Machine Learning Markdown Optimizer for Perishable Inventory
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Solution Overview
Problem
Current retail markdown strategies for perishable items rely on predefined policies and human intuition, failing to optimize markdown processes based on data-driven analytics, leading to inefficiencies in reducing spoilage and improving sales and margins.
Innovation Solution
A machine learning model is trained on historical data from various retailer systems to predict optimal markdown parameters, including when to initiate markdowns, markdown levels, and quantities, integrating these predictions into existing workflows to enhance decision-making and reduce spoilage and shrinkage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of substance
If predefined markdown policies and human discretion are used to manage perishable inventory, then operational simplicity is maintained, but spoilage reduction and sales optimization are insufficient
Solution Approach 1:
The patent replaces manual, intuition-based markdown decision-making with an automated machine learning model that analyzes historical data and predicts optimal markdown parameters. This substitution of mechanical human judgment with an automated intelligent system enables data-driven decisions that reduce spoilage while managing process complexity through automation.
Solution Approach 2:
The system enables self-service by allowing the machine learning model to autonomously generate markdown recommendations without requiring human intervention for each decision. The model continuously learns from data and automatically adjusts markdown strategies, reducing the need for manual oversight while improving spoilage reduction outcomes.
2Measurement precision
If manual store walk-throughs are used to identify at-risk inventory, then implementation simplicity is maintained, but identification accuracy and responsiveness are insufficient
Solution Approach 1:
The patent replaces manual store walk-throughs with an automated machine learning system that continuously monitors inventory data. This substitution enables real-time, accurate identification of at-risk items without the time consumption and human error associated with manual assessments, significantly improving both precision and speed of inventory risk detection.
Solution Approach 2:
The system implements continuous monitoring and assessment of inventory through automated data collection and analysis. Unlike periodic manual walk-throughs, the machine learning model operates continuously, constantly updating risk assessments and markdown recommendations to respond immediately to changing inventory conditions, thereby eliminating time losses and improving identification accuracy.
3Adaptability or versatility
If predefined markdown policies are applied uniformly, then process consistency is maintained, but flexibility and optimality for individual items are insufficient
Solution Approach 1:
The patent applies local quality by generating customized markdown recommendations for each individual item based on its specific characteristics, historical performance, and current conditions. Rather than uniform policies, the machine learning model tailors markdown strategies to local item needs, improving flexibility and optimality while managing complexity through automated individualized analysis.
Solution Approach 2:
The system implements dynamic markdown strategies that adapt to changing conditions for each item. The machine learning model continuously updates predictions based on new data, allowing markdown parameters to evolve over time rather than remaining static. This dynamic approach provides flexibility and adaptability while the automation manages the complexity of tracking and adjusting individual item strategies.
Data Source
AI summary
A network-based service is provided that utilizes a machine learning model (MLM) trained on a variety of data from disparate systems of a retailer to generate predictive guidance/parameters for a price markdown process. The predictive guidance includes a markdown prediction that indicates whether an item should or should not be marked down. For an item designated for markdown, the MLM also generates markdown parameters including a markdown level for the item and a quantity of the item to be marked down. The predictions of the MLM are optimized to reduce item shrink, reduce item spoilage, increase item sales, and increase item margins. The service can be integrated into existing retailer systems and services to provide optimal markdown instructions for perishable items that are data-driven and objective.