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.
Highlights
Received: 29 December 2020In supply chain management (SCM), each manufacturer observes customer demand to decide production quantity
The forecasted demand is incorporated with the mathematical model and the total profit with the Machine Learning (ML)-Radio Frequency Identification (RFID) model is observed to be higher than it is with the traditional system
The proposed ML-RFID model provided a higher profit than the traditional model by reducing the unreliability
Summary
In supply chain management (SCM), each manufacturer observes customer demand to decide production quantity. Using smart technology, such as RFID, gives an extra advantage for getting real-time information about the products in the inventory Incorporating these features takes supply chain management to an advanced level as compared to traditional systems. By implementing a service-level restriction in this supply chain model, the product quality is improved by optimizing the model through a distribution-free approach ([6]) This improved service strategy for the consignment stock is applied in this research to extend the traditional model to gives a higher profit. As the demand is forecasted using LSTM, the manufacturer produces goods based on the predicted future demand ([16]) In this case, the holding cost or the shortage cost is minimized, and the total profit is much higher than the existing traditional model.
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