Next Article in Journal
Integration of Gasification and Solid Oxide Fuel Cells (SOFCs) for Combined Heat and Power (CHP)
Next Article in Special Issue
Real-World Failure Prevention Framework for Manufacturing Facilities Using Text Data
Previous Article in Journal
Special Issue on “Process Modeling in Pyrometallurgical Engineering”
Previous Article in Special Issue
Integrating FMEA and the Kano Model to Improve the Service Quality of Logistics Centers
Article

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

1
Department of Industrial & Management Engineering, Hanyang University, Ansan 15588, Korea
2
Department of Industrial Engineering, Yonsei University, 50 Yonsei-ro, Sinchon-dong, Seodaemun-gu, Seoul 03722, Korea
*
Author to whom correspondence should be addressed.
Academic Editor: Zhiwei Gao
Processes 2021, 9(2), 247; https://doi.org/10.3390/pr9020247
Received: 29 December 2020 / Revised: 21 January 2021 / Accepted: 26 January 2021 / Published: 29 January 2021
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. View Full-Text
Keywords: smart supply chain management; machine learning; environment; unreliability; radio frequency identification smart supply chain management; machine learning; environment; unreliability; radio frequency identification
Show Figures

Figure 1

MDPI and ACS Style

Sardar, S.K.; Sarkar, B.; Kim, B. Integrating Machine Learning, Radio Frequency Identification, and Consignment Policy for Reducing Unreliability in Smart Supply Chain Management. Processes 2021, 9, 247. https://doi.org/10.3390/pr9020247

AMA Style

Sardar SK, Sarkar B, Kim B. Integrating Machine Learning, Radio Frequency Identification, and Consignment Policy for Reducing Unreliability in Smart Supply Chain Management. Processes. 2021; 9(2):247. https://doi.org/10.3390/pr9020247

Chicago/Turabian Style

Sardar, Suman K., Biswajit Sarkar, and Byunghoon Kim. 2021. "Integrating Machine Learning, Radio Frequency Identification, and Consignment Policy for Reducing Unreliability in Smart Supply Chain Management" Processes 9, no. 2: 247. https://doi.org/10.3390/pr9020247

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop