Markdown Optimization Algorithm for Retail Inventory Sell-Through
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Solution Overview
Problem
Retailers face challenges in determining optimal markdown plans to maximize revenue while meeting inventory sell-through targets, as existing methods often result in sub-optimal solutions due to complex non-linear constraints and the need to balance individual store targets with company-wide goals, leading to unnecessary discounts and revenue loss.
Innovation Solution
A computer-implemented system and method that generates near-optimum product markdown plans by generating feasible markdown schedules, removing dominated schedules, and using a two-part solving algorithm involving a limited exact algorithm and dynamic programming to ensure revenue within a threshold percentage of a revenue upper bound, while considering inventory pooling targets and pricing constraints.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If traditional markdown optimization methods are used, then implementation simplicity is maintained, but revenue maximization is compromised due to sub-optimal solutions
Solution Approach 1:
The patent segments the markdown optimization problem into distinct components: generating feasible markdown schedules that satisfy individual store constraints, identifying dominated schedules to eliminate, and selecting optimal schedules that meet company-wide inventory targets. This segmentation allows the complex problem to be solved systematically while achieving near-optimum revenue outcomes.
Solution Approach 2:
The patent performs preliminary actions by first generating all feasible markdown schedules for each store that satisfy individual constraints before considering company-wide targets. Dominated schedules are identified and removed in advance, creating a refined set of candidate schedules. This preliminary processing enables the final selection to focus only on viable options, improving both efficiency and optimality.
2Productivity
If individual store markdown targets are optimized independently, then local revenue is maximized, but company-wide inventory sell-through targets may not be met
Solution Approach 1:
The patent implements feedback mechanisms where the selection of markdown schedules for individual stores is constrained by company-wide inventory sell-through targets. The system evaluates how individual store decisions impact overall company targets and adjusts selections accordingly. This feedback loop ensures that local optimization does not compromise global objectives, achieving both local revenue maximization and company-wide target achievement.
Solution Approach 2:
The patent merges individual store markdown plans with company-wide inventory targets by selecting combinations of store-level schedules that collectively satisfy both local and global constraints. The integration process combines the benefits of local optimization with the requirements of company-wide performance, creating a unified markdown strategy that achieves both objectives simultaneously.
3Productivity
If aggressive markdowns are applied to meet inventory targets, then sell-through is improved, but revenue is reduced due to unnecessary discounts
Solution Approach 1:
The patent changes the parameter selection approach by evaluating multiple feasible markdown schedules with different discount levels and timing patterns. Instead of applying aggressive markdowns uniformly, the system selects from a range of parameter combinations (markdown percentages, timing, duration) that achieve inventory targets while minimizing revenue loss. This parameter optimization allows flexible adjustment to balance sell-through and revenue preservation.
Solution Approach 2:
The patent applies partial action by selecting markdown schedules that provide just enough discount to meet inventory targets rather than applying excessive discounts. The system identifies the minimum necessary markdown intensity required to achieve sell-through objectives, avoiding unnecessary revenue erosion. This controlled application of markdowns ensures adequate inventory movement while preserving maximum possible revenue.
Data Source
AI summary
Computer-implemented systems and methods generate a near-optimum product markdown plan for a plurality of uniform pricing levels having a required inventory sell-through target over all of the plurality of uniform pricing levels. A plurality of feasible markdown schedules are generated for the uniform pricing level, where each of the plurality of feasible markdown schedules meets all individual constraints for the uniform pricing level. All dominated feasible markdown schedules are removed for the uniform pricing level to generate one or more candidate markdown schedules for the uniform pricing level. A near-optimum product markdown plan is generated, where generating the near-optimum product markdown plans includes executing a limited exact algorithm solver for a plurality of iterations, and executing a dynamic programming solver if no product markdown plan generated by the limited exact algorithm solver is within the threshold percentage of the revenue upper bound.