TY - GEN
T1 - Sales prediction analysis in determining new minimarket stores
AU - Edwardo, Timothy Orvin
AU - Ruldeviyani, Yova
N1 - Publisher Copyright:
© 2020 IEEE.
Copyright:
Copyright 2021 Elsevier B.V., All rights reserved.
PY - 2020/10/19
Y1 - 2020/10/19
N2 - PT XYZ is a company engaged in the retail minimarket business in Indonesia. In running its business, one of the key activities involved is opening a new minimarket store. The audit team will analyze proposals to predict sales of would-be new stores, however, the results of the predictions are often not following reality, so research is needed to predict sales more accurately. This study aims to analyze the prediction of minimarket stores sales using deep learning technique to determine new minimarket stores. The model can predict 53.18% stores that achieve the target prediction and 28.32% stores that have the potential to achieve the target in the future, which indicates an increase in predicting stores on target compared to the branch office method which only predicts 31.2% of stores that achieve the target and 31.62% of potential stores. Thus, the approval decision of new minimarket stores which predicted achieve its target can be more accurate than the branch office method. The audit team will use the model to predict the store sales and consider the result for approving the proposal. Factors that had a significant influence on sales were rack size, store age, distance between competitors, domain location, and store type.
AB - PT XYZ is a company engaged in the retail minimarket business in Indonesia. In running its business, one of the key activities involved is opening a new minimarket store. The audit team will analyze proposals to predict sales of would-be new stores, however, the results of the predictions are often not following reality, so research is needed to predict sales more accurately. This study aims to analyze the prediction of minimarket stores sales using deep learning technique to determine new minimarket stores. The model can predict 53.18% stores that achieve the target prediction and 28.32% stores that have the potential to achieve the target in the future, which indicates an increase in predicting stores on target compared to the branch office method which only predicts 31.2% of stores that achieve the target and 31.62% of potential stores. Thus, the approval decision of new minimarket stores which predicted achieve its target can be more accurate than the branch office method. The audit team will use the model to predict the store sales and consider the result for approving the proposal. Factors that had a significant influence on sales were rack size, store age, distance between competitors, domain location, and store type.
KW - Data Mining
KW - Deep Learning
KW - Retail
KW - Sales Prediction
UR - http://www.scopus.com/inward/record.url?scp=85099607436&partnerID=8YFLogxK
U2 - 10.1109/ICITSI50517.2020.9264911
DO - 10.1109/ICITSI50517.2020.9264911
M3 - Conference contribution
AN - SCOPUS:85099607436
T3 - 2020 International Conference on Information Technology Systems and Innovation, ICITSI 2020 - Proceedings
SP - 79
EP - 84
BT - 2020 International Conference on Information Technology Systems and Innovation, ICITSI 2020 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 6th International Conference on Information Technology Systems and Innovation, ICITSI 2020
Y2 - 19 October 2020 through 23 October 2020
ER -