| dc.description.abstract | For companies assembling products in general and companies specializing in high-tech
electrical equipment lines in particular, ensuring that demand is forecasted accurately,
and the assembly lines always operate smoothly is a prerequisite factor affecting the
ability of responsiveness to their customers. The timely supply of necessary components
to assemble each product line is also an indispensable daily responsibility. This study
proposed an efficient combination of the Demand Forecasting (DF) model and the
Capacitated Lot-sizing Problem (CLSP) under time – varying demand in the case of
Schneider Electric Vietnam Co., Ltd (SEV). In the first stage, demand prediction was
implemented based on the Long Short-term Memory (LSTM) method and Support
Vector Machine (SVM) algorithm with the primary input data of historical sales data
for a sample SKU. Next, the results of this stage will be validated to select the better
method and obtain the final predicted demand. In the second stage, in addition to
demand forecasting, the input data is supplemented with a Bill of Materials (BOM),
inventory status data, ordering cost, holding cost, supply capacities, and minimum order
quantity. The Exact method was utilized to solve the lot-sizing problem in terms of
limited supply by using CPLEX software. The results of the research indicate that the
performance of LSTM is superior to that of SVM no matter what metrics are represented
in this case. Finally, the outputs obtained are optimal lot size, minimum total cost, and
time to order each component to conduct an effective assembly. | en_US |
