, um zu prüfen, ob Sie einen Vollzugriff auf diese Publikation haben.
Sammelband Kein Zugriff
Enterprise & Business Management
A Handbook for Educators, Consultants, and Practitioners- Herausgeber:innen:
- Verlag:
- 2020
Publikation durchsuchen
Bibliographische Angaben
- Auflage
- 1/2020
- Copyrightjahr
- 2020
- ISBN-Print
- 978-3-8288-4255-7
- ISBN-Online
- 978-3-8288-7230-1
- Verlag
- Tectum, Baden-Baden
- Reihe
- Enterprise & Business Management
- Sprache
- Englisch
- Seiten
- 412
- Produkttyp
- Sammelband
Inhaltsverzeichnis
KapitelSeiten
- Titelei/Inhaltsverzeichnis Kein Zugriff Seiten I - XIV
- Autor:innen: |
- Learning Objectives Kein ZugriffAutor:innen: |
- Chapter Outline Kein ZugriffAutor:innen: |
- Autor:innen: |
- 1 Introduction Kein ZugriffAutor:innen: |
- Autor:innen: |
- 2.1 The Role of Reverse Logistics in Sustainable Supply Chain Operations Kein ZugriffAutor:innen: |
- Autor:innen: |
- 3.1 Impacts of Industry 4.0 on Supply Chain Operations towards Sustainability Kein ZugriffAutor:innen: |
- Autor:innen: |
- 4.1 A Qualitative Research Approach Kein ZugriffAutor:innen: |
- 5 Conclusions Kein ZugriffAutor:innen: |
- 6 References Kein ZugriffAutor:innen: |
- 7 Key Terms Kein ZugriffAutor:innen: |
- 8 Questions for Further Study Kein ZugriffAutor:innen: |
- 9 Exercises Kein ZugriffAutor:innen: |
- 10 Further Reading Kein ZugriffAutor:innen: |
- Autor:innen: | |
- Learning Objectives Kein ZugriffAutor:innen: | |
- Chapter Outline Kein ZugriffAutor:innen: | |
- Autor:innen: | |
- Autor:innen: | |
- 1.1 Problem Statement and Company Background Kein ZugriffAutor:innen: | |
- 1.2 Motivation Kein ZugriffAutor:innen: | |
- Autor:innen: | |
- 2.1 Retail Applications Kein ZugriffAutor:innen: | |
- 2.2 SKU Segmentation Kein ZugriffAutor:innen: | |
- 2.3 Inventory Management Kein ZugriffAutor:innen: | |
- Autor:innen: | |
- Autor:innen: | |
- 3.1.1 Current System Design Kein ZugriffAutor:innen: | |
- 3.1.2 Interviews and Business Context Kein ZugriffAutor:innen: | |
- 3.2 Decision Frame Kein ZugriffAutor:innen: | |
- Autor:innen: | |
- 3.3.1 Ordering Flow Kein ZugriffAutor:innen: | |
- 3.3.2 Decision Frame in Safety Stock Calculation Kein ZugriffAutor:innen: | |
- Autor:innen: | |
- 3.4.1 System Dynamics Model Parameters Kein ZugriffAutor:innen: | |
- 3.4.2 DPS Simulation Parameters Kein ZugriffAutor:innen: | |
- Autor:innen: | |
- 4.1 Standard Deviation of Demand or Forecast Error Kein ZugriffAutor:innen: | |
- 4.2 Fixed vs. Dynamic Cycle Service Levels Kein ZugriffAutor:innen: | |
- 4.3 Improvements on Dynamic Cycle Service Levels Kein ZugriffAutor:innen: | |
- Autor:innen: | |
- Autor:innen: | |
- 5.1.1 Operations of System Dynamics Model Kein ZugriffAutor:innen: | |
- 5.2 Benefits of Using Dynamic Cycle Service Levels Kein ZugriffAutor:innen: | |
- 6 Conclusion Kein ZugriffAutor:innen: | |
- 7 References Kein ZugriffAutor:innen: | |
- 8 Key Terms Kein ZugriffAutor:innen: | |
- 9 Questions for Further Study Kein ZugriffAutor:innen: | |
- 10 Exercises Kein ZugriffAutor:innen: | |
- 11 Further Reading Kein ZugriffAutor:innen: | |
- Autor:innen: |
- Learning Objectives Kein ZugriffAutor:innen: |
- Chapter Outline Kein ZugriffAutor:innen: |
- Autor:innen: |
- 1 Introduction Kein ZugriffAutor:innen: |
- 2 Readiness for Industry 4.0 Kein ZugriffAutor:innen: |
- 3 A Roadmap for Industry 4.0 Kein ZugriffAutor:innen: |
- 4 Case Study: Current Situation in Turkey Kein ZugriffAutor:innen: |
- Conclusion Kein ZugriffAutor:innen: |
- References Kein ZugriffAutor:innen: |
- Key Terms Kein ZugriffAutor:innen: |
- Questions for Further Study Kein ZugriffAutor:innen: |
- Exercises Kein ZugriffAutor:innen: |
- Further Reading Kein ZugriffAutor:innen: |
- Autor:innen: | |
- Learning Objectives Kein ZugriffAutor:innen: | |
- Chapter Outline Kein ZugriffAutor:innen: | |
- Autor:innen: | |
- 1 Introduction Kein ZugriffAutor:innen: | |
- 2 Effects of Industry 4.0 on the Shop-Floor Kein ZugriffAutor:innen: | |
- 3 Literature Review Kein ZugriffAutor:innen: | |
- 4 Research Methodology Kein ZugriffAutor:innen: | |
- Autor:innen: | |
- 5.1 Pre-Industry 4.0 Stage Kein ZugriffAutor:innen: | |
- 5.2 Industry 4.0 Initiation Stage Kein ZugriffAutor:innen: | |
- 5.3 Industry 4.0 Implementation Phase Kein ZugriffAutor:innen: | |
- 6 Conclusion Kein ZugriffAutor:innen: | |
- References Kein ZugriffAutor:innen: | |
- Key Terms Kein ZugriffAutor:innen: | |
- Questions for Further Study Kein ZugriffAutor:innen: | |
- Exercises Kein ZugriffAutor:innen: | |
- Further Reading Kein ZugriffAutor:innen: | |
- Autor:innen:
- Learning Objectives Kein ZugriffAutor:innen:
- Chapter Outline Kein ZugriffAutor:innen:
- Autor:innen:
- 1 Introduction Kein ZugriffAutor:innen:
- Autor:innen:
- 2.1 The Principles of Industry 4.0 Technology Kein ZugriffAutor:innen:
- 2.2 Factors Affecting Industry 4.0 Kein ZugriffAutor:innen:
- Autor:innen:
- 3.1 Milk Supply Process of Sample Company Kein ZugriffAutor:innen:
- 3.2 Acceptance of Milk and Milk Processing Kein ZugriffAutor:innen:
- Autor:innen:
- 3.3.1 CAN Bus (Controller Area Network Bus) System Kein ZugriffAutor:innen:
- 3.3.2 GPS (Global Positioning System) Kein ZugriffAutor:innen:
- 3.3.3 Temperature and Moisture Sensors Kein ZugriffAutor:innen:
- 4 Current Situation Assessment Kein ZugriffAutor:innen:
- Autor:innen:
- 5.1 Traceability of Product and Service Using RFID Kein ZugriffAutor:innen:
- 5.2 Interoperability with IoT and Cyber Physical Systems Kein ZugriffAutor:innen:
- 5.3 Intelligent Systems Kein ZugriffAutor:innen:
- 5.4 Robots, Automatic Machines and Unmanned Transportation Vehicles Kein ZugriffAutor:innen:
- 5.5 Customized Services and Products Kein ZugriffAutor:innen:
- 5.6 Globalizing Systems Kein ZugriffAutor:innen:
- 6 Threats Coming with Industry 4.0 Kein ZugriffAutor:innen:
- 7 Conclusion Kein ZugriffAutor:innen:
- References Kein ZugriffAutor:innen:
- Key Terms Kein ZugriffAutor:innen:
- Questions for Further Study Kein ZugriffAutor:innen:
- Exercises Kein ZugriffAutor:innen:
- Further Reading Kein ZugriffAutor:innen:
- Autor:innen: |
- Learning Objectives Kein ZugriffAutor:innen: |
- Chapter Outline Kein ZugriffAutor:innen: |
- Autor:innen: |
- 1 Introduction Kein ZugriffAutor:innen: |
- Autor:innen: |
- 2.1 Cyber physical systems Kein ZugriffAutor:innen: |
- 2.2 Industry 4.0 and Industrial Internet of Things (IIoT) difference Kein ZugriffAutor:innen: |
- 3 Smart Factories Kein ZugriffAutor:innen: |
- 4 Relation between Industry 4.0 and Smart Factory Kein ZugriffAutor:innen: |
- 5 Conclusions Kein ZugriffAutor:innen: |
- 6 References Kein ZugriffAutor:innen: |
- Key Terms Kein ZugriffAutor:innen: |
- Questions for Further Study Kein ZugriffAutor:innen: |
- Exercises Kein ZugriffAutor:innen: |
- Further Reading Kein ZugriffAutor:innen: |
- Autor:innen: | |
- Classification of technology Kein ZugriffAutor:innen: | |
- Relationship between business and technology Kein ZugriffAutor:innen: | |
- Business view on managing technologies Kein ZugriffAutor:innen: | |
- Technology management and innovation Kein ZugriffAutor:innen: | |
- How to review technological innovation? Kein ZugriffAutor:innen: | |
- Relationship between technology and market Kein ZugriffAutor:innen: | |
- How to choose technology management methodologies? Which factors have to be considered? Kein ZugriffAutor:innen: | |
- Prerequisites for a successful methodology? Kein ZugriffAutor:innen: | |
- Benefits of using a methodology Kein ZugriffAutor:innen: | |
- When you have chosen a methodology review it consequently Kein ZugriffAutor:innen: | |
- Strategic technology lifecycle Kein ZugriffAutor:innen: | |
- Autor:innen: |
- Learnign Objectives Kein ZugriffAutor:innen: |
- Chapter Outline Kein ZugriffAutor:innen: |
- Autor:innen: |
- 1 Introduction Kein ZugriffAutor:innen: |
- Autor:innen: |
- 2.1 The First Industrial Revolution Kein ZugriffAutor:innen: |
- 2.2 The Second Industrial Revolution Kein ZugriffAutor:innen: |
- 2.3 The Third Industrial Revolution Kein ZugriffAutor:innen: |
- 2.4 The Fourth Industrial Revolution Kein ZugriffAutor:innen: |
- Autor:innen: |
- 3.1 Technologies of Industry 4.0 Kein ZugriffAutor:innen: |
- 4 Techno Parks Kein ZugriffAutor:innen: |
- Autor:innen: |
- Autor:innen: |
- 5.1.1 The level of awareness about Industry 4.0 Kein ZugriffAutor:innen: |
- 5.1.2 Technologies of Industry 4.0 Kein ZugriffAutor:innen: |
- 5.1.3 Application areas Kein ZugriffAutor:innen: |
- 5.1.4 Effect of Size and Establishment Year Kein ZugriffAutor:innen: |
- Conclusion Kein ZugriffAutor:innen: |
- References Kein ZugriffAutor:innen: |
- Key Terms Kein ZugriffAutor:innen: |
- Questions for Further Study Kein ZugriffAutor:innen: |
- Exercises Kein ZugriffAutor:innen: |
- Further Reading Kein ZugriffAutor:innen: |
- Autor:innen: |
- Learning Objectives Kein ZugriffAutor:innen: |
- Chapter Outline Kein ZugriffAutor:innen: |
- Autor:innen: |
- Autor:innen: |
- 1.1 Why is Industry 4.0 Important? Kein ZugriffAutor:innen: |
- 1.2 What is Outsourcing? Kein ZugriffAutor:innen: |
- 1.3 Why do Organizations Outsource? Kein ZugriffAutor:innen: |
- 1.4 Some Samples for the Outsourcing Reasons in Different Countries Kein ZugriffAutor:innen: |
- 2 Convergence Point of These Two Phenomena „Outsourcing and Industry 4.0“: Technology Kein ZugriffAutor:innen: |
- 3 The Relationship between the Institutional Logic, Pragmatism, Industry 4.0 and Outsourcing Kein ZugriffAutor:innen: |
- Conclusion Kein ZugriffAutor:innen: |
- References Kein ZugriffAutor:innen: |
- Key Terms Kein ZugriffAutor:innen: |
- Questions for Further Studies in the Field Kein ZugriffAutor:innen: |
- Exercises Kein ZugriffAutor:innen: |
- Further Reading Kein ZugriffAutor:innen: |
- Autor:innen: |
- Definitions Kein ZugriffAutor:innen: |
- Stakeholders Kein ZugriffAutor:innen: |
- Software products Kein ZugriffAutor:innen: |
- Software product evaluation criteria Kein ZugriffAutor:innen: |
- Attributes of good software Kein ZugriffAutor:innen: |
- Classification of software process models Kein ZugriffAutor:innen: |
- Generic software process models Kein ZugriffAutor:innen: |
- Engineering process model Kein ZugriffAutor:innen: |
- Hybrid process models Kein ZugriffAutor:innen: |
- Spiral model Kein ZugriffAutor:innen: |
- Potential problems of process models Kein ZugriffAutor:innen: |
- Process visibility Kein ZugriffAutor:innen: |
- Questions on Software engineering Kein ZugriffAutor:innen: |
- Autor:innen: |
- Learning Objectives Kein ZugriffAutor:innen: |
- Chapter Outline Kein ZugriffAutor:innen: |
- Autor:innen: |
- 1 Introduction Kein ZugriffAutor:innen: |
- 2 Definition and Scope of ERP Kein ZugriffAutor:innen: |
- 3 Development of ERP System Kein ZugriffAutor:innen: |
- 4 Fundamental Features of ERP System Kein ZugriffAutor:innen: |
- 5 Components of ERP System Kein ZugriffAutor:innen: |
- 6 ERP Software in Turkey Kein ZugriffAutor:innen: |
- Autor:innen: |
- 7.1 Foreign ERP Providers Kein ZugriffAutor:innen: |
- 7.2 Turkey ERP Providers Kein ZugriffAutor:innen: |
- 8 Definition of Information and Information Safety Kein ZugriffAutor:innen: |
- Autor:innen: |
- 9.1 ERP Information Safety Gaps Kein ZugriffAutor:innen: |
- 10 Conclusions Kein ZugriffAutor:innen: |
- 11 References Kein ZugriffAutor:innen: |
- 12 Key Terms Kein ZugriffAutor:innen: |
- 13 Questions for Further Study Kein ZugriffAutor:innen: |
- 14 Exercises Kein ZugriffAutor:innen: |
- 15 Further Reading Kein ZugriffAutor:innen: |
- Autor:innen:
- Learning Objectives Kein ZugriffAutor:innen:
- Chapter Outline Kein ZugriffAutor:innen:
- Autor:innen:
- 1 Introduction Kein ZugriffAutor:innen:
- 2 Background Kein ZugriffAutor:innen:
- 3 Artificial Neural Networks Kein ZugriffAutor:innen:
- 4 Materials and Methods Kein ZugriffAutor:innen:
- 5 Design and Implementation Kein ZugriffAutor:innen:
- 6 Results and Discussion Kein ZugriffAutor:innen:
- 7 Conclusions and Recommendations Kein ZugriffAutor:innen:
- 8 References Kein ZugriffAutor:innen:
- 9 Key TErms Kein ZugriffAutor:innen:
- 10 Questions for Further Study Kein ZugriffAutor:innen:
- 11 Exercises Kein ZugriffAutor:innen:
- 12 Further Reading Kein ZugriffAutor:innen:
- Autor:innen: |
- Learning Objectives Kein ZugriffAutor:innen: |
- Chapter Outline Kein ZugriffAutor:innen: |
- Autor:innen: |
- 1 Introduction Kein ZugriffAutor:innen: |
- Autor:innen: |
- 2.1 Study area Kein ZugriffAutor:innen: |
- 2.2 Data collection Kein ZugriffAutor:innen: |
- 2.3 Length–weight relationship (LWR) equation Kein ZugriffAutor:innen: |
- 2.4 Artificial Neural Networks (ANNs) Kein ZugriffAutor:innen: |
- 2.5 Normalization Kein ZugriffAutor:innen: |
- 2.6 Estimation Accuracy Validation Kein ZugriffAutor:innen: |
- 2.7 Statistics Kein ZugriffAutor:innen: |
- 2.8 Data Editing for MATLAB Kein ZugriffAutor:innen: |
- Autor:innen: |
- 3.1 Tinca tinca Kein ZugriffAutor:innen: |
- 3.2 LENGTH˗WEIGHT RELATIONSHIPS (LWR) Kein ZugriffAutor:innen: |
- 3.3 ARTIFICIAL NEURAL NETWORKS (ANNs) Kein ZugriffAutor:innen: |
- 4 Results and Discussion Kein ZugriffAutor:innen: |
- 5 References Kein ZugriffAutor:innen: |
- 6 Key Terms Kein ZugriffAutor:innen: |
- 7 Questions for Further Study Kein ZugriffAutor:innen: |
- 8 Exercises Kein ZugriffAutor:innen: |
- 9 Further Reading Kein ZugriffAutor:innen: |
- Autor:innen: | |
- Definitions Kein ZugriffAutor:innen: | |
- Knowledge generation Kein ZugriffAutor:innen: | |
- Knowledge classification Kein ZugriffAutor:innen: | |
- Transforming knowledge Kein ZugriffAutor:innen: | |
