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Integrating Machine Learning, Radio Frequency Identification, and Consignment Policy for Reducing Unreliability in Smart Supply Chain Management

Title
Integrating Machine Learning, Radio Frequency Identification, and Consignment Policy for Reducing Unreliability in Smart Supply Chain Management
Author
김병훈
Keywords
smart supply chain management; machine learning; environment; unreliability; radio frequency identification
Issue Date
2021-01
Publisher
MDPI
Citation
PROCESSES, v. 9, Issue. 2, Article no. 247, 16pp
Abstract
Adopting smart technologies for supply chain management leads to higher profits. The manufacturer and retailer are two supply chain players, where the retailer is unreliable and may not send accurate demand information to the manufacturer. As an advanced smart technology, Radio Frequency Identification (RFID) is implemented to track and trace each product’s movement on a real-time basis in the inventory. It takes this supply chain to a smart supply chain management. This research proposes a Machine Learning (ML) approach for on-demand forecasting under smart supply chain management. Using Long-Short-Term Memory (LSTM), the demand is forecasted to obtain the exact demand information to reduce the overstock or understock situation. A measurement for the environmental effect is also incorporated with the model. A consignment policy is applied where the manufacturer controls the inventory, and the retailer gets a fixed fee along with a commission for selling each product. The manufacturer installs RFID technology at the retailer’s place. Two mathematical models are solved using a classical optimization technique. The results from those two models show that the ML-RFID model gives a higher profit than the existing traditional system.
URI
https://www.mdpi.com/2227-9717/9/2/247https://repository.hanyang.ac.kr/handle/20.500.11754/167021
ISSN
2227-9717
DOI
10.3390/pr9020247
Appears in Collections:
COLLEGE OF ENGINEERING SCIENCES[E](공학대학) > INDUSTRIAL AND MANAGEMENT ENGINEERING(산업경영공학과) > Articles
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