簡易檢索 / 詳目顯示

研究生: 蔡政宏
Tsai, Cheng-Hung
論文名稱: 傳統產業數碼轉型 - 以印刷業為例
Digital Transformation for Traditional Industries --- Taking the example of printing business
指導教授: 周志遠
Chou, Jerry
口試委員: 周哲維
Chou, Wade
賴冠州
Lai, Kuan-Chou
學位類別: 碩士
Master
系所名稱: 教務處 - 智慧製造跨院高階主管碩士在職學位學程
AIMS Fellows
論文出版年: 2021
畢業學年度: 109
語文別: 英文
論文頁數: 84
中文關鍵詞: 印刷產業數碼轉型數碼印刷工業3.5唯一碼一物一碼可變印紋電子商務平台UNISON架構決策方法
外文關鍵詞: Printing Business, Digital Printing, Unique Identification (UID) label, Variant Data Printing (VDP)
相關次數: 點閱:2下載:0
分享至:
查詢本校圖書館目錄 查詢臺灣博碩士論文知識加值系統 勘誤回報
  • 論文摘要

    傳統產業的數碼轉型是攸關企業生存的關鍵因素, 而現今面對艱困環境挑戰的正是這些半自動化或是以人力為主的中小企業。

    德國在提出工業4.0整合IT及OT的架構下, 以大量的自動化設備及產線環境感測設施, 建構完整的即時資訊體系, 以達成實時間控的目的。這些對台灣中小型的傳統產業而言都還太過遙遠, 相對的, 簡禎富教授所提出的工業3.5理念, 以人為主軸, 輔以各式強化工具來增進產線效率, 做為過渡階段的務實方法, 成為一個重要的指引, 帶領中小企業一步一腳印的走向數碼轉型之路。

    本研究論文將以傳統印刷產業為基礎, 引用工業3.5的理念及UNISON架構的決策方法, 思考新的印刷產業營運模式、善用先進資訊技術與平台並進行驗證。 內容中提出利用數碼印刷的可變印紋特性, 產生唯一碼標籤與商品結合, 並在電子商務平台上進行線上的行銷活動, 希望在競爭激烈的產業生存戰場中, 能脫穎而出, 開創一片新局。

    關鍵詞 : 印刷產業、數碼轉型、中小企業、數碼印刷、工業3.5、唯一碼、一物一碼、可變印紋、電子商務平台、UNISON架構決策方法


    ABSTRACT

    Digital transformation had been one of the crucial factors for traditional manufacturing companies to survive in harsh competition environments nowadays. Those are usually small and medium size business(SMB) companies with partial automatic production lines and labor-intensive workflows.

    Industry 4.0, which tends to implement fully automated production lines by integrating information technologies (IT) and operation technologies (OT), seems to be a good idea for industry upgrade, however, it is too far away for SMB companies to reach. Moreover, the massive capital investments in environmental sensors, computers and network connections among machines are also big challenges to them. In this case, Industry 3.5 was raised as the middle stage from Industry 3.0 to 4.0 and the relative strategies are provided for small and medium companies. This is a practical way for traditional manufacturing business to equipped with reasonable IT investments based on OT demands and domain know-how.

    In this paper, we will focus on the ancient and traditional manufacturing --- the printing business and explore the ways of digital transformation for printing business. Digital printing technologies has become the major driving force nowadays to fulfill the demands of customers in variant data printing. Digital transformation of printing business must integrate the new technology solutions and provide fully automatically information flows in order to meet the challenges of digital era.
    A new business model leveraging the power of digital printing and variant data printing are proposed and verified in this discussion. Of course, by implementing unique identification labels on products, brand name customers will be able to build up the communication bridges directly to end users, and the data collected on the service platform will help the brand name customers to conduct precisely marketing events and obtain more market shares.
    This is also a totally new business model prototype for printing business to migrate from traditional industry to information service business and then start the magic journey of digital transformation. The concept also matches with industry 3.5 philosophy --- to provide effective and reasonable AI tools for production lines based on problem solving demands. Also, UNISON framework is leveraged for decision making. This is how traditional industries can go further step by step.

