Inventory Optimization with Priority Bands in Constrained Networks
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Solution Overview
Problem
Existing techniques for optimizing inventory in supply chains with feasibility constraints are inefficient and fail to provide accurate customer service levels, as they either decrease demand ad hoc or simulate flow without adequately accounting for constraints.
Innovation Solution
The method involves assigning service level bands with priorities and calculating corresponding inventory bands, generating a feasible supply chain plan that satisfies these bands in order of priority until a constrained network is depleted, ensuring accurate customer service levels are met while considering feasibility constraints.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If ad hoc demand decrease or simulation techniques are used to account for feasibility constraints, then inventory optimization is attempted, but the accuracy of customer service levels is insufficient
Solution Approach 1:
The patent segments the inventory optimization problem into discrete priority levels and time periods. It divides demand into constrained and unconstrained portions, and segments the solution into iterative steps that process different priority levels separately. This segmentation enables precise calculation of service levels for each segment while maintaining computational efficiency through systematic processing.
2Measurement precision
If feasibility constraints are fully considered in inventory optimization, then accurate service levels are achieved, but computational complexity increases
Solution Approach 1:
The patent performs preliminary actions by first identifying and separating constrained demand from unconstrained demand before the main optimization process. It pre-calculates priority levels and constriction amounts, and prepares the demand structure in advance. This preliminary organization simplifies the subsequent iterative optimization steps and reduces overall computational complexity while maintaining accuracy.
Solution Approach 2:
The patent introduces dynamics by implementing an iterative algorithm that adaptively adjusts the constrained demand portion in each iteration based on the previous iteration's results. The constriction amount dynamically changes as the optimization progresses, allowing the system to converge to an accurate solution while managing computational complexity through progressive refinement rather than exhaustive calculation.
3Manufacturing precision
If iterative optimization with priority levels is implemented, then inventory accuracy improves, but processing time increases
Solution Approach 1:
The patent applies partial action by iteratively processing only the constrained portion of demand at each priority level rather than recalculating the entire inventory optimization problem. It focuses computational effort on the specific segments that require adjustment due to constraints, leaving the unconstrained portions unchanged. This partial processing significantly reduces processing time while maintaining the accuracy improvements gained through iterative optimization.
Data Source
AI summary
In one embodiment, optimizing inventory includes accessing service level band sets. Each service level band set is associated with a policy group, and includes service level bands. Each service level band of a service level band set has a service level priority with respect to any other service level bands of the same service level band set. An inventory band set is determined for each service level band set. Each inventory band set includes inventory bands, where each inventory band satisfies a corresponding service level band assuming an unconstrained network. Each inventory band of an inventory band set has an inventory priority with respect to any other inventory bands of the same inventory band set. A feasible supply chain plan that satisfies the inventory band sets is generated in order of the inventory priorities until a constrained network is depleted.