Clustering-based Sales Forecasting in a Forklift Distributor

Pratiwi Eka Puspita; Tülin İnkaya; Mehmet Akansel

doi:10.29137/umagd.473977

EN

Clustering-based Sales Forecasting in a Forklift Distributor

Abstract

Sales forecasting refers to the prediction of future demand based on past data. A vast literature on sales forecasting has accumulated due to its vital role in balancing demand and supply. Among these, data mining has emerged as a powerful tool to facilitate sales forecasting. In this study, we use data mining methods for accurate and reliable sales forecasts in a forklift distributor company. Monthly sales data for 100 different types of forklifts between 1998 and 2016 are used. The proposed forecasting methodology includes three steps. First, products with similar sales patterns are determined using hierarchical clustering. Dynamic time warping is applied to calculate the similarities among product sales data. Second, features are extracted and selected for each cluster. In addition to the features adopted from the literature, four new features are proposed to characterize intermittency. Multivariate adaptive regression splines model is used for feature selection. Third, support vector regression is used to predict future sales of each product cluster. Finally, the performance of the proposed approach is evaluated according to forecasting error and complexity. The numerical analysis shows that the proposed approach gives reasonable accuracy with less complexity.

Keywords

Data mining,clustering,forecasting,multivariate adaptive regression splines (MARS),dynamic time warping (DTW),support vector regression (SVR)

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Details

Primary Language

English

Subjects

-

Journal Section

Research Article

Authors

Pratiwi Eka Puspita This is me

Tülin İnkaya

Mehmet Akansel This is me

Publication Date

January 31, 2019

Submission Date

June 7, 2018

Acceptance Date

August 27, 2018

Published in Issue

Year 2019 Volume: 11 Number: 1

DOI

https://doi.org/10.29137/umagd.473977

IZ

https://izlik.org/JA44AY32YJ

APA

Puspita, P. E., İnkaya, T., & Akansel, M. (2019). Clustering-based Sales Forecasting in a Forklift Distributor. International Journal of Engineering Research and Development, 11(1), 25-40. https://doi.org/10.29137/umagd.473977

AMA

1.Puspita PE, İnkaya T, Akansel M. Clustering-based Sales Forecasting in a Forklift Distributor. IJERAD. 2019;11(1):25-40. doi:10.29137/umagd.473977

Chicago

Puspita, Pratiwi Eka, Tülin İnkaya, and Mehmet Akansel. 2019. “Clustering-Based Sales Forecasting in a Forklift Distributor”. International Journal of Engineering Research and Development 11 (1): 25-40. https://doi.org/10.29137/umagd.473977.

EndNote

Puspita PE, İnkaya T, Akansel M (January 1, 2019) Clustering-based Sales Forecasting in a Forklift Distributor. International Journal of Engineering Research and Development 11 1 25–40.

IEEE

[1]P. E. Puspita, T. İnkaya, and M. Akansel, “Clustering-based Sales Forecasting in a Forklift Distributor”, IJERAD, vol. 11, no. 1, pp. 25–40, Jan. 2019, doi: 10.29137/umagd.473977.

ISNAD

Puspita, Pratiwi Eka - İnkaya, Tülin - Akansel, Mehmet. “Clustering-Based Sales Forecasting in a Forklift Distributor”. International Journal of Engineering Research and Development 11/1 (January 1, 2019): 25-40. https://doi.org/10.29137/umagd.473977.

JAMA

1.Puspita PE, İnkaya T, Akansel M. Clustering-based Sales Forecasting in a Forklift Distributor. IJERAD. 2019;11:25–40.

MLA

Puspita, Pratiwi Eka, et al. “Clustering-Based Sales Forecasting in a Forklift Distributor”. International Journal of Engineering Research and Development, vol. 11, no. 1, Jan. 2019, pp. 25-40, doi:10.29137/umagd.473977.

Vancouver

1.Pratiwi Eka Puspita, Tülin İnkaya, Mehmet Akansel. Clustering-based Sales Forecasting in a Forklift Distributor. IJERAD. 2019 Jan. 1;11(1):25-40. doi:10.29137/umagd.473977

Kırıkkale University, Faculty of Engineering and Natural Science, 71450 Yahşihan / Kırıkkale, Türkiye.

ijerad@kku.edu.tr

Clustering-based Sales Forecasting in a Forklift Distributor

Clustering-based Sales Forecasting in a Forklift Distributor

Abstract

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Published in Issue

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