零售加盟業之時效性商品短期銷售預測｜國立臺灣科技大學博碩士論文系統

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研究生：	吳志恩 Chih-En Wu
論文名稱：	零售加盟業之時效性商品短期銷售預測 Short-term Sales Forecasting of Perishable Goods for Retailing Franchisees
指導教授：	楊朝龍 Chao-Lung Yang
口試委員:	郭人介 Ren-Jieh Kuo 曹譽鐘 Yu-Chung Tsao
學位類別：	碩士 Master
系所名稱：	管理學院 - 工業管理系 Department of Industrial Management
論文出版年：	2015
畢業學年度：	103
語文別：	英文
論文頁數：	76
中文關鍵詞：	零售加盟業、時效性商品、銷售預測
外文關鍵詞：	Retailing Franchisee, Perishable Good, Sales Forecasting
相關次數：	點閱：195 下載：9
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在連鎖企業體系中，加盟店跟直營店在經營策略上有諸多不同。由於加盟店之經營者必須自負盈虧，因此對風險承擔程度相對較直營店低。若銷售商品為具有時效性的商品，例如：麵包、生鮮食品，加盟店在訂單數量上通常較為保守，因為若未在時效期限內銷售完畢就必須進行要銷毀。此一保守的訂購策略，對加盟業之總公司而言會影響到利潤的增長；此外，當特定商品過早售罄，亦是銷售上的機會成本損失。為解決此一具時效性商品之訂單及庫存管理議題，本研究發展出一套運用銷售模式辨識 (Pattern recognition) 進行銷售速率預測之資料分析架構，期望能提供加盟店管理者更準確之銷售預測分析。本研究以麵包加盟店之案例進行銷售點 (Point of Sale)的資料收集。針對每日銷售資料進行資料分群，得以找出具特徵性之每日銷售速率曲線。分群的結果亦將作為銷售模式辨識的參考依據，藉由以當日之前數小時之銷售特徵研判後續銷售之狀況，以作為訂單調整的依據。實驗的結果發現，若針對特定銷售數量有成長空間之模式進行銷售量之預測分析，將可預估出商品可能增長的銷售空間，此乃銷售預測之應用。透過此方法的應用，總公司可提出各種激勵措施及各分店銷售業績績效表現之評核方法，作為整個加盟體系之各加盟店訂單決策管理之用。

In a franchisee chain system, the franchise organization grants the right to a franchisee to represent it and sells its products, and franchisee can only order products from the system. Due to buy-out policy of the product ordering, franchisees forecast the demand and control the inventory locally based on their sales knowledge or experience. While the franchisee products are perishable goods with short preservation time, the ordering strategy of franchisees tends to more conservative to avoid the waste. This conservative ordering might lead to profit lose. Therefore, understanding the sales pattern for each store is critical for inventory control and sales promotion. This research aims to develop a prediction model to provide more accurate sales estimation for the perishable-good franchisees. A case study of bakery franchisee stores which sell breads with one-day preservation time was studied, and point of sales (POS) data from multiple franchise stores were collected and analyzed. In order to provide the better sales forecasting in a relatively short period, the data clustering method was used to study sales pattern considering different influence factors such as weather and holiday. The pattern recognition method was utilized to indicate the sales patterns of breads in each store by using the early period of inventory level. The experimental result shows that the indicating sales pattern can be used to predict the sales of the remaining operation hours. The daily prediction on short-term sales provides a systemic method to franchisee for a better ordering strategy.

摘要	i
ABSTRACT	ii
誌謝	iii
CONTENTS	iv
LIST OF TABLES	vii
LIST OF FIGURES	viii
CHAPTER 1 INTRODUCTION	1
CHAPTER 2 LITERATURE REVIEW	5
2.1 Franchisers and Franchisees	5
2.2 Point of Sale (POS) System and Prediction Model	5
2.3 Perishable Goods and Unperishable Goods	7
2.4 Clustering Analysis	9
2.4.1	Hierarchical Clustering	9
2.4.2	Determination of Clusters	10
2.5 K-fold Cross Validation	11
CHAPTER 3 DATA ANALYSIS	13
3.1 POS Data	13
3.1.1	Data Collection	13
3.1.2	Data Description	13
3.1.3	The Shortages of Supply	16
3.1.4	Inventory Data	18
3.2 Summary of POS Data	19
3.3 Data Analysis (ANOVA)	20
3.4 Basic Statistical Information	21
CHAPTER 4 RESEARCH METHODOLOGY	24
4.1 Problem Definition	24
4.2 Assumptions	25
4.3 Research Structure	26
4.4 Mathematical Formulation	27
4.4.1	Normalization	29
4.4.2	Normalization for Early Sold-out Time	31
4.4.3	Sales Pattern Recognition	31
4.4.4	Order Level Adjustment	32
CHAPTER 5 EXPERIMENTAL RESULTS	34
5.1 Basic Statistics of Data	34
5.2 Sales Pattern Clustering by Factor Combination	38
5.2.1	Cluster K Determination	38
5.3 Cross Validation and Discussion of Errors	41
5.3.1	5-fold Cross Validation	41
5.3.2	Experimental Error	42
5.3.3	Error Discussion	46
CHAPTER 6 CONCLUSION	48
6.1 Conclusion and Discussion	48
6.2 Future Research	49
REFERENCES	51
APPENDIX	53
A.	The Basic Statistical Information of Different Combinations	53
B.	The Mean of MAPE by Different Clusters Of Different Combinations	55
C.	The Error Rate of Misclassification	59
D.	Main Code: Inventory Level (R Language)	63
E.	Main Code: Clustering with Different Clusters and Clustering Centers (R Language)	65
F.	Main Code: Forecasting Error (R Language)	66