    Keywords: Printing Business, Digital Transformation (DX), SMB, Digital Printing, Industry 3.5, Unique Identification (UID) label, Variant Data Printing (VDP), e-commerce, UNISON

    CONTENTS 目錄 論文摘要 II ABSTRACT III ACKNOWLEDGE V CONTENTS VI FIGURES IX TABLES X Chapter 1 --- Introduction 11 1.1 Printing Industry Challenges 11 1.1.1 Digital printing 11 1.1.2 Manufacturing data collection 12 1.1.3 Operation know-how transfer 12 1.1.4 Printing business digital transformation 13 1.2 Unique Identification labels --- a variable data printing solution 14 1.3 Objective & Contribution 18 Chapter 2 --- Related Works 21 2.1 Current studies on printing business 21 2.2 Parallel Processing Technologies 23 2.3 Bloom Filter Algorithm 24 2.4 Computer visions for defect detection 25 Chapter 3 --- Research Framework and Domain Knowledge 27 3.1 Industry 3.5 approaches 27 3.1.1 UNISON framework for decision making 30 3.1.2 Industry 3.5 approach 30 3.2 UID label printing with unique ID numbers 32 3.3 Unique ID code generation and verification 33 3.4 Parallel processing for UID production 35 3.5 Information linkage for smart manufacturing 36 3.6 UID platform services 36 Chapter 4 --- Printing Business Issues and Solutions 40 4.1 Digital transformation initiatives 40 4.1.1 Missions 40 4.1.2 Problems definition by UNISON 41 4.2 Parallel Processing for UID duplication checking 43 4.2.1 UID duplication checking 43 4.2.2 Parallel Process computing 45 4.2.3 POC results and achievements 50 4.3 Bloom Filter Algorithm for UID verification 52 4.3.1 Search mechanism for huge data in database 52 4.3.2 Bloom Filter 52 4.3.3 Optimized Bloom Filter parameters 56 4.4 e-commerce platform for UID activities 58 4.4.1 MOTLAB services on e-commerce platform 59 4.4.2 Service system design & deployment 61 4.4.3 Cloud-native CI/CD environment build up 63 4.4.4 UID activation and system integration 65 4.5 Future works --- Label Defect detections 68 4.5.1 Current processes of defect inspections 68 4.5.2 AI model trial 70 4.5.3 Summary 74 Chapter 5 --- Conclusions 76 5.1 Adopt UNISON decision making framework 76 5.2 Leverage Parallel processing technologies for UID duplication checks 77 5.3 Implement Bloom Filter Algorithm for UID verifications 78 5.4 Provide MOTLAB service platform for UID activities 79 5.5. Recognize the importance of data quality 80 REFERENCES 83

