Data mining applications in a forklift distributor

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Tezin Türü: Yüksek Lisans

Tezin Yürütüldüğü Kurum: Türkiye

Tezin Onay Tarihi: 2018

Tezin Dili: İngilizce

Öğrenci: PRATIWI EKA PUSPITA

Danışman: Tülin İnkaya

Özet:

Sales forecasting has a vital role in today's business environment. In a company, accurate and reliable sales forecasting is the fundamental basis for production planning processes. In this study, a data mining-based forecasting methodology is proposed for a forklift distributor. Monthly sales data for 100 different types of forklifts between years 1998 and 2016 are used. The proposed methodology has three stages. In the first stage, items with similar sales patterns are identified using hierarchical clustering. Dynamic time warping (DTW) is used for measuring the similarities among the items. The number of clusters is determined using the heterogeneity and homogeneity criteria. For each cluster, cluster prototypes are found based on cluster medoids and DTW barycenter averaging (DBA) method. In the second stage, features are extracted. In addition to the features that characterize amount, trend, growth, and volatility, new features are proposed to identify the intermittency in the data. Also, the important features are selected using multivariate adaptive regression splines (MARS). Then, support vector regression (SVR) is used as a forecasting model for each cluster prototype. In the final stage, the proposed approach is evaluated according to inventory performance. The numerical analysis shows that the proposed methodology forecasts the sales with reasonable accuracy and low complexity, and provides a reduction in inventory management costs.