Supply Chain Optimization: Machine Learning Applications in Inventory Management for E-commerce

This study looks at how machine learning (ML) methods can be used in e-commerce inventory management to make demand forecasting and inventory optimisation more accurate and efficient. The literature review shows how important it is for online stores to keep track of their goods well and how machine learning algorithms might be able to help with this problem. Several machine learning techniques are talked about, such as supervised learning, reinforcement learning, and unsupervised learning, along with how they can be used in inventory management. The study makes up the suggested architecture for ML-based inventory management: importing data, preparing data, finding features, storing data, and the ML system. Real-world examples, like how Amazon uses AI in its forecasting, show that ML can be used in e-commerce product management and show its benefits. Real-world examples show that decision tree models and deep learning methods like recurrent neural networks (RNNs) and convolutional neural networks (CNNs) are useful for predicting demand and optimising supplies. But problems are pointed out, such as bad data, a lack of computing power, and models that are hard to understand. In the future, researchers will look into how to fix problems with machine learning algorithms, how to use new technologies like bitcoin and augmented reality, and how to make sure that AI is used in an ethical way and that customer data is kept safe. The study finds that machine learning has a lot of potential to change how e-commerce inventory is managed, which could lead to more efficiency and better decisions in the digital market.