    REFERENCES

    1. Kletti, J., Manufacturing execution system-MES. 2007: Springer.
    2. Ku, C.-C., C.-F. Chien, and K.-T. Ma, Digital transformation to empower smart production for Industry 3.5 and an empirical study for textile dyeing. Computers & Industrial Engineering, 2020. 142: p. 106297.
    3. Dhingra, U. and S.S. Sharma, Impact of unique identification systems. Indian Journal of Economics and Development, 2019. 7(8).
    4. Ilie-Zudor, E., et al., A survey of applications and requirements of unique identification systems and RFID techniques. Computers in Industry, 2011. 62(3): p. 227-252.
    5. Gupta, N., Unique identification code system for entity verification. 2019, Google Patents.
    6. Pandey, D. and K. Pandey. Improvement of Searching Methodology for Network Applications using Bloom Filters and Hashing. in 2020 Sixth International Conference on Parallel, Distributed and Grid Computing (PDGC). 2020. IEEE.
    7. Peng, B., Digital printing technology and its application in packaging printing. The International Journal of Electrical Engineering & Education, 2021: p. 0020720921996609.
    8. Cheng, F. Application Analysis of Digital Printing Technology in Packaging Printing. in E3S Web of Conferences. 2020. EDP Sciences.
    9. Haik, O., et al. A Novel Inspection System For Variable Data Printing Using Deep Learning. in The IEEE Winter Conference on Applications of Computer Vision. 2020.
    10. Qiu and Chang, Algorithms and architectures for parallel processing. 2020: Springer.
    11. Bahrami, M. and M. Singhal, The role of cloud computing architecture in big data, in Information granularity, big data, and computational intelligence. 2015, Springer. p. 275-295.
    12. Jorgensen, S.G., Bloom filter with memory element. 2015, Google Patents.
    13. Debnath, B., et al. BloomFlash: Bloom Filter on Flash-Based Storage. in 2011 31st International Conference on Distributed Computing Systems. 2011.
    14. Villalba-Diez, J., et al., Deep Learning for Industrial Computer Vision Quality Control in the Printing Industry 4.0. Sensors, 2019. 19(18): p. 3987.
    15. Voulodimos, A., et al., Deep Learning for Computer Vision: A Brief Review. Computational Intelligence and Neuroscience, 2018. 2018: p. 7068349.
    16. Hecht, O. and G. Dishon. Automatic optical inspection (AOI). in 40th Conference Proceedings on Electronic Components and Technology. 1990. IEEE.
    17. Hsieh, R.-J., J. Chou, and C.-H. Ho. Unsupervised online anomaly detection on multivariate sensing time series data for smart manufacturing. in 2019 IEEE 12th Conference on Service-Oriented Computing and Applications (SOCA). 2019. IEEE.
    18. Chien, C.-F., H.-K. Wang, and W.-H. Fu, Industry 3.5 Framework of an Advanced Intelligent Manufacturing System: Case Studies from Semiconductor Intelligent Manufacturing. Management Review, 2018. 37(3): p. 105-121.
    19. Chien, C.-F., Decision analysis and management: A unison framework for total decision quality enhancement. Yeh-Yeh Book Gallery, Taipei, Taiwan, 2005.
    20. Chien, C.-F., T.-Y. Hong, and H.-Z. Guo, An empirical study for smart production for TFT-LCD to empower Industry 3.5. Journal of the Chinese Institute of Engineers, 2017. 40(7): p. 552-561.
    21. Butzin, B., F. Golatowski, and D. Timmermann. Microservices approach for the internet of things. in 2016 IEEE 21st International Conference on Emerging Technologies and Factory Automation (ETFA). 2016. IEEE.
    22. Soon, T.J., QR code. Synthesis Journal, 2008. 2008: p. 59-78.
    23. Hsu, C.-H., et al. Energy-aware task consolidation technique for cloud computing. in 2011 IEEE Third International Conference on Cloud Computing Technology and Science. 2011. IEEE.
    24. Ghazi, M.R. and D. Gangodkar, Hadoop, MapReduce and HDFS: a developers perspective. Procedia Computer Science, 2015. 48: p. 45-50.
    25. Karun, A.K. and K. Chitharanjan. A review on hadoop—HDFS infrastructure extensions. in 2013 IEEE conference on information & communication technologies. 2013. IEEE.
    26. Yang, X.Y., Z. Liu, and Y. Fu. MapReduce as a programming model for association rules algorithm on Hadoop. in The 3rd International Conference on Information Sciences and Interaction Sciences. 2010. IEEE.
    27. Bhavsar, S., J. Rangras, and K. Modi, Automating Container Deployments Using CI/CD, in Data Science and Intelligent Applications. 2021, Springer. p. 423-429.
    28. Wettinger, J., U. Breitenbücher, and F. Leymann. Standards-based DevOps automation and integration using TOSCA. in 2014 IEEE/ACM 7th International Conference on Utility and Cloud Computing. 2014. IEEE.
    29. Chitradevi, B. and P. Srimathi, An overview on image processing techniques. International Journal of Innovative Research in Computer and Communication Engineering, 2014. 2(11): p. 6466-6472.

    QR CODE