# An Efficient Intelligent Semi-Automated Warehouse Inventory Stocktaking System

arXiv: https://arxiv.org/abs/2309.12365 · HTML: https://mlsanity.com/paper/2309.12365/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 1 of its 33 references that are in the map.
- First posted on arXiv 13 Sep 2023.

## Builds on (most cited first, 1 of 1)

- [HybNet: A Hybrid Deep Learning -- Matched Filter Approach for IoT Signal Detection](https://mlsanity.com/paper/2111.10557/) (2021)

## Abstract (arXiv)

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