- Tacit to Tacit Kein ZugriffAutor:innen: | |
- Explicit to Tacit Kein ZugriffAutor:innen: | |
- Tacit to Explicit Kein ZugriffAutor:innen: | |
- Explicit to Explicit Kein ZugriffAutor:innen: | |
- Knowledge management components Kein ZugriffAutor:innen: | |
- Strategies, processes and metrics Kein ZugriffAutor:innen: | |
- How to develop a knowledge strategy? Kein ZugriffAutor:innen: | |
- Knowledge management architecture Kein ZugriffAutor:innen: | |
- Aspects of Secure Knowledge Management Kein ZugriffAutor:innen: | |
- Security Strategies Kein ZugriffAutor:innen: | |
- Security processes Kein ZugriffAutor:innen: | |
- Metrics Kein ZugriffAutor:innen: | |
- Techniques Kein ZugriffAutor:innen: | |
- Knowledge management cycle Kein ZugriffAutor:innen: | |
- Knowledge management technologies Kein ZugriffAutor:innen: | |
- People and systems Kein ZugriffAutor:innen: | |
- People Kein ZugriffAutor:innen: | |
- Systems Kein ZugriffAutor:innen: | |
- Two ways to generate and use knowledge Kein ZugriffAutor:innen: | |
- Knowledge cycle Kein ZugriffAutor:innen: | |
- Levers Kein ZugriffAutor:innen: | |
- Principles of effective learning Kein ZugriffAutor:innen: | |
- understanding Kein ZugriffAutor:innen: | |
- skills Kein ZugriffAutor:innen: | |
- processes Kein ZugriffAutor:innen: | |
- The goal of knowledge management metrics Kein ZugriffAutor:innen: | |
- Autor:innen: |
- Learning Objectives Kein ZugriffAutor:innen: |
- Chapter Outline Kein ZugriffAutor:innen: |
- Autor:innen: |
- 1 Introduction Kein ZugriffAutor:innen: |
- Autor:innen: |
- 2.1 Study area Kein ZugriffAutor:innen: |
- 2.2 Data collection Kein ZugriffAutor:innen: |
- 2.3 Length–weight relationship (LWR) equation Kein ZugriffAutor:innen: |
- 2.4 Artificial Neural Networks (ANNs) Kein ZugriffAutor:innen: |
- 2.5 Normalization Kein ZugriffAutor:innen: |
- 2.6 Estimation Accuracy Validation Kein ZugriffAutor:innen: |
- 2.7 Statistics Kein ZugriffAutor:innen: |
- 2.8 Data Editing for MATLAB Kein ZugriffAutor:innen: |
- 3 Literature Review Kein ZugriffAutor:innen: |
- 4 Results Kein ZugriffAutor:innen: |
- 5 Discussion Kein ZugriffAutor:innen: |
- 6 References Kein ZugriffAutor:innen: |
- 7 Key Terms Kein ZugriffAutor:innen: |
- 8 Questions for Further Study Kein ZugriffAutor:innen: |
- 9 Exercises Kein ZugriffAutor:innen: |
- 10 Further Reading Kein ZugriffAutor:innen: |
- Autor:innen: |
- Learning Objectives Kein ZugriffAutor:innen: |
- Chapter Outline Kein ZugriffAutor:innen: |
- Autor:innen: |
- Autor:innen: |
- 1.1 Definition and Evolution of Industry 4.0 Kein ZugriffAutor:innen: |
- Autor:innen: |
- 2.1 Autonomous Robots Kein ZugriffAutor:innen: |
- 2.2 Big Data and Analytics Kein ZugriffAutor:innen: |
- 2.3 Simulation Kein ZugriffAutor:innen: |
- 2.4 System Integration Kein ZugriffAutor:innen: |
- 2.5 Cybersecurity Kein ZugriffAutor:innen: |
- 2.6 The Industrial Internet of Things (IIoT) Kein ZugriffAutor:innen: |
- 2.7 The Cloud Kein ZugriffAutor:innen: |
- 2.8 Additive Manufacturing Kein ZugriffAutor:innen: |
- 2.9 Augmented Reality Kein ZugriffAutor:innen: |
- Autor:innen: |
- 3.1 Companies and Overall Economy Kein ZugriffAutor:innen: |
- 3.2 Managers and Employees Kein ZugriffAutor:innen: |
- 3.3 Countries, Regions, Cities and Transnational Relations Kein ZugriffAutor:innen: |
- 3.4 Individual and the Society Kein ZugriffAutor:innen: |
- Autor:innen: |
- 4.1 Definition and History of Planned Obsolescence Kein ZugriffAutor:innen: |
- Autor:innen: |
- 4.2.1 Obsolescence of Function Kein ZugriffAutor:innen: |
- 4.2.2 Obsolescence of Quality Kein ZugriffAutor:innen: |
- 4.2.3 Obsolescence of Desirability Kein ZugriffAutor:innen: |
- 5 Conclusions Kein ZugriffAutor:innen: |
- 6 References Kein ZugriffAutor:innen: |
- Key Terms Kein ZugriffAutor:innen: |
- Questions for Further Study Kein ZugriffAutor:innen: |
- Exercises Kein ZugriffAutor:innen: |
- Further Reading Kein ZugriffAutor:innen: |
- Autor:innen: |
- Learning Objectives Kein ZugriffAutor:innen: |
- Chapter Outline Kein ZugriffAutor:innen: |
- Autor:innen: |
- 1 Introduction Kein ZugriffAutor:innen: |
- Autor:innen: |
- 2.1 Literature Review Kein ZugriffAutor:innen: |
- 3 Conclusion Kein ZugriffAutor:innen: |
- References Kein ZugriffAutor:innen: |
- Key Terms Kein ZugriffAutor:innen: |
- Questions for Further Study Kein ZugriffAutor:innen: |
- Exercises Kein ZugriffAutor:innen: |
- Further Reading Kein ZugriffAutor:innen: |
- About the Chapter Contributors Kein Zugriff Seiten 407 - 412
Literaturverzeichnis (643 Einträge)
Es wurden keine Treffer gefunden. Versuchen Sie einen anderen Begriff.
- Kenton, W. (2018). End-To-End. [online] Investopedia. Available at: https://www.investopedia.com/terms/e/end-to-end.asp Google Scholar öffnen doi.org/10.5771/9783828872301
- Kenton, W. (2018). Functional Obsolescence. [online] Investopedia. Available at: https://www.investopedia.com/terms/f/functional-obsolescence.asp Google Scholar öffnen doi.org/10.5771/9783828872301
- Kenton, W. (2018). Vertical Integration. [online] Investopedia. Available at: https://www.investopedia.com/terms/v/verticalintegration.asp Google Scholar öffnen doi.org/10.5771/9783828872301
- Kenton, W. (2018). What are some examples of horizontal integration?. [online] Investopedia. Available at: https://www.investopedia.com/ask/answers/051315/what-are-some-examples-horizontal-integration.asp Google Scholar öffnen doi.org/10.5771/9783828872301
- Kessler, T. and Brendel, J. (2016). Planned Obsolescence and Product-Service Systems: Linking Two Contradictory Business Models. Google Scholar öffnen doi.org/10.5771/9783828872301
- Keynes, J. (1931). Economic Possibilities for our Grandchildren. Google Scholar öffnen doi.org/10.5771/9783828872301
- Khaleeli, H. (2015). End of the line for stuff that's built to die?. [online] the Guardian. Available at: https://www.theguardian.com/technology/shortcuts/2015/mar/03/has-planned-obsolesence-had-its-day-design Google Scholar öffnen doi.org/10.5771/9783828872301
- Knight, E. (2014). The Art of Corporate Endurance. [online] Harvard Business Review. Available at: https://hbr.org/2014/04/the-art-of-corporate-endurance Google Scholar öffnen doi.org/10.5771/9783828872301
- Kim, E. (2018). Amazon's $775 million deal for robotics company Kiva is starting to look really smart. [online] Business Insider. Available at: https://www.businessinsider.com/kiva-robots-save-money-for-amazon-2016-6 Google Scholar öffnen doi.org/10.5771/9783828872301
- Kumari, P. and Kaur, P. (2018). A survey of fault tolerance in cloud computing. Journal of King Saud University – Computer and Information Sciences. Google Scholar öffnen doi.org/10.5771/9783828872301
- Kuppelwieser, V., Klaus, P., Manthiou, A. and Boujena, O. (2018). Consumer responses to planned obsolescence. Journal of Retailing and Consumer Services, 47, pp.157 – 165. Google Scholar öffnen doi.org/10.5771/9783828872301
- London, B. (1932). Ending the Depression Through Planned Obsolescence. Google Scholar öffnen doi.org/10.5771/9783828872301
- Lueth, K. (2015). IoT basics: Getting started with the Internet of Things. [online] Available at: https://iot-analytics.com/wp/wp-content/uploads/2015/03/2015-March-Whitepaper-IoT-basics-Getting-started-with-the-Internet-of-Things.pdf Google Scholar öffnen doi.org/10.5771/9783828872301
- Mearian, L. (2017). MIT creates 3D printed graphene that’s lighter than air, 10X stronger than steel. [online] Computerworld. Available at: https://www.computerworld.com/article/3155102/emerging-technology/mit-creates-3d-printed-graphene-thats-lighter-than-air-10x-stronger-than-steel.html Google Scholar öffnen doi.org/10.5771/9783828872301
- Naím, M. (2013). The End of Power: From Boardrooms to Battlefields and Churches to States, Why Being In Charge Isn't What It Used to Be. Google Scholar öffnen doi.org/10.5771/9783828872301
- Neagle, C. (2013). 10 augmented reality technologies you should know about. [online] Network World. Available at: https://www.networkworld.com/article/2358001/data-center/90615-10-augmented-reality-technologies-you-should-know-about.html#slide2 [Accessed 21 Dec. 2018]. Google Scholar öffnen doi.org/10.5771/9783828872301
- OECD. (2011). Divided We Stand: Why Inequality Keeps Rising. [online] Available at: http://www.oecd.org/els/soc/49499779.pdf Google Scholar öffnen doi.org/10.5771/9783828872301
- Ohnemus, T. (2018). Digital Twin Excellence: Two Shining Examples. [online] Digitalistmag.com. Available at: https://www.digitalistmag.com/iot/2018/06/14/digital-twin-excellence-2-shining-examples-06175901 Google Scholar öffnen doi.org/10.5771/9783828872301
- Orbach, B. (2004). The Durapolist Puzzle: Monopoly Power in Durable-Goods Market. Yale Journal on Regulation, 21, pp.67 – 118. Google Scholar öffnen doi.org/10.5771/9783828872301
- Parrott, A. and Warshaw, L. (2017). Industry 4.0 and the digital twin Manufacturing meets its match. [online] Deloitte Insights. Available at: https://www2.deloitte.com/insights/us/en/focus/industry-4-0/digital-twin-technology-smart-factory.html Google Scholar öffnen doi.org/10.5771/9783828872301
- Qin, J., Liu, Y. and Grosvenor, R. (2016). A Categorical Framework of Manufacturing for Industry 4.0 and Beyond. Procedia CIRP, 52, pp.173 – 178. Google Scholar öffnen doi.org/10.5771/9783828872301
- Packard, V. (1960). The Waste Makers. Great Britain, London: Longmans. Google Scholar öffnen doi.org/10.5771/9783828872301
- Patidar, S., Rane, D. and Jain, P. (2012). A Survey Paper on Cloud Computing. 2012 Second International Conference on Advanced Computing & Communication Technologies. Google Scholar öffnen doi.org/10.5771/9783828872301
- Ramsey, M. and MacMillan, D. (2015). Carnegie Mellon Reels After Uber Lures Away Researchers. [online] WSJ. Available at: https://www.wsj.com/articles/is-uber-a-friend-or-foe-of-carnegie-mellon-in-robotics-1433084582 Google Scholar öffnen doi.org/10.5771/9783828872301
- Rangnekar, D. (2002). R&D appropriability and planned obsolescence: empirical evidence from wheat breeding in the UK (1960–1995). Industrial and Corporate Change, 11(5), pp.1011 – 1029. Google Scholar öffnen doi.org/10.5771/9783828872301
- Rimal, B., Choi, E. and Lumb, I. (2009). A Taxonomy and Survey of Cloud Computing Systems. 2009 Fifth International Joint Conference on INC, IMS and IDC. Google Scholar öffnen doi.org/10.5771/9783828872301
- Rivera, J. and Lallmahomed, A. (2016). Environmental implications of planned obsolescence and product lifetime: a literature review. International Journal of Sustainable Engineering, 9(2), pp.119 – 129. Google Scholar öffnen doi.org/10.5771/9783828872301
- Rodič, B. (2017). Industry 4.0 and the New Simulation Modelling Paradigm. Organizacija, 50(3), pp.193 – 207. Google Scholar öffnen doi.org/10.5771/9783828872301
- Rüßmann, M., Lorenz, M., Gerbert, P., Waldner, M., Justus, J., Engel, P. and Harnisch, M. (2018). Industry 4.0: The Future of Productivity and Growth in Manufacturing Industries. [online] Inovasyon.org. Available at: http://www.inovasyon.org/pdf/bcg.perspectives_Industry.4.0_2015.pdf Google Scholar öffnen doi.org/10.5771/9783828872301
- Saikia, D. and Devi, Y. (n.d.). FAULT TOLEREANE TECHNIQUES AND ALGORITHMS IN CLOUD COMPUTING. [online] Ijcscn.com. Available at: https://www.ijcscn.com/Documents/Volumes/vol4issue1/ijcscn2014040101.pdf Google Scholar öffnen doi.org/10.5771/9783828872301
- Savu, L. (2011). Cloud Computing: Deployment Models, Delivery Models, Risks and Research Challenges. 2011 International Conference on Computer and Management (CAMAN). Google Scholar öffnen doi.org/10.5771/9783828872301
- Schuh, G., Potente, T., Wesch-Potente, C., Weber, A. and Prote, J. (2014). Collaboration Mechanisms to Increase Productivity in the Context of Industrie 4.0. Procedia CIRP, 19, pp.51 – 56. Google Scholar öffnen doi.org/10.5771/9783828872301
- Schwab, K. (2016). The Fourth Industrial Revolution. World Economic Forum. Google Scholar öffnen doi.org/10.5771/9783828872301
- Scott, M. (2017). 3D Printing Will Change The Way We Make Things And Design Them In 2017. [online] Forbes.com. Available at: https://www.forbes.com/sites/mikescott/2017/01/25/3d-printing-will-change-the-way-we-make-things-in-2017/#107a1385310e Google Scholar öffnen doi.org/10.5771/9783828872301
- Singh, S., Jeong, Y. and Park, J. (2016). A survey on cloud computing security: Issues, threats, and solutions. Journal of Network and Computer Applications, 75, pp.200 – 222. Google Scholar öffnen doi.org/10.5771/9783828872301
- Sivarajah, U., Kamal, M., Irani, Z. and Weerakkody, V. (2017). Critical analysis of Big Data challenges and analytical methods. Journal of Business Research, 70, pp.263 – 286. Google Scholar öffnen doi.org/10.5771/9783828872301
- Stewart, I. (1959). Day Conference in Gloucestershire. Occupational Therapy: the Official Journal of the Association of Occupational Therapists, 22(11), pp.14 – 15. Google Scholar öffnen doi.org/10.5771/9783828872301
- Stock, T. and Seliger, G. (2016). Opportunities of Sustainable Manufacturing in Industry 4.0. Procedia CIRP, 40, pp.536 – 541. Google Scholar öffnen doi.org/10.5771/9783828872301
- Strausz, R. (2009). Planned Obsolescence as an Incentive Device for Unobservable Quality. The Economic Journal, 119(540), pp.1405 – 1421. Google Scholar öffnen doi.org/10.5771/9783828872301
- Swan, P. (1972). Optimum Durability, Second-Hand Markets, and Planned Obsolescence. Journal of Political Economy, 80(3, Part 1), pp.575 – 585. Google Scholar öffnen doi.org/10.5771/9783828872301
