets, while still achieving near-optimal global results.
2Productivity
If node configuration is changed to meet high demand, then shipment handling efficiency improves, but the cost of node reconfiguration increases
Solution Approach 1:
The patent implements dynamic node configurations that can be adjusted based on demand fluctuations. The system determines optimal configurations for different demand scenarios and time periods, allowing nodes to transition between configurations as needed. This dynamic approach enables the system to meet high demand efficiently while minimizing reconfiguration costs by only making changes when and where necessary, rather than maintaining static over-provisioned configurations.
Solution Approach 2:
The optimization model evaluates multiple configuration parameters for each node including throughput capacity, equipment levels, and operational constraints. By systematically varying these parameters across different scenarios and optimizing their combinations, the system identifies configurations that achieve high shipment handling efficiency at minimal reconfiguration cost, finding the optimal balance between capability and investment.
3Reliability
If throughput configuration is increased to meet demand, then demand fulfillment capability improves, but the capacity installation cost increases
Solution Approach 1:
The patent applies partial action by determining the minimum necessary throughput configuration needed to meet demand within service level constraints, rather than over-provisioning all nodes to maximum capacity. The optimization model calculates the precise throughput levels required for each node based on assigned demand, ensuring sufficient fulfillment capability while avoiding excessive capacity installation costs that would result from uniform high-capacity configurations across all nodes.
Data Source
AI summary
Methods and systems for optimizing a ship-from-shore process are provided. A decision model receives forecasted demand and node data representing a plurality of possible shipping node configurations. The decision model generates output data that includes an assignment of the forecasted demand to nodes among the plurality of nodes based on the shipping cost data and the node data while optimizing a supply chain objective subject to a plurality of constraints, the plurality of constraints including a fulfillment of the forecasted demand within a predetermined delivery service level, and identifies an optimal configuration for each shipping node. In some aspects, the decision model segments the node data to improve efficiency of analysis.