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研究生:吳志恩
研究生(外文):Chih-En Wu
論文名稱:零售加盟業之時效性商品短期銷售預測
論文名稱(外文):Short-term Sales Forecasting of Perishable Goods for Retailing Franchisees
指導教授:楊朝龍楊朝龍引用關係
指導教授(外文):Chao-Lung Yang
口試委員:楊朝龍
口試委員(外文):Chao-Lung Yang
口試日期:2015-06-11
學位類別:碩士
校院名稱:國立臺灣科技大學
系所名稱:工業管理系
學門:商業及管理學門
學類:其他商業及管理學類
論文種類:學術論文
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 FranchiseePerishable GoodSales Forecasting
相關次數:
  • 被引用被引用:2
  • 點閱點閱:284
  • 評分評分:
  • 下載下載:60
  • 收藏至我的研究室書目清單書目收藏:1
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
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