Optimizing Stock Accuracy with AI and Blockchain for Better Inventory Management - Putra, Fengki Eka; Khasanah, Miftakhul; Anwar, Muhammad Rehan (ADI Journal on Recent Innovation, 2025)
The optimization of stock Opname accuracy is crucial for effective inventory management and improved sales outcomes. Traditional inventory management systems often struggle with accuracy due to manual processes and lack of real time data, leading to inefficiencies and sales losses. This study examines the impact of integrating AI and Blockchain technology to enhance stock Opname accuracy, optimize inventory management, and improve sales performance in business operations. A quantitative research approach was employed, utiliz- ing AI algorithms for predictive analytics and Blockchain for secure, transpar- ent recordkeeping. The results indicate a 30% improvement in stock Opname accuracy, a 60% reduction in inventory discrepancies, and a 50% decrease in reconciliation time. Additionally, businesses implementing AI and Blockchain experienced a 15% increase in sales performance and a 67% reduction in stock- out issues. These findings highlight that the combination of AI and Blockchain significantly reduces human error, enhances real time tracking, and provides tamper proof records, leading to more efficient inventory management and in- creased sales outcomes. This study contributes to the field by providing empirical evidence on the effectiveness of AI and Blockchain in inventory manage- ment, offering a framework for businesses seeking to improve stock Opname accuracy and optimize operational efficiency. The integration of these technologies presents a promising solution for modern inventory management, enabling businesses to respond to demand fluctuations with greater precision.