                                

[1] Stanworth, J., Stanworth, C., Customer Service Franchising – A Trend or a Deviant Case? International Small Business Journal, 1999. 17(3): p. pp. 74-78.
[2] Clarkin, J.E. and S.M. Swavely, The Importance of Personal Characteristics in Franchisee Selection. Journal of Retailing and Consumer Services, 2006. 13(2): p. 133-142.
[3] Jambulingam, T. and J.R. Nevin, Influence of Franchisee Selection Criteria on Outcomes Desired by the Franchisor. Journal of Business Venturing, 1999. 14(4): p. 363-395.
[4] Ramanathan, U., Supply Chain Collaboration for Improved Forecast Accuracy of Promotional Sales. International Journal of Operations & Production Management, 2012. 32(5-6): p. 676-695.
[5] Zhao, L.-d. and L. Tang. Multi-Period Demand-Driven Dynamic Pricing Model for Fresh Food in Supermarket. in Service Systems and Service Management, 2007 International Conference on. 2007.
[6] Van Donselaar, K., et al., Inventory Control of Perishables in Supermarkets. International Journal of Production Economics, 2006. 104(2): p. 462-472.
[7] Chu, C.-H. and C.-Y. Ku, A Portfolio Analysis of Regular and Franchise for Chain Store 2000.
[8] Falbe, C.M., T.C. Dandridge, and A. Kumar, The Effect of Organizational Context on Entrepreneurial Strategies in Franchising. Journal of Business Venturing, 1999. 14(1): p. 125-140.
[9] Wang, V. and C.-H. Lai, Franchise fee, competition and economic growth. Economic Modelling, 2011. 28(5): p. 2090-2099.
[10] Maruyama, M. and Y. Yamashita, Franchise Fees and Royalties: Theory and Empirical Results. Review of Industrial Organization, 2012. 40(3): p. 167-189.
[11] Du, H. and B. Cui. Sale Forecasting Method in Dynamic Environment Based on ARMA(1,1). in Electric Information and Control Engineering (ICEICE), 2011 International Conference on. 2011.
[12] Zliobaite, I., J. Bakker, and M. Pechenizkiy, Towards Context Aware Food Sales Prediction. 2009 Ieee International Conference on Data Mining Workshops, ed. Y. Saygin, et al. 2009. 94-99.
[13] Meulstee, P. and M. Pechenizkiy. Food Sales Prediction: "If Only It Knew What We Know". in Data Mining Workshops, 2008. ICDMW '08. IEEE International Conference on. 2008.
[14] Zhu, J.S., POS Data and Your Demand Forecast. First International Conference on Information Technology and Quantitative Management, 2013. 17: p. 8-13.
[15] Yagi, K., et al. Optimized Sequential Pattern Mining from Point Of Sales Data. in Data Engineering Workshops, 2005. 21st International Conference on. 2005.
[16] Sundararaman, K., et al. Baseline Prediction of Point of Sales Data for Trade Promotion Optimization. in Communications and Information Technology (ICCIT), 2012 International Conference on. 2012.
[17] Williams, B.D., et al., Predicting Retailer Orders with POS and Order Data: The Inventory Balance Effect. European Journal of Operational Research, 2014. 232(3): p. 593-600.
[18] Hanssens, D.M., Order Forecasts, Retail Sales, and the Marketing Mix for Consumer Durables. Journal of Forecasting, 1998. 17(3-4): p. 327-346.
[19] Lin, W., Y. Jinyao, and F. YuanJing. Data Mining Algorithms and Statistical Analysis for Sales Data Forecast. in Computational Sciences and Optimization (CSO), 2012 Fifth International Joint Conference on. 2012.
[20] Ren, S., T.M. Choi, and N. Liu, Fashion Sales Forecasting With a Panel Data-Based Particle-Filter Model. Systems, Man, and Cybernetics: Systems, IEEE Transactions on, 2014. PP(99): p. 1-1.
[21] Tang, C.S., et al., The Benefits of Advance Booking Discount Programs: Model and Analysis. Management Science, 2004. 50(4): p. 465-478.
[22] Yihang, L. and L. Lieli. Sales Forecasting through Fuzzy Neural Networks. in Electronic Computer Technology, 2009 International Conference on. 2009.
[23] Fakharudin, A.S., M.A.M. Hamza, and M.U. Johan. Newspaper Vendor Sales Prediction Using Artificial Neural Networks. in Education Technology and Computer, 2009. ICETC '09. International Conference on. 2009.
[24] Kung-Hsing Chen and W.-I. Lee, The Convenience Stores’ Fresh Food Commodity Sales Forecasting Model-- Discussion 「Artificial Neural Networks」 and 「Ordinary Day and Holiday Moving Average method」Comparison, 2006, National Kaohsiung First University of Science and Technology.
[25] Tan;, P.-n., M. Steinbach;, and V. Kumar, Introduction to Data Mining, 2012, Addison-Wesley Educational Publishers Inc.
[26] Kohavi, R. A study of cross-validation and bootstrap for accuracy estimation and model selection. in Proceedings of the Fourteenth International Joint Conference on Artificial Intelligence 2 (12): 1137–1143. 1995.

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