考慮競爭者的零售業展店決策模型

零售業的展店決策問題在過去研究中被廣泛討論，研究者透過預測單店的來客率決定最佳設址位置，然而多數研究者在參數設定上，因缺乏實際資料，大多假設模型參數為定值，這形成一個缺口，模型的參數無法依地區性而有所變化。本研究在誤差最小化下，求解最符合該地區消費行為的模型參數，並進一步建構展店決策模型。透過實際資料找出模型的參數，使模型符合該區消費者行為，亦修改商店面積的參數設定，將原模型中商店面積的邊際遞減效果，修正為先遞增後遞減，並納入其他重要因子：商店位於地下室/地平面、其他競爭者影響（在地傳統市場等）、大眾運輸系統帶來的人流量。透過營業額估計模型預測各店營業額，並依國際會計準則放入成本函數，在追求總體營業淨利最大化下，求出最佳的展店位置與商店面積，提供管理者在區域中有效地找到最佳展店的位置與商店面積，進而使得市場的總利潤最大化。此外，部分零售業者因重視消費者隱私，無提供會員卡等服務來獲得消費者的資料，無法準確分析消費者行為。本研究的模型透過歷史營業額資料與政府公開資料庫資訊，即可準確估計營業額與展店位置，提供零售業者未來行銷與營運決策方面的建議。

關鍵字

營業額估計；展店決策；赫夫模型

並列摘要

Estimating the consumer arrival rate of a store to make an optimal location decision was widely discussed in the previous studies. Researchers used the constants to valid the parameters in those models. However, the influences of the parameters are different in various regions; no study has clarified how to fit the parameters with real revenue data. To fill this gap, first, this study constructs the revenue estimating model to find the optimal parameters of the factors minimize the error. Then construct the retail location and store size decision model. Second, this study modifies the attractiveness function of the store size in Huff model from marginal decreasing curve to sigmoid curve. Moreover, this study includes other important factors such as the influence of the underground site, the influence of other competitiveness, and the influence of the population in the subway station to satisfy the real state. In addition, this study uses International Financial Reporting Standards (IFRSs) to construct the cost function in the model to maximize the operating income by finding out the optimal location and the store size of the new store. This study contributes to the retail managers by providing the suggestion of open a new store and predict the consumer behavior without consumers’ membership card information. By using the historical revenue data and open data from the government can estimate the location and size of new store accurately.

並列關鍵字

Revenue estimating model ； Retail location and store size decision model ； Huff model

參考文獻

[1]. Aboolian, R., Berman, O., & Krass, D. (2007). Competitive facility location and design problem. European Journal of Operational Research, 182(1), 40-62. doi:10.1016/j.ejor.2006.07.021

[2]. Aguirregabiria, V., & Vicentini, G. (2016). Dynamic Spatial Competition Between Multi-Store Retailers. The Journal of Industrial Economics, 64(4), 710-754. doi:10.1111/joie.12112

[3]. Alizadeh, M., Mahdavi-Amiri, N., & Shiripour, S. (2015). Modeling and solving a capacitated stochastic location-allocation problem using sub-sources. Soft Computing, 20(6), 2261-2280. doi:10.1007/s00500-015-1640-6

[5]. Aros-Vera, F., Marianov, V., & Mitchell, J. E. (2013). p-Hub approach for the optimal park-and-ride facility location problem. European Journal of Operational Research, 226(2), 277-285. doi:10.1016/j.ejor.2012.11.006

[7]. Ben-Akiva, M. E., & Lerman, S. R. (1985). Discrete choice analysis: theory and application to travel demand (Vol. 9): MIT press.

國際替代計量

考慮競爭者的零售業展店決策模型

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