請用此 Handle URI 來引用此文件：
http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/10778完整後設資料紀錄
| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 任立中 | |
| dc.contributor.author | Ping Lin | en |
| dc.contributor.author | 林平 | zh_TW |
| dc.date.accessioned | 2021-05-20T21:58:01Z | - |
| dc.date.available | 2020-12-31 | |
| dc.date.available | 2021-05-20T21:58:01Z | - |
| dc.date.copyright | 2010-07-22 | |
| dc.date.issued | 2010 | |
| dc.date.submitted | 2010-07-20 | |
| dc.identifier.citation | References
1. 陳靜怡（民96）。購買量與購買時程雙變量之預測－層級貝氏潛藏行為模型之建構。台北：國立台灣大學國際企業學研究所博士論文。 2. 林柏全（民97）。解讀聯發科的成長密碼。新竹：國立交通大學管理學院碩士在職專班經營管理組碩士論文。 3. 呂玉敏（民94）。應用雙變量層級貝氏模型於顧客價值分析——以購物網站為例。台北：國立台灣大學商學研究所碩士論文。 4. 魏以恩（民93）。以 Bass 擴散模式為基礎建立跨國新產品之銷售預測模型－以在台上映之美國電影為例。台北：國立台灣大學國際企業學系碩士論文。 5. 吳語軒（民94）。層級貝氏購買期間模型之比較。台北：國立台灣大學國際企業學系碩士論文。 6. Allenby, G. M., L. C. Jen, et al. (1996). “Economic trends and being trendy: The influence of consumer confidence on retail fashion sales.” Journal of Business & Economic Statistics 14(1): 103-111. 7. Berling, P. (2008). “The capital cost of holding inventory with stochastically mean-reverting purchase price.” European Journal of Operational Research 186(2): 620-636. 8. Chang, C. T., (2002). “Extended Economic Order uantity Model under Cash Discount and Payment Delay.” Information and Management Sciences 13(3): 57-69. 9. Clarke, H. R., (1987). “Economic order quantities with discounting.” Engineering Costs and Production Economics 11 (2): 15-22. 10. Dalrymple, D.J. (1975). “Sales Forecasting Methods and Accuracy.” Business Horizons. December: 69-73. 11. Dalrymple, D.J. (1987). “Sales Forecasting Practices.” International Journal of Forecasting 3: 380-91. 12. de Alba, E. and M. Mendoza (2001). “Forecasting an accumulated series based on partial accumulation: A Bayesian method for short series with seasonal patterns.” Journal of Business & Economic Statistics 19(1): 95-102. 13. Fisher, M. and K. Rajaram (2000). “Accurate retail testing of fashion merchandise: Methodology and application.” Marketing Science 19(3): 266-278. 14. Ghysels, E. and M. Nerlove (1988). “SEASONALITY IN SURVEYS - A COMPARISON OF BELGIAN, FRENCH AND GERMAN BUSINESS TESTS.” European Economic Review 32(1): 81-99. 15. Herbig, P., J. Milewicz, et al. (1994). “Differences in Forecasting Behavior Between Industrial Product Firms and Consumer Product Firms.” Journal of Business and Industrial Marketing 9(1): 60–69. 16. Himmelberg, C. P. and B. C. Petersen (1994). “RESEARCH-AND-DEVELOPMENT AND INTERNAL FINANCE - A PANEL STUDY OF SMALL FIRMS IN HIGH-TECH INDUSTRIES.” Review of Economics and Statistics 76(1): 38-51. 17. Hua, Z. S., B. Zhang, et al. (2007). “A new approach of forecasting intermittent demand for spare parts inventories in the process industries.” Journal of the Operational Research Society 58(1): 52-61. 18. Jen, L. C., C. H. Chou, et al. (2003). “A Bayesian approach to modeling purchase frequency.” Marketing Letters 14(1): 5-20. 19. Lackman, C. L. (2007). “Forecasting sales for a B2B product category: case of auto component product.” Journal of Business & Industrial Marketing 22(4-5): 228-235. 20. Lenk, P. (2001). Bayesian Inference and Markov Chain Monte Carlo: 199-240. 21. Lo, S. L., F. K. Wang, et al. (2008). “Forecasting for the LCD monitor market.” Journal of Forecasting 27(4): 341-356. 22. Lynn, G. S., S. P. Schnaars, et al. (1999). “Survey of new product forecasting practices in industrial high technology and low technology businesses.” Industrial Marketing Management 28(6): 565-571. 23. McDade, S., T. A. Oliva, et al. (2010). “Forecasting organizational adoption of high-technology product innovations separated by impact: Are traditional macro-level diffusion models appropriate?” Industrial Marketing Management 39(2): 298-307. 24. Miltenburg, J. and H. C. Pong (2007). “Order quantities for style goods with two order opportunities and Bayesian updating of demand. Part I: no capacity constraints.” International Journal of Production Research 45(7): 1643-1663. 