Markdown Schedule Optimization via Segmented Search
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Solution Overview
Problem
Current methods for determining markdown schedules during product discontinuation are inefficient, particularly when dealing with thousands of products in thousands of stores, as they rely on slow general optimization algorithms and do not account for product-level preferences or real-time inventory and demand fluctuations, lacking the ability for on-demand re-optimization and integration with inventory allocation and promotional planning.
Innovation Solution
A computer-implemented method for markdown schedule optimization that identifies measurable objectives, allocates inventory from distribution centers, determines schedules of price reductions, and updates the schedule based on sales data, using permissible price steps and convergence criteria to optimize inventory exhaustion or profit maximization, incorporating business rules and stochastic hedging for dynamic pricing adjustments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If general optimization algorithms are used for markdown schedule determination, then optimization coverage is comprehensive, but computation speed is slow
Solution Approach 1:
The patent segments the markdown optimization problem into two distinct phases: (1) an offline pre-computation phase that establishes the demand model and calculates optimal markdown schedules for future dates, and (2) an online real-time phase that only determines markdowns for today based on current inventory and sales data. This segmentation allows the system to use comprehensive optimization methods for future planning while achieving fast real-time performance for immediate decisions.
Solution Approach 2:
The system performs preliminary computation of markdown schedules for future dates in advance, storing these pre-calculated optimal schedules. When real-time markdown determination is needed, the system simply retrieves and adjusts these pre-computed schedules rather than performing full optimization calculations, dramatically reducing computation time while maintaining optimization quality.
2Ease of operation
If markdown schedules are determined statically, then implementation is simple, but adaptability to real-time demand fluctuations is poor
Solution Approach 1:
The patent implements dynamic markdown scheduling where the optimal markdown schedule is recalculated each day based on updated inventory levels and actual sales performance. The system uses a demand model that incorporates time-dependent factors and promotional lift effects to adaptively adjust markdown amounts, ensuring optimal performance despite changing conditions while maintaining automated simplicity.
3Device complexity
If product-level preferences are not considered, then allocation process is simplified, but inventory allocation accuracy decreases
Solution Approach 1:
The system applies local quality by incorporating store-specific and product-specific characteristics into the inventory allocation and markdown optimization process. The demand model includes store-level parameters and product-level elasticity variations, allowing the system to tailor markdown strategies to local conditions while maintaining a unified automated framework that does not require manual intervention.
Data Source
AI summary
Markdown optimization may be performed using a computer system. Markdown objectives may include margin maximization or inventor minimization. Markdown schedule optimization uses an efficient, iterative, targeted combinatorial search. First, permissible discount steps are identified, then for each permissible discount step, a subspace of permissible step dates is identified. Elements from the date step subspace and price reduction subspace are combined and evaluated against the markdown objectives. If an improved markdown schedule is located, a new subspace of permissible step dates is defined to search the subregion of the solution space where the improved markdown optimization schedule was identified. Scheduling of inventory allocation from a distribution center is also disclosed.