DESIGNING THE SMART WAREHOUSE: KEY AUTOMATION CRITERIA FOR SUSTAINABLE AND SCALABLE OPERATIONS

Year 2025, Volume: 12 Issue: 2, 47 - 56, 31.12.2025

Naveen Kukkala

https://izlik.org/JA95UM49LW

Abstract

Purpose- Warehouses face challenges due to rising demands for efficient, sustainable, large-scale operations, making automation essential
for enhancing processes and reducing costs. This paper compares three automation models LSTM, Prophet, and Logistic Regression that can
improve warehouse management, particularly in sales forecasting and reorder prediction.
Methodology- Using actual warehouse sales data from Kaggle, time-series models (LSTM and Prophet) were built for daily sales forecasting
and Logistic Regression for reorder quantities on each item. The models evaluated each model's ability to project warehouse sales and
determine replenishment timing.
Findings- Results suggest that LSTM provided better forecasting results than Prophet, with lower MSE, RMSE, and MAE values, modelling
both short-term volatility and long-term trends. Logistic Regression showed high accuracy and decent precision, though low recall suggests
it missed many reorder cases.
Conclusion- While LSTM models can improve decision-making in warehouse management, further development of classification models is
essential to enhance reorder prediction accuracy, increase recall, and prevent stockouts.

Keywords

Warehouse management , automation , LSTM , reorder prediction , inventory management , smart warehouses

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