深度學習於智慧零售預測模型之研究：以便利商店時效性商品為例

A Deep Learning Approach of Forecasting Model in Smart Retail: A Case Study of Time-Critical Goods at Convenience Store

歐宗殷(Tsung-Yin Ou)；洪志洋(Chih-Young Hung)；林哲瑋(Che-Wei Lin)

智慧零售 ； 便利商店 ； 時效性商品 ； 隨機森林 ； 深度學習 ； Smart Retail ； Convenience Store ； Time-Critical Goods ； Random Forest ； Deep Learning

科技管理學刊

23卷3期（2018 / 09 / 01）

69 - 91

繁體中文

近年來智慧零售的議題甚囂塵上，許多零售業者整合新興技術亟欲打造新型態的虛實整合零售業，但無論應用或整合了那些資訊技術，其真正用意都是希望能打造一個以大數據重新驅動消費者體驗的新零售模式，以先進科技和資料分析技術改變企業的營運流程與內涵。在便利商店中，時效性商品的銷售佔比重日漸攀升，店鋪管理者雖可透過POS (Point of Sales)系統得知歷史銷售數據和訂購建議進行下單採購，然而時效性商品常因外在環境影響，使得每日的需求有所變動，若能提高商品銷售的預測準確度將可提高營收並降低成本，本研究以便利商店時效性商品為研究對象，嘗試用新的方法來建構更為精準的預測模型。本研究整合多元數據集包括歷史銷售數據、店內促銷活動資訊、外部環境變數等資料，採用深度學習方法建立便利商店時效性商品銷售預測模式，以個案公司過去兩年於12家分店的資料，每家分店各54項的銷售數據作為建立預測模式的資料，結果顯示，使用隨機森林進行特徵變數篩選，以CNN與RNN建立銷售預測模型，並採用MAE和MSE指標進行誤差評比，準確度明顯優於ARIMA、MLF、RBF、SVM之預測結果，顯見深度學習對於預測時效性商品的適用能力確實較為優異。

In recent years, smart retailing has become a hot topic in convenience store industry. Many retailers are integrating in emerging technologies to create a new type of retail business. The intention of those applications or integrations by information technology is to create a new retail model with big data by customer experience. Advanced technology and data analysis technology are adapted to this kind of new retail model to change the operation and process in convenience stores. In convenience stores industry, the percentage of Time-Critical Goods is growing up. Managers can place orders through historical sales data and ordering advice by POS system currently. However, the demand of Time-Critical Goods is impacted due to external environment changing. It will increase profits and decrease unnecessary expenses if the accuracy of forecasting of the sales be improved. The purpose of this study is to use advanced methods to construct more accurate forecasting model for sales of Time-Critical Goods in convenience stores. Mmultivariate datasets including historical sales data, in-store promotion, external environmental variables etc., are integrated in this research. Research team use deep learning methods to establish forecasting model of sales of Time-Critical Goods in convenient store. The data comes from 12 branch stores of case company and each 54 items of sales data is used for models establishing. After using (i) random forests for feature variable screening and (ii) sales forecasting model by CNN and RNN, and (iii) MAE and MSE for error analysis, the accuracy of forecasting is better than the sales forecast model constructed by ARIMA、MLF、RBF and SVM methods. The result showed that deep learning methodology is suitable for forecasting of sales of Time-Critical Goods in convenient store.

