Integrating Machine Learning, Radio Frequency Identification, and Consignment Policy for Reducing Unreliability in Smart Supply Chain Management
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 Identificat...
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doaj-52d03be2e7684fcc837bbf2813323ac72021-01-30T00:01:56ZengMDPI AGProcesses2227-97172021-01-01924724710.3390/pr9020247Integrating Machine Learning, Radio Frequency Identification, and Consignment Policy for Reducing Unreliability in Smart Supply Chain ManagementSuman Kalyan Sardar0Biswajit Sarkar1Byunghoon Kim2Department of Industrial & Management Engineering, Hanyang University, Ansan 15588, KoreaDepartment of Industrial Engineering, Yonsei University, 50 Yonsei-ro, Sinchon-dong, Seodaemun-gu, Seoul 03722, KoreaDepartment of Industrial & Management Engineering, Hanyang University, Ansan 15588, KoreaAdopting 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.https://www.mdpi.com/2227-9717/9/2/247smart supply chain managementmachine learningenvironmentunreliabilityradio frequency identification |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Suman Kalyan Sardar Biswajit Sarkar Byunghoon Kim |
spellingShingle |
Suman Kalyan Sardar Biswajit Sarkar Byunghoon Kim Integrating Machine Learning, Radio Frequency Identification, and Consignment Policy for Reducing Unreliability in Smart Supply Chain Management Processes smart supply chain management machine learning environment unreliability radio frequency identification |
author_facet |
Suman Kalyan Sardar Biswajit Sarkar Byunghoon Kim |
author_sort |
Suman Kalyan Sardar |
title |
Integrating Machine Learning, Radio Frequency Identification, and Consignment Policy for Reducing Unreliability in Smart Supply Chain Management |
title_short |
Integrating Machine Learning, Radio Frequency Identification, and Consignment Policy for Reducing Unreliability in Smart Supply Chain Management |
title_full |
Integrating Machine Learning, Radio Frequency Identification, and Consignment Policy for Reducing Unreliability in Smart Supply Chain Management |
title_fullStr |
Integrating Machine Learning, Radio Frequency Identification, and Consignment Policy for Reducing Unreliability in Smart Supply Chain Management |
title_full_unstemmed |
Integrating Machine Learning, Radio Frequency Identification, and Consignment Policy for Reducing Unreliability in Smart Supply Chain Management |
title_sort |
integrating machine learning, radio frequency identification, and consignment policy for reducing unreliability in smart supply chain management |
publisher |
MDPI AG |
series |
Processes |
issn |
2227-9717 |
publishDate |
2021-01-01 |
description |
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. |
topic |
smart supply chain management machine learning environment unreliability radio frequency identification |
url |
https://www.mdpi.com/2227-9717/9/2/247 |
work_keys_str_mv |
AT sumankalyansardar integratingmachinelearningradiofrequencyidentificationandconsignmentpolicyforreducingunreliabilityinsmartsupplychainmanagement AT biswajitsarkar integratingmachinelearningradiofrequencyidentificationandconsignmentpolicyforreducingunreliabilityinsmartsupplychainmanagement AT byunghoonkim integratingmachinelearningradiofrequencyidentificationandconsignmentpolicyforreducingunreliabilityinsmartsupplychainmanagement |
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