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Author: 黃振益
Chen-I Huang
Thesis Title: 應用智慧型手機的市場反饋數據進行可靠度分析
Using the field return data of smartphones to analyze its reliability
Advisor: 王福琨
Fu-Kwun Wang
Committee: 杜志挺
Chih-ting Du
許總欣
Tsung-Shin Hsu
林則孟
James T. Lin
陳鴻基
Houn-Gee Chen
Degree: 博士
Doctor
Department: 管理學院 - 管理研究所
Graduate Institute of Management
Thesis Publication Year: 2013
Graduation Academic Year: 101
Language: 英文
Pages: 60
Keywords (in Chinese): 智慧型手機市場反饋數據區間設限資料最大概似法
Keywords (in other languages): Smartphones, Field return data, Interval censored data, Maximum likelihood estimation
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  • 近年來智慧型手機裝置已經成為人們每日不可或缺的重要工具與資訊獲得媒介。智慧型手機的正常使用與品質穩定度因素對於消費者、手機銷售商與製造商有著不同的影響層面,對於後兩者而言其可靠度與失效率的分析數據探討研究便顯得日益重要。由於智慧型手機的設計能力及製造技術不斷更新,客戶對智慧型手機之可靠性要求也不斷的相對提高。因此,本研究使用國內某手機維修商所蒐集而得的品牌A、B、C三款不同智慧型手機於銷售後報修數據來進行探討,本研究假設三品牌不同款的品質是一致。利用統計分析手法與工具軟體計算出四種失效機率分佈函數(Exponential、Log-normal、Log-logistic、Weibull)之各種參數值。並運用最大概似法(Maximum likelihood estimation, MLE)評估這三款智慧型手機的最大概似值(Log-likelihood value),以決定那款智慧型手機最適合的失效機率分佈。計算出品牌A、B、C三款智慧型手機的分佈市場預估失效率。本研究所得之結論將能提供在這市場主要角色如消費者、手機銷售商與製造商等對於智慧型手機產品之品質、銷售策略、售後保固產品服務內容包裝與製程改善等有所助益。


    In recent years, smartphones have become indispensable tools and media for obtaining information, and the standard usage and quality stability factors of smartphones have a significant and varied influence on consumers, mobile phone retailers, and manufacturers. Consequently, investigative and data analysis studies exploring smartphone reliability and failure rates have become increasingly important for mobile phone retailers and manufacturers. The design capabilities and manufacturing technologies for smartphones are continuously upgraded, and consumer demands for reliability increase correspondingly. Therefore, this study investigated after-sales repair and maintenance data obtained from a Taiwanese mobile phone maintenance provider for three brands of smartphones (A, B, and C). We assumed that the quality of the different models of the three brands is consistent. Statistical analysis techniques and software were used to calculate the parameter values of four failure probability distribution functions (i.e., exponential, log-normal, log-logistic, and Weibull). The maximum likelihood estimation (MLE) method was also employed to assess the log-likelihood value and determine the most appropriate failure probability distribution for each smartphone. Finally, we calculated the predicted market failure rate for the A, B, and C smartphone brands. The results and conclusion obtained in this study can benefit important market players, such as consumers, mobile phone retailers, and manufacturers, regarding smartphone quality, sales strategies, after-sales warranty service packages, and manufacturing process improvements.

    Chapter 1 Introduction ........................................1 1.1 Research background .......................................1 1.2 Research objectives .......................................3 1.3 Research procedure ........................................4 Chapter 2 Literature review ...................................8 2.1 Smartphone technologies and applications ..................8 2.2 Reliability and field return data .........................9 2.3 Failure concept and definition ...........................11 2.4 Data types ...............................................15 Chapter 3 Methodology ........................................20 3.1 Survival analysis method .................................20 3.2 Introduction to interval-censored data estimation ........22 3.3 Reliability functions and product life ...................23 3.4 Maximum likelihood estimation ............................31 3.5 Analysis procedure .......................................34 Chapter 4 Data analysis ......................................37 4.1 Smartphone failure data ..................................37 4.2 Smartphone survival analysis .............................39 4.3 Failure rate prediction and efficacy assessment ..........40 4.4 Log-likelihood and lifetime values for the three brands ..41 4.5 Discussions ..............................................42 Chapter 5 Conclusion and future studies ......................55 5.1 Conclusion ...............................................55 5.2 Future studies ...........................................56 References ...................................................59

