A retail demand forecasting model based on data mining techniques

Irem Islek; Sule Gunduz Oguducu

doi:10.1109/ISIE.2015.7281443

A retail demand forecasting model based on data mining techniques

Irem Islek, Sule Gunduz Oguducu

Idea Teknoloji Çözümleri

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

28 Citations (Scopus)

Abstract

This paper addresses the problem of forecasting various product demands of main distribution warehouses. Demand forecasting is the activity of building forecasting models to estimate the quantity of a product that customers will purchase. It is affected from numerously different factors such as warehouse region size, customer count, product type etc. When the number of the distribution warehouses and products increases, it becomes considerably hard to estimate the demand of customers. In this study, we provide an appropriate methodology for demand forecasting which is capable of overcoming the aforementioned limitations while providing a high estimation accuracy. The proposed methodology clusters similar warehouses according to their sale behavior using bipartite graph clustering. After that, hybrid forecasting phase which combines moving average model and Bayesian Network machine learning algorithm is applied. Our experimental results on real data set show that this approach considerably improves the forecasting performance.

Original language	English
Title of host publication	Proceedings - 2015 IEEE 24th International Symposium on Industrial Electronics, ISIE 2015
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	55-60
Number of pages	6
ISBN (Electronic)	9781467375542
DOIs	https://doi.org/10.1109/ISIE.2015.7281443
Publication status	Published - 28 Sept 2015
Externally published	Yes
Event	24th IEEE International Symposium on Industrial Electronics, ISIE 2015 - Buzios, Rio de Janeiro, Brazil Duration: 3 Jun 2015 → 5 Jun 2015

Publication series

Name	IEEE International Symposium on Industrial Electronics
Volume	2015-September

Conference

Conference	24th IEEE International Symposium on Industrial Electronics, ISIE 2015
Country/Territory	Brazil
City	Buzios, Rio de Janeiro
Period	3/06/15 → 5/06/15

Bibliographical note

Publisher Copyright:
© 2015 IEEE.

Keywords

Bayesian networks
Bipartite graph
Bipartite graph clustering
Demand forecasting
Moving Average
Multilayer perceptron algorithm (MLP)
Supply chain

Access to Document

10.1109/ISIE.2015.7281443

Cite this

Islek, I., & Oguducu, S. G. (2015). A retail demand forecasting model based on data mining techniques. In Proceedings - 2015 IEEE 24th International Symposium on Industrial Electronics, ISIE 2015 (pp. 55-60). Article 7281443 (IEEE International Symposium on Industrial Electronics; Vol. 2015-September). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ISIE.2015.7281443

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title = "A retail demand forecasting model based on data mining techniques",

abstract = "This paper addresses the problem of forecasting various product demands of main distribution warehouses. Demand forecasting is the activity of building forecasting models to estimate the quantity of a product that customers will purchase. It is affected from numerously different factors such as warehouse region size, customer count, product type etc. When the number of the distribution warehouses and products increases, it becomes considerably hard to estimate the demand of customers. In this study, we provide an appropriate methodology for demand forecasting which is capable of overcoming the aforementioned limitations while providing a high estimation accuracy. The proposed methodology clusters similar warehouses according to their sale behavior using bipartite graph clustering. After that, hybrid forecasting phase which combines moving average model and Bayesian Network machine learning algorithm is applied. Our experimental results on real data set show that this approach considerably improves the forecasting performance.",

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author = "Irem Islek and Oguducu, {Sule Gunduz}",

note = "Publisher Copyright: {\textcopyright} 2015 IEEE.; 24th IEEE International Symposium on Industrial Electronics, ISIE 2015 ; Conference date: 03-06-2015 Through 05-06-2015",

year = "2015",

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day = "28",

doi = "10.1109/ISIE.2015.7281443",

language = "English",

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Islek, I & Oguducu, SG 2015, A retail demand forecasting model based on data mining techniques. in Proceedings - 2015 IEEE 24th International Symposium on Industrial Electronics, ISIE 2015., 7281443, IEEE International Symposium on Industrial Electronics, vol. 2015-September, Institute of Electrical and Electronics Engineers Inc., pp. 55-60, 24th IEEE International Symposium on Industrial Electronics, ISIE 2015, Buzios, Rio de Janeiro, Brazil, 3/06/15. https://doi.org/10.1109/ISIE.2015.7281443

A retail demand forecasting model based on data mining techniques. / Islek, Irem; Oguducu, Sule Gunduz.
Proceedings - 2015 IEEE 24th International Symposium on Industrial Electronics, ISIE 2015. Institute of Electrical and Electronics Engineers Inc., 2015. p. 55-60 7281443 (IEEE International Symposium on Industrial Electronics; Vol. 2015-September).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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KW - Bayesian networks

KW - Bipartite graph

KW - Bipartite graph clustering

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KW - Moving Average

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A retail demand forecasting model based on data mining techniques

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Keywords

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