ELEVATING CUSTOMER EXPERIENCES AND MAXIMIZING PROFITS WITH PREDICTABLE STOCKOUT PREVENTION MODELLING
Keywords:
Predictive Modelling, Stockout Prevention, Customer Experience, Inventory Management, Machine Learning, Retail Analytics, Profit Maximization, Demand Forecasting, Supply Chain Optimization.Abstract
Stockouts are still a big problem in today's fast-paced retail world. They hurt customer satisfaction and business profits. This study looked into how well predictive stockout prevention modeling works to make customers happier and make the most money. We used a quantitative research strategy and machine learning methods like Random Forest and ARIMA to predict how much inventory we would need based on past sales and demand patterns. We looked at simulated data from 20 retail units before and after the model was put into use. The results showed that stockouts happened 66.67% less often, customer satisfaction went up 37.5%, and profit margins went up 33.3%. The predictive model was very accurate (MAPE: 7.2%, R²: 0.87), which showed that it was reliable and could be used in real life. The study found that predictive inventory modeling can help change reactive stock management into a proactive, data-driven approach, which is necessary for keeping customers loyal and making money in the long run.