- Thoben, K., Wiesner, S. and Wuest, T. (2017). “Industrie 4.0” and Smart Manufacturing – A Review of Research Issues and Application Examples. International Journal of Automation Technology, 11(1), pp.4 – 16. Google Scholar öffnen doi.org/10.5771/9783828872301
- Tofail, S., Koumoulos, E., Bandyopadhyay, A., Bose, S., O’Donoghue, L. and Charitidis, C. (2018). Additive manufacturing: scientific and technological challenges, market uptake and opportunities. Materials Today, 21(1), pp.22 – 37. Google Scholar öffnen doi.org/10.5771/9783828872301
- Utaka, A. (2000). Planned obsolescence and marketing strategy. Managerial and Decision Economics, 21(8), pp.339 – 344. Google Scholar öffnen doi.org/10.5771/9783828872301
- Vaidya, S., Ambad, P. and Bhosle, S. (2018). Industry 4.0 – A Glimpse. Procedia Manufacturing, 20, pp.233 – 238. Google Scholar öffnen doi.org/10.5771/9783828872301
- Waldman, M. (1996). Planned Obsolescence and the R&D Decision. The RAND Journal of Economics, 27(3), p.583. Google Scholar öffnen doi.org/10.5771/9783828872301
- Waslo, R., Lewis, T., Hajj, R. and Carton, R. (2017). [online] Www2.deloitte.com. Available at: https://www2.deloitte.com/content/dam/insights/us/articles/3749_Industry4-0_cybersecurity/DUP_Industry4-0_cybersecurity.pdf Google Scholar öffnen doi.org/10.5771/9783828872301
- Wetterstrand, K. (2015). The Cost of Sequencing a Human Genome. [online] National Human Genome Research Institute (NHGRI). Available at: https://www.genome.gov/sequencingcosts/ Google Scholar öffnen doi.org/10.5771/9783828872301
- White, L. (1969). The American Automobile Industry in the Post War Period. Google Scholar öffnen doi.org/10.5771/9783828872301
- World Economic Forum. (2015). Data-Driven Development Pathways for Progress. [online] Available at: http://www3.weforum.org/docs/WEFUSA_DataDrivenDevelopment_Report2015.pdf Google Scholar öffnen doi.org/10.5771/9783828872301
- Zissis, D. and Lekkas, D. (2012). Addressing cloud computing security issues. Future Generation Computer Systems, 28(3), pp.583–592. Google Scholar öffnen doi.org/10.5771/9783828872301
- Industry 4.0 and Big Data Literature Review by Burcu OZCAN, Cevher HİLAL AYTAC Google Scholar öffnen doi.org/10.5771/9783828872301
- Abouelmehdi, K., Beni-hssane, A., Khaloufi H., et. al. (2017). Big Data Security and Privacy in Healthcare: A Review. Procedia Comput Science, 113,73–80. Google Scholar öffnen doi.org/10.5771/9783828872301
- Ackermann, K. and Angus, S.D. (2014). A Resource Efficient Big Data Analysis Method for the Social Sciences: The Case of Global IP Activity. Procedia Comput Science, 29, 2360–2369. Google Scholar öffnen doi.org/10.5771/9783828872301
- Aikat, J., Carsey T.M., Fecho K., et al. (2017). Scientific Training in the Era of Big Data: A New Pedagogy for Graduate Education. Big Data, 5 (1), 12–18. Google Scholar öffnen doi.org/10.5771/9783828872301
- Akhavan-Hejazi, H., Mohsenian-Rad, H. (2018). Power Systems Big Data Analytics: An Assessment of Paradigm Shift Barriers and Prospects. Energy Reports, 4, 91–100. Google Scholar öffnen doi.org/10.5771/9783828872301
- Arslantekin, S. and Doğan, K. (2016). Big Data: Its Importance, Structure and Current Status. DTCF Journal, 56, 15–36. Google Scholar öffnen doi.org/10.5771/9783828872301
- Baccarelli, E., Cordeschi, N., Mei A., et. al. (2016). Energy-efficient Dynamic Traffic Offloading and Reconfiguration of Networked Data Centers for Big Data Stream Mobile Computing: Review, Challenges and A Case Study. IEEE Network, 30 (2), 54–61. Google Scholar öffnen doi.org/10.5771/9783828872301
- Bartevyan, L. Industry 4.0 – Summary Report. (2015). DLG: Expert Report. (Report No:5), 1–8. Google Scholar öffnen doi.org/10.5771/9783828872301
- Bello-Orgaz, G., Jung, J.J., Camacho, D. (2016). Social Big Data: Recent Achievements and New Challenges. Information Fusion, 28, 45–59. Google Scholar öffnen doi.org/10.5771/9783828872301
- Benjelloun, F.Z., Lahcen, A.A., Belfkih, S. (2015, March). An Overview of Big Data Opportunities, Applications and Tools. 2015 Intelligent Systems and Computer Vision (ISCV), 1–6. Google Scholar öffnen doi.org/10.5771/9783828872301
- Blazquez, D. and Domenech, J. (2018). Big Data Sources and Methods for Social and Economic Analyses. Technological Forecasting & Social Change, 130, 99–113. Google Scholar öffnen doi.org/10.5771/9783828872301
- Brettel, M., Friederichsen, N., Keller, M. et. al. (2014). How Virtualization, Decentralization and Network Building Change the Manufacturing Landscape: An Industry 4.0 Perspective. International Journal of Information and Computer Engineering, 8 (1), 37–44. Google Scholar öffnen doi.org/10.5771/9783828872301
- Cackett, D. Information Management and Big Data, A Reference Architecture. White paper. Redwood Shores: Oracle Corporation. 2013; Accessed: 20 April 2016. https://www.oracle.com/technetwork/topics/entarch/articles/info-mgmt-big-data-ref-arch-1902853.pdf Google Scholar öffnen doi.org/10.5771/9783828872301
- Can, A.V. and Kıymaz, M. (2016). Reflection of Information Technologies to Retail Sector: Impact of Industry 4.0 to Accounting Departments. Journal of Suleyman Demirel University Institute of Social Sciences, Special Issue, 107–117. Google Scholar öffnen doi.org/10.5771/9783828872301
- Celesti, A., Celesti, F., Fazio, M., et. al. (2017). Are Next-Generation Sequencing Tools Ready for the Cloud? Trsoends in Biotechnology, 35 (6), 486–489. Google Scholar öffnen doi.org/10.5771/9783828872301
- Chang, V. (2018). A Proposed Social Network Analysis Platform for Big Data Analytics. Technological Forecasting and Social Change, 130, 57–68. Google Scholar öffnen doi.org/10.5771/9783828872301
- Chen, R. and Lazer, M. (2013). Sentiment Analysis of Twitter Feeds for the Prediction of Stock Market Movement. Accessed: 25.01.2013. http://cs229.stanford.edu/proj2011/ChenLazer-SentimentAnalysisOfTwitterFeedsForThePredictionOfStockMarketMovement.pdf. Google Scholar öffnen doi.org/10.5771/9783828872301
- Chen, P. (2018). Medical Big Data Applications: Intertwined Effects and Effective Resource Allocatiın Strategies Identified Through IRA-NRM Analysis. Technologies Forecasting and Social Change, 150–164. Google Scholar öffnen doi.org/10.5771/9783828872301
- Chen, D.Q., Preston, D.S., Swink, M. (2015). How the Use of Big Data Analytics Affects Value Creation in Supply Chain Management. Journal of Management Information Systems, 32 (4), 4–39. Google Scholar öffnen doi.org/10.5771/9783828872301
- Chung, M.K. (2018). Statistical Challenge of Big Brain Network Data. Statistics & Probability Letters, 136, 78–82. Google Scholar öffnen doi.org/10.5771/9783828872301
- Corte-Real, N., Ruivo, P., Oliveira, T. (2014). The Diffusion Stages of Business Intelligence & Analytics (BI&A): A Systematic Mapping Study. Procedia Technology, 16, 172–179. Google Scholar öffnen doi.org/10.5771/9783828872301
- Davenport, T.H. (2014). Big Data at Work: Dispelling the Myths, Uncovering the Opportunities. Boston Massachusetts: Harvard Business Press. Google Scholar öffnen doi.org/10.5771/9783828872301
- Dong, X., Li, R., He, H., et al. (2015). Secure Sensitive Data Sharing on a Big Data Platform. Tsinghua Science and Technology, 20 (1), 72–80. Google Scholar öffnen doi.org/10.5771/9783828872301
- Du, D., Li, A., Zhang, L. (2014). Survey on the Applications of Big Data in Chinese Real Estate Enterprise. Procedia Computer Science, 30, 24–33. Google Scholar öffnen doi.org/10.5771/9783828872301
- EBSO,Aegean Region Chamber of Industry (2015). Industry 4.0. Research Directorate. Google Scholar öffnen doi.org/10.5771/9783828872301
- Elragal, A. (2014). ERP and Big Data: The Inept Couple. Procedia Technology,16, 242–249. Google Scholar öffnen doi.org/10.5771/9783828872301
- Erevelles, S., Fukawa, N., Swayne, L. (2016). Big Data Consumer Analytics and the Transformation of Marketing. Journal of Business Research, 69 (2), 897–904. Google Scholar öffnen doi.org/10.5771/9783828872301
- Fessele, K.L. (2018). The Rise of Big Data in Oncology. Seminars in Oncology Nursing, 34 (2), 168–176. Google Scholar öffnen doi.org/10.5771/9783828872301
- Gandomi, A. and Haider, M. (2015). Beyond the Hype: Big Data Concepts, Methods, and Analytics. International Journal of Information Management,35 (2), 137–144. Google Scholar öffnen doi.org/10.5771/9783828872301
- Germano, G. (2015). How Pfizer is Using Big Data to Power Patient Care. Forbes. Accessed:03.09.2015. http://www.forbes.com/sites/matthewherper/2015/02/17/how-pfizer-is-using-big-data-to-power-patient-care/#3a8f6ee8ceb4 Google Scholar öffnen doi.org/10.5771/9783828872301
- Gleaser, E.L., Kominers, S.D., Luca, M., et al. (2016). Big Data and Big Cities: The Promises and Limitations of Improved Measures of Urban Life. Economic Inquiry, 56 (1),114–137. Google Scholar öffnen doi.org/10.5771/9783828872301
- Golzer, P., Simon, L., Cato, P., et al. (2015). Designing Global Manufacturing Networks Using Big Data. Procedia CIRP, 33,191–196. Google Scholar öffnen doi.org/10.5771/9783828872301
- Gu, F., Ma, B., Guo, J. et al. (2017). Internet of Things and Big Data as Potential Solutions to thSe Problems in Waste Electrical and Electronic Equipment Management: An exploratory Study. Waste Management, 68, 434–448. Google Scholar öffnen doi.org/10.5771/9783828872301
- Gursakal, N. (2014). Big Data. Bursa: Dora Publishing. Google Scholar öffnen doi.org/10.5771/9783828872301
- Hardy, K. and Maurushat, A. (2017). Opening Up Government Data for Big Data Analysis and Public Benefit. Computer Law & Security Review, 33 (1), 30–37. Google Scholar öffnen doi.org/10.5771/9783828872301
- Hashem, I.A.T., Yaqoob, I., Anuar, N.B., et al. (2015). The Rise of “Big Data” on Cloud Computing: Review and Open Research Issues. Information Systems, 47, 98–115. Google Scholar öffnen doi.org/10.5771/9783828872301
- He, X., Ai, Q., Qiu, R., et al. (2017). A Big Data Architecture Design for Smart Grids Based on Random Matrix Theory. IEEE Transactions on Smart Grid, 8 (2), 674–686. Google Scholar öffnen doi.org/10.5771/9783828872301
- Hu, F., Yang, C., Schnase, J.L., et al. (2018). ClimateSpark: An In-memory Distributed Computing Framework for Big Climate Data Analytics. Computers & Geosciences, 2018, 115, 154–166. Google Scholar öffnen doi.org/10.5771/9783828872301
- Huda, M., Jasmi, K. A., Embong, W.H., et al. (2018). Nurturing Compassion-Based Empathy: Innovative Approach in Higher Education. In M. Badea, & M. Suditu (Eds.), Violence Prevention and Safety Promotion in Higher Education Settings. Hershey, 154–173. Google Scholar öffnen doi.org/10.5771/9783828872301
- Hure, E., Picot-Coupey, K., Ackermann, C. (2017). Understanding Omni-channel Shopping Value: A Mixed-method Study. Journal of Retailing and Consumer Services, 3, 314–330. Google Scholar öffnen doi.org/10.5771/9783828872301
- Hurwitz, J., Nugent, A., Halper, F., et al. (2013). Big Data for Dummies. Hoboken, NJ: For Dummies, sa Wiley Brand Google Scholar öffnen doi.org/10.5771/9783828872301
- Iafrate, F. (2015). From Big Data to Smart Data. Hoboken, NJ: ISTE Ltd, John Wiley and Sons Inc., Google Scholar öffnen doi.org/10.5771/9783828872301
- International Controller Association-ICV (2017). Industrie 4.0- Controlling in the Age of Intelligent Networks. Dream Car of the Dream Factory of the ICV- 2015. https://www.icv-controlling.com/fileadmin/Assets/Content/AK/Ideenwerkstatt/Files/Dream_Car_Industrie_4.0_EN.pdf Google Scholar öffnen doi.org/10.5771/9783828872301
- Ji, W., Wang, L. (2017). Big Data Analytics Based Fault Prediction for Shop Floor Scheduling. Journal of Manufacturing Systems, 43 (1), 187–194. Google Scholar öffnen doi.org/10.5771/9783828872301
- Jian, Q., Ying, L., Grosvenor, R. (2016). A Categorical Framework of Manufacturing for Industry 4.0 and Beyond. Procedia CIRP, 52, 173–178. Google Scholar öffnen doi.org/10.5771/9783828872301
- Jin, X., Wah, B.W., Cheng, X., et al. (2015). Significance and Challenges of Big Data Research. Big Data Research, 2 (2), 59–64. Google Scholar öffnen doi.org/10.5771/9783828872301
- Karim, A., Siddiga, A., Safdar, Z., et al. (2017). Big Data Management in Participatory Sensing: Issues, Trends and future Directions. Future Generation Computer Systems. Available online. https://doi.org/10.1016/j.future.2017.10.007 Google Scholar öffnen doi.org/10.5771/9783828872301
- Khakifrooz, M., Chien, C.F., Chen, Y. (2017). Bayesian Inference For Mining Semiconductor Manufacturing Big Data for Yield Enhancement and Smart Production to Empower Industry 4.0. Applied Soft Computing, 68, 990–999. Google Scholar öffnen doi.org/10.5771/9783828872301
- Kobusinska, A., Pawluczuk, K., Brzezinski, J. (2018). Big Data Fingerprinting Information Analytics for Sustainability. Future Generation Computer Systems, 86, 1321–1337. Google Scholar öffnen doi.org/10.5771/9783828872301
- Laney, D. (2001). 3D Data Management: Controlling Data Volume, Velocity, and Variety (). META Group. Google Scholar öffnen doi.org/10.5771/9783828872301
- Lee, R.J., Sener, I.N., Mokhtarian, P.L., et al. (2017). Relationships Between the Online and In-store Shopping Frequency of Davis, California Residents. Transportation Research Part A: Policy and Practice, 100, 40–52. Google Scholar öffnen doi.org/10.5771/9783828872301
- Li, J., Xu, L., Tang, L., et al. (2018) Big Data in Tourism Research: A Literature Review. Tourism Management, 68, 301–323. Google Scholar öffnen doi.org/10.5771/9783828872301
