AI and Inventory Management — Team Research Project

For many retail businesses, inventory management is a critical success factor, and technological advances are making it easier. This research paper examines how artificial intelligence can improve inventory management in retail, the benefits companies can expect when it is applied well, and a closer look at Amazon Forecast — a machine learning service built by Amazon Web Services for demand planning. The paper was produced by a team for an Artificial Intelligence Challenge.

Research contributor (team project)

The team identified demand forecasting as the retail process with the most room for improvement through AI. Traditional methods rely on basic statistical techniques and historical sales data, which often miss complex factors driving demand. The paper details how machine learning improves forecasting through: improved accuracy (analyzing large volumes of sales data alongside economic indicators, weather, and promotions), granular insights at the category, store, or product level, dynamic forecasting that adapts to new data in real time, integration with external data sources such as market research and competitor data, and automatic detection of seasonality and emerging trends.

The paper walks through Amazon Forecast end to end: importing structured historical data, automated data cleaning, choosing a domain such as inventory planning and setting scope parameters, selecting and training the forecasting algorithm, evaluating model reliability with the provided metrics and visual tools, and finally generating and exporting forecasts for use in other applications.

The team concluded that Amazon Forecast can process large volumes of data with less human error, optimize inventory by predicting consumer demand, seasonality, and pricing effects, and reduce both overstock and stockouts — saving companies time and money. It also weighs the limitations: heavy reliance on large amounts of quality historical data, longer training times for big datasets, limited explainability of results, and the risk of AI hallucinations producing inaccurate forecasts.