Artificial Intelligence Approach to Business Process Re-Engineering the Information Flow of Warehouse Shipping Orders: An Italian Case Study

Artificial intelligence is revolutionizing and significantly shaping the work environment. It not only presents an innovative and unique challenge, but it also creates numerous opportunities in various sectors. This study aims to examine a case of artificial intelligence use in the logistics sector,...

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Bibliographic Details
Published in:Applied sciences 2024-11, Vol.14 (21), p.9894
Main Authors: Merli, Matteo, Ciarapica, Filippo Emanuele, Varghese, Kishore Chalakkal, Bevilacqua, Maurizio
Format: Article
Language:English
Subjects:
Algorithms
Artificial intelligence
Automation
Business operations
Case studies
Competitive advantage
Decision making
Efficiency
Image retrieval
Information management
Literature reviews
logistic
Logistics
Logistics services
shipping notes
Supply chain management
text recognition algorithms
warehouse
Warehouses
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Summary:Artificial intelligence is revolutionizing and significantly shaping the work environment. It not only presents an innovative and unique challenge, but it also creates numerous opportunities in various sectors. This study aims to examine a case of artificial intelligence use in the logistics sector, specifically the implementation of text recognition algorithms that use advanced artificial intelligence technology to perform tasks and activities typically carried out by human operators. The implementation under analysis was carried out at an Italian company in Tolentino, in the province of Macerata. The implementation of this tool focuses on the ability to autonomously read shipping notes, with the aim of simplifying the flow of information in the goods receipt phase and reducing the workload of warehouse staff. As illustrated in the following chapters, when artificial intelligence follows several trainings, technical improvements and structural changes in shipping notes, the flow is greatly simplified, significantly facilitating this logistics phase.
ISSN:2076-3417
2076-3417
DOI:10.3390/app14219894