- Mahyoub, F.H., Siddiqui, M.A., Dahab, M.Y. (2014). Building an Arabic Sentiment Lexicon Using Semi-Supervised Learning. Journal of King Saud University-Computer and Information Sciences, 26 (4), 417–424. Google Scholar öffnen doi.org/10.5771/9783828872301
- Manogaran, G., Thota, C., Lopez, D., et al. (2017). Big Data Knowledge System in Healthcare. Internet of Things and Big Data Technologies for Newt Generation Healthcare, 23, 133–157. Google Scholar öffnen doi.org/10.5771/9783828872301
- Manyika, J., Chui, M., Brown, B., et al. (2011). Big Data: The Next Frontier for Innovation, Competition, and Productivity. McKinsey Global Institute. https://www.mckinsey.com/business-functions/digital-mckinsey/our-insights/big-data-the-next-frontier-for-innovation Google Scholar öffnen doi.org/10.5771/9783828872301
- Matz, S., Netzer, O. (2017). Using Big Data As a Window into Consumers’ Psychology. Current Opinion in Behavioral Sciences, 18, 7–12. Google Scholar öffnen doi.org/10.5771/9783828872301
- Mayer-Schönberger, V., Cukier, K. (2013). Big Data – A Revolution to Transform Your Life, Work and Thinking. İstanbul: Paloma Publisher. Google Scholar öffnen doi.org/10.5771/9783828872301
- Mehta, N. and Pandit, A. (2018). Concurrence of Big Data Analytics and Healthcare: A systematic Review. International Journal of Medical Informatics, 114, 57–65. Google Scholar öffnen doi.org/10.5771/9783828872301
- Melie-Garcia, L., Draganski, B., Ashburner, J., et al. (2018). Multiple Linear Regression: Bayesian Inference for Distributed and Big Data in the Medical Informatics Platform of the Human Brain Project. The Preprint Server For Biology. Electronic preprint. https://doi.org/10.1101/242883 Google Scholar öffnen doi.org/10.5771/9783828872301
- Mendelson, D. (2017). Legal Protections for Personal Health Information in the Age of Big Data – A Proposal for Regulatory Framework. Ethics, Medicine and Public Health, 3, 37–55. Google Scholar öffnen doi.org/10.5771/9783828872301
- Moktadir, M.A., Ali, S.M., Paul, S.K., et al. (2018). Barries to Big Data Analtyics in Manufacturing Supply Chains: A Case Study from Bangladesh. Computers & Industrial Engineering. Available online. https://doi.org/10.1016/j.cie.2018.04.013 Google Scholar öffnen doi.org/10.5771/9783828872301
- Morabito, V. (2015). Big Data and Analytics. Berlin: Springer International Publishing. Google Scholar öffnen doi.org/10.5771/9783828872301
- Niemi, T., Nurminen, J.K., Liukkonen, J., et al. (2018). Towards Green Big Data an CERN. Future Generation Computer Systems, 81, 103–113. Google Scholar öffnen doi.org/10.5771/9783828872301
- Pries, K.H., Dunnigan, R. (2015). Big Data Analytics: A Practical Guide for Managers. New York: CRC Press.Taylor & Francis Group. Google Scholar öffnen doi.org/10.5771/9783828872301
- Qian, J., Li, P., Yue, X., et al. (2015). Hierarchical Attribute Reduction Algorithms for Big Data Using MapReduce. Knowledge-Based Systems, 73, 18–31. Google Scholar öffnen doi.org/10.5771/9783828872301
- Ramakrishnan, R., Sridharan, B., Kasturi, P., et al. (2017 May). Azure Data Lake Store: A Hyperscale Distributed File Service for Big Data Analytics. Proceedings of the 2017 ACM International Conference on Management of Data. doi:10.1145/3035918.3056100 Google Scholar öffnen doi.org/10.5771/9783828872301
- Salleh, K.A. and Janczewski, L. (2016). Technological, Organizational and Environmental Security and Privacy Issues of Big Data: A Literature Review. Procedia Computer Science, 100, 19–28. Google Scholar öffnen doi.org/10.5771/9783828872301
- Samuel, A., Sarfraz, M.I., Haseeb, H., et al. (2015). A Framework for Composition and Enforcement of Privacy-Aware and Context-Driven Authorization Mechanism for Multimedia Big Data. IEEE Transactions and Multimedia,17 (9), 1484–1494. Google Scholar öffnen doi.org/10.5771/9783828872301
- Sayer, S., Ulker, A. (2014). Product Lifecycle Management. Engineer & the Machinery Magazine,55 (657), 65–72. Google Scholar öffnen doi.org/10.5771/9783828872301
- Schwab, K. (2016). The Fourth Industrial Revolution. İstanbul: Optimist Publications. Google Scholar öffnen doi.org/10.5771/9783828872301
- Shi, Y. (2014). Big Data: History, Current Status, and Challenges Going Forward. The Bridge, A Global View of Big Data, 44 (6), 6–11. Google Scholar öffnen doi.org/10.5771/9783828872301
- Shin, D.H. and Choi, M.J. (2015). Ecological Views of Big Data: Perspectives and Issues. Telematics and Informatics,32 (2), 311–320. Google Scholar öffnen doi.org/10.5771/9783828872301
- Siegel, E. (2013). Predictive Analytics: The Power to Predict Who Will Click, buy, Lie, or Die. Hoboken, N.J: Wiley. Google Scholar öffnen doi.org/10.5771/9783828872301
- Siemens, (2015) On the Way to Industrie 4.0 – The Digital Enterprise Industry 4.0 Way. https://www.siemens.com/press/pool/de/events/2015/digitalfactory/2015-04-hannovermesse/presentation-e.pdf Google Scholar öffnen doi.org/10.5771/9783828872301
- Sinan, A. (2016). A New Theme for Production: Industry 4.0. Journal of Life Economics, 3 (2), 19–30. Google Scholar öffnen doi.org/10.5771/9783828872301
- Smith, S.M. and Nichols, T.E. (2018). Statistical Challenges in Big Data Human Neuroimaging, Neuroview, 97 (2), 263–268. Google Scholar öffnen doi.org/10.5771/9783828872301
- Smiths, G., Pivert, O., Yager, R., et al. (2018). A Soft Computing Approach to Big Data Summarization. Fuzzy Sets and Systems, 348, 4–20. Google Scholar öffnen doi.org/10.5771/9783828872301
- Soroka, A., Liu, Y., Hani, L., et al. (2017). Big Data Driven Customer Insights for SMEs in Redistributed Manufacturing, Procedia CIRP, 63, 692–697. Google Scholar öffnen doi.org/10.5771/9783828872301
- Su, Z., Xu, Q., Qi, Q. (2016). Big Data in Mobile Social Networks: A QoE- Oriented Framework. Browse Journal & Magazines, 30 (1), 52–57. Google Scholar öffnen doi.org/10.5771/9783828872301
- Tiwari, S., Wee, H.M. (2018). Daryanto Y. Big Data Analytics in Supply Chain Management Between 2010 and 2016: Insights to Industries. Computers & Industrial Engineering, 115, 319–330. Google Scholar öffnen doi.org/10.5771/9783828872301
- Torrecilla, J.L. and Romo, J. (2018). Data Learning From Big Data. Statitics & Probability Letters, 136, 15–19. Google Scholar öffnen doi.org/10.5771/9783828872301
- Wamba, S.F., Gunasekaran, A., Akter, S., et al. (2017). Big Data Analytics and Firm Performance: Effects of Dynamic Capabilities. Journal of Business Research, 70, 356–365. Google Scholar öffnen doi.org/10.5771/9783828872301
- Weichselbraun, A., Gindl, S., Scharl, A. (2014). Enriching semantic knowledge bases for opinion mining in big data applications. Knowledge Based Systems 69, 78–85. Google Scholar öffnen doi.org/10.5771/9783828872301
- Williams, M.L., Burnap, P., Sloan, L. (2017). Crime Sensing With Big Data: The Affordances and Limitations of Using Open-source Communications to Estimate Crime Patterns. The British Journal of Criminology,57 (2),320–340. Google Scholar öffnen doi.org/10.5771/9783828872301
- Witkowski, K. (2016). Internet of Things, Big Data, Industry 4.0 – Innovative Solutions in Logistics and Supplu Chains Management. Procedia Engineer, 182, 763–769. Google Scholar öffnen doi.org/10.5771/9783828872301
- Wu, K., Cui, L., Tseng, M., et al. (2017). Applying Big Data with Fuzzy Dematel to Discover the Critical Factors for Employee Engagement in Developing Sustainability for the Hospitality Industry under Uncertainty. In Supply Chain Management in the Big Data Era (pp. 218–253). IGI Global. doi:10.4018/978–1–5225–0956–1.ch012 Google Scholar öffnen doi.org/10.5771/9783828872301
- Wu, K., Liao, C., Tseng, M., et al. (2017). Toward Sustainability: Using Big Data to Explore the Decisive Attributes of Supply Chain Risks and: Uncertainties. Journal of Cleaner Production, 142 (2), 663–676. Google Scholar öffnen doi.org/10.5771/9783828872301
- Xiang, Z., Schwartz, Z., Gerdes, J.H., et al. (2015). What Can Big Data and Text Analytics Tell Us About Hotel Guest Experience and Satisfaction? International Journal of Hospitality Management, 44, 120–130. Google Scholar öffnen doi.org/10.5771/9783828872301
- Xie, L., Draizen, E.J., Bourne, P.E. (2016). Harnessing Big Data for Systems Pharmacology. Annual Review of Pharmacology and Toxicology, 57, 245–262. Google Scholar öffnen doi.org/10.5771/9783828872301
- Yin, S. and Kaynak, O. (2015). Big Data for Modern Industry: Challenges and Trends [Point of View]. Proceedings of the IEEE, 103 (2), 143–146. Google Scholar öffnen doi.org/10.5771/9783828872301
- Young, S.D. (2015). A “Big Data” Approach to HIV Epidemiology and Prevention. Preventive Medicin, 70, 17–18. Google Scholar öffnen doi.org/10.5771/9783828872301
- Zaki, M., Theodoulidis, B., Shapira, P., et al. (2017). The Role of Big Data to Facilitate Redistributed Manufacturing Using a Co-creation Lens: Patterns from Consumer Goods. Procedia CIRP, 63, 680–685. Google Scholar öffnen doi.org/10.5771/9783828872301
- Zeide, E. (2017). The Structural Consequences of Big Data-Driven Education. Big Data, 5 (2), 165–172. Google Scholar öffnen doi.org/10.5771/9783828872301
- Zhong, R.Y, Xu, C., Chen, C., et al. (2017). Big Data Analytics for Physical Internet-based Intelligent Manufacturing Shop Floors. Internet Journal of Production Research, 55 (9), 2610–2621. Google Scholar öffnen doi.org/10.5771/9783828872301
- Integration of Industry 4.0 Principles into Reverse Logistics Operations for Improved Value Creation: A Case Study of a Mattress Recycling Company by Özden Tozanlı, Elif Kongar Google Scholar öffnen doi.org/10.5771/9783828872301
- Agrawal, S., Singh, R. K. & Murtaza, Q. 2015. A Literature Review And Perspectives In Reverse Logistics. Resources, Conservation And Recycling, 97, 76-92. Google Scholar öffnen doi.org/10.5771/9783828872301
- Ahi, P. & Searcy, C. 2013. A Comparative Literature Analysis Of Definitions For Green And Sustainable Supply Chain Management. Journal Of Cleaner Production, 52, 329–341. Google Scholar öffnen doi.org/10.5771/9783828872301
- Bartodziej, C. J. 2016. The Concept Industry 4.0: An Empirical Analysis Of Technologies And Applications In Production Logistics, Springer. Google Scholar öffnen doi.org/10.5771/9783828872301
- Brettel, M., Friederichsen, N., Keller, M. & Rosenberg, M. 2014. How Virtualization, Decentralization And Network Building Change The Manufacturing Landscape: An Industry 4.0 Perspective. International Journal Of Mechanical, Industrial Science And Engineering, 8, 37–44. Google Scholar öffnen doi.org/10.5771/9783828872301
- Cascadealliance 2017. The State Of The Mattress Recycling Industry. Google Scholar öffnen doi.org/10.5771/9783828872301
- Chopra, S. & Meindl, P. 2007. Supply Chain Management. Strategy, Planning & Operation. Das Summa Summarum Des Management. Springer. Google Scholar öffnen doi.org/10.5771/9783828872301
- Deep. 2018. Mattress Recycling [Online]. Department Of Energy & Environmental Protection. Available: Http://Www.Ct.Gov/Deep/Cwp/View.Asp?A=2714&Q=482160&Deepnav_Gid=1645%20. Google Scholar öffnen doi.org/10.5771/9783828872301
- Efendigil, T., Önüt, S. & Kongar, E. 2008. A Holistic Approach For Selecting A Third-Party Reverse Logistics Provider In The Presence Of Vagueness. Computers & Industrial Engineering, 54, 269–287. Google Scholar öffnen doi.org/10.5771/9783828872301
- Gbce. 2018. Bye Bye Mattress Recycling Program [Online]. Greater Community Bridgeport Enterprises. Available: Https://Greenteambpt.Com/Bye-Bye-Mattress-Recycling-Program/. Google Scholar öffnen doi.org/10.5771/9783828872301
- Handfield, R. B. & Nichols, E. L. 1999. Introduction To Supply Chain Management, Upper Saddle River, Nj: Prentice Hall. Google Scholar öffnen doi.org/10.5771/9783828872301
- Hofmann, E. & Rüsch, M. 2017. Industry 4.0 And The Current Status As Well As Future Prospects On Logistics. Computers In Industry, 89, 23–34. Google Scholar öffnen doi.org/10.5771/9783828872301
- Kagermann, H., Lukas, W.-D. & Wahlster, W. 2011. Industrie 4.0: Mit Dem Internet Der Dinge Auf Dem Weg Zur 4. Industriellen Revolution. Vdi Nachrichten, 13, 11. Google Scholar öffnen doi.org/10.5771/9783828872301
- Kagermann, H., Wahlster, W. & Helbig, J. 2012. Im Fokus: Das Zukunftsprojekt Industrie 4.0: Handlungsempfehlungen Zur Umsetzung. Bericht Der Promotorengruppe Kommunikation. Forschungsunion. Google Scholar öffnen doi.org/10.5771/9783828872301
- Lasi, H., Kemper, H.-G., Fettke, P., Feld, T. & Hoffmann, M. 2014. Industry 4.0. Business & Information Systems Engineering, 6, 239–242. Google Scholar öffnen doi.org/10.5771/9783828872301
- Porter, M. E. 1985. Competitive Advantage: Creating And Sustaining Superior Performance. 1985. New York: Free Press. Google Scholar öffnen doi.org/10.5771/9783828872301
- Stock, T. & Seliger, G. 2016. Opportunities Of Sustainable Manufacturing In Industry 4.0. Procedia Cirp, 40, 536–541. Google Scholar öffnen doi.org/10.5771/9783828872301
- Tozanli, O., Duman, G., Kongar, E. & Gupta, S. 2017. Environmentally Concerned Logistics Operations In Fuzzy Environment: A Literature Survey. Logistics, 1, 4. Google Scholar öffnen doi.org/10.5771/9783828872301
- Tuck. 2018. Mattresses [Online]. Tuck Advancing Better Sleep. Available: Https://Www.Tuck.Com/Mattresses/. Google Scholar öffnen doi.org/10.5771/9783828872301
- Wef. 2017. Impact Of The Fourth Industrial Revolution On Supply Chains [Online]. World Economic Forum. Available: Http://Www3.Weforum.Org/Docs/Wef_Impact_Of_The_Fourth_Industrial_Revolution_On_Supply_Chains_.Pdf [Accessed October 2017. Google Scholar öffnen doi.org/10.5771/9783828872301
