Conclusion - Graduate School of the University of Ulsan

The NARX developed in this study resulted in a promising result since it employed near optimal parameters including number of input-output delays, node numbers, and parsimonious variables. The parsimonious variables showed the positive effect in boosting the network's learning process. Meanwhile, since there is no specific method to identify an exact optimal number of nodes for the network, the procedure used in study may be applied in determining a satisfying node number.

The model’s improvement reflects a better performance for the company’s production.

Approaching higher accuracy in forecasting the actual demand placed by the main contractor of the automotive industry aims to relieve uncertainty in managing inventory. Efficient inventory management mainly affects the dilemma of profitable supply chain management which is whether to keep low inventory level in order to keep the cost under control or high inventory level in order to assure the availability of products for actual demand. The supplier tends to eliminate waste and unnecessary processes to minimize safety stock which costs in maintenance and expiration of the individual product, while the high inventory level can guarantee to satisfy the order. So, the significant loss will be used to address the decision by stabilizing the inventory level. Furthermore, the forecasting also impacts the dilemma of the supply chain system in positioning factories and warehouses. An optimal distance between the first-tier supplier’s upstream suppliers and the main contractor can be confidently considered with a known demand of individual raw material. The precise distance strongly contributes to a profitable production planning by providing particular capacity preparation for human resource management and commodity for product categorizing in procurement.

Since the model was built based on a specific dataset in the certain scenario of a first- tier supplier, adapting this study result in other companies, parameters will be required to alter based on the various complexity and characteristics of the respective dataset. As the availability of data depends on shared information between the company and partner, different methods will be applied. For further study, other approaches such as deep learning and hybrid models can be applied to study alternative insight and performance.

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