###### 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. L 20221 17 February  
Smart Manufacturing is a concept and technology for achieving enterprise-wide optimization by connecting both industrial devices and production processes to a network through the convergence of ICT and manufacturing industries. This provides process optimization, efficiency, and flexibility in production facilities for problems such as production, process control and repair of existing factories, and workplace safety. With these advances in technology, mass customization to satisfy the detailed requirements of various customers basically requires on-demand factory technology. Mass customization refers to new production and marketing methods that lower costs through mass production of customized products and services, and requires multiple capabilities, but the most important factor is managing product processes and supply chains to respond to changes in customer needs. The development of machine learning has had a major effect across many industries. Among them, Smart Manufacturing has an environment where various machine-learning solutions such as a massive amount of data are gathered by means of various sensors and investment is concentrated. In fact, various solutions have already been developed and applied to energy efficiency [1; 2; 3; 4; 5], equipment-condition monitoring [6; 7; 8; 9; 10; 11], and product-defect detection [12; 13; 14] in Smart Manufacturing construction. In particular, the application of machine-learning algorithms to inventory management can help to effectively reduce costs for many companies. Until now, we have been predicting market demand based on human intuition or on poorly performing data-prediction techniques. On the other hand, by means of machine learning, it is possible to predict future outcomes effectively by using accumulated data. In fact, research is currently under way to predict market demand. In the area where data collection such as the electric-power market is organized, various demand-forecasting systems based on machine learning have already been introduced to the business solution system. For SMEs that small produce a large variety of products, a systematic inventory-management system is needed to minimize losses caused by inventories and provide a smooth supply of products. However, most SMEs do not have systematic inventory management, and many companies manage inventories based on handwritten inventories created by judging orders for products by intuition.