A study on the forecasting product demand at Q Mart in Kuala Terengganu

Product demand forecasting is an important process that helps businesses plan production, manage inventory, and meet customer needs. However, many organizations face difficulties in accurately predicting product demand. This study aims to identify the common problems that affect the accuracy of product demand forecasting at Q Mart Kuala Terengganu, is to address the issues of inadequate stock management and erroneous demand forecasting that frequently arise in small retail enterprises. Because decisions are typically dependent on experience and manual checking, Q Mart confronts issues including overstock and stock outs The research focuses on factors such as inaccurate historical data, changes in customer preferences, seasonal demand, and market uncertainties. The manager will be able to see whether goods are in high or low demand by utilizing Power BI to display the prediction results on an interactive dashboard. Additionally, it shows how predictive analytics may help small retailers transition from manual to data-driven decision-making in order to increase long-term productivity and profit. The findings show that poor data quality and unexpected market changes are among the main challenges faced by businesses when forecasting product demand.