隨著工業科技時代的演進，德國政府率先各國發佈了未來科技的新趨勢「工業4.0」，「工業4.0」是以推動製造業智慧化轉型為宗旨。而在台灣，行政院將德國所推行的「工業4.0」命名為「生產力4.0」，並在每年一度的科技會報當中核定推動我國「生產力4.0」發展方案，將其「生產力4.0」分類為農業4.0、製造業4.0以及商業4.0等三方向發展。
在其製造業智慧化的主要發展的體現下關注的是物聯網、雲端運算以及大數據分析結合的應用。而本研究把主要焦點放在大數據分析中，所謂的大數據是指具有「快速」產生「多變」的「大量」資料等特性的數據。在大數據分析中，所分析的資料是屬於非結構化的資料，非結構化的資料是指沒有固定格式，還沒有明確分析邏輯的資料，例如：工業資料、醫療數據資料等等。
本研究將以Fargo醫療集團的個案作為研究題材，並根據它的數據資料庫進行醫療管理分析，而在個案研究數據資料庫裡資料是屬於非結構化的醫療人力大數據資料，所以必須經由前處理過後才能進行醫療管理預測的部分，經由預測過後的結果希望能夠提供相關業者醫療管理決策分析的參考。
本研究將數個時間序列分析預測的方法，包括天真法、簡單移動平均法、指數平滑法以及簡單線性迴歸等應用到這個特殊資料庫中，並研究利用R程式語言軟體撰寫程式進行實驗分析，當中發現簡單線性迴歸較適合這類型的資料，具有較好的大數據預測成效。

With the evolution of the information technology, German government proposes the trend of the revolution “Industry 4.0.” In Taiwan, our government renamed the “Industry 4.0” to ‘‘Productivity 4.0,” and they promote the ‘‘Productivity 4.0” development program at the annual tech-report in our country, regarding the ‘‘Productivity 4.0” classified as ‘‘Agriculture 4.0,” ‘Manufacturing 4.0” and ‘‘Commercial 4.0.”
The purpose of the 4th industry revolution is to transform the manufacturing industry more intelligent in addition to machine automation. Intelligent manufacturing industry focuses on the integration of Internet of Things, cloud computing and big data analytics. In this study, we focus on the big data analytics, having three properties: velocity, variety and volume. In big data analytics, all of the data is unstructured data, which means that data with no fixed data structure and undefined logical relationships, such as industry data, healthcare data, etc.
In this thesis, we conduct big data analysis and use dataset from the Fargo’s medical group as case study in the field of healthcare management. The data in the case study’s database belongs to unstructured dataset, so the dataset has to be preprocessed in order to do healthcare management prediction. By conducting big data analytics to perform predictive analysis, our experiment results can provide valuable information in the healthcare management.
Several time series prediction methods, including naive method, moving average method, exponential smoothing method and simple linear regression. Experiment results using the R program found out that the simple linear regression is more suitable and more effective in this type of data as well as big data analytics.

中文參考文獻
于宗先(1972)，經濟預測，大中國出版社。
方世杰(1988)，市場預測方法一百種，書泉出版社。
方匡南、朱建平、姜葉飛(2015)，R語言資料分析活用範例詳解，碁峰資訊。
行政院科技會報(2015)，行政院生產力4.0發展方案。
何應欽(2011)，作業管理，滄海書局。
李鈞(2011)，生產管理，前程文化。
呂宗學、蘇慧貞(2013)，海量資料分析在醫療照護領域的應用，醫療品質專欄，17(6)，652–661。
韋康博(2015)，工業4.0，書泉出版社，商周出版。
經濟部工業局(2015)，生產力4.0推動作法與未來展望，台北。
賴奕銓(2010)，作業管理原理，雙葉書廊。
韓偉(2015)，巨量資料的第一步R語言與商業應用，上奇資訊。
簡禎富、許嘉裕(2014)，資料挖礦與大數據分析，前程文化。
施雅月、賴錦慧(2007)，資料探勘，台灣培生教育。
謝明瑞(2015)，大數據分析-以柯P現象為例，http://www.npf.org.tw/2/14788。
GEO DISCOVERY(2008)，http://museum02.digitalarchives.tw/teldap/2008/GeoDiscovery/5-4-1.html。
 
英文參考文獻
Adam, E.-E., Berry W.-L., Whybar, D.-C., (1978), “An experimental comparison of exponential and adaptive smoothing forecasting models using actual operating data,” Computer and Industrial Engineering, 2(2), 91–98.
Bergs, J., Heerinckx, P., Verelst, S., (2014), “Knowing what to expect, forecasting monthly emergency department visits: A time-series analysis,” International Emergency Nursing, 22, 112–115.
