Incorporating Machine Learning In RFID Inventory Management System To Optimize Logistics Operations

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dc.contributor.advisorSipos, Csanád
dc.contributor.authorWehbe, Mahmoud
dc.contributor.departmentDE--Műszaki Kar
dc.date.accessioned2024-06-19T10:13:04Z
dc.date.available2024-06-19T10:13:04Z
dc.date.created2024-05-21
dc.description.abstractThis thesis demonstrates a potential incorporation of Machine Learning (ML) and Radio Frequency Identification (RFID) into logistics and inventory management. Moreover, it demonstrates the capacity of these technologies to enhance precision and efficiency. The term "RFID" succinctly denotes the technology under consideration. Technological improvements such as these facilitate the reduction of errors, optimization of inventory levels, and improvement of decision-making via the provision of real-time monitoring and predictive capabilities. Multiple firms have undertaken case studies demonstrating increased transparency, reduced costs, and enhanced customer satisfaction. Among the corporations mentioned are Amazon and Walmart. The significant return on investment and long-term benefits are substantial, but, there are specific challenges to address, such as the intricacy of system integration and the substantial initial costs. In addition, the thesis proposes that blockchain and the Internet of Things (IoT) might be among the next technical developments.
dc.description.correctorLB
dc.description.courseEngineering Management
dc.description.degreeMSc/MA
dc.format.extent63
dc.identifier.urihttps://hdl.handle.net/2437/373815
dc.language.isoen
dc.rights.accessHozzáférhető a 2022 decemberi felsőoktatási törvénymódosítás értelmében.
dc.subjectLogistics
dc.subjectRFID
dc.subjectMachine learning
dc.subjectAI
dc.subject.dspaceEngineering Sciences::Engineering
dc.titleIncorporating Machine Learning In RFID Inventory Management System To Optimize Logistics Operations
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