Developing an app for streamlined inventory tracking with barcode scanning and load planning optimization - Teng, Yan Xin (2025)
Inventory management and load planning are important processes for organizations that handle large volumes of goods. However, many small and medium-sized enterprises (SMEs) still rely on manual record-keeping and random cargo loading practices due to the high cost and complexity of existing systems. These outdated practices often result in inaccurate stock records, inefficient use of vehicle space, and delays in distribution caused by time-consuming and unstructured load adjustments. To address these challenges, this project developed a Streamlined Inventory Tracking Application that integrates barcode scanning for fast and accurate stock management with an optimized load planning module. The application was implemented using React Native for mobile development and Firebase Firestore as the backend database to enable real-time data synchronization, while a binary tree bin packing algorithm was applied to generate efficient cargo loading arrangements. The methodology combined throwaway prototyping and incremental development, ensuring continuous refinement based on feedback and iterative improvements. The system was tested for functionality, usability, and performance, demonstrating improved stock accuracy, reduced manual workload, and improved space utilization compared to traditional manual methods. The results indicate that the proposed system is both affordable and practical for SMEs, offering a user-friendly solution that enhances operational efficiency. It is recommended that future improvements include focus on role-based access control, advanced reporting, and support for irregular cargo shapes to further increase usability and applicability.