Cam, M.-L., Daoud, A., Zmeureanu, R., (2016), “Forecasting electric demand of supply fan using data mining techniques,” Energy, 101, 541–557.
Cha, S., Abusharekh, A., Abidi, S.-S., (2015), “Towards a big Health data analytics platform,” IEEE First International Conference on Big Data Computing Service and Applications, 233–241.
Chen, Y., Kelton, C.-M.-L., Jing Y., Guo, J.-J., Li, X., Pharm N.-C., (2008), “Utilization, price, and spending trends for antidepressants in the US medicaid program,” Research in Social and Administrative Pharmacy, 4, 244–257.
Chu, F.-L., (2009), “Forecasting tourism demand with ARMA-based methods,” Tourism Management, 30, 740–751.
Demchenko, Y., Laat, C.-D., Membrey, P., (2014), “Defining architecture components of the big data ecosystem,” Collaboration Technologies and Systems, 104–112.
Faller, C., Feldmüller, D., (2015), “Industry 4.0 learning factory for regional SMEs,” Procedia CIRP, 32, 88–91.
Fan, S., Lau, R.-Y.K., Zhao, J.-L., (2015), “Demystifying big data analytics for business intelligence through the lens of marketing mix,” Big Data Research, 2, 28–32.
Flood, M.-D., Lemieux, V.-L., Varga, M., Wong, B.L.-W., (2016), “The application of visual analytics to financial stability monitoring,” Journal of Financial Stability. (10.1016/j.jfs.2016.01.006)
Gadepally, V., Kepner, J., (2014) “Big data dimensional analysis,” High Performance Extreme Computing Conference, 1–6.
Gandomi, A., Haider, M., (2015), “Beyond the hype: Big data concepts, methods, and analytics,” International Journal of Information Management, 35, 137–144.
Hashem, I.-A.-T., Chang, V., Anuar, N.-B., Adewole, K., Yaqoob, I., Gani, A., Ahmed, E., Chiroma, H., (2016), “The role of big data in smart city,” International Journal of Information Management, 36, 748–758.
Hay, K.-L., Bull, B.-S., (2009), “Statistical clues to postoperative blood loss: Moving averages applied to medical data,” Blood Cells, Molecules, and Diseases, 43, 250–255.
Heizer, J., Render, B., (2003), Principles of Operations Management, Pearson Education Inc, 7th ed.
Herland, M., Khoshgoftaar, T.-M., Wald, R., (2013), “Survey of clinical data mining applications on big data in health informatics,” International Conference on Machine Learning and Applications, 2, 465–472.
Ho, S.-L., Xie, M., (2016), “The use of ARIMA models for reliability forecasting and analysis,” Computers and Industrial Engineering, 32, 213–216.
Hoot, N.-R., Zhou, C., Jones, I., Aronsky, D., (2007), “Measuring and forecasting emergency department crowding in real time,” Health policy and clinical practice/original research, 49, 747–755.
Huang, M.-L., Huang, T.-W., Zhang, X., (2016), “A novel virtual node approach for interactive visual analytics of big datasets in parallel coordinates,” Future Generation Computer Systems, 55, 510–523.
Iversen, T., (2000), “Potential effect of internal markets on hospitals’ waiting time,” European Journal of Operational Research, 121, 467–475.
Ji, H., Songlin, W., Qinglin, W., Xiaonan, C., (2012), “Douhe reservoir flood forecasting model based on data mining technology,” Procedia Environmental Sciences, 12, 93–98.
John, E.-H., Dean, W.-W., (2005), Business Forecasting, Pearson Prentice Hall Companies Inc, 8th ed.
Khachatryan, D., (2014), Fargo Health Group: Managing the Demand for Medical Examinations Using Predictive Analytics, Harvard Business Publishing, USA.
Kwon, O., Lee, N., Shin, B., (2014), “Data quality management, data usage experience and acquisition intention of big data analytics,” International Journal of Information Management, 34, 387–394.
Larburu, N., Bults, R., Sinderen, M.-V., Hermens, H., (2016), “Quality-of-data management for telemedicine systems,” Procedia Computer Science, 63, 451–458.
Lee, J., Kao, H.-A., Yang, S., (2014), “Service innovation and smart analytics for industry 4.0 and big data environment,” Procedia CIRP, 16, 3–8.
Lee, J., Lapira, E., Bagheri, B., Kao, H.-A., (2013), “Recent advances and trends in predictive manufacturing systems in big data environment,” Manufacturing Letters, 1, 38–41.
Li, M., Chen, G., Lin, Z., Cai, B., (2009), “Combination forecasting model based on drift,” International Joint Conference on Computational Sciences and Optimization, 443–446.