- Dynamic Customer Service Levels: Evolving Safety Stock Requirements for Changing Business Needs by Daniel Patrick Covert, Joaquin Alberto Ortiz Millan, Tugba Efendigil Google Scholar öffnen doi.org/10.5771/9783828872301
- Armstrong, David J. "Sharpening inventory management." Harvard Business Review 63.6 (1985): 42–58. Google Scholar öffnen doi.org/10.5771/9783828872301
- Bijvank, Marco. "Periodic review inventory systems with a service level criterion." Journal of the Operational Research Society 65.12 (2014): 1853–1863. Google Scholar öffnen doi.org/10.5771/9783828872301
- Dubelaar, Chris, Garland Chow, and Paul D. Larson. "Relationships between inventory, sales and service in a retail chain store operation." International Journal of Physical Distribution & Logistics Management 31.2 (2001): 96–108. Google Scholar öffnen doi.org/10.5771/9783828872301
- Emmelhainz, Larry W., Margaret A. Emmelhainz, and James R. Stock. "Logistics implications of retail stockouts." Journal of Business Logistics 12.2 (1991): 129. Google Scholar öffnen doi.org/10.5771/9783828872301
- Flores, Benito E., and D. Clay Whybark. "Implementing multiple criteria ABC analysis." Journal of Operations Management 7.1 – 2 (1987): 79–85. Google Scholar öffnen doi.org/10.5771/9783828872301
- Flores, Benito E., David L. Olson, and V. K. Dorai. "Management of multicriteria inventory classification." Mathematical and Computer modelling 16.12 (1992): 71–82. Google Scholar öffnen doi.org/10.5771/9783828872301
- Koottatep, Pakawkul, and Jinqian Li. Promotional forecasting in the grocery retail business. Diss. Massachusetts Institute of Technology, 2006. Google Scholar öffnen doi.org/10.5771/9783828872301
- Millstein, Mitchell A., Liu Yang, and Haitao Li. "Optimizing ABC inventory grouping decisions." International Journal of Production Economics 148 (2014): 71–80. Google Scholar öffnen doi.org/10.5771/9783828872301
- Mohammaditabar, Davood, Seyed Hassan Ghodsypour, and Chris O'Brien. "Inventory control system design by integrating inventory classification and policy selection." International Journal of Production Economics 140.2 (2012): 655–659. Google Scholar öffnen doi.org/10.5771/9783828872301
- Ng, Wan Lung. "A simple classifier for multiple criteria ABC analysis." European Journal of Operational Research 177.1 (2007): 344–353. Google Scholar öffnen doi.org/10.5771/9783828872301
- Porras, Eric, and Rommert Dekker. "An inventory control system for spare parts at a refinery: An empirical comparison of different re-order point methods." European Journal of Operational Research 184.1 (2008): 101–132. Google Scholar öffnen doi.org/10.5771/9783828872301
- Ramanathan, Ramakrishnan. "ABC inventory classification with multiple-criteria using weighted linear optimization." Computers & Operations Research 33.3 (2006): 695–700. Google Scholar öffnen doi.org/10.5771/9783828872301
- Silver, Edward Allen, Pyke, David F., & Peterson, Rein. (1998). Inventory management and production planning and scheduling (Vol. 3, p. 30). New York: Wiley. Google Scholar öffnen doi.org/10.5771/9783828872301
- Taylor, J. C., & Fawcett, S. E. (2001). Retail on‐shelf performance of advertised items: an assessment of supply chain effectiveness at the point of purchase. Journal of Business Logistics, 22(1), 73–89. Google Scholar öffnen doi.org/10.5771/9783828872301
- Teunter, Ruud H., M. Zied Babai, and Aris A. Syntetos. "ABC classification: service levels and inventory costs." Production and Operations Management 19.3 (2010): 343–352. Google Scholar öffnen doi.org/10.5771/9783828872301
- Van Kampen, Tim J., Renzo Akkerman, and Dirk Pieter van Donk. "SKU classification: a literature review and conceptual framework." International Journal of Operations & Production Management 32.7 (2012): 850–876. Google Scholar öffnen doi.org/10.5771/9783828872301
- Thomopoulos, Nick T. "Promotion Forecasts" Demand Forecasting for Inventory Control. Springer International Publishing, 2015. 71–87. Google Scholar öffnen doi.org/10.5771/9783828872301
- Timofeev, Roman. "Classification and regression trees (cart) theory and applications." Humboldt University, Berlin (2004). Google Scholar öffnen doi.org/10.5771/9783828872301
- Yang, Liu. Optimizing inventory for profitability and order fulfillment improvement: Integrating Inventory Classification and Control Decisions under Non-Stationary Demand For Profit Maximization and Integrating Inventory Classification and Control Decisions to Maximize Order Fulfillment Measures. Diss. University of Missouri-Saint Louis, 2016. Google Scholar öffnen doi.org/10.5771/9783828872301
- Yu, Min-Chun. "Multi-criteria ABC analysis using artificial-intelligence-based classification techniques." Expert Systems with Applications 38.4 (2011): 3416–3421. Google Scholar öffnen doi.org/10.5771/9783828872301
- Zhang, Rachel Q., Wallace J. Hopp, and Chonawee Supatgiat. "Spreadsheet implementable inventory control for a distribution center." Journal of Heuristics 7.2 (2001): 185–203. Google Scholar öffnen doi.org/10.5771/9783828872301
- Zhou, Peng, and Liwei Fan. "A note on multi-criteria ABC inventory classification using weighted linear optimization." European journal of operational research 182.3 (2007): 1488–1491. Google Scholar öffnen doi.org/10.5771/9783828872301
- Industry 4.0: Is Your Country Ready? by Serpil Erol, Gül Didem Batur Sir Google Scholar öffnen doi.org/10.5771/9783828872301
- Cabinet Office. “Report on the 5th science and technology basic plan”, Cabinet Office of Japan, Tokyo, 2015. Google Scholar öffnen doi.org/10.5771/9783828872301
- Conseil national de l’industrie. “The new face of industry in France”, French National Industry Council, Paris, 2013. Google Scholar öffnen doi.org/10.5771/9783828872301
- European Commission, “Factories of the Future PPP: Towards Competitive EU Manufacturing”, European Commission, Bruxelles, 2016. Google Scholar öffnen doi.org/10.5771/9783828872301
- European Parliament’s Committee on Industry, Research and Energy, Study for ITRE, “Industry 4.0”, Policy Department A: Economic and Scientific Policy, Brussels, 2016. Google Scholar öffnen doi.org/10.5771/9783828872301
- Evans, P.C. & Annunziata, M. “Industrial internet: pushing the boundaries of minds and machines”, General Electric, Boston, 2012. Google Scholar öffnen doi.org/10.5771/9783828872301
- Foresight. “The future of manufacturing: a new era of opportunity and challenge for the UK”, UK Government Office for Science, London, 2013. Google Scholar öffnen doi.org/10.5771/9783828872301
- Gentner, S. “Industry 4.0: Reality, Future or just Science Fiction? How to Convince Today’s Management to Invest in Tomorrow’s Future! Successful Strategies for Industry 4.0 and Manufacturing IT”, CHIMIA International Journal for Chemistry, Vol. 70, No. 9, 2016, pp. 628–633. Google Scholar öffnen doi.org/10.5771/9783828872301
- https://www.statista.com/statistics/667634/leading-countires-industry-40-worldwide/ Google Scholar öffnen doi.org/10.5771/9783828872301
- Kagermann, H., Wahlster, W. & Helbig, J. “Recommendations for implementing the strategic initiative Industrie 4.0”, Final Report of the Industrie 4.0 Working Group of Acatech, Berlin, 2013. Google Scholar öffnen doi.org/10.5771/9783828872301
- Kang, H.S., Lee, J.Y., Choi, S., Kim, H., Park, J.H., Son, J.Y., Kim, B.H. & Do Noh, S. “Smart manufacturing: Past research, present findings, and future directions”, International Journal of Precision Engineering and Manufacturing-Green Technology, Vol. 3, No. 1, 2016, pp. 111–128. Google Scholar öffnen doi.org/10.5771/9783828872301
- Li, K. “Made in China 2025”, State Council of China, Beijing, 2015. Google Scholar öffnen doi.org/10.5771/9783828872301
- Liao, Y., Deschamps, F., Loures, EDFR & Ramos, L.F.P., "Past, present and future of Industry 4.0-a systematic literature review and research agenda proposal", International journal of production research, Vol. 55, No. 12, 2017, pp. 3609–3629. Google Scholar öffnen doi.org/10.5771/9783828872301
- Mueller, E., Chen, X. L., & Riedel, R. “Challenges and requirements for the application of industry 4.0: a special insight with the usage of cyber-physical system”, Chinese Journal of Mechanical Engineering, Vol. 30, No. 5, 2017, pp. 1050–1057. Google Scholar öffnen doi.org/10.5771/9783828872301
- National Research Foundation. “Research, innovation and enterprise (RIE) 2015 plan” Prime Minister’s Office of Singapore, Singapore, 2016. Google Scholar öffnen doi.org/10.5771/9783828872301
- Rafael R., Shirley A.J. & Liveris, A. “Report to the president accelerating u.s. advanced manufacturing”, The President’s Council of Advisors on Science and Technology, Washington, 2014. Google Scholar öffnen doi.org/10.5771/9783828872301
- Ridgway, K., Clegg, C.W. & Williams, D.J. “The factory of the future. Future of Manufacturing Project: Evidence Paper 29”, Government Office for Science, London, 2013. Google Scholar öffnen doi.org/10.5771/9783828872301
- Siemieniuch, C.E., Sinclair, M.A. & deC Henshaw, M.J. “Global Drivers, Sustainable Manufacturing and Systems Ergonomics”, Applied Ergonomics, Vol. 51, 2015, pp. 104–119. Google Scholar öffnen doi.org/10.5771/9783828872301
- World Economic Forum, ‘Readiness for the Future of Production Report’, World Economic Forum’s System Initiative on Shaping the Future of Production, 2018. Google Scholar öffnen doi.org/10.5771/9783828872301
- Transformation of Shop Floor with Industry 4.0: Guidelines for Manufacturing Companies by Fatma DEMIRCAN KESKIN, Haluk SOYUER, Hakan OZKARA Google Scholar öffnen doi.org/10.5771/9783828872301
- Abersfelder, S., Bogner, E., Heyder, A. and Franke, J. (2016). “Application and Validation of an Existing Industry 4.0 Guideline for the Development of Specific Recommendations for Implementation”, Advanced Materials Research, 1140: 465–472. Google Scholar öffnen doi.org/10.5771/9783828872301
- Almada-Lobo, F. (2015). The Industry 4.0 revolution and the future of Manufacturing Execution Systems (MES), Journal of Innovation Management, 3(4), 16–21. Google Scholar öffnen doi.org/10.5771/9783828872301
- Berger, C., Berlak, J. and Reinhart, G. (2016). Service-based Production Planning and Control of Cyber-Physical Production Systems, BLED 2016 Proceedings, pp: 491–502. Google Scholar öffnen doi.org/10.5771/9783828872301
- Cachada, A., Pires, F., Barbosa, J., & Leitão, P. (2017, October). Petri nets approach for designing the migration process towards industrial cyber-physical production systems. In Industrial Electronics Society, IECON 2017–43rd Annual Conference of the IEEE (pp. 3492–3497). IEEE. Google Scholar öffnen doi.org/10.5771/9783828872301
- Erol, S., Schumacher, A. and Sihn, W. (2016). “Strategic guidance towards Industry 4.0 – a three-stage process model”. International Conference on Competitive Manufacturing 2016 (COMA'16), At Stellenbosch, South Africa. Google Scholar öffnen doi.org/10.5771/9783828872301
- Gorecky, D., Schmitt, M., Loskyll, M., & Zühlke, D. (2014, July). Human-machine-interaction in the industry 4.0 era. In Industrial Informatics (INDIN), 2014 12th IEEE International Conference on(pp. 289–294). Ieee. Google Scholar öffnen doi.org/10.5771/9783828872301
- Industrie 4.0 Reifegrad – Selbstcheck f¨ur Unternehmen. 2016. URL:https://ihk-industrie40.de/selbstcheck/. Google Scholar öffnen doi.org/10.5771/9783828872301
- Kagermann, H., Wahlster, W. and Helbig, J. (2013). “Securing the future of German manufacturing industry, Recommendations for implementing the strategic initiative INDUSTRIE 4.0“, Final report of the Industrie 4.0“ Working Group,“Report_“Industrie 4.0“_engl.pdf, Frankfurt, April 2013. Google Scholar öffnen doi.org/10.5771/9783828872301
- Khedher, A.B., Henry, S., and Bouras, A. (2011). Integration between MES and Product Lifecycle Management. 2011 IEEE 16th Conference on Emerging Technologies & Factory Automation (ETFA). Google Scholar öffnen doi.org/10.5771/9783828872301
- Klein, K., Franke, M., Hribernik, K., Coscia, E., Balzert, S., Sutter, J., and Thoben, K. D. (2014, November). Potentials of future internet technologies for digital factories. In Computer Systems and Applications (AICCSA), 2014 IEEE/ACS 11th International Conference on (pp. 734–741). IEEE. Google Scholar öffnen doi.org/10.5771/9783828872301
- Kucharska, E., Grobler- Debska, K., Gracel, J., and Jagodzinski, M. (2015). “Idea of Impact of ERP-APS-MES Systems Integration on the Effectiveness of Decision Making Process in Manufacturing Companies”. In Kozielski, S., Mrozek, D., Kasprowski, P., Malysiak-Mrozek, B., Kostrzewa, D. (Eds.). Beyond Databases, Architectures and Structures. 11th International Conference, BDAS 2015, Ustroń, Poland, May 26–29, 2015, Proceedings. Google Scholar öffnen doi.org/10.5771/9783828872301
- Lee, J., Bagheri, B., and Kao, H. A. (2015). A cyber-physical systems architecture for industry 4.0-based manufacturing systems. Manufacturing Letters, 3, 18–23. Google Scholar öffnen doi.org/10.5771/9783828872301
- Lee, J., Holgado, M., Kao, H. A., & Macchi, M. (2014). New thinking paradigm for maintenance innovation design. IFAC Proceedings Volumes, 47(3), 7104–7109. Google Scholar öffnen doi.org/10.5771/9783828872301
- Leyh, C., Bley, K., Schäffer, T., & Forstenhäusler, S. (2016, September). SIMMI 4.0-a maturity model for classifying the enterprise-wide it and software landscape focusing on Industry 4.0. In Computer Science and Information Systems (FedCSIS), 2016 Federated Conference on (pp. 1297–1302). IEEE. Google Scholar öffnen doi.org/10.5771/9783828872301
