Open AccessForecasting new product demand using machine learning
Author(s) -
P. S. Smirnov,
Vladimir Anatolievich Sudakov
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1925/1/012033
Subject(s) - computer science , demand forecasting , implementation , product (mathematics) , gradient boosting , operations research , boosting (machine learning) , order (exchange) , industrial engineering , artificial intelligence , economics , engineering , mathematics , geometry , finance , random forest , programming language
The problem of predicting demand for a new product based on its characteristics and description is critical for various industrial enterprises, wholesale and retail trade and, especially, for modern highly competitive sector of air transportation, since solving this problem will optimize production, management and logistics in order to maximize profits and minimize costs. Classic demand forecasting methods assume the availability of sales data for a certain historical period, which is obviously not the case when concerning a new product. Most research papers are limited either to a specific category of goods or use sophisticated marketing methods. This paper proposes the use of machine learning methods. We used data about new product demand from the Ozon online store. The input data of the algorithm are characteristics such as the price, name, category and text description of the product. To solve the regression problem, various implementations of the gradient boosting algorithm were used, such as XGBoost, Light GBM, Cat Boost. The forecast accuracy is now about 4.00. The proposed system can be used both independently and as part of another more complex system.