Kooperativer Bibliotheksverbund

UID:

Format: 1 Online-Ressource (XV, 267 p. 75 illus., 45 illus. in color)

ISBN: 9789819986576

Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 978-981-9986-56-9

Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 978-981-9986-58-3

Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 978-981-9986-59-0

Language: English

DOI: 10.1007/978-981-99-8657-6

URL: Volltext  (URL des Erstveröffentlichers)

UID:

Format: 1 Online-Ressource (XV, 221 p. 35 illus)

Edition: 1st ed. 2024

ISBN: 9783031557798

Series Statement: CSR, Sustainability, Ethics & Governance

Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 978-3-031-55778-1

Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 978-3-031-55780-4

Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 978-3-031-55781-1

Language: English

DOI: 10.1007/978-3-031-55779-8

URL: Volltext  (URL des Erstveröffentlichers)

UID:

Format: 1 Online-Ressource (XXIII, 450 p. 7 illus., 2 illus. in color)

Edition: 1st ed. 2024

ISBN: 9783031452895

Series Statement: Palgrave Macmillan Studies in Banking and Financial Institutions

Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 978-3-031-45288-8

Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 978-3-031-45290-1

Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 978-3-031-45291-8

Language: English

DOI: 10.1007/978-3-031-45289-5

URL: Volltext  (URL des Erstveröffentlichers)

UID:

Format: 1 Online-Ressource (145 Seiten)

Edition: 1st ed

ISBN: 9781000961034

Series Statement: Sustainable Manufacturing Technologies Series

Content: This book presents innovative breakthroughs in Operational Excellence that can solve the operational issues of smart factories. The book illustrates various tools and techniques of Lean Six Sigma 4.0 and details their suitability for manufacturing and service systems

