Simultaneous Inventory Reorder Point and Quantity Optimization
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Solution Overview
Problem
Current methodologies for computing optimal order quantities in inventory management are static and disjointed, failing to account for service level targets and demand and lead time variability, leading to inadequate visibility and performance in supply chains.
Innovation Solution
A system and method for simultaneous computation of optimal order point and optimal order quantity, which iteratively determines the impact of changes in reorder point and reorder quantity on inventory performance across multiple levels of a supply chain, ensuring desired service levels are met by optimizing these parameters dynamically.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If EOQ formula is used for computing order quantity, then calculation simplicity is improved, but service level target compliance deteriorates
Solution Approach 1:
The patent transforms the static EOQ formula into a dynamic optimization model where order quantity and reorder point are computed simultaneously based on current inventory levels, demand variability, and service level targets. The system continuously adjusts parameters based on real-time conditions rather than using fixed offline calculations.
Solution Approach 2:
The invention changes the parameters from simple average demand and fixed costs to include demand variability (standard deviation), lead time variability, and service level targets. This allows the system to compute optimal order quantities that explicitly account for uncertainty and meet specified service levels.
2Device complexity
If static offline EOQ calculations are used, then computational complexity is reduced, but inventory performance visibility deteriorates
Solution Approach 1:
The system implements feedback loops where inventory performance metrics are continuously monitored and fed back into the optimization model. This allows the system to adjust order quantities and reorder points based on actual performance versus targets, providing visibility into how well service level goals are being met.
Solution Approach 2:
The patent introduces an intermediary optimization engine that connects raw inventory data with performance metrics. This intermediary computes the relationship between order parameters and service level outcomes, providing visibility that neither simple EOQ nor complex simulation alone could deliver.
3Ease of operation
If reorder point and order quantity are computed separately, then calculation process is simplified, but optimization effectiveness deteriorates
Solution Approach 1:
The patent merges the separate computations of reorder point and order quantity into a unified simultaneous optimization model. Both parameters are determined together in an iterative process that considers their interdependence, leading to more effective inventory control than sequential methods.
Data Source
AI summary
A system is disclosed for simultaneous computation of optimal order point and optimal order quantity. The system includes one or more memory units and on ore more processing units, collectively configured to receive initial inputs, initialize a first, at least second and final locations and the initial inputs and compute a first baseline inventory performance of the first level. The system is further configured to compute at least a second inventory performance of the at least second level and perform optimization iterations by simultaneously determining a change in inventory performance for the first and the at least second level when the re-order point (R) is incremented by a specified R increment value and when the re-order quantity is incremented by a specified Q increment value. The system is further configured to report the reorder point and reorder quantity for the first, the at least second, and the final location.