Clustering-based Sales Forecasting in a Forklift Distributor

Year 2019, Volume: 11 Issue: 1, 25 - 40, 31.01.2019

Pratiwi Eka Puspita Tülin İnkaya , Mehmet Akansel

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

Cited By: 3

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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