In the retail landscape, demand forecasting is one of the most critical foundations for accurate, timely, and effective inventory management. Most retailers and distributors struggle to have the right product at the right place, in the right quantity and at the right time to meet the demands of their customers. Inventory management is typically the single biggest capital investment a company makes, with the majority of a retailer’s working capital tied up in its inventory investment. One of the biggest challenges associated with demand forecasting for business executives today is understanding the impact of demand volatility on demand forecasting. To address this challenge, companies often add excess inventory to protect against inaccurate forecasts. Various studies have shown that excess inventory holding costs due to storage, obsolescence, spoilage, and taxes could increase costs by 20 percent to 30 percent. With the advent of advanced AI and Machine Learning models, retail companies are increasingly using demand forecasting to optimize inventory. With democratization of AI and Machine learning even small and medium sized businesses can leverage powerful AI/ ML technologies to create accurate forecasting models. With improved forecasting, companies can drive better decisions regarding cash flow, risk management, capacity planning, and workforce planning.