Abstract
Over the last decade, the development of machine-learning models has enabled the design of sophisticated regression models. For this reason, studies have been conducted to design predictive models using machine learning in various industries. In particular, in terms of inventory management, forecasting models predict historical market demand, predict future demand, and enable systematic inventory management. However, in most small and medium enterprise (SMEs), there is no systematic management of data, and because of the lack of data and the volatility of random data, it is difficult for prediction models to work well. Since the predictive model is a core function derived from the management of the enterprise’s inventory data, the poor performance of the model causes the company’s inventory data-management system to be degraded. Companies that have poor inventory data because of this vicious cycle will continue to have difficulty introducing data-management systems. In this paper, we propose a framework that can reliably predict the inventory data of a firm by modeling the volatility of a firm stochastically. The framework makes the prediction using the point prediction model by means of LSTM(Long Short Term Memory), the 2D kernel density function, and the prediction result reflecting inventory-management cost. Through various experiments, the necessity of interval prediction in demand prediction and the validity of the cost-effective prediction model through the readjustment function were shown.
Highlights
Since the global financial crisis, the global manufacturing industry has faced growth limitations due to the long-term economic recession and rising labor and raw material costs
We propose an efficient demand-forecasting algorithm that reflects the characteristics of small and medium enterprise (SMEs)
In terms of forecasting the volume of stock, the characteristics of SMEs are that they do not have a lot of data, because they have no systematic management, and the random variability is very large
Summary
Since the global financial crisis, the global manufacturing industry has faced growth limitations due to the long-term economic recession and rising labor and raw material costs. A prediction system that can indicate the reliability of the data together with the output of the predicted results by means of machine learning is needed for the SMEs. In this paper, we first design a demand-prediction model based on LSTM (Long-Short-Term Memory). In order to predict the demand of fashion companies, the development of algorithms from a data-driven perspective was studied by using machine learning techniques and identifying important predictors. Many of these studies show that demand forecasting is very difficult. Ref. [32] conducted a study on smart metering customers in general homes, and deep-RNN surpassed ARIMA 19.5%, SVR 13.1%, and general RNN 6.5%, in terms of RMSE, compared to the latest techniques of predicting loads in the home
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