Duty Cost Optimization Across Supply Chain Network Paths
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Solution Overview
Problem
Existing supply chain network models fail to efficiently consider duty costs and taxes, leading to inefficient and costly supply chain operations, particularly in complex networks with numerous sites and products, and require substantial computing resources.
Innovation Solution
A computer-implemented method and system that calculates and optimizes duty and tax costs across complex supply chains by enumerating path solutions, detecting and removing loops, and using network optimization models to minimize costs while fulfilling orders.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing supply chain network models are used to analyze and optimize shipping and production, then supply chain operations can be predicted and planned, but duty costs and taxes are not efficiently considered leading to costly operations
Solution Approach 1:
The patent segments the supply chain network into discrete paths and path fragments, allowing duty costs to be calculated and optimized for each segment independently. This segmentation enables the system to handle complex multi-country supply chains by breaking them down into manageable units that can be enumerated and optimized separately, resolving the contradiction between accurate cost prediction and computational complexity
Solution Approach 2:
The patent performs preliminary enumeration of all possible paths and path fragments before optimization. By pre-calculating the complete set of potential supply chain routes and their associated duty costs, the system prepares data structures that facilitate efficient optimization without repeatedly computing costs during the optimization process, thereby reducing overall computational complexity while maintaining prediction accuracy
2Productivity
If multiple path solutions are enumerated to optimize duty costs, then cost-effective fulfillment is achieved, but substantial computing resources are required
Solution Approach 1:
The patent extracts and removes loops from the enumerated paths, keeping only the essential acyclic path fragments needed for optimization. This extraction eliminates redundant computational iterations while preserving all unique supply chain routes, thereby reducing computing resource requirements while maintaining the ability to find cost-effective solutions
Solution Approach 2:
The patent enumerates all possible paths initially (excessive action) but then prunes the solution space by removing loops and redundant paths. This approach ensures no cost-effective solution is missed in the initial enumeration, but subsequent pruning reduces the computational burden for the actual optimization, balancing thoroughness with resource efficiency
3Productivity
If loop detection and removal is implemented in path enumeration, then computational efficiency is improved, but the complexity of the algorithm increases
Solution Approach 1:
The patent uses depth-first search to create and traverse copies of the path structure, maintaining a current path being built and comparing it against previously visited paths to detect loops. This copying approach simplifies loop detection by working with explicit path representations rather than abstract graph theory, improving computational efficiency while keeping the algorithm accessible and implementable
Data Source
AI summary
Input data provides an order of the quantity of finished goods to a site. Software is programmed for: accessing data defining an architecture of the supply chain network with sites, and a location of each site; enumerating one or more path solutions along the supply chain network to fulfill the order, each path solution comprising path fragments connecting two sites, a path fragment defining movement of a sub-quantity of the finished goods or raw materials; determining a cost associated with each of the plurality of path fragments, the cost comprising a duty rate, the duty rate associated with a particular path fragment based on the locations of the two sites connected by the particular path fragment and the sub-quantity of the finished goods or finished goods raw materials moved between the two sites; determining one or more optimal path solutions; and outputting the optimal path solutions for display.