A
system for real-time monitoring and management of inventory using IoT data, consisting of: a
central processing unit (CPU) configured to receive data from a variety of
Internet of Things (IoT) sensors distributed throughout a retail or warehouse environment; a series of IoT sensors deployed on product storage units, wherein the sensors include at least one of the following systems: RFID readers, weight sensors, temperature sensors, motion detectors and camera-based
image processing systems, each of the sensors being configured to capture real-
time data regarding product availability, product movement and environmental conditions within the environment; an AI-powered analytics engine that is operationally coupled with the
central processing unit, the analytics engine being configured to process the data from the IoT sensors using
machine learning techniques to predict future product availability, detect anomalies in product movement, and generate real-time alerts regarding inventory status; a
user interface that displays real-
time data from IoT sensors, with the
user interface also displaying AI-generated stock replenishment recommendations and informing warehouse managers about detected anomalies and predicted stock requirements; and a communication network that connects the IoT sensors, the
central processing unit and the
user interface, with the network transmitting data in real time to enable
continuous monitoring and dynamic adjustments to
inventory management.