Efficient Stock Management Using Computer Vision

Warehouses and product showrooms need competent strategies for ensuring that the products are accessible on demand. Optimization of inventory levels and efficient management can be implemented using machine learning algorithms. The proposed work optimizes the inventory and minimizes the stock outs, overstock and the needed stock for efficient stock management. To predict the amount of stock needed over the 90 days, a prediction model called using Long Short-Term Memory (LSTM) is proposed. To ensure accurate results and accuracy an organized workflow has been set to handle the algorithms development, testing and prediction operations and obtains the accuracy result with Mean Squared Error from 0 to 1. Manufacturers can boost productivity, improve inventory levels and quickly adapt to changing customer needs by putting this predictive model into effect. Overall, this analysis allows enterprises to keep adequate stock levels which leads to consistent revenue and customer satisfaction.