- Lichtblau, K., Stich, V., Bertenrath, R., Blum, M., Bleider, M., Millack, A., Schmitt, K., Schmitz, E. & M.S.: IMPULS – Industrie 4.0- Readiness, (2015). Google Scholar öffnen doi.org/10.5771/9783828872301
- Lins, R. G., Guerreiro, B., Schmitt, R., Sun, J., Corazzim, M., & Silva, F. R. (2017, October). A novel methodology for retrofitting CNC machines based on the context of industry 4.0. In Systems Engineering Symposium (ISSE), 2017 IEEE International (pp. 1–6). IEEE. Google Scholar öffnen doi.org/10.5771/9783828872301
- Madkan, P. (2014). Empirical Study of ERP Implementation Strategies-Filling Gaps between the Success and Failure of ERP Implementation Process. International Journal of Information & Computation Technology, 4(6), 633–642. Google Scholar öffnen doi.org/10.5771/9783828872301
- Naedele, M., Chen, H-M.,Kazman,R., Cai, Y.,Xiao, L. and Silva, C.V.A. (2015). Manufacturing execution systems: A vision for managing software development. The Journal of Systems and Software, 101: 59–68. Google Scholar öffnen doi.org/10.5771/9783828872301
- Panetto, H., and Molina, A. (2008). Enterprise Integration and Interoperability in Manufacturing Systems: trends and issues. Computers in Industry, 59(7), 641–646. Google Scholar öffnen doi.org/10.5771/9783828872301
- Porter, M.E., and Heppelmann, J.E. (2015). How Smart, Connected Products Are Transforming Companies, Harvard Business Review, 1–9. Google Scholar öffnen doi.org/10.5771/9783828872301
- PricewaterhouseCoopers: The Industry 4.0 / Digital Operations Self Assessment, (2016). Google Scholar öffnen doi.org/10.5771/9783828872301
- Romero, D., Stahre, J., Wuest, T., Noran, O., Bernus, P., Fast-Berglund, Å., & Gorecky, D. (2016, October). Towards an operator 4.0 typology: a human-centric perspective on the fourth industrial revolution technologies. In INTERNATIONAL CONFERENCE ON COMPUTERS & INDUSTRIAL ENGINEERING (CIE46) (pp. 1–11). Google Scholar öffnen doi.org/10.5771/9783828872301
- Romero, D., and Vernadat, F. (2016). Enterprise information systems state of the art: Past, present and future trends. Computers in Industry, 79, 3–13. Google Scholar öffnen doi.org/10.5771/9783828872301
- Roy, R., Stark, R., Tracht, K., Takata, S., & Mori, M. (2016). Continuous maintenance and the future–Foundations and technological challenges. CIRP Annals, 65(2), 667–688. Google Scholar öffnen doi.org/10.5771/9783828872301
- Sanders, A., Elangeswaran, C., and Wulfsberg, J. (2016). “Industry 4.0 Implies Lean Manufacturing: Research Activities in Industry 4.0 Function as Enablers for Lean Manufacturing”, Journal of Industrial Engineering and Management, 9(3), 812–833. Google Scholar öffnen doi.org/10.5771/9783828872301
- Schumacher, A., Erol, S., & Sihn, W. (2016). A maturity model for assessing industry 4.0 readiness and maturity of manufacturing enterprises. Procedia CIRP, 52, 161–166. Google Scholar öffnen doi.org/10.5771/9783828872301
- Seitz, K-F., and Nyhuis, P. (2015). Cyber-Physical Production Systems Combined with Logistic Models – A Learning Factory Concept for an Improved Production Planning and Control. Procedia CIRP, (32), 92–97. Google Scholar öffnen doi.org/10.5771/9783828872301
- Shrouf, F., Ordieres, J., and Miragliotta, G. (2014). Smart Factories in Industry 4.0: A Review of the Concept and of Energy Management Approached in Production Based on the Internet of Things Paradigm. Industrial Engineering and Engineering Management (IEEM), 2014 IEEE International Conference on, 9–12 Dec. 2014, 697–701. Google Scholar öffnen doi.org/10.5771/9783828872301
- Stojkić, Z., Veža, I., and Bošnjak, I. (2016). A Concept Of Information System Implementation (CRM and ERP) Within Industry 4.0. 26TH DAAAM International Symposium On Intelligent Manufacturing and Automation, 912–919. Google Scholar öffnen doi.org/10.5771/9783828872301
- Tao, F., Cheng, J., Qi, Q., Zhang, M., Zhang, H., & Sui, F. (2018). Digital twin-driven product design, manufacturing and service with big data. The International Journal of Advanced Manufacturing Technology, 94(9–12), 3563–3576. Google Scholar öffnen doi.org/10.5771/9783828872301
- Tao, F., & Zhang, M. (2017). Digital twin shop-floor: a new shop-floor paradigm towards smart manufacturing. Ieee Access, 5, 20418–20427. Google Scholar öffnen doi.org/10.5771/9783828872301
- Theorin, A., Bengtsson, K., Provost, J., Lieder, M., Johnsson, C., Lundholm, T., and Lennartson, B. (2016). An event-driven manufacturing information system architecture for Industry 4.0, International Journal of Production Research, 1–15. Google Scholar öffnen doi.org/10.5771/9783828872301
- Uhlemann, T. H. J., Lehmann, C., & Steinhilper, R. (2017). The digital twin: Realizing the cyber-physical production system for industry 4.0. Procedia Cirp, 61, 335–340. Google Scholar öffnen doi.org/10.5771/9783828872301
- Wang, H., Liu, L., Fei, Y., and Liu, T. (2016). A collaborative manufacturing execution system oriented to discrete manufacturing enterprises. Concurrent Engineering, 24(4), 330–343. Google Scholar öffnen doi.org/10.5771/9783828872301
- Wang, X., Ong, S. K., & Nee, A. Y. (2016). A comprehensive survey of augmented reality assembly research. Advances in Manufacturing, 4(1), 1–22. Google Scholar öffnen doi.org/10.5771/9783828872301
- Zainal, Z. (2007). Case study as a research method. Jurnal Kemanusiaan, (9), 1–6. Google Scholar öffnen doi.org/10.5771/9783828872301
- Zhuang, C., Liu, J., & Xiong, H. (2018). Digital twin-based smart production management and control framework for the complex product assembly shop-floor. The International Journal of Advanced Manufacturing Technology, 96(1–4), 1149–1163. Google Scholar öffnen doi.org/10.5771/9783828872301
- A Review on Cold Chain Management for Industry 4.0 by Cagla Ediz Google Scholar öffnen doi.org/10.5771/9783828872301
- Atzori, L., Iera, A., & Morabito, G. (2010). The Internet Of Things: A Survey. Computer Networks, 54(15), 2787–2805. Google Scholar öffnen doi.org/10.5771/9783828872301
- Barata, J., Rupino Da Cunha, P., & Stal, J. (2018). Mobile Supply Chain Management In The Industry 4.0 Era: An Annotated Bibliography And Guide For Future Research. Journal of Enterprise Information Management, 31(1), 173–192. Google Scholar öffnen doi.org/10.5771/9783828872301
- Barreto, L., Amaral, A., & Pereira, T. (2017). Industry 4.0 Implications In Logistics: An Overview. Procedia Manufacturing, 13, 1245–1252. Google Scholar öffnen doi.org/10.5771/9783828872301
- Benešová, A., & Tupa, J. (2017). Requirements For Education And Qualification Of People In Industry 4.0. Procedia Manufacturing, 11, 2195–2202. Google Scholar öffnen doi.org/10.5771/9783828872301
- Bouzakis, A., & Overmeyer, L. (2012, November). Simulation Analysis For The Performance Of Integrated HF RFID Antennas. In Computer Modeling And Simulation (EMS), 2012 Sixth Uksim/AMSS European Symposium On,391–394, IEEE. Google Scholar öffnen doi.org/10.5771/9783828872301
- Corallo, A., Latino, M. E., & Menegoli, M. (2018). From Industry 4.0 to Agriculture 4.0: A Framework to Manage Product Data in Agri-Food Supply Chain for Voluntary Traceability. Int. J. Nutr. Food Eng., 12(5). Google Scholar öffnen doi.org/10.5771/9783828872301
- Dombrowski, U., Richter, T., & Krenkel, P. (2017). Interdependencies Of Industrie 4.0 & Lean Production Systems: A Use Cases Analysis. Procedia Manufacturing, 11, 1061–1068. Google Scholar öffnen doi.org/10.5771/9783828872301
- Drath, R., & Horch, A. (2014). Industrie 4. 0: Hit Or Hype? IEEE Ind Electron Mag, 8(2):56–58. Google Scholar öffnen doi.org/10.5771/9783828872301
- Erkollar, A. & Oberer, B. (2017). Endüstri 4.0 Ve Ulaşımda Kullanımı. Transist 2017, 493–498. Google Scholar öffnen doi.org/10.5771/9783828872301
- Fleisch, E., Weinberger, M., & Wortmann, F. (2015). Business Models And The Internet Of Things. In Interoperability And Open-Source Solutions For The Internet Of Things, 6–10, Springer, Cham. Google Scholar öffnen doi.org/10.5771/9783828872301
- Gunhan, T., Demir, V., & Bilgen, H. (2006). Çiftlik Tipi Süt Soğutma Tanklarının Performans Değerlerinin Deneysel Olarak Belirlenmesi. Tarım Makinaları Bilimi Dergisi, 2(4). Google Scholar öffnen doi.org/10.5771/9783828872301
- Hermann, M., Pentek, T., & Otto, B. (2016, January). Design Principles For Industrie 4.0 Scenarios. In System Sciences (HICSS), 2016 49th Hawaii International Conference On,3928–3937, IEEE. Google Scholar öffnen doi.org/10.5771/9783828872301
- Hofmann, E., & Rüsch, M. (2017). Industry 4.0 And The Current Status As Well As Future Prospects On Logistics. Computers In Industry, 89, 23–34. Google Scholar öffnen doi.org/10.5771/9783828872301
- Kara, İ. (2009). CAN Haberleşme Protokolünün İncelenmesi Ve Bir Sıcaklık Kontrol Sistemine Uygulanması (Doctoral Dissertation). Google Scholar öffnen doi.org/10.5771/9783828872301
- Muneeswaran. A. (2015). Automotive Diagnostics Communication Protocols AnalysisKWP2000, IOSR Journal of Electronics and Communication Engineering (IOSR-JECE), Volume 10, Issue 1, Ver. 1 (Jan – Feb. 2015), 20–31. Google Scholar öffnen doi.org/10.5771/9783828872301
- Olsen, P., & Borit, M. (2013). How to define traceability. Trends in food science & technology, 29(2), 142–150. Google Scholar öffnen doi.org/10.5771/9783828872301
- Onat, O. (2018). Sürücüsüz Otomobil de Kaza Yapar, CNN Turk, 20.03.2018. Google Scholar öffnen doi.org/10.5771/9783828872301
- Lasi, H., Fettke, P., Kemper, H. G., Feld, T., & Hoffmann, M. (2014). Industry 4.0. Business & Information Systems Engineering, 6, 239–242. Google Scholar öffnen doi.org/10.5771/9783828872301
- Lu Y., Industry 4.0: A Survey On Technologies, Applications And Open Research İssues, In Journal Of Industrial Information Integration, Volume 6, 2017, Pages 1–10. Google Scholar öffnen doi.org/10.5771/9783828872301
- Oberer, B., & Erkollar, A. (2017), Sustainable Cities Need Smart Transportation: The Industry 4.0 Transportation Matrix. Transist 2017, 188–197. Google Scholar öffnen doi.org/10.5771/9783828872301
- Ozgüven, M. M. (2016), Radyo Frekansli (Rf) Pedometre Tasarimi. (Master Thesis), Gaziosmanpaşa University, Tokat. Google Scholar öffnen doi.org/10.5771/9783828872301
- Shafiq, S. I., Sanin, C., Szczerbicki, E., & Toro, C. (2015). Virtual Engineering Object/Virtual Engineering Process: A Specialized Form Of Cyber Physical System For Industrie 4.0. Procedia Computer Science, 60, 1146–1155. Google Scholar öffnen doi.org/10.5771/9783828872301
- Sung, T. K. (2018). Industry 4.0: A Korea perspective. Technological Forecasting and Social Change, 132, 40–45. Google Scholar öffnen doi.org/10.5771/9783828872301
- Suru Yonetimli Buyukbas Sagım Sistemleri, Sezer Tarım Teknolojileri, http://www.sezermac.com/index.php?sayfa=detay&act=view&code=507&cat=413&catname=S%FCr%FC%20Y%F6netimli%20B%FCy%FCkba%FE%20Sa%F0%FDm%20Sistemleri, accessed on 2.6.2018. Google Scholar öffnen doi.org/10.5771/9783828872301
- TC Milli Eğitim Bakanlığı (2013). Gıda Teknolojisi, Sütü İşletmeye Alma, Ankara,. Google Scholar öffnen doi.org/10.5771/9783828872301
- Tanrıvermiş, H., & Mülayim, Z. G. (1997). Sanayinin Neden Olduğu Çevre Kirliliğinin Tarıma Verdiği Zararların Değerinin Biçilmesi: Samsun Gübre (TÜGSAS) Ve Karadeniz Bakır (KBI) Sanayileri Örneği,. J. Agriculture And Forestry, 23, 337–345. Google Scholar öffnen doi.org/10.5771/9783828872301
- Thames, L., & Schaefer, D. (2016). Software-Defined Cloud Manufacturing For Industry 4.0. Procedia CIRP, 52, 12–17. Google Scholar öffnen doi.org/10.5771/9783828872301
- Tjahjono, B., Esplugues, C., Ares, E., & Pelaez, G. (2017). What Does Industry 4.0 Mean To Supply Chain? Procedia Manufacturing, 13, 1175–1182. Google Scholar öffnen doi.org/10.5771/9783828872301
- Tupa, J., Simota, J., & Steiner, F. (2017). Aspects Of Risk Management Implementation For Industry 4.0. Procedia Manufacturing, 11, 1223–1230. Google Scholar öffnen doi.org/10.5771/9783828872301
- Connection between industry 4.0 and smart factories by Elif Nurten, Cagla Seneler Google Scholar öffnen doi.org/10.5771/9783828872301
- Alcin, S. (2016). ÜRETİM İÇİN YENİ BİR İZLEK: SANAYİ 4.0. Journal of Life Economics, 3(8), pp.19 – 19. Google Scholar öffnen doi.org/10.5771/9783828872301
- AZoNano.com. (2005). What is Nanotechnology and What Can It Do?. [online] Available at: https://www.azonano.com/article.aspx?ArticleID=1134 Google Scholar öffnen doi.org/10.5771/9783828872301
- Burke, R., Mussomeli, A., Laaper, S., Hartigan, M. and Sniderman, B. (2017). The smart factory. [online] Deloitte Insights. Available at: https://www2.deloitte.com/insights/us/en/focus/industry-4-0/smart-factory-connected-manufacturing.html Google Scholar öffnen doi.org/10.5771/9783828872301
- Chen, B., Wan, J., Shu, L., Li, P., Mukherjee, M. and Yin, B. (2018). Smart Factory of Industry 4.0: Key Technologies, Application Case, and Challenges. IEEE Access, 6, pp.6505 – 6519. Google Scholar öffnen doi.org/10.5771/9783828872301
- Cleaveland, P. (2006). What is a smart sensor? – Control Engineering. [online] Control Engineering. Available at: https://www.controleng.com/articles/what-is-a-smart-sensor/ Google Scholar öffnen doi.org/10.5771/9783828872301
- Correia, M. (2014). Industrie 4.0 Framework, Challenges and Perspectives. [online] Recipp.ipp.pt. Available at: http://recipp.ipp.pt/bitstream/10400.22/7110/1/DM_CorreiaMiguel_2014_MEM.pdf Google Scholar öffnen doi.org/10.5771/9783828872301
