Retail Waste Analytics With ML Recommendations for Store Loss Reduction
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Solution Overview
Problem
Retailers lack a clear method to efficiently manage waste, leading to significant losses in sales, and there is no existing guidance for timely identification of high waste levels and effective reduction strategies.
Innovation Solution
A system utilizing machine learning models to analyze waste data, detect anomalies, identify causal relationships, and generate actionable insights and recommendations for waste reduction across various store levels, including store, department, and item levels.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If machine learning models are used to analyze waste data and generate recommendations, then waste reduction effectiveness is improved, but system complexity increases
Solution Approach 1:
The system segments the waste analysis process into distinct modules: data collection module, machine learning analysis module, recommendation generation module, and visualization module. Each module handles specific tasks independently, making the complex system more manageable and maintainable while preserving the effectiveness of ML-based waste reduction.
Solution Approach 2:
The patent introduces an intermediary layer between raw waste data and actionable recommendations. This intermediary processing layer uses machine learning models to transform raw data into insights, and then into standardized recommendations. This mediation simplifies the overall system architecture by abstracting the complexity of ML algorithms from the end users while maintaining effective waste reduction.
2Measurement precision
If comprehensive waste data is collected and analyzed across multiple stores, then waste management precision is improved, but data processing time increases
Solution Approach 1:
The system performs preliminary data cleaning, normalization, and feature engineering when data is first collected. Preprocessing operations such as handling missing values, outlier detection, and data transformation are completed in advance before analysis. This preliminary action reduces processing time during subsequent analysis phases while maintaining high measurement precision through thorough data preparation.
Solution Approach 2:
The patent implements selective data processing based on store priorities and waste patterns. Instead of uniformly processing all store data with equal intensity, the system applies partial processing to high-priority stores showing critical waste patterns, while using lighter processing for stores with stable performance. This partial action approach maintains sufficient precision for waste management while significantly reducing overall processing time.
3Ease of operation
If actionable recommendations are generated for store operations, then ease of operation is improved, but information processing complexity increases
Solution Approach 1:
The system extracts only the most critical and actionable information from complex waste data analysis. Instead of presenting all analytical details, the patent identifies and extracts key recommendations that directly impact waste reduction decisions. This extraction process simplifies the information presented to store operators while maintaining the essential insights needed for effective waste management operations.
Solution Approach 2:
The patent transforms complex analytical parameters into actionable operational parameters. Machine learning outputs are converted into practical recommendations with specific actions, timeframes, and expected impacts. This parameter transformation simplifies information processing by presenting data in terms that are directly applicable to store operations, improving ease of operation without losing critical information.
Data Source
AI summary
Systems and methods for generating insights and recommendations to reduce waste in retail stores are disclosed. In some embodiments, a disclosed method includes: obtaining waste data of a plurality of stores; selecting, from the plurality of stores, at least one store based on the waste data; generating, based on the waste data and at least one machine learning model, recommendation data for the at least one store to take at least one action to reduce waste; and providing the recommendation data to the at least one store.