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工业 4.0 中的智能仓库——系统文献综述。

Intelligent Warehouse in Industry 4.0-Systematic Literature Review.

机构信息

Faculty of Mechanical Engineering, Wroclaw University of Science and Technology, 50-370 Wroclaw, Poland.

出版信息

Sensors (Basel). 2023 Apr 19;23(8):4105. doi: 10.3390/s23084105.


DOI:10.3390/s23084105
PMID:37112446
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10146052/
Abstract

The development of Industry 4.0 (I4.0) and the digitization and automation of manufacturing processes have created a demand for designing smart warehouses to support manufacturing processes. Warehousing is one of the fundamental processes in the supply chain, and is responsible for handling inventory. Efficient execution of warehouse operations often determines the effectiveness of realized goods flows. Therefore, digitization and its use in exchanging information between partners, especially real-time inventory levels, is critical. For this reason, the digital solutions of Industry 4.0 have quickly found application in internal logistics processes and enabled the design of smart warehouses, also known as Warehouse 4.0. The purpose of this article is to present the results of the conducted review of publications on the design and operation of warehouses using the concepts of Industry 4.0. A total of 249 documents from the last 5 years were accepted for analysis. Publications were searched for in the Web of Science database using the PRISMA method. The article presents in detail the research methodology and the results of the biometric analysis. Based on the results, a two-level classification framework was proposed, which includes 10 primary categories and 24 subcategories. Each of the distinguished categories was characterized based on the analyzed publications. It should be noted that in most of these studies, the authors' attention primarily focused on the implementation of (1) Industry 4.0 technological solutions, such as IoT, augmented reality, RFID, visual technology, and other emerging technologies; and (2) autonomous and automated vehicles in warehouse operations processes. Critical analysis of the literature also allowed us to identify the current research gaps, which will be the subject of further research by the authors.

摘要

工业 4.0(I4.0)的发展以及制造过程的数字化和自动化,对设计智能仓库以支持制造过程提出了需求。仓储是供应链的基本流程之一,负责处理库存。仓库作业的高效执行通常决定了实现货物流动的效率。因此,数字化及其在合作伙伴之间交换信息(尤其是实时库存水平)中的应用至关重要。出于这个原因,工业 4.0 的数字解决方案已迅速在内部物流流程中得到应用,并实现了智能仓库的设计,也称为仓库 4.0。本文的目的是展示使用工业 4.0 概念对仓库设计和运营进行的文献综述的结果。从过去 5 年中总共接受了 249 篇文献进行分析。使用 PRISMA 方法在 Web of Science 数据库中搜索出版物。本文详细介绍了研究方法和生物计量分析的结果。基于结果,提出了一个两级分类框架,包括 10 个主要类别和 24 个子类。根据分析的出版物对每个区分的类别进行了描述。值得注意的是,在这些研究中,大多数研究的重点主要是实施(1)工业 4.0 技术解决方案,例如物联网、增强现实、RFID、视觉技术和其他新兴技术;以及(2)仓库作业过程中的自主和自动化车辆。对文献的批判性分析还使我们能够确定当前的研究差距,这将是作者进一步研究的主题。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/70f9/10146052/2e72761fabb7/sensors-23-04105-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/70f9/10146052/797353b20c67/sensors-23-04105-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/70f9/10146052/2edacbcf0b1e/sensors-23-04105-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/70f9/10146052/b0db28a0d611/sensors-23-04105-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/70f9/10146052/a09bfcaa6e48/sensors-23-04105-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/70f9/10146052/8d344033a6ff/sensors-23-04105-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/70f9/10146052/4ee6950270fc/sensors-23-04105-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/70f9/10146052/2e72761fabb7/sensors-23-04105-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/70f9/10146052/797353b20c67/sensors-23-04105-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/70f9/10146052/2edacbcf0b1e/sensors-23-04105-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/70f9/10146052/b0db28a0d611/sensors-23-04105-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/70f9/10146052/a09bfcaa6e48/sensors-23-04105-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/70f9/10146052/8d344033a6ff/sensors-23-04105-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/70f9/10146052/4ee6950270fc/sensors-23-04105-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/70f9/10146052/2e72761fabb7/sensors-23-04105-g007.jpg

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[6]
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本文引用的文献

[1]
Maintenance Performance in the Age of Industry 4.0: A Bibliometric Performance Analysis and a Systematic Literature Review.

Sensors (Basel). 2023-1-27

[2]
Driver injuries in heavy vs. light and medium truck local crashes, 2010-2019.

J Safety Res. 2022-12

[3]
A robot localization proposal for the RobotAtFactory 4.0: A novel robotics competition within the Industry 4.0 concept.

Front Robot AI. 2022-11-15

[4]
A heterogeneous multi-modal medical data fusion framework supporting hybrid data exploration.

Health Inf Sci Syst. 2022-8-26

[5]
UWB indoor positioning optimization algorithm based on genetic annealing and clustering analysis.

Front Neurorobot. 2022-7-26

[6]
Research on AGV trackless guidance technology based on the global vision.

Sci Prog. 2022

[7]
Intelligent monitoring method of tridimensional storage system based on deep learning.

Environ Sci Pollut Res Int. 2022-10

[8]
Research on Intelligent Warehousing and Logistics Management System of Electronic Market Based on Machine Learning.

Comput Intell Neurosci. 2022

[9]
Development and testing of an explorative BPM acceptance model: Insights from the COVID-19 pandemic.

PLoS One. 2021

[10]
Internet of things for perishable inventory management systems: an application and managerial insights for micro, small and medium enterprises.

Ann Oper Res. 2021-10-6

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