Perishable Inventory Markdown Optimization via Machine Learning
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current retail strategies for managing perishable inventory focus on operational aspects like markdowns rather than decision-making optimization, leading to inefficiencies in reducing spoilage-based shrinkage, particularly in departments like produce, deli, bakery, prepared foods, seafood, and meat, where margins are thin and spoilage accounts for 3.1% of overall shrinkage.
Innovation Solution
A method utilizing machine-learning models to determine optimal markdown levels, unit quantities, and durations for perishable items based on predicted customer behavior and item affinities, integrating exploration and exploitation predictors with item affinity analyzers to provide data-driven recommendations for markdown decisions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If predefined markdown policies and worker discretion are used to manage perishable inventory, then operational simplicity is maintained, but spoilage-based shrinkage reduction is suboptimal and inflexible
Solution Approach 1:
The patent replaces manual worker discretion and predefined markdown policies with an automated machine-learning-based system that analyzes inventory data, customer behavior, and item affinities to dynamically determine optimal markdown strategies, thereby reducing spoilage while maintaining operational simplicity
Solution Approach 2:
The system enables self-service markdown optimization by automatically processing inventory data and generating markdown recommendations without requiring manual worker intervention, allowing the system to continuously adapt to changing conditions while reducing human labor requirements
2Loss of substance
If machine-learning models are used to optimize markdown decisions, then spoilage reduction and sales maximization are improved, but system complexity increases
Solution Approach 1:
The patent segments the markdown optimization problem into distinct machine-learning models: an exploration predictor that identifies customers likely to purchase marked-down items, an exploitation predictor that forecasts sales impact, and an item affinity analyzer that determines item relationships, allowing each component to be developed and optimized independently
Solution Approach 2:
The patent introduces an item affinity analyzer as an intermediary component that processes raw inventory and sales data to identify relationships between items, serving as a bridge between data collection and markdown decision-making, thereby simplifying the overall system architecture
3Loss of substance
If markdowns are applied to clear perishable inventory, then spoilage is reduced, but overall store sales and profitability may be compromised
Solution Approach 1:
The patent dynamically adjusts markdown parameters (discount level, duration, target items) based on real-time analysis of inventory characteristics, customer behavior patterns, and item affinities, optimizing the balance between spoilage reduction and sales maximization rather than applying fixed markdown rules
Solution Approach 2:
The patent implements feedback loops where the exploitation predictor continuously monitors actual sales outcomes against predicted outcomes, and the exploration predictor refines its customer identification based on observed purchase behaviors, allowing the system to learn and improve its markdown strategies over time to maintain profitability
Data Source
Figure 1
Figure 2A
Figure 2B
AI summary
A system and methods for fine-grain predictive guidance for item markdowns are provided. The predictive guidance indicates whether an item should or should not be marked down, a markdown level for any markdown, a total number of units for any markdown, a time duration for any markdown, a store location to place an item associated with the markdown, and a listing of customers who are likely to purchase a marked down item when provided a targeted promotion. The predictive guidance utilizes, as input, the output produced by multiple predictive services and weighs those respective outputs to optimize item markdown sales and overall sales of the store.