Estimasi Permintaan Berbasis AI dan E-Commerce: Peran Analitik Big Data, Akurasi Prediksi, dan Optimasi - Taryana Taryana; Ahmad Syamil; Soleman Soleman (Institut Teknologi dan Bisnis (ITB) Semarang, 2026)
This study aims to analyze the effect of artificial intelligence (AI)-based demand forecasting and big data analytics on inventory optimization through prediction accuracy among e-commerce actors or marketplace sellers in Curug. This research employed an explanatory quantitative approach involving 100 respondents selected through purposive sampling based on predefined criteria relevant to the research objectives. Data were collected using a structured Likert-scale questionnaire and analyzed using Structural Equation Modeling Partial Least Squares (SEM-PLS) by evaluating the measurement model, structural model, and mediation effects. The findings reveal that AI-based demand forecasting and big data analytics have a positive and significant effect on prediction accuracy. Furthermore, prediction accuracy has a positive and significant effect on inventory optimization and significantly mediates the relationship between AI-based demand forecasting, big data analytics, and inventory optimization. These findings indicate that the integration of AI and big data analytics contributes to more accurate demand prediction, leading to improved inventory management performance. The implication of this study suggests that e-commerce actors should improve data quality, analytical capability, and the adoption of predictive technologies to support more accurate, efficient, and responsive inventory decisions, thereby enhancing operational performance and competitiveness in responding to dynamic market demand.