LSTM 및 Conv1D-LSTM을 사용한 공급 사슬의 티어 예측
Prediction of Tier in Supply Chain Using LSTM and Conv1D-LSTM
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Supply chain managers seek to achieve global optimization by solving problems in the supply chain's business process. However, companies in the supply chain hide the adverse information and inform only the beneficial information, so the information is distorted and cannot be the information that describes the entire supply chain. In this case, supply chain managers can directly collect and analyze supply chain activity data to find and manage the companies described by the data. Therefore, this study proposes a method to collect the order-inventory information from each company in the supply chain and detect the companies whose data characteristics are explained through deep learning. The supply chain consists of Manufacturer, Distributor, Wholesaler, Retailer, and training and testing data uses 600 weeks of time series inventory information. The purpose of the experiment is to improve the detection accuracy by adjusting the parameter values of the deep learning network, and the parameters for comparison are set by learning rate (lr = 0.001, 0.01, 0.1) and batch size (bs = 1, 5). Experimental results show that the detection accuracy is improved by adjusting the values of the parameters, but the values of the parameters depend on data and model characteristics.
Abstract
Supply chain managers seek to achieve global optimization by solving problems in the supply chain's business process. However, companies in the supply chain hide the adverse information and inform only the beneficial information, so the information is distorted and cannot be the information that describes the entire supply chain. In this case, supply chain managers can directly collect and analyze supply chain activity data to find and manage the companies described by the data. Therefore, this study proposes a method to collect the order-inventory information from each company in the supply chain and detect the companies whose data characteristics are explained through deep learning. The supply chain consists of Manufacturer, Distributor, Wholesaler, Retailer, and training and testing data uses 600 weeks of time series inventory information. The purpose of the experiment is to improve the detection accuracy by adjusting the parameter values of the deep learning network, and the parameters for comparison are set by learning rate (lr = 0.001, 0.01, 0.1) and batch size (bs = 1, 5). Experimental results show that the detection accuracy is improved by adjusting the values of the parameters, but the values of the parameters depend on data and model characteristics.
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