- Deloitte Insights. (2018). Forces of change: Industry 4.0. [online] Available at: https://www2.deloitte.com/insights/us/en/focus/industry-4-0/overview.html Duivenvoorden, C. (2017). The Beginners Guide To The Industry 4.0 – Industry4Magazine. [online] Industry4Magazine. Available at: https://industry4magazine.com/the-beginners-guide-to-the-industry-4-0-f45b93a95649 Google Scholar öffnen doi.org/10.5771/9783828872301
- Jazdi, N. (2014). Cyber physical systems in the context of Industry 4.0. 2014 IEEE International Conference on Automation, Quality and Testing, Robotics. Google Scholar öffnen doi.org/10.5771/9783828872301
- Lee, E. (2008). Cyber Physical Systems: Design Challenges. 2008 11th IEEE International Symposium on Object and Component-Oriented Real-Time Distributed Computing (ISORC). Google Scholar öffnen doi.org/10.5771/9783828872301
- Lee, E (2015). The Past, Present and Future of Cyber-Physical Systems: A Focus on Models. Sensors, 15(3), pp.4837 – 4869. Google Scholar öffnen doi.org/10.5771/9783828872301
- Lee, I. and Lee, K. (2015). The Internet of Things (IoT): Applications, investments, and challenges for enterprises. Business Horizons, 58(4), pp.431 – 440. Google Scholar öffnen doi.org/10.5771/9783828872301
- Lee, J., Bagheri, B. and Kao, H. (2015). A Cyber-Physical Systems architecture for Industry 4.0-based manufacturing systems. Manufacturing Letters, 3, pp.18 – 23. Google Scholar öffnen doi.org/10.5771/9783828872301
- Lee, J., Davari, H., Singh, J. and Pandhare, V. (2018). Industrial Artificial Intelligence for industry 4.0-based manufacturing systems. Manufacturing Letters, 18, pp.20 – 23. Google Scholar öffnen doi.org/10.5771/9783828872301
- Lueth, K. (2014). Why it is called Internet of Things: Definition, history, disambiguation. [online] Iot-analytics.com. Available at: https://iot-analytics.com/internet-of-things-definition/ Marr, B. (2016). What Everyone Must Know About Industry 4.0. [online] Forbes.com. Available at: https://www.forbes.com/sites/bernardmarr/2016/06/20/what-everyone-must-know-about-industry-4-0/#6f0059f3795f Google Scholar öffnen doi.org/10.5771/9783828872301
- OTTO Motors. (n.d.). 5 Key Industry 4.0 Technologies Changing Manufacturing. [online] Available at: https://ottomotors.com/blog/5-industry-4-0-technologies Google Scholar öffnen doi.org/10.5771/9783828872301
- Rojko, A. (2017). Industry 4.0 Concept: Background and Overview. International Journal of Interactive Mobile Technologies (iJIM), 11(5), p.77. Google Scholar öffnen doi.org/10.5771/9783828872301
- Rghioui, A. (2017). Internet of Things: Visions, Technologies, and Areas of Application. Automation, Control and Intelligent Systems, 5(6), p.83. Google Scholar öffnen doi.org/10.5771/9783828872301
- Sawe, B. (2017). What Was The Second Industrial Revolution?. [online] WorldAtlas. Available at: https://www.worldatlas.com/articles/what-was-the-second-industrial-revolution.html Scheuermann, C., Verclas, S. and Bruegge, B. (2015). Agile Factory – An Example of an Industry 4.0 Manufacturing Process. 2015 IEEE 3rd International Conference on Cyber-Physical Systems, Networks, and Applications. Google Scholar öffnen doi.org/10.5771/9783828872301
- Schwab, K. (n.d.). The fourth industrial revolution. Google Scholar öffnen doi.org/10.5771/9783828872301
- Shrouf, F., Ordieres, J. and Miragliotta, G. (2014). Smart factories in Industry 4.0: A review of the concept and of energy management approached in production based on the Internet of Things paradigm. 2014 IEEE International Conference on Industrial Engineering and Engineering Management. Google Scholar öffnen doi.org/10.5771/9783828872301
- Sniderman, B., Mahto, M. and Cotteleer, M. (2016). Industry 4.0 and manufacturing ecosystems. [online] Deloitte Insights. Available at: https://www2.deloitte.com/insights/us/en/focus/industry-4-0/manufacturing-ecosystems-exploring-world-connected-enterprises.html Google Scholar öffnen doi.org/10.5771/9783828872301
- Weallans, S. (2018). IIoT And Industry 4.0: The Basics You Need to Know | Sensors Magazine. [online] Sensorsmag.com. Available at: https://www.sensorsmag.com/components/iiot-and-industry-4-0-basics-you-need-to-know Google Scholar öffnen doi.org/10.5771/9783828872301
- Wright, G. (2018). Smart factories just got smarter. [online] Manufacturingglobal.com. Available at: https://www.manufacturingglobal.com/technology/smart-factories-just-got-smarter Google Scholar öffnen doi.org/10.5771/9783828872301
- Techno-Parks on the Digital Transformation by Gizem ATAK, Ferhan ÇEBİ Google Scholar öffnen doi.org/10.5771/9783828872301
- Ahuett-Garza, H., & Kurfess, T. (2018). A brief discussion on the trends of habilitating technologies for Industry 4.0 and Smart manufacturing. Manufacturing Letters. Google Scholar öffnen doi.org/10.5771/9783828872301
- Auger, P., Barnir, A., & Gallaugher, M. J. (2003). Business Process Digitization, Strategy, and the Impact of Firm Age and Size: The Case of the Magazine Publishing Industry. Journal of Business Venturing, (6) 18, 789–814. Google Scholar öffnen doi.org/10.5771/9783828872301
- Atak G. (2018), The Role of Technopark Companies in the Development of the Fourth Industrial Revolution in Turkey,. Istanbul Technical University, Unpublished master’s thesis Google Scholar öffnen doi.org/10.5771/9783828872301
- Btgm.sanayi.gov.tr (2018). Date retrieved 28.12.2018, from https://btgm.sanayi.gov.tr/DokumanGetHandler.ashx?dokumanId=c2f 7d4c9–5cde-461e-b2ee-1966309073d2. Google Scholar öffnen doi.org/10.5771/9783828872301
- Fırat, S. Ü., & Fırat, O. Z. (2017). Sanayi 4.0 Devrimi Üzerine Karşılaştırmalı Bir İnceleme: Kavramlar, Küresel Gelişmeler ve Türkiye. Toprak İşveren Dergisi, (114), 10–23. Google Scholar öffnen doi.org/10.5771/9783828872301
- Fuchs, C. (2018). Industry 4.0: The Digital German Ideology. tripleC: Communication, Capitalism & Critique. Open Access Journal for a Global Sustainable Information Society, 16(1), 280–289. Google Scholar öffnen doi.org/10.5771/9783828872301
- Gubán, M., & Kovács, G. (2017). Industry 4.0 Conception. Acta Technical Corviniensis-Bulletin of Engineering, 10(1), 111. Google Scholar öffnen doi.org/10.5771/9783828872301
- IASP (2017). Date retrieved 23.10.2017, from https://www.iasp.ws/OurIndustry/Definitions. Google Scholar öffnen doi.org/10.5771/9783828872301
- İçten, T., & Bal, G. (2017). Artırılmış Gerçeklik Teknolojisi Üzerine Yapılan Akademik Çalışmaların İçerik Analizi. Bilişim Teknolojileri Dergisi, 10(4), 401–415. (IASP 2017) Doyduk, H. B. B., & Tiftik, C. (2017). Nesnelerin İnterneti: Kapsamı, Gelecek Yönelimi ve İş Fırsatları. Third Sector Social Economic Review, 52(3), 127–147. Google Scholar öffnen doi.org/10.5771/9783828872301
- Kai-Oliver Zander MS, M., & MEng, K. R. (2015). An Analysis of the Potential of Company's Inter-Cooperation on Shop-Floor Level Through the Utilization of Cyber-Physical Production Systems. In Proceedings of the International Annual Conference of the American Society for Engineering Management. (p.1). American Society for Engineering Management (ASEM). Google Scholar öffnen doi.org/10.5771/9783828872301
- Kiran, V. (2016). Trends 2016: Big Data, IoT take the plunge. Voice & Data; New Delhi. Google Scholar öffnen doi.org/10.5771/9783828872301
- Koçak, A., & Diyadin, A. (2018). Sanayi 4.0 Geçiş Süreçlerinde Kritik Başarı Faktörlerinin DEMATEL Yöntemi ile Değerlendirilmesi. Ege Akademik Bakis, 18(1), 107–120. Google Scholar öffnen doi.org/10.5771/9783828872301
- Leitão, P., Colombo, A. W., & Karnouskos, S. (2016). Industrial automation based on cyber-physical systems technologies: Prototype implementations and challenges. Computers in Industry, 81, 11–25. Google Scholar öffnen doi.org/10.5771/9783828872301
- Magruk, A. (2016). Uncertainty in the Sphere of the Industry 4.0-Potential Areas to Research. Business, Management and Education, 14(2), 275. Google Scholar öffnen doi.org/10.5771/9783828872301
- Official Gazette 24454 (2001). Date retrieved 23.10.2017, from http://www.resmigazete.gov.tr/eskiler/2001/07/20010706.htm#1. Google Scholar öffnen doi.org/10.5771/9783828872301
- Ozdogan, O. (2017). Endüstri 4.0. Ankara: Pusula yayın. Google Scholar öffnen doi.org/10.5771/9783828872301
- Pekol, Ö., & Erbas, B. Ç. (2011). Patent Sisteminde Türkiye'deki Teknoparkların Yeri/Technopark in Turkey: Patent System Perspective. Ege Akademik Bakis, 11(1), 1327. Google Scholar öffnen doi.org/10.5771/9783828872301
- Schwab, K. (2017). Dördüncü Sanayi Devrimi. Istanbul: Optimist. Google Scholar öffnen doi.org/10.5771/9783828872301
- Siegel, D. S., Westhead, P., & Wright, M. (2003). Science Parks and The Performance of New Technology-Based Firms: A Review of Recent UK Evidence and an Agenda for Future Research. Small Business Economics, 20(2), 177–184. Google Scholar öffnen doi.org/10.5771/9783828872301
- Soysal, M., & Pamuk, N. S. (2018). Yeni Sanayi Devrimi Endüstri 4.0 Üzerine Bir İnceleme. Verimlilik Dergisi, (1), 41–66. Google Scholar öffnen doi.org/10.5771/9783828872301
- TÜBİTAK (2016). Ar-Ge Reform Paketi Tanıtım Toplantısı Yapıldı. Türkiye Bilimsel ve Teknolojik Araştırmalar Merkezi, 14 Ocak 2016. Date retrieved: 23.04.2017, from https://www.tubitak.gov. tr /tr/haber/ar-gereform-paketi-tanitim-programi-yapildi. Google Scholar öffnen doi.org/10.5771/9783828872301
- Türkiye Odalar ve Borsalar Birliği (2016). Akıllı Fabrikalar Geliyor. TOBB Ekonomik Forum Dergisi, 259, 16–27. Google Scholar öffnen doi.org/10.5771/9783828872301
- TÜSİAD & BCG (2016). Türkiye’nin Küresel Rekabetçiliği için Bir Gereklilik Olarak Sanayi 4.0: Gelişmekte Olan Ekonomi Perspektifi. İstanbul: TÜSİAD. Google Scholar öffnen doi.org/10.5771/9783828872301
- An Evaluative Perspective from Institutional Logic and Pragmatism on the Relationship between Industry 4.0 and Outsourcing by Mustafa Aldülmetin DİNÇER, Yasemin ÖZDEMİR Google Scholar öffnen doi.org/10.5771/9783828872301
- Adler, P. S. Making the HR Outsourcing Decision. MIT Sloan Management Review, 45(1), 2003, 53–60. Google Scholar öffnen doi.org/10.5771/9783828872301
- Alexander, M., and D. Young, Strategic Outsourcing. Long Range Planning, 29(1), 1996,116–119. Google Scholar öffnen doi.org/10.5771/9783828872301
- Alford, R. R. and R. Friedland,. Powers of Theory: Capitalism, the State, and Democracy. Cambridge: Cambridge University Press,1985. Google Scholar öffnen doi.org/10.5771/9783828872301
- Arnold, U. New Dimensions of Outsourcing: A Combination of Transaction Cost Economics and the Core Competencies Concept. European Journal of Purchasing & Supply Management, 6(1), 2000, 23–29. Google Scholar öffnen doi.org/10.5771/9783828872301
- Aron, R., E. K. Clemons, and S. Reddi, “Just Right Outsourcing: Understending and Managing Risk”, Journal of Management Information Systens, Vol. 22, Issue 2, 2005, Pp. 37–55 (Https://and.Tandfonline.Com/Doi/Abs/10.1080/07421222.2005.11045852, 28.08.2018) Google Scholar öffnen doi.org/10.5771/9783828872301
- Arora, A., and A. Gambardella, Complementarity and External Linkages: The Strategies of the Large Firms in Biotechnology. the Journal of Industrial Economics, 1990, 361–379. Google Scholar öffnen doi.org/10.5771/9783828872301
- Aubert, B.A., M. Patry, and S. Rivard, “A Transaction Cost Approach to Outsourcing Behavior: Some Empirical Evidence”, Information & Management, Vol. 30, 1996, Pp. 51–64. Google Scholar öffnen doi.org/10.5771/9783828872301
- Aubert, B. A., M. Patry, and S. Rivard, “Assessing the Risk of IT Outsourcing”, Proceedings of the Thirty-First Hawai International Conference on System Sciences, 9 January 1998, Kohala Coast, USA. 1998. Google Scholar öffnen doi.org/10.5771/9783828872301
- Bartodziej, C. J. The Concept Industry 4.0: An Empirical Analysis of Technologies and Applications in Production Logistics. Springer. 2016. Google Scholar öffnen doi.org/10.5771/9783828872301
- Battilana, J. 2006. ‘Agency and Institutions: The Enabling Role of Individuals’ Social Position,’ Organization, Forthcoming. Google Scholar öffnen doi.org/10.5771/9783828872301
- Beaulieu, M., Roy, J., & S. Landry, Logistics Outsourcing in the Healthcare Sector: Lessons from A Canadian Experience. Canadian Journal of Administrative Sciences/Revue Canadienne des Sciences De l'Administration, 35(4), 2018, 635–648.DOI: 10.1002/CJAS.1469. Google Scholar öffnen doi.org/10.5771/9783828872301
- Belcourt, M. “Outsourcing — the Benefits and the Risks”, Human Resource Management Review, Vol. 16, 2006, P. 269–279. Google Scholar öffnen doi.org/10.5771/9783828872301
- Berger, P. and T. Luckmann, the Social Construction of Reality. New York: Doubleday Anchor. 1967. Google Scholar öffnen doi.org/10.5771/9783828872301
- Crouch, C. Complementarıty, Morgan, G., Campbell, J., Crouch, C., Pedersen, O. K., & Whitley, R. (Eds.). In the Oxford Handbook of Comparative Institutional Analysis. Oxford University Press.2010. Google Scholar öffnen doi.org/10.5771/9783828872301
- El Mokrini, A., E. Dafaoui, A. Berrado, and A. El Mhamedi, “An Approach to Risk Assessment for Outsourcing Logistics: Case of Pharmaceutical Industry”, IFAC-Papers Online, Vol. 49, Pp. 1239–1244, 2016. Google Scholar öffnen doi.org/10.5771/9783828872301
- Greenwood, R. & C. R. Hinings, Understanding Strategic Change: The Contribution of Archetypes. Academy of Management Journal, 36(5), 1993,1052–1081. Google Scholar öffnen doi.org/10.5771/9783828872301
- Gupta, U. G. & A. Gupta, Outsourcing the IS Function: Is It Necessary For Your Organization?, Information Systems Management, 9(3), 1992, 44–47. Google Scholar öffnen doi.org/10.5771/9783828872301