Note: Description based on publisher supplied metadata and other sources , Cover -- Half Title -- Series Information -- Title Page -- Copyright Page -- Table of Contents -- Preface -- About the Editors -- List of Contributors -- 1 Overview of Lean Six Sigma 4.0 for Operational Excellence -- 1.1 Introduction -- 1.2 Know-How On Lean Six Sigma 4.0 -- 1.3 Enablers of Lean Six Sigma 4.0 -- 1.4 Barriers to Lean Six Sigma 4.0 -- 1.5 Enabling Technologies of Lean Six Sigma 4.0 -- 1.6 Applications of Lean Six Sigma 4.0 to Industrial Sectors -- 1.7 Lesson Learned and Inferences -- References -- 2 Connection Between Lean Six Sigma (LSS) and Industrial IoT for Operational Excellence -- 2.1 Introduction -- 2.2 Theoretical Background -- 2.2.1 Industrial IoT -- 2.2.2 Lean Six Sigma -- 2.2.3 Industrial IoT and LSS Integration -- 2.3 Research Methodology -- 2.4 Results and Discussion -- 2.4.1 Enablers of Integrated IIoT With LSS -- 2.4.2 Barriers of Integrated IIoT With LSS -- 2.5 Conclusion -- References -- 3 Integrating Lean Six Sigma and Industry 4.0 -- 3.1 Introduction -- 3.2 Lean Six Sigma (LSS) -- 3.2.1 Traces of LSS -- 3.3 Industry 4.0 -- 3.3.1 Traces of I4.0 -- 3.4 Integration of I4.0 and LSS -- 3.4.1 Foundation of Integrating I4.0 and LSS -- 3.4.1.1 Requirements for Implementing I4.0 Are Established By LSS -- 3.4.1.2 While Each Is Being Deployed Independently, I4.0 and LSS Complement One Another -- 3.4.2 I4.0 and LSS: Integrating for Complementary Benefits -- 3.4.3 Potential Benefits of the Integration -- 3.4.3.1 The Influence of I4.0 Technologies On Operational Performance May Be Optimized With the Help of a Foundation Provided By LSS -- 3.4.3.2 Improve the Efficacy of Horizontal Integration -- 3.4.3.3 Improved Process Mapping -- 3.4.3.4 Performance Measuring -- 3.4.3.5 Instrumentation for Assessing Machine Health -- 3.4.3.6 Shorten the Time Required for Setting Up -- 3.4.3.7 Production Monitoring in Real-Time -- References , 4 Mapping Fourth Industrial Revolution Enabling Technologies -- 4.1 Introduction -- 4.1.1 Objectives -- 4.2 Literature Review -- 4.3 Methods -- 4.3.1 Identification of the Industry 4.0 Enabling Technologies -- 4.3.2 Industry 4.0 Maturity Index Analysis -- 4.4 Results and Discussions -- 4.4.1 Correlations Between Enabling Technologies of Industry 4.0 and Maturity Levels -- 4.5 Conclusion -- Acknowledgments -- Funding -- Declaration of Interest Statement -- JEL Code -- References -- 5 Lean, Six Sigma, and Industry 4.0 Technologies Connection and Inclusion -- 5.1 Introduction -- 5.2 Literature Review -- 5.2.1 Background On Lean Six Sigma Tools and Principles -- 5.2.2 Lean Tools and Principles and Their Purpose -- 5.2.3 Background On Industry 4.0 Technologies -- 5.2.4 I4.0 and Lean Six Sigma (LSS) Integration -- 5.2.5 Effects of I4.0/Smart Manufacturing With Lean Integration On Performance -- 5.2.6 Lean Manufacturing Problems and I4.0's Recommended Solutions -- 5.2.7 Effects of I4.0/Smart Manufacturing With Six Sigma On Performance -- 5.2.8 I4.0's Sustainability Features -- 5.3 Conclusions and Future Scope -- References -- 6 Exploring Critical Success Factors for LSS 4.0 Implementation: A Combined Systematic Literature Review and Interpretive Structural Modelling Approach -- 6.1 Introduction -- 6.2 Systematic Literature Review of LSS 4.0 Critical Success Factors -- 6.2.1 Background On LSS4.0 as an Integrated Approach -- 6.2.2 Identification of CSFs for Adopting LSS 4.0 -- 6.3 Research Methodology -- 6.4 Interpretive Structural Modeling - ISM Model -- 6.4.1 Questionnaire Development -- 6.4.2 Data Collection -- 6.4.3 SSIM Development -- 6.4.4 Initial Reachability Matrix Formation -- 6.4.5 Final Reachability Matrix Formation -- 6.4.6 Level Partitions -- 6.4.7 ISM-Based Model -- 6.4.8 MICMAC Analysis -- 6.5 Results, Discussion and Research Implications , 6.5.1 Theoretical and Practical Implications -- 6.6 Conclusion, Limitations and Future Research Perspectives -- References -- 7 Application of Industry 4.0, Digital Transformation, and Lean Six Sigma to Detect Cold Weld Defects -- 7.1 Introduction -- 7.2 Lean Six Sigma 4.0 Literature Review -- 7.3 Methodology -- 7.4 Results -- 7.4.1 Define -- 7.4.2 Measure -- 7.4.3 Analyze -- 7.4.4 Improve -- 7.4.5 Control -- 7.5 Conclusions -- References -- 8 Application of Lean Six Sigma 4.0 in Seed Potato Value Chain Performance Improvement -- 8.1 Introduction -- 8.2 Research Methodology -- 8.3 Current State Mapping -- 8.4 Future State Mapping -- 8.5 Conclusion -- References -- 9 Unleashing the Potential of Industry 4.0 in India: Opportunities, Challenges, and the Road Ahead -- 9.1 Introduction -- 9.2 Why Industry 4.0? -- 9.3 Global Success Stories -- 9.4 Limitations of the Existing Indian Manufacturing Scenario -- 9.5 Efficacy of Industry 4.0 in India -- 9.6 Challenges to Deploying Industry 4.0 in India -- 9.7 Enablers for Industry 4.0 in India -- 9.8 Indian Case Studies -- 9.9 Discussion of Indian Readiness to Deploy Industry 4.0 -- 9.10 Vital Recommendations -- 9.11 Conclusions -- References -- 10 Role of Lean Six Sigma 4.0 and Digital Forensic Tools in Cyber Investigation -- 10.1 Introduction -- 10.2 Digital Forensics -- 10.3 Lean Six Sigma 4.0 -- 10.4 Procedure to Transform Conventional Forensics Into a Digital Scenario -- 10.4.1 Standard Operating Protocol (SOP) of Investigation -- 10.5 Role of LSS 4.0 in Forensic DNA -- 10.5.1 DMAIC Process in Digital Forensics -- 10.6 Recommendations for Future Directions -- 10.7 Conclusion -- References -- Index

Additional Edition: Erscheint auch als Druck-Ausgabe Rathi, Rajeev Lean Six Sigma 4. 0 for Operational Excellence under the Industry 4. 0 Transformation Milton : Taylor & Francis Group,c2023 ISBN 9781032464190

Language: English

UID:

Format: 1 Online-Ressource (xv, 325 Seiten) , Diagramme, Karten

ISBN: 9789819973095

Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 978-981-99-7308-8

Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 978-981-99-7310-1

Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 978-981-99-7311-8

Language: English

Keywords: Aufsatzsammlung

DOI: 10.1007/978-981-99-7309-5

URL: Volltext  (URL des Erstveröffentlichers)

UID:

Format: 1 Online-Ressource (187 Seiten)

