AI-driven pricing and stock replenishment decisions

AI integration in pricing and replenishment is increasingly recognized as a source of competitiveness and efficiency in retail. This study examines how demographic factors influence awareness of AI-based price optimization. Data from 100 respondents, collected via a structured questionnaire, was analyzed using multiple linear regression to evaluate the effects of gender, age, occupation, education, and professional experience on awareness scores. While the overall regression model was not strongly explanatory, certain predictors—occupation (students), education level (undergraduates), and age group (above 45 years)—showed significant effects. These findings align with existing literature that emphasizes the importance of education and work experience in technology adoption. The results highlight the need for targeted training and awareness initiatives to address demographic gaps in AI readiness. For retail managers, the study provides practical insights into workforce preparedness for AI deployment in pricing and inventory decisions, contributing to both academic understanding and managerial practice.