LEADER 03934nam 22004333 450 001 9910847595903321 005 20240508080251.0 010 $a3-658-44453-3 035 $a(CKB)5860000000528023 035 $a(MiAaPQ)EBC31319773 035 $a(Au-PeEL)EBL31319773 035 $a(Exl-AI)31319773 035 $a(EXLCZ)995860000000528023 100 $a20240508d2024 uy 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aImplementation and Benefits of Digital Twin on Decision Making and Data Quality Management 205 $a1st ed. 210 1$aWiesbaden :$cSpringer Vieweg. in Springer Fachmedien Wiesbaden GmbH,$d2024. 210 4$d©2024. 215 $a1 online resource (188 pages) 311 $a3-658-44452-5 327 $aIntro -- Foreword by Atilla Wohllebe -- Foreword by Stefan Waitzinger -- Acknowledgment -- Contents -- Abbreviations -- List of Figures -- List of Tables -- 1 Introduction -- 2 Literature Review -- 2.1 Basic Definitions -- 2.1.1 Data and Big Data -- 2.1.2 Analytics -- 2.1.3 Digital Twin -- 2.1.4 Decision Making -- 2.2 Data Quality Management -- 2.2.1 Data Quality -- 2.2.2 Data Quality Management -- 2.2.3 Corporate Data Quality Management -- 2.2.4 Data Quality Dimensions -- 2.3 Digital Twin -- 2.3.1 Digital Twin for Decision Making -- 2.3.2 Process Digital Twin -- 2.3.3 Five-Dimensional Digital Twin -- 2.3.4 Requirements -- 2.3.5 Industry Dissemination -- 2.3.6 Benefits -- 2.4 Decision Support System -- 2.4.1 Decision Support System -- 2.4.2 Model-Driven Decision Support System -- 2.4.3 Characteristics -- 2.5 Summary-Digital Twin-Driven Decision-Making Model -- 3 Objectives -- 3.1 Strategic Positioning -- 3.2 Digital Twin-Driven Decision-Making Model -- 3.3 Operational Effectiveness -- 4 Materials and Methods of Dissertation -- 4.1 Research Design -- 4.2 Data Collection and Sample Description -- 4.2.1 Data Collection Procedure -- 4.2.2 Sample Description -- 4.3 Methods for Data Analysis -- 5 Results and Evaluation -- 5.1 Data Analysis-Preliminary Study -- 5.1.1 Strategic Positioning -- 5.1.2 The Digital Twin-Driven Decision-Making Model -- 5.2 Data Analysis-Main Study -- 5.2.1 Strategic Positioning -- 5.2.2 The Digital Twin-Driven Decision-Making Model -- 5.2.3 Operational Effectiveness -- 6 Conclusions and Recommendations -- 6.1 Conclusion of Hypotheses -- 6.2 Recommendations -- 6.3 Practical Implications -- 6.4 Limitations -- 7 New Scientific Results -- 8 Summary -- References. 330 $aThis dissertation by Florian Blaschke explores the implementation and benefits of digital twin technology in decision-making and data quality management. With a focus on applications within manufacturing and e-commerce, the work highlights how digital twins, as virtual representations of physical systems, can optimize operations, enhance data accuracy, and support informed decision-making processes. The study delves into the potential of digital twins to improve productivity, reduce costs, and personalize consumer experiences. It emphasizes the importance of data quality management and suggests that digital twins will become increasingly vital in research and practice, particularly in the context of Industry 4.0. The book targets professionals and academics interested in the intersection of management, IT, and emerging technologies.$7Generated by AI. 606 $aDigital twins (Computer simulation)$7Generated by AI 606 $aData integrity$7Generated by AI 615 0$aDigital twins (Computer simulation) 615 0$aData integrity. 700 $aBlaschke$b Florian$01777111 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910847595903321 996 $aImplementation and Benefits of Digital Twin on Decision Making and Data Quality Management$94296569 997 $aUNINA