Retail Inventory Waste and Profit Optimization: A Machine Learning-Based Business Analysis Practice

This article introduces a machine learning-based retail inventory optimization project. By analyzing 100,000 transaction records from 50 stores and 63 product categories, it identifies key drivers of inventory waste and proposes actionable business recommendations such as dynamic pricing and inventory planning, aiming to help retailers balance product supply and profit optimization.

The retail industry has long faced the challenge of balancing inventory waste and profit: billions of dollars are lost annually due to improper inventory management, with three main root causes being perishable goods expiration, inefficient promotions, and inaccurate inventory planning. Targeting this pain point, the project explores waste drivers and actionable strategies by analyzing 100,000 transaction data across 5 regions from 2023 to 2024.

Dataset and Preprocessing

The project uses a dataset from 2023 to 2024 containing 41 variables (inventory, pricing, promotions, etc.), which undergoes data cleaning (missing value validation, type consistency check) and feature engineering (avoiding confusion from abnormal promotion data, considering regional seasonal differences).

Predictive Model Construction

Based on exploratory analysis results, regression, decision tree, or ensemble learning methods are used to build models, quantifying the impact of various factors on inventory waste and profit to enable risk prediction and intervention.

1. Waste Drivers: High inventory waste is strongly correlated with low sales turnover; frozen ready-to-eat food has the highest waste ratio, reflecting supply chain challenges (temperature control, shelf life, demand forecasting difficulty).
2. Discount-Profit Relationship: Profit margins drop significantly when discount levels exceed a threshold, challenging the traditional belief that 'clearing stock is better than scrapping'.
3. Value of Inventory Freshness: The longer a product stays on the shelf, the lower its marginal contribution, supporting the investment value of the FIFO principle and precise replenishment systems.

• Dynamic Price Reduction Optimization: Adjust discounts in real time based on remaining shelf life, sales speed, and inventory levels to balance turnover and profit.
• Low-Turnover Category Control: Reduce safety stock, shorten replenishment cycles, or even remove some categories from certain stores, balancing stockout risk and waste costs.
• Category-Specific Policies: Differentiated management (high inventory for high-turnover low-loss categories, strict control for frozen ready-to-eat categories).
• Waste Risk Monitoring Dashboard: Display real-time warning indicators for stores/categories to enable proactive prevention.

The project uses Python ecosystem tools: Pandas/NumPy for data processing, Matplotlib/Seaborn for visualization, and Jupyter Notebook for interactive analysis. The project structure is modular (data cleaning → EDA → modeling → recommendations), facilitating reproducibility and customization.

Industry Implications

Provides a data-driven retail optimization paradigm, adapting to the Chinese market (high e-commerce penetration, high fresh food proportion) and new formats such as community group buying and instant retail.

Limitations and Extensions

Limitations: The dataset does not fully cover the complexity of the real environment (e.g., competitor behavior, macroeconomics); implementation requires organizational changes and system integration. Future directions: Introduce time series analysis, integrate external data (weather/holidays), develop real-time recommendation systems, and use A/B testing to verify strategy effectiveness.