Abstract:
Based on the data given by PT. X, there are 34,800 Kg product or material SVN
stored in the warehouse which categorized as expired. The issue was coming from
a bad forecast since the company did not apply standard method to execute their
demand. However, after the proposed improvement was simulated by comparing
2 methods, there are improvement in the error rate.
Backpropagation Neural Network become the best method that
suitable with the pattern of SVN demand. It can be proven by the
result of Mean Absolute Percentage Error (MAPE) and Mean
Squared Error (MSE). The MAPE value of Backpropagation
Neural Network (BPNN) with structure 2-20-1 is 17.52% with
1,613,248.51 of MSE. However, Double Moving Average Method
did not show a significant improvement for forecasting the demand
since the MAPE is high which yielded at 149.1% following with
18,079,648.70 of MSE.
The best forecast method that can be implemented to forecast the
demand of material SVN at PT. X is by using Backpropagation
Neural Network with structure 2-20-1.
By implementing the BPNN method, there is a chance to reducing
the total expired stock value about 50%.
By this evidence, it is crystal clear that if the company implementing the proposed
methods, the benefits would be gathered from the level of forecasting accuracy
which resulting into expired stock reduction.
