Employ a classification model to categorize inventory levels into UNDER_STOCKED, OVER_STOCKED, and CORRECTLY_STOCKED, providing monthly reports for inventory adjustments. This approach simplifies decision-making by categorizing inventory status but may lack precision in quantifying exact order amounts.

Utilize a clustering algorithm to identify groups of popular items based on historical sales data, enabling the logistics team to increase stock for these items. While this method highlights trends, it does not directly predict the quantity of inventory needed for each item.

Apply a regression model to forecast the additional inventory required each month, offering predictions at the month's start for inventory enhancement. Regression models can predict quantities but may not fully account for temporal patterns like seasonality.

Implement a time series forecasting model to predict monthly sales per item, guiding the logistics team in inventory ordering based on these forecasts. This method excels in capturing trends, seasonality, and other temporal dynamics in sales data, providing precise predictions for inventory needs.

Combine a time series forecasting model with a classification model to not only predict monthly sales per item but also categorize inventory status for additional insights. This dual approach offers both quantitative predictions and qualitative assessments for comprehensive inventory management.