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A hybrid method for forecasting new product sales based on fuzzy clustering and deep learning

Peng Yin (School of Management, University of Science and Technology of China, Hefei, China)
Guowei Dou (Institute of Big Data Intelligent Management and Decision, College of Management, Shenzhen University, Shenzhen, China)
Xudong Lin (Institute of Big Data Intelligent Management and Decision, College of Management, Shenzhen University, Shenzhen, China)
Liangliang Liu (School of Public Finance and Taxation, Nanjing University of Finance and Economics, Nanjing, China)

Kybernetes

ISSN: 0368-492X

Article publication date: 4 February 2020

Issue publication date: 13 November 2020

Abstract

Purpose

The purpose of this paper is to solve the problem of low accuracy in new product demand forecasting caused by the absence of historical data and inadequate consideration of influencing factors.

Design/methodology/approach

A hybrid new product demand forecasting model combining clustering analysis and deep learning is proposed. Based on the product similarity measurement, the weight of product similarity attributes is realized by using the method of fuzzy clustering-rough set, which provides a basis for the acquisition and collation of historical sales data of similar products and the determination of product similarity. Then the prediction error of Bass model is adjusted based on similarity through a long short-term memory neural network model, where the influencing factors such as product differentiation, seasonality and sales time on demand forecasting are embedded. An empirical example is given to verify the validity and feasibility of the model.

Findings

The results emphasize the importance of considering short-term impacts when forecasting new product demand. The authors show that useful information can be mined from similar products in demand forecasting, where the seasonality, product selling cycles and sales dependencies have significant impacts on the new product demand. In addition, they find that even in the peak season of demand, if the selling period has nearly passed the growth cycle, the Bass model may overestimate the product demand, which may mislead the operational decisions if it is ignored.

Originality/value

This study is valuable for showing that with the incorporation of the evaluation method on product similarity, the forecasting model proposed in this paper achieves a higher accuracy in forecasting new product sales.

Keywords

Acknowledgements

The authors are grateful to the editor and the anonymous referees for their constructive comments, which substantially helped the authors improve the quality of the manuscript. The authors gratefully acknowledge the financial support from the National Natural Science Foundation of China [Grant Nos. 71701135, 71991461, 91846301], Natural Science Foundation of Guangdong Province [Grant No. 2017A030310246] and Guangdong Province Soft Science Research Project [Grant No.2019A101002074].

Citation

Yin, P., Dou, G., Lin, X. and Liu, L. (2020), "A hybrid method for forecasting new product sales based on fuzzy clustering and deep learning", Kybernetes, Vol. 49 No. 12, pp. 3099-3118. https://doi.org/10.1108/K-10-2019-0688

Publisher

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Emerald Publishing Limited

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