The Application of Artificial Neural Network (ANN) with Backpropagation Algorithm to Predict Supply Chain Demand
محورهای موضوعی : Statistical methods in industrial engineeringRizki Agam Syahputra 1 , Kasmawati Kasmawati 2 , Prima Denny Sentia 3
1 - Departmen of Industrial Engineering, Faculty of Engineering, University of Teuku Umar, Meulaboh, Indonesia
2 - Departmen of Industrial Engineering, Faculty of Engineering, University of Teuku Umar, Meulaboh, Indonesia
3 - Center for Artificial Intelligence Technology, Universiti Kebangsaan Malaysia, Bangi, Selangor, Malaysia
کلید واژه: Artificial Neural Network, Supply Chaim Management, Machine Learning,
چکیده مقاله :
Managing a proper supply chain plays an important role in determining competitiveness within companies, hence many companies adopt forecasting methods to understand the expectation of the customer. In this study, Artificial Neural Network (ANN) model with a backpropagation algorithm is developed and applied to predict the supply chain demand in one of the steel tower production in Aceh, Indonesia. As a statistical method, The ANN is utilized by classifying data into the data training and data testing sequence. Both data are evaluated with the Mean Square Error (MSE) together with the correlation coefficient (R) measurement and Mean Absolute Percentage Error (MAPE) for the data testing process to ensure the credibility of the model. Based on the result, this study found that the ANN model predicts that the total demand reaching to 4,226 for the next 12 production periods. The ANN model showed optimal prediction performance with MSE and R values of 0.000985 and 0.99393, for the training data respectively. The forecast result shows the highest demand in period 11 with 491 units, and the lowest demand is predicted to happen in period 1 with only 183 predicted demands. Therefore based on the result of ANN prediction, the ANN can be used as an effective method to predict future demand in the future.
Managing a proper supply chain plays an important role in determining competitiveness within companies, hence many companies adopt forecasting methods to understand the expectation of the customer. In this study, Artificial Neural Network (ANN) model with a backpropagation algorithm is developed and applied to predict the supply chain demand in one of the steel tower production in Aceh, Indonesia. As a statistical method, The ANN is utilized by classifying data into the data training and data testing sequence. Both data are evaluated with the Mean Square Error (MSE) together with the correlation coefficient (R) measurement and Mean Absolute Percentage Error (MAPE) for the data testing process to ensure the credibility of the model. Based on the result, this study found that the ANN model predicts that the total demand reaching to 4,226 for the next 12 production periods. The ANN model showed optimal prediction performance with MSE and R values of 0.000985 and 0.99393, for the training data respectively. The forecast result shows the highest demand in period 11 with 491 units, and the lowest demand is predicted to happen in period 1 with only 183 predicted demands. Therefore based on the result of ANN prediction, the ANN can be used as an effective method to predict future demand in the future.
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