    [1] Andrews, J. and B. Moss, Reliability and Risk Assessment, The American Society of Mechanical Engineers, New York, NY (2002).
    [2] Bharatendra, K. and S. Nanua, Reliability Analysis and Prediction with Warranty Data, CRC Press, New York, NY (2009).
    [3] Blischke, W. R. and P. Murthy, Reliability: Modeling, Prediction, and Optimization, Wiley, New York, NY (2000).
    [4] Chen, S., F. Sun and J. Yang, “A new method of hard disk drive MTTF projection using data from and early life test,” Annual Reliability and Maintainability Symposium, 252–257 (1999).
    [5] Collins, J. A. and H. M. Bratt, “The failure-experience matrix: A useful design tool,” Journal of Engineering for Industry, 98(3), 1074-1079 (1976).
    [6] Carolina, M., “Hype cycle for mobile device technologies 2010,” Available online at: www.gartner.com (accessed October 10, 2012).
    [7] Cisco Visual Networking Index, “Global mobile data traffic forecast update, 2011–2016,” Available online at: www.cisco.com (accessed December 10, 2012).
    [8] Hintze, J., NCSS 2004, NCSS LCC, Kaysville, Utah, UT (2007).
    [9] Hong, Y. and W. Q. Meeker, “Field-failure and warranty prediction based on auxiliary use-rate information,” Technometrics, 52(2), 148-159 (2010).
    [10] HRD 4, “Handbook of reliability data for components used in telecommunications systems,” Available online at: infostore.saiglobal.com (accessed October 23, 2012).
    [11] Ian, F. A., S. Mohanty and J. Xie, “A ubiquitous mobile communication architecture for next-generation heterogeneous wireless systems," Communications Magazine, 43(6), 29-36 (2005).
    [12] Jeffrey, H. and G. Borriello, “Location systems for ubiquitous computing,” Computer, 34(8), 57-66 (2001).
    [13] Kalbfleisch, J. D. and J. F. Lawless, “Estimation of reliability in field-performance studies,” Technometrics, 30(4), 365–378 (1988).
    [14] Klein, J. P. and M. L. Moeschberger, Survival Analysis Techniques for Censored and Truncated Data, Springer, New York, NY (2003).
    [15] Kaplan, E. L. and P. Meier, “Nonparametric estimation from incomplete observations,” Journal of the American Statistical Association, 53(282), 457-481 (1958).
    [16] Lawless, J. F., M. J. Crowder and K. A. Lee, “Analysis of reliability and warranty claims in products with age and usage scales,” Technometrics, 51(1), 14-24 (2009).
    [17] Lewis, C. D. International and Business Forecasting Methods, Butterworth-Heinemann, London, UK (1982).
    [18] Lin, C. F., Survival Analysis, YehYeh Book Gallery, Taiwan (2008).
    [19] Marcos, E., G. Philip and I. Kosuke, “A framework for warranty prediction during product development,” Proceedings of 2005 ASEA International Mechanical Engineering Congress & Exposition, 1-8 (2008).
    [20] Meeker, W. Q. and A. Luis, Statistical Methods for Reliability Data, Wiley, New York, NY (1998).
    [21] Miller, R. G., Survival Analysis, Wiley, New York, NY (1982).
    [22] Nick, D., “Smart phones: Capabilities and trends in 2011 and beyond,” Available online at: ovum.com (accessed October 23, 2012).
    [23] Oh, Y. S. and D. S. Bai, “Field data analysis with additional after-warranty failure data,” Reliability Engineering & System Safety, 72(1), 12-25 (2001)
    [24] Pan, R., “A bayes approach to reliability prediction using data from accelerated life tests and field failure observations,” Quality and Reliability Engineering International, 25(2), 229-240 (2009)
    [25] Perera, U. D., “Reliability index - A method to predict failure rate and monitor maturity of mobile phones,” Reliability and Maintainability Symposium, 2006, Newport Beach, CA (2006).
    [26] Rausand, M. and A. Hoyland, System Reliability Theory – Models and Statistical Methods, Wiley, New York, NY (1994).
    [27] Suzuki, K., “Role of field performance data and its analysis,” in N. Balakrishnan, Recent Advances in Life Testing and Reliability, CRC Press, New York, NY, 141-151 (1995).
    [28] Singpurwalla, N. D. and S. Wilson, “The warranty problem: its statistical and game theoretic aspects,” SIAM Review, 35(1), 17-42 (1993).
    [29] Tobias, P. A. and T. David, Applied Reliability, Chapman and Hall, Texas (2011).
    [30] Wang, C. H., Reliability Engineering Technical Manual, Chinese Society for Quality, Taipei (1996).
    [31] Wayne, L., “IHS iSuppli wireless communications smartphone report cumulative Android smartphone shipments will exceed 1 billion in 2013,” Available online at: www.isuppli.com (accessed December 11, 2012).
    [32] Wilson, S., T. Joyce and E. Lisay, “Reliability estimation from field return data,” Lifetime Data Analysis, 15(3), 397-410 (2009).
    [33] Wu, H. and W. Q. Meeker, “Early detection of reliability problems using information from warranty databases,” Technometrics, 44(2), 120–133 (2002).
    [34] Zhang, S., F. B. Sun and R. Gough, “Application of an empirical growth model and multiple imputation in hard disk drive field return prediction,” International Journal of Reliability, Quality and Safety Engineering, 17(6), 565-577 (2010).

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