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@article{ART001455209},
author={Lee, Sang Youb and Jaehwan Kim},
title={Sales Forecasting Model for the Department Store Development},
journal={Korea Real Estate Review},
issn={2092-5395},
year={2010},
volume={20},
number={1},
pages={139-162}

TY - JOUR
AU - Lee, Sang Youb
AU - Jaehwan Kim
TI - Sales Forecasting Model for the Department Store Development
JO - Korea Real Estate Review
PY - 2010
VL - 20
IS - 1
PB - korea real estate research institute
SP - 139
EP - 162
SN - 2092-5395
AB - As the growth of department stores, which have led the distribution industry, started to decrease recently, large-scale discount stores have emerged as a new format of retail business and taken the central position in the distribution industry. As large-scale discount stores gain more and more momentum in opening, there appears a shift to the competition structure between department stores and large-scale discount stores. Despite the latter's remarkable growth, however, it should be noted that the two retail formats deal with different items in each product category and attract consumers with different preferences. Thus approaches to opening between them should naturally be different. In addition to the old approach toward opening a distribution facility including location analysis and market potential(MP) analysis, they should consider the unique characteristics of department stores to estimate sales. Thus this study divided the main variables to affect the sales of department stores into population and economic factors, location factors, internal environment factors, and differential factors. Then the investigator selected their input variables. Based on the factors to affect sales and sales data, I devised an estimation model of regression analysis and artificial neural networks. Based on the national statistics and the data of A department stores across the nation from the first quarter of 2002 to the fourth quarter of 2006, I compared the estimated and actual sales of 2007 and reviewed the model's accuracy. In order to compare and assess total 505 cases by the regions and analysis methods, I divided the model composition into four(the Seoul metropolitan area(including Seoul), the Seoul metropolitan area(excluding Seoul), the rest of the nation, and the entire nation) and made estimations. As a result, the Seoul metropolitan area(including Seoul) model showed the highest estimating power at 96.6%. Using the model with the best estimation of sales, I predicted the sales of a new A department store for 2008. The relative importance of the input variables used in estimating sales turned out to influence the sales of a new department store. Thus it's suggested that sales should multiply when they compose the store's MD(merchandise) based on those variables.
KW - Sales Forecasting;Artificial Neural Networks;Regression Analysis
DO -
UR -
ER -

Lee, Sang Youb and Jaehwan Kim. (2010). Sales Forecasting Model for the Department Store Development. Korea Real Estate Review, 20(1), 139-162.

Lee, Sang Youb and Jaehwan Kim. 2010, "Sales Forecasting Model for the Department Store Development", Korea Real Estate Review, vol.20, no.1 pp.139-162.

Lee, Sang Youb, Jaehwan Kim "Sales Forecasting Model for the Department Store Development" Korea Real Estate Review 20.1 pp.139-162 (2010) : 139.

Lee, Sang Youb, Jaehwan Kim. Sales Forecasting Model for the Department Store Development. 2010; 20(1), 139-162.

Lee, Sang Youb and Jaehwan Kim. "Sales Forecasting Model for the Department Store Development" Korea Real Estate Review 20, no.1 (2010) : 139-162.

Lee, Sang Youb; Jaehwan Kim. Sales Forecasting Model for the Department Store Development. Korea Real Estate Review, 20(1), 139-162.

Lee, Sang Youb; Jaehwan Kim. Sales Forecasting Model for the Department Store Development. Korea Real Estate Review. 2010; 20(1) 139-162.

Lee, Sang Youb, Jaehwan Kim. Sales Forecasting Model for the Department Store Development. 2010; 20(1), 139-162.

Lee, Sang Youb and Jaehwan Kim. "Sales Forecasting Model for the Department Store Development" Korea Real Estate Review 20, no.1 (2010) : 139-162.