- Gutek, G. Philosophical, Ideological, and theoretical Perspectives on Education. New Jersey: Pearson, 2014, pp. 76,100. ISBN 978–0–13–285238–8. Google Scholar öffnen doi.org/10.5771/9783828872301
- Haour, G. Stretching the Knowledge‐Base of the Enterprise Through Contract Research. R&D Management, 22(2), 1992, 177–182. Google Scholar öffnen doi.org/10.5771/9783828872301
- Heng, S. Industry 4.0. Upgrade Des Industriestandorts Deutschland Steht Bevor. In: DB Research Management. Frankfurt A. M., 2014. Google Scholar öffnen doi.org/10.5771/9783828872301
- Hofmann, E., & M. RüSch, Industry 4.0 and the Current Status as well as Future Prospects on Logistics. Computers in Industry, 89, 2017, 23–34. Google Scholar öffnen doi.org/10.5771/9783828872301
- Howells, J. Research and Technology Outsourcing, Technology Analysis & Strategic Management, 11:1, 1999, 17–29, Google Scholar öffnen doi.org/10.5771/9783828872301
- Huff, S. L. Outsourcing of Information Services. Business Quarterly, 55(4), 1991, 62–65. Google Scholar öffnen doi.org/10.5771/9783828872301
- Jackall, R. Moral Mazes: The World of Corporate Managers. International Journal of Politics, Culture, and Society, 1(4), 1988, 598–614. Google Scholar öffnen doi.org/10.5771/9783828872301
- Kagermann, AND., AND. Wahlster, J. Helbig, Umsetzungsempfehlungen für das Zukunftsprojekt Industry 4.0. Abschlussbericht Des Arbeitskreises Industry 4.0. Deutschlands Zukunft Als Produktionsstandort Sichern. In: Promotorengruppe Kommunikation der Forschungsunion Wirtschaft – Wissenschaft. Berlin, 2013. Google Scholar öffnen doi.org/10.5771/9783828872301
- Kakabadse, A., and N. Kakabadse. “Trends in Outsourcing: Contrasting USA and Europe”, European Management Journal Vol. 20, No. 2, 2002, Pp. 189–198. Google Scholar öffnen doi.org/10.5771/9783828872301
- Kosnik, T., D. J. Wong-Mingji, & K. Hoover, Outsourcing Vs Insourcing in the Human Resource Supply Chain: A Comparison of Five Generic Models. Personnel Review, 35(6), 2006,671–684. Google Scholar öffnen doi.org/10.5771/9783828872301
- Leeman, D., & D. Reynolds, Trust and Outsourcing: Do Perceptions of Trust Influence the Retention of Outsourcing Providers in the Hospitality Industry?, International Journal of Hospitality Management, 31(2), 2012, 601–608. Google Scholar öffnen doi.org/10.5771/9783828872301
- Li-Jun, Z. “Research on Analysis and Control of Enterprise Logistics Outsourcing Risks”, Energy Procedia, Vol. 17,2012, Pp. 1268–1273. Google Scholar öffnen doi.org/10.5771/9783828872301
- Masten. S, K. Crocker, Efficient Adaptation in Long-Term Contracts: Take or Pay Provisions For Natural Gas. American Economic Review,1985, 75,1085–1096. Google Scholar öffnen doi.org/10.5771/9783828872301
- Mccauley, A. Know the Benefits and Costs of Outsourcing Services. Canadian HR Reporter, 13(17), 18–19. Meyer, John and., R. Richard Scott (Eds). 1983. Organizational Environments: Ritual and Rationality, Beverly Hills, CA: Sage, 2000. Google Scholar öffnen doi.org/10.5771/9783828872301
- Meyer, J. and., J. Boli, G. M. Thomas, and F. O. Ramirez. ‘World Society and the Nation-State,’ American Journal of Sociology 103, 1997, 144–181. Google Scholar öffnen doi.org/10.5771/9783828872301
- Ngwenyama, O. K., & N. Bryson, Making the Information Systems Outsourcing Decision: A Transaction Cost Approach to Analyzing Outsourcing Decision Problems. European Journal of Operational Research, 115(2),1999, 351–367. Google Scholar öffnen doi.org/10.5771/9783828872301
- Oshima, M., T. Kao, & J. Tower, Achieving Post-Outsourcing Success. Human Resources Planning, 28(2), 7–12. Outsourcing and the Implications for Human Resource Development. (2000). Journal of Management Development, 19(8), 2005, 694–699. Google Scholar öffnen doi.org/10.5771/9783828872301
- Quélin, B., & F. Duhamel, Bringing Together Strategic Outsourcing and Corporate Strategy: Outsourcing Motives and Risks. European Management Journal, 21(5), 2003, 647–661. Google Scholar öffnen doi.org/10.5771/9783828872301
- Rennunga, F. M., C. T. Luminosua, and A. Draghici, “Strategic Management – Managing the Potential Complexity-Risks in Outsourcing”, Procedia Economics and Finance, Vol. 26, 2015, Pp. 757–763. Google Scholar öffnen doi.org/10.5771/9783828872301
- Ringe, M. J. (1992). The Contract Research Business in the United Kingdom. The European Dimension. EUR 14578 EN. Research Evaluation. Science and Technology Policy Series. Google Scholar öffnen doi.org/10.5771/9783828872301
- Scott, AND. R. [1995] 2001. Institutions and Organizations, 2nd Edn. Thousand Oaks, CA: Sage,1992. Google Scholar öffnen doi.org/10.5771/9783828872301
- Scott, AND. R., M. Ruef, P. Mendel, and C. Caronna, Institutional Change and Health Care Organizations: From Professional Dominance to Managed Care. Chicago: University of Chicago Press, 2000. Google Scholar öffnen doi.org/10.5771/9783828872301
- Spath, D. (Ed.), O. Ganschar, S. Gerlach, M. Hämmerle, T. Krause, S. Schlund, Studie: Produktionsarbeit Der Zukunft – Industrie 4.0 (2013). Retrieved June 8, 2015, From Http://and.Iao.Fraunhofer.De/Images/Iao-News/ Produktionsarbeit-Der-Zukunft.Pdf. Google Scholar öffnen doi.org/10.5771/9783828872301
- Thornton, P. AND., & AND. Ocasio, Institutional Logics. The Sage Handbook of Organizational Institutionalism, 840, 2008, 99–128.,Pdf Google Scholar öffnen doi.org/10.5771/9783828872301
- Wang, E. T. Transaction Attributes and Software Outsourcing Success: An Empirical Investigation of Transaction Cost theory. Information Systems Journal, 12(2), 2002, 153–181. Google Scholar öffnen doi.org/10.5771/9783828872301
- Weick, K. E. Educational Organizations as Loosely Coupled Systems. Administrative Science Quarterly, 21, 1–19, 1976. Google Scholar öffnen doi.org/10.5771/9783828872301
- Weick, K. E. Management of Organizational Change Among Loosely Coupled Elements. Inp. S. Goodman &As-Sociates (Eds.), Change in Organizations (Pp. 375–408). San Francisco: Jossey-Bass, 1982. Google Scholar öffnen doi.org/10.5771/9783828872301
- William, J. The Meaning of Truth. Retrieved 5 March, 1909/2015. Google Scholar öffnen doi.org/10.5771/9783828872301
- https://Tez.Yok.Gov.Tr/Ulusaltezmerkezi/Tezsorgusonucyeni.Jsp Google Scholar öffnen doi.org/10.5771/9783828872301
- Usage of Enterprise Resource Planning (ERP) in Turkey and Information Safety by Recep Benzer, Emre Akar Google Scholar öffnen doi.org/10.5771/9783828872301
- Al-Mashari, M., Al-Mudimigh, A., & Zairi, M. Enterprise resource planning: A taxonomy of critical factors. European journal of operational research, 146(2), 352–364. 2003. Google Scholar öffnen doi.org/10.5771/9783828872301
- Alsmadi, I., Burdwell, R., Aleroud, A., Wahbeh, A., Al-Qudah, M. A., & Al-Omari, A. Introduction to Information Security. In Practical Information Security (pp. 1–16). Springer, Cham. 2018. Google Scholar öffnen doi.org/10.5771/9783828872301
- Anonymous, Security and threats in ERP. Https://Cpm.Com.Tr/Tr/Erp-Blog/Erpde-Guvenlik-Ve-Tehditler. 2018. Google Scholar öffnen doi.org/10.5771/9783828872301
- Avunduk, H., & Güleryüz, Ö. Enterprise Resource Planning (ERP) and an Analysis of the Effects to Managerial Decisions: A Qualitative Research in Textile Firm. Journal of Current Researches on Business and Economics, 8(1), 41–52. 2018. Google Scholar öffnen doi.org/10.5771/9783828872301
- Aydoğan, E.. Enterprise Resource Planning, TSA Dergisi Yı l:2 S:2, Ağustos 2008, s.109. 2008 Google Scholar öffnen doi.org/10.5771/9783828872301
- Başaran, A. Cyberspace Arion Press (In Turkish). 2017. Google Scholar öffnen doi.org/10.5771/9783828872301
- Başaran, A. Http://Alperbasaran.Com/Kurumsal-Kaynak-Planlama-Yazilimi-Erp-Guvenligi/. 2018. Google Scholar öffnen doi.org/10.5771/9783828872301
- Braggs, S. ERP: the state of the industry. Arc. Insights 12 ECL, New York. 2005. Google Scholar öffnen doi.org/10.5771/9783828872301
- Canbek Gürol, Sağıroğlu Şeref, Bilgi, Bilgi Güvenliği Ve Süreçleri Üzerine Bir İnceleme, Politeknik Dergisi, Cilt: 9, Sayı:3, 2006, S.165 4 Türkiye Bilişim Derneği, Bilişim Sistemleri Güvenliği El Kitabı, Sürüm 1.0, Ankara, Mayıs 2006, S.3 (In Turkish). Google Scholar öffnen doi.org/10.5771/9783828872301
- Çetinkaya, M. Implementation of Information Security Management System in Institutions. Akademik Bilişim 2008, Çanakkale Onsekiz Mart Üniversitesi, Çanakkale, 30 Ocak- 01 Şubat 2008, S.511 (In Turkish). 2008. Google Scholar öffnen doi.org/10.5771/9783828872301
- Demir, B. Information Security in the Accounting Information Systems. The Journal of Accounting and Finance, (26), 147–156. 2005. Google Scholar öffnen doi.org/10.5771/9783828872301
- Erkan, Turan. Erman. ERP Enterprise Resource Planning. Ankara: Atılım Üniversitesi. (Turkish) Enterprise Resource Planning. 2008. Google Scholar öffnen doi.org/10.5771/9783828872301
- Habertürk, https://www.haberturk.com/sap-ve-oracle-in-guvenlik-aciklari-binlerce-sirketi-tehlikeye-soktu-2076352-ekonomi alıntı tarihi: 26 Temmuz 2018. 2018 Google Scholar öffnen doi.org/10.5771/9783828872301
- İnal, İ. WEB based ERP for SME's: An evaluation of Turkey's ERP vendors, Msc Thesis. Balıkesir University Graduate School of Natural and Applied Sciences. 103 page. 2004. Google Scholar öffnen doi.org/10.5771/9783828872301
- Keçek, G. & Yıldırım, E. Enterprise Resource Planning and The Importance For Company Electronic Journal of Social Sciences 8 :240–258. 2014. Google Scholar öffnen doi.org/10.5771/9783828872301
- Laudon, C. K., & Laudon, P. J. Information Systems in the Enterprise, Managing the Digital Firm, 8/E. Prentice Hall. 2004. Google Scholar öffnen doi.org/10.5771/9783828872301
- Loh TC, Koh SCL Critical elements for a successful enterprise resource planning implementation in small-and medium-sized enterprises. Int J Prod Res 42(17):3433–3455. 2004. Google Scholar öffnen doi.org/10.5771/9783828872301
- Manettı J. How technology is transforming manufacturing. Production and Inventory Management Journal 42(1), 54–64. 2001. Google Scholar öffnen doi.org/10.5771/9783828872301
- Montalbano, E. Onapsis Report Report: Cybercriminals target difficult-to-secure ERP systems with new attacks https://www.onapsis.com/research/reports/erp-security-threat-report 2018. Google Scholar öffnen doi.org/10.5771/9783828872301
- Ross, J.W. and Vitale, M.R. The ERP revolution, surviving vs. Thriving. Information Systems Frontiers; special issue on The Future of Enterprise Resource Planning Systems 2(2), 233–241. 2000. Google Scholar öffnen doi.org/10.5771/9783828872301
- Sumner, M., Enterprise resource planning, Upper Saddle River, New Jersey: Prentice-Hall. 2005. Google Scholar öffnen doi.org/10.5771/9783828872301
- Usmanij, PA, Khosla R, Chu M-T Successful product or successful system? User satisfaction measurement of ERP software. J Intell Manuf 24(6):1131–1144. 2013. Google Scholar öffnen doi.org/10.5771/9783828872301
- Vural, Y, Sağıroğlu Ş., A Review On Enterprise Information Security And Standards. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi, Cilt 23, No 2, Ankara, 2008, S.509. 2008. Google Scholar öffnen doi.org/10.5771/9783828872301
- Machine learning approaches for prediction of service times in health information systems by Mete Eminağaoğlu Google Scholar öffnen doi.org/10.5771/9783828872301
- Aha, D. W., Kibler, D., & Albert, M. K. (1991). “Instance-based learning algorithms”, Machine Learning, Vol. 6 No. 1, pp. 37–66. Google Scholar öffnen doi.org/10.5771/9783828872301
- Akaike, H. (1981). “Likelihood of a model and information criteria”, Journal of Econometrics, Vol. 16 No. 1, pp. 3–14. Google Scholar öffnen doi.org/10.5771/9783828872301
- Bernardi, R. Constantinides, P., & Nandhakumar, J. (2017). “Challenging Dominant Frames in Policies for IS Innovation in Healthcare through Rhetorical Strategies”, Journal of the Association for Information Systems, Vol. 18 No. 2, pp. 81–112. Google Scholar öffnen doi.org/10.5771/9783828872301
- Bishop, C. M. (2006). Pattern Recognition and Machine Learning, Springer Science + Business Media LLC, New York. Google Scholar öffnen doi.org/10.5771/9783828872301
- Breil, B. Fritz, F., Thiemann, V., & Dugas, M. (2011). “Mapping turnaround times (TAT) to a generic timeline: a systematic review of TAT definitions in clinical domains”, BMC Medical Informatics and Decision Making, Vol. 11 No. 34, pp. 1–12. Google Scholar öffnen doi.org/10.5771/9783828872301
- Broomhead, D. S., & Lowe, D. (1988). “Radial basis functions, multi-variable functional interpolation and adaptive networks (Technical report)”, Royal Signals and Radar Establishment, Report no. 4148, UK. Google Scholar öffnen doi.org/10.5771/9783828872301
- Buduma, N., & Locascio, N. (2017). Fundamentals of Deep Learning, O’Reilly Media, Inc., USA. Google Scholar öffnen doi.org/10.5771/9783828872301
- Chen, S. (2014). "Information needs and information sources of family caregivers of cancer patients", Aslib Journal of Information Management, Vol. 66 No. 6, pp. 623–639. Google Scholar öffnen doi.org/10.5771/9783828872301
- Dasu T. & Johnson, T. (2003). Exploratory Data Mining and Data Cleaning, John Wiley & Sons Inc., New Jersey. Google Scholar öffnen doi.org/10.5771/9783828872301
- Eibe, F. (2014). Fully supervised training of Gaussian radial basis function networks in WEKA. Retrieved from https://www.cs.waikato.ac.nz/~eibe/pubs/ rbf_networks_in_weka_ description.pdf Google Scholar öffnen doi.org/10.5771/9783828872301
- Eminağaoğlu M., & Vