An Efficient Intelligent Semi-Automated Warehouse Inventory Stocktaking System

TL;DR

This paper presents an intelligent semi-automated warehouse inventory system that leverages bar code, distributed applications, and big data analytics to improve accuracy, automation, and decision-making in inventory management.

Contribution

It introduces a novel integrated system combining perception technologies and big data analytics for smarter, more efficient inventory stocktaking.

Findings

Enhanced monitoring accuracy and frequency.

Improved forecasting precision with AI techniques.

Reduced inventory costs and optimized stock levels.

Abstract

In the context of evolving supply chain management, the significance of efficient inventory management has grown substantially for businesses. However, conventional manual and experience-based approaches often struggle to meet the complexities of modern market demands. This research introduces an intelligent inventory management system to address challenges related to inaccurate data, delayed monitoring, and overreliance on subjective experience in forecasting. The proposed system integrates bar code and distributed flutter application technologies for intelligent perception, alongside comprehensive big data analytics to enable data-driven decision-making. Through meticulous analysis, system design, critical technology exploration, and simulation validation, the effectiveness of the proposed system is successfully demonstrated. The intelligent system facilitates second-level monitoring, high-frequency checks, and artificial intelligence-driven forecasting, consequently enhancing the automation, precision, and intelligence of inventory management. This system contributes to cost reduction and optimized inventory sizes through accurate predictions and informed decisions, ultimately achieving a mutually beneficial scenario. The outcomes of this research offer