Lin, W.-T., (1989), “Modeling and forecasting hospital patient movements: unilabiate and multiple time series approaches,” International Journal of Forecasting, 5, 195–208.
Lo, S.-C., Hall, R.-W., (2008), “The design of real-time logistics information system for trucking industry,” Computers and Operations Research, 35, 3439–3451.
Makridakis, S., Wheelwright, S., (1973), “Integrating forecasting and planning,” Long Range Planning, 6(3), 53–63.
Miguel, F.-E., Hughes, P., Gulijk, C.-V., (2016), “Visual analytics for text-based railway incident reports,” Safety Science, 89, 72–76.
Nowotarski, J., Liu, B., Weron, R., Hong, T., (2016), “Improving short term load forecast accuracy via combining sister forecasts,” Energy, 98, 40–49.
Pandey, S., Tokekar, V., (2014), “Prominence of MapReduce in big data processing,” Fourth International Conference on Communication Systems and Network Technologies, 555–560.
Parise, S., Iyer, B., Vesset, D., (2012), “Four Strategies to capture and create value from big data,” Ivey Business Journal, 76(4), 1–9.
Seematter-Bagnoud, L., Fustinoni, S., Dung, D.-H., Santos-Eggimann, B., Koehn, V., Bize, R., Oettli, A., Wasserfallen, J.-B., (2015), “Comparison of different methods to forecast hospital bed needs,” European Geriatric Medicine, 6, 262–266.
Siddiqa, A., Hashem, I.-A.-T., Yaqoob, I., Marjani, M., Shamshirband, S., Gani, A., Nasaruddin, F., (2016), “A survey of big data management: Taxonomy and state-of-the-art,” Journal of Network and Computer Applications, 71, 151–166.
Slawnych, M.-P., Nieminen, T., Kähönen, M., Kavanagh, K.-M., Lehtimäki, T., Ramadan, D., Viik, J., Aggarwal, S.-G., Lehtinen, R., Ellis, L., Nikus, K., Exner, D.-V., (2009), “Post-exercise assessment of cardiac repolarization alternant in patients with coronary artery disease using the modified moving average method,” Journal of the American College of Cardiology, 53(13), 1130–1137.
Stevenson, W.-J., (2008), Operations Management, McGraw-Hill Companies Inc, 10th ed.
Stock, T., Seliger, G., (2016), “Opportunities of sustainable manufacturing in industry 4.0,” Procedia CIRP, 40, 536–541.
Swan, M., (2015), “Philosophy of big data expanding the human-data relation with big data science services,” IEEE First International Conference on Big Data Computing Service and Applications, 468–477.
Tandberg, D., Tibbetts, J., Sklar, D.-P., (1989), “Time series forecasts of ambulance run volume,” The American Journal of Emergency Medicine, 16(3), 232–237.
Waller, M.-A., Fawcett, S.-E., (2013), “Data science, predictive analytics, and big data: a revolution that will transform supply chain design and management,” Journal of Business Logistics, 34(2), 77–84.
Wang, S., Wan, J., Zhang, D., Li, D., Zhang, C., (2016), “Towards smart factory for industry 4.0: A self-organized multi-agent system with big data based feedback and coordination,” Computer Networks, 101, 158–168.
Wang, W.-C., Chau, K.-W., Qiu, L., Chen, Y.-B., (2015), “Improving forecasting accuracy of medium and long-term runoff using artificial neural network based on EEMD decomposition,” Environmental Research, 139, 46–54.
Wilson, J.-H., Keating, B., (2009), Business Forecasting with Forecast XTM, McGraw-Hill Companies Inc, 6th ed.
Xie, J., Wang, Y., Zheng, X., Yang, Q., Wang, T., Zou, Y., Xing, J., Dong, Y., (2016), “Modeling and forecasting acinetobacter baumannii resistance to set appropriate use of cefoperazone-sulbactam: Results from trend analysis of antimicrobial consumption and development of resistance in a tertiary care hospital,” American Journal of Infection, 43, 861–4.
YalçÕn, N., Köse, U., (2010), “What is search engine optimization: SEO?” Procedia Social and Behavioral Sciences, 9, 487–493.
Yang, Y., Yu, C., (2015), “Prediction models based on multivariate statistical methods and their applications for predicting railway freight volume,” Neurocomputing, 158, 210–215.
Zhang, J., Huang, M.-L., Wang, W.-B., Lu, L.-F., Meng, Z.-P., (2014), “Big data density analytics using parallel coordinate visualization,” International Conference on Computational Science and Engineering, 1115–1120.
Zhang, X.-I., Zhu, T., Luo, L., He, C.-Z., Cao, Y., Shi, Y.-K., (2013), “Forecasting emergency department patient flow using markov chain,” International Conference on Service Systems and Service Management, 278–282.