Edition: 1st ed

ISBN: 9781000995138

Content: This book covers aspects of data science and predictive analytics used in oil and gas industry by looking into the challenges of data processing and data modelling unique to this industry. It includes upstream management, intelligent/digital well, value chain integration, crude basket forecasting and so forth

Note: Description based on publisher supplied metadata and other sources , Cover -- Half Title -- Title Page -- Copyright Page -- Dedication -- Contents -- Preface -- About the Editors -- Contributors -- Acknowledgments -- Chapter 1 Understanding the Oil and Gas Sector and Its Processes: Upstream, Downstream -- 1.1 Introduction -- 1.2 Identification of the Geological Origins of Petroleum Reservoirs and Reservoir Fluids -- 1.3 History of the Oil and Gas Industry -- 1.3.1 Ancient Oil and Gas Industry -- 1.3.2 Modern Oil and Gas Industry -- 1.3.3 The Role of the Russian Oil and Gas Industry -- 1.3.4 Royal Dutch Shell in the East Indies -- The Structure of the Modern Oil and Gas Industry -- 1.4.1 Upstream -- 1.4.2 Midstream -- 1.4.3 Downstream -- 1.5 Differences between the Conventional and Unconventional Reservoirs -- 1.5.1 Conventional Reservoirs -- 1.5.2 Unconventional Reservoirs -- 1.6 A List of the Various Disciplines that Make Up Petroleum Engineering -- 1.6.1 Exploration Stage -- 1.6.2 The Appraisal Stage -- 1.6.3 The Development Stage -- 1.6.4 Production Stage -- 1.7 Analyzing Rudimentary Engineering Methods in Exploration and Production -- 1.7.1 Role of Geoscientists -- 1.7.2 Methods of Exploration -- 1.7.3 Methods of Production -- 1.8 Interpretation of Cross-Plots -- Acknowledgement -- References -- Chapter 2 IT Technologies Impacting the Petroleum Sector -- 2.1 Introduction -- 2.2 GIS and Remote Sensing -- 2.2.1 Case Study of GIS in the Petroleum Industry 1: OMV Enterprise GIS -- 2.2.2 GIS Case Study 2: Assessment of Hurricane Effects on Gulf of Mexico Oil and Gas Production Predictions -- 2.3 Image Processing -- 2.4 SCADA and Telemetry -- 2.4.1 Telemetry -- 2.5 Geological and Geophysical Parameters -- 2.6 Introduction to ANN and Automation -- 2.6.1 ANN in the Petroleum Industry -- 2.6.2 Exploration -- 2.6.3 Drilling -- 2.6.4 Production -- 2.6.5 Reservoir -- 2.7 Different Types of Surveys -- 2.7.1 Seismic Surveys , 2.7.2 3D Seismic Survey -- 2.8 Cloud Technologies -- 2.8.1 Cloud Technologies in the Petroleum Industry -- 2.8.2 Cloud Storage Application of Petroleum Industry -- 2.9 Conclusion -- Bibliography -- Chapter 3 Data Handling Techniques in the Petroleum Sector -- 3.1 Introduction -- 3.2 BigData in O& -- G -- 3.3 BigData Administration in the Oil and Gas Sector -- 3.3.1 Data Attainment -- 3.3.2 Data Processing -- 3.4 Contemporary Frameworks in the Oil and Gas Sector -- 3.5 BigData Prospects and Challenges in the Oil and Gas Sector -- 3.5.1 Asset Enactment Controlling -- 3.5.2 Asset Risk Valuation -- 3.5.3 Virtual Operational Drill -- 3.5.4 Disaster Reaction Training -- 3.5.5 Lack of Standardization -- 3.5.6 Data Ownership and Sharing -- 3.5.7 Functionality -- 3.6 Rami Dossier in the Case of the Oil and Gas Industry -- 3.6.1 Requirement of Flexibility in the Upstream Oil and Gas Business -- 3.6.2 Identification of Mobility -- 3.6.3 Readiness Assessment Framework -- 3.7 Major Risks to Companies -- 3.8 Risk Analysis /Assessment for Organization -- Bibliography -- Chapter 4 Predictive Modelling Concepts in Petroleum Sector -- 4.1 Overview -- 4.2 Statistical Methods -- 4.2.1 Parametric vs Non-Parametric Methods -- 4.2.2 Regression and Classification -- 4.2.3 Performance Metrics for Classification and Regression -- 4.2.3.1 Performance Metrics for Classification-Confusion Matrix -- 4.2.3.2 Performance Metrics for Regression Model -- 4.3 Machine Learning Concepts -- 4.3.1 Background -- 4.3.2 Machine Learning