Inventory Demand Forecasting using Machine Learning and Python

• Python
• Machine Learning

A major requirement for small/medium-sized businesses is Inventory Management since a lot of money and skilled labor has to be invested to do so. E-commerce giants use Machine Learning models to maintain their inventory based on demand for a particular item. Inventory Management can be extended as a service to small/medium sized businesses to improve their sales and predict the demand of various products. Demand forecasting is a crucial part of all businesses and brings up the following question: How much stock of an item should a company/business keep to meet the demands, i.e., what should the predicted demand of a product be? Among its many benefits, a predictive forecast is a key enabler for a better customer experience through the reduction of out-of-stock situations, and for lower costs due to better planned inventory and less write-off items. Any supply chain management must control manufacturing, storage, transportation, sales, and services in a way to meet all business demands within time. An optimum goal is to produce and store just enough to meet current needs with certain par reserves. With adequate data, we can forecast consumer behavior and demands for a specific product or service. With the help of time series analysis, we can recognize patterns and check for any correlation between storage and consumption. It incorporates seasonal fluctuations, consumer habits, and trends. Thus, if modeled adequately, it can predict future behavior from the correlation observed and thus help find its causation, which will help management teams avoid excess stockings and reduce capital investment on standing assets and reserves.