25. Neelamegham, R. and P. Chintagunta (1999). “A Bayesian model to forecast new product performance in domestic and international markets.” Marketing Science 18(2): 115-136. 26. Nicolau, J. L. and F. J. Mas (2005). “Stochastic modeling - A three-stage tourist choice process.” Annals of Tourism Research 32(1): 49-69. 27. OECD, (2009). Guide To Measuring The Information Society : 19-20 28. Sawhney, M. S. and J. Eliashberg (1996). “A parsimonious model for forecasting gross box-office revenues of motion pictures.” Marketing Science 15(2): 113-131. 29. Stangl, D. K. (1995). “PREDICTION AND DECISION-MAKING USING BAYESIAN HIERARCHICAL-MODELS.” Statistics in Medicine 14(20): 2173-2190. 30. Thomas, D. J., D. P. Warsing, et al. (2009). “Forecast Updating and Supplier Coordination for Complementary Component Purchases.” Production and Operations Management 18(2): 167-184. 31. van de Klundert, T. (2008). “Looking back, looking ahead: Biased technological change, substitution and the wage gap.” Journal of Macroeconomics 30(2): 707-713. 32. Yelland, P. M. (2010). “Bayesian forecasting of parts demand.” International Journal of Forecasting 26(2): 374-396. 33. Yelland, P. M., S. Kim, et al. (2010). “A Bayesian Model for Sales Forecasting at Sun Microsystems.” Interfaces 40(2): 118-129. | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/10778 | - |
| dc.description.abstract | Abstract
Sales forecast has been an integral part of business planning, especially to the high-tech industry where product life cycle is short and intensive capital expenditure is required. However, forecasts in industrial product are usually less accurate and companies tend to adopt different forecast practices compared with consumer product industry. Coupled with the fact that the market structure for high-tech industry has been undergone several waves of evolutions, forecast method should be modified to adjust for improvements. This research paper proposed using a 2 level Hierarchical Bayesian Model that takes customer heterogeneity into account. The first level will address the aggregate factors affecting sales in the industry, whereas the second level utilizes firm specific factors to explain variations among customer purchasing behavior. Empirical analysis was accomplished with the data that recorded sales volume and firm attributes of 8 key accounts from an IC design company. | en |
| dc.description.provenance | Made available in DSpace on 2021-05-20T21:58:01Z (GMT). No. of bitstreams: 1 ntu-99-R97724052-1.pdf: 474761 bytes, checksum: 8ef58cc9ee41bee972c4da7553926f1a (MD5) Previous issue date: 2010 | en |
| dc.description.tableofcontents | Contents
Abstract i Chapter 1 Preface 1 1.1 Background 1 1.2 Research Purpose 3 1.3 Framework 4 Chapter 2 Literature Review 5 2.1 High-tech Industry Overview 6 2.2 Industrial Product Forecast 12 2.3 Heterogeneity Among Purchasers 15 2.4 Multi-level Forecast Model 18 Chapter 3 Research Method 20 3.1 Hierarchical Bayesian Method 20 3.2 Markov Chain Monte-Carlo Methodology 23 Chapter 4 Empirical Analysis 24 4.1 Sample Summary 24 4.2 Model Construction 29 4.3 Results & Forecast 37 Chapter 5 Conclusion 50 5.1 Managerial Implications & Suggestions 50 5.2 Limitation & Future Directions 51 References 53 | |
| dc.language.iso | en | |
| dc.title | 以層級貝氏模型預測廠商異質性下之銷售量—以晶片廠商為例 | zh_TW |
| dc.title | Forecasting Sales Volume of Industrial Product with Firm Heterogeneity—Case of Mobile Phone Chipset | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 98-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 謝明慧,陳厚銘 | |
| dc.subject.keyword | 預測,工業品,層級貝氏, | zh_TW |
| dc.subject.keyword | forecast,industrial product,Hierarchical Bayesian, | en |
| dc.relation.page | 56 | |
| dc.rights.note | 同意授權(全球公開) | |
| dc.date.accepted | 2010-07-21 | |
| dc.contributor.author-college | 管理學院 | zh_TW |
| dc.contributor.author-dept | 國際企業學研究所 | zh_TW |
| 顯示於系所單位： | 國際企業學系 | |
文件中的檔案：
| 檔案 | 大小 | 格式 | |
|---|---|---|---|
| ntu-99-1.pdf | 463.63 kB | Adobe PDF | 檢視/開啟 |
系統中的文件，除了特別指名其著作權條款之外，均受到著作權保護，並且保留所有的權利。