Inventory Allocation and Pricing Optimization via Min-Cost Flow
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Solution Overview
Problem
Current price optimization systems for retailers fail to consider inventory allocation among multiple locations, leading to suboptimal pricing and revenue maximization due to uncertainty in demand parameters, especially price elasticity, which affects inventory management and markdown strategies.
Innovation Solution
A system that optimizes inventory allocation and pricing by using a network flow model to allocate inventory from multiple fulfillment centers to customer groups, considering historical sales data and demand models, and employing a min-cost network flow approach to determine optimal prices and inventory levels, accounting for parameter uncertainty through a log-linear demand model and uniform distribution of price elasticity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If price optimization systems focus on maximizing revenue through markdown strategies, then revenue generation is improved, but inventory allocation across multiple locations is not optimized leading to suboptimal pricing decisions
Solution Approach 1:
The patent combines inventory allocation optimization with price markdown optimization into a unified system. The inventory allocation module determines optimal distribution of inventory across multiple locations, while the price optimization module uses this allocation to generate markdown strategies. These previously separate functions are merged to ensure pricing decisions account for actual inventory distribution, resolving the contradiction between revenue maximization and allocation complexity.
Solution Approach 2:
The patent introduces an intermediary inventory allocation module that acts as a bridge between demand forecasting and price optimization. This intermediary takes inventory constraints and allocation decisions as input, and provides optimized inventory distribution as output to the price optimization module. This intermediary structure enables the system to handle allocation complexity systematically while maintaining revenue optimization goals.
2Adaptability or versatility
If inventory is allocated across multiple fulfillment centers, then customer service coverage is improved, but determining optimal allocation and pricing becomes computationally complex
Solution Approach 1:
The patent segments the inventory optimization problem into distinct modular components: demand forecasting module, inventory allocation module, and price optimization module. Each module handles a specific aspect of the overall problem, processing inputs and generating outputs for the next module. This segmentation reduces computational complexity by breaking down the monolithic optimization problem into manageable segments that can be solved sequentially rather than simultaneously across multiple fulfillment centers.
Solution Approach 2:
The patent performs preliminary inventory allocation optimization before executing price markdown strategies. The inventory allocation module first determines the optimal distribution of inventory across fulfillment centers based on demand forecasts and service requirements. This preliminary action establishes the inventory foundation that subsequent pricing decisions will operate upon, simplifying the overall optimization by separating allocation decisions from pricing decisions in a sequential manner.
Data Source
AI summary
Embodiments optimize inventory allocation of a retail item, where the retail item is allocated from a plurality of different fulfillment centers to a plurality of different customer groups. Embodiments receive historical sales data for the retail item and estimate demand model parameters. Embodiments generate a network including first nodes corresponding to the fulfillment centers, second nodes corresponding to the customer groups, and third nodes between the first nodes and the second nodes, each of the third nodes corresponding to one of the second nodes. Embodiments generate an initial feasible inventory allocation from the first nodes to the second nodes and solves a minimum cost flow problem for the network to generate an optimal inventory allocation.