Methods -- 4.3.2.1 Linear Regression -- 4.3.2.2 Support Vector Machine -- 4.3.2.3 Strength Weakness Opportunities Threats (SWOT) Analysis -- 4.4 The Artificial Neural Network and its Application in the Oil and Gas Sector -- 4.4.1 Introduction -- 4.4.2 Generic Development of ANN Models -- 4.4.3 Applications in the Oil and Gas Industry -- 4.4.3.1 Exploration , 4.4.3.2 Reservoir -- 4.4.3.3 Drilling -- 4.5 Concepts of Deep Neural Networks -- 4.5.1 Introduction -- 4.5.2 DNN Models -- 4.5.3 Model Evaluation Metrics -- 4.6 Case Study -- Bibliography -- Chapter 5 Supply Chain Management in the Oil and Gas Business -- 5.1 Introduction -- 5.2 Challenges in SCM -- 5.2.1 Inventory Management -- 5.2.2 Warehouse Management -- 5.2.3 Logistics -- 5.2.4 Reduction of Transport Costs -- 5.2.5 Processing a Large Amount of Information -- 5.2.6 Delay in Delivery -- 5.3 Opportunities in the Supply Chain -- 5.3.1 Customer Requirement Process -- 5.3.2 Sourcing and Supplier Management -- 5.4 Analysis -- 5.5 Introduction to ERP for SCM -- 5.5.1 Main Features of ERP -- 5.5.2 Role of ERP in SCM -- 5.5.3 Advantages of ERP in SCM -- 5.6 Case Study -- 5.6.1 Geographic Information System (GIS) Solution Enhances Inventory Control -- Bibliography -- Chapter 6 Prescriptive Analysis and Its Application in Oil and Gas Business -- 6.1 Introduction -- 6.2 Introduction to Different Types of Analytics -- 6.3 Basics of Prescriptive Analysis -- 6.4 Reservoir Simulator (Petrel) -- 6.5 Refining Simulator (Aspen-HySYS) -- 6.6 AI and Its Application in Simulation -- 6.7 Case Study on Upstream -- 6.7.1 Case Study 1: Optimizing the Exploration of Shale Oil -- 6.7.2 Case 2: Permian Basin Unconventional Hydrocarbon Extraction -- 6.8 Case Study on Downstream -- 6.8.1 Case Study 1: Supply Chain 4.0 -- 6.8.2 Supply Chain and Predictive Analytics: -- 6.8.3 Supply Chain and Big Data Analytics: -- 6.8.4 Case Study: The Norwegian Continental Shelf -- Bibliography -- Chapter 7 Future Challenges in Petroleum Sector and IT Solutions -- 7.1 Introduction -- 7.2 Challenges in the Oil and Gas Industry -- 7.2.1 Reduce Cost of Production to Remain Competitive in the Market -- 7.2.2 Improving Performance to Ensure the Valourization of Assets , 7.2.3 Reducing Carbon Footprint to Meet Stringent Governmental Standards -- 7.3 Data as a New Oil -- 7.3.1 Data Refining -- 7.3.2 Data Quality -- 7.3.3 Data Requires Infrastructure -- 7.3.4 Difference between Data and Oil -- 7.4 Challenges of Data Integration in the Oil and Gas Sector -- 7.5 Reducing Production Cost through IT Technologies -- 7.6 Case Study -- Conclusion -- Bibliography -- Chapter 8 Oil and Gas Industry in Context of Industry 4.0 -- 8.1 Introduction -- 8.2 Concepts of the Oil and Gas Industry 4.0 -- 8.3 Concepts of Digital Oilfields -- 8.4 Cloud Integration in the Oil and Gas Domain -- 8.5 Digital Transformation -- 8.6 Agile Business Transformation -- 8.7 Digital Leadership -- 8.8 Case Study -- 8.8.1 List of Case Studies on the Oil and Gas Industry -- 8.8.2 Analyzing the Barriers to Adopt Industrial Revolution in India -- References -- Index

Additional Edition: Erscheint auch als Druck-Ausgabe Srivastava, Kingshuk Understanding Data Analytics and Predictive Modelling in the Oil and Gas Industry Milton : Taylor & Francis Group,c2023 ISBN 9781032413891

Language: English

UID:

Format: 1 Online-Ressource (x, 343 Seiten) , Illustrationen, Diagramme

ISBN: 9783031554087

Series Statement: Springer Studies in Media and Political Communication

Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 978-3-031-55407-0

Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 978-3-031-55409-4

Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 978-3-031-55410-0

Language: English

Keywords: Aufsatzsammlung

DOI: 10.1007/978-3-031-55408-7

URL: Volltext  (URL des Erstveröffentlichers)