LAPSE:2022.0102
Published Article
LAPSE:2022.0102
Integrating Machine Learning, Radio Frequency Identification, and Consignment Policy for Reducing Unreliability in Smart Supply Chain Management
October 25, 2022
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.
Keywords
environment, Machine Learning, radio frequency identification, smart supply chain management, unreliability
Subject
Suggested Citation
Sardar SK, Sarkar B, Kim B. Integrating Machine Learning, Radio Frequency Identification, and Consignment Policy for Reducing Unreliability in Smart Supply Chain Management. (2022). LAPSE:2022.0102
Author Affiliations
Sardar SK: Department of Industrial & Management Engineering, Hanyang University, Ansan 15588, Korea [ORCID]
Sarkar B: Department of Industrial Engineering, Yonsei University, 50 Yonsei-ro, Sinchon-dong, Seodaemun-gu, Seoul 03722, Korea [ORCID]
Kim B: Department of Industrial & Management Engineering, Hanyang University, Ansan 15588, Korea [ORCID]
Journal Name
Processes
Volume
9
Issue
2
First Page
247
Year
2021
Publication Date
2021-01-29
Published Version
ISSN
2227-9717
Version Comments
Original Submission
Other Meta
PII: pr9020247, Publication Type: Journal Article
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LAPSE:2022.0102
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doi:10.3390/pr9020247
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Oct 25, 2022
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License
CC BY 4.0
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Version History
[v1] (Original Submission)
Oct 25, 2022
 
Verified by curator on
Oct 25, 2022
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v1
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https://psecommunity.org/LAPSE:2022.0102
 
Original Submitter
Mina Naeini
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