1. Arunraj, N. S.,Ahrens, D.(2015).A hybrid seasonal autoregressive integrated moving average and quantile regression for daily food sales forecasting.International Journal of Production Economics,170,321-335.
2. Box, G. E.,Jenkins, G. M.,Reinsel, G. C.,Ljung, G. M.(2015).Time series analysis: forecasting and control.NJ:John Wiley & Sons.
3. Breiman, L.(1996).Bagging predictors.Machine learning,24(2),123-140.
4. Chen, T.(2018).University of Michigan-Dearborn.
5. Cortes, C.,Vapnik, V.(1995).Support-vector networks.Machine learning,20(3),273-297.
6. Di Persio, L.,Honchar, O.(2016).Artificial neural networks approach to the forecast of stock market price movements.International Journal of Economics and Management Systems,1,158-162.
7. Diane, E. K.(1990).Machine Learning.Training and Development Journal,44(12),24-29.
8. Doganis, P.,Alexandridis, A.,Patrinos, P.,Sarimveis, H.(2006).Time series sales forecasting for short shelf-life food products based on artificial neural networks and evolutionary computing.Journal of Food Engineering,75(2),196-204.
9. Goodwin, T. K.(2017).Tasmanian School, Business and Economics University of Tasmania.
10. Hapfelmeier, A.,Ulm, K.(2013).A new variable selection approach using random forests.Computational Statistics & Data Analysis,60,50-69.
11. Hashemi, M.,Hall, M.(2018).Identifying the responsible group for extreme acts of violence through pattern recognition.5th International Conference on HCI in Business, Government, and Organizations,Las Vegas, NV, USA:
12. Henson, T.,Huxhold, W.,Bowman, D.(1993).An enhanced neural network learning algorithm with simulated annealing.Third Workshop on Neural Networks: Academic/Industrial/NASA/Defense
13. Huber, J.,Gossmann, A.,Stuckenschmidt, H.(2017).Cluster-based hierarchical demand forecasting for perishable goods.Expert systems with applications,76,140-151.
14. Ibrahim, N. F.,Wang, X.(2017).Mining Social Network Content of Online Retail Brands: A Machine Learning Approach.ECISM 2017 11th European Conference on Information Systems Management
15. Islam Molla, M.(2016).University of Missouri-Columbia.
16. Kitapci, O.,Akdogan, C.,Dortyol, I. T.(2014).The impact of service quality dimensions on patient satisfaction, repurchase intentions and word-of-mouth communication in the public healthcare industry.Procedia-Social and Behavioral Sciences,148,161-169.
17. Kraus, M.,Feuerriegel, S.,Oztekin, A.(2018).Deep learning in business analytics and operations research: Models, applications and managerial implications.Business Analytics: Defining the field and identifying a research agenda
18. Liu, X., Lee, D., & Srinivasan, K. (2017), “Large scale cross category analysis of consumer review content on sales conversion leveraging deep learning” From: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2848528.
19. Liu, Y.,Zhao, J.,Gao, J.(2014).Weather sensitive demand forecasting method based on SVR for shoes products.2014 6th International Conference on Knowledge and Smart Technology (KST)
20. Ma, J.,Cheng, J. C.(2016).Identifying the influential features on the regional energy use intensity of residential buildings based on Random Forests.Applied energy,183,193-201.
21. Ma, S.,Fildes, R.,Huang, T.(2016).Demand forecasting with high dimensional data: The case of SKU retail sales forecasting with intra-and inter-category promotional information.European Journal of Operational Research,249(1),245-257.
22. Moon, S.,Kwon, J.,Jung, S. U.,Bae, Y. H.(2018).The impact of individual differences in weather sensitivity on weather-related purchase intentions.International Journal of Market Research,60(1),104-117.
23. Mou, S.,Ji, Y.,Tian, C.(2018).Retail Time Series Prediction Based on EMD and Deep Learning.2018 International Conference on Network Infrastructure and Digital Content (IC-NIDC)
24. Andrew Ng, Jiquan Ngiam, Chuan Yu Foo, Yifan Mai & Caroline Suen (2014), “UFLDL Tutorial: Convolutional Neural Network” From:http://ufldl.stanford.edu/tutorial/supervised/ConvolutionalNeuralNetwork/.
25. Ougiaroglou, S.,Nanopoulos, A.,Papadopoulos, A. N.,Manolopoulos, Y.,Welzer-Druzovec, T.(2007).Adaptive k-nearest-neighbor classification using a dynamic number of nearest neighbors.East European Conference on Advances in Databases and Information Systems,Berlin:
26. Qiu, X.,Zhang, L.,Ren, Y.,Suganthan, P. N.,Amaratunga, G.(2014).Ensemble deep learning for regression and time series forecasting.Computational Intelligence in Ensemble Learning (CIEL), 2014 IEEE Symposium
27. Ramos, P.,Santos, N.,Rebelo, R.(2015).Performance of state space and ARIMA models for consumer retail sales forecasting.Robotics and computer-integrated manufacturing,34,151-163.
28. Song, Q.,Shepperd, M.(2011).Predicting software project effort: A grey relational analysis based method.Expert Systems with Applications,38(6),7302-7316.
29. Tsoumakas, G.(2018).A survey of machine learning techniques for food sales prediction.Artificial Intelligence Review,52,441-447.
30. Vapnik, V. N.(1995).The nature of statistical learning theory.New York:Springer.
31. Wong, W. K.,Guo, Z. X.(2010).A hybrid intelligent model for medium-term sales forecasting in fashion retail supply chains using extreme learning machine and harmony search algorithm.International Journal of Production Economics,128(2),614-624.
32. Xiao, X. C.,Wang, X. Q.,Fu, K. Y.,Zhao, Y. J.(2012).Grey relational analysis on factors of the quality of web service.Physics Procedia,33,1992-1998.
33. Zhan, Y.,Luo, Y.,Deng, X.,Grieneisen, M. L.,Zhang, M.,Di, B.(2018).Spatiotemporal prediction of daily ambient ozone levels across China using random forest for human exposure assessment.Environmental Pollution,233,464-473.
34. Zhang, Y.,Gong, D. W.,Cheng, J.(2017).Multi-objective particle swarm optimization approach for cost-based feature selection in classification.IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB),14(1),64-75.
35. 周秉誼(2016)，「淺談Deep Learning 原理及應用」，國立臺灣大學計算機及資訊網路中心電子報，取自http://www.cc.ntu.edu.tw/chinese/epaper/0038/20160920_3805.html。
36. 陳弘忠(2014)。碩士論文(碩士論文)。義守大學工業工程與管理學系。
37. 葉怡成(2004)。類神經網路模式應用與實作。台北:儒林圖書公司。

1. 劉學承,曾敬勳,李昕潔(2021)。Processing Big Data Problems using Modular Fuzzy Inference Systems。科技管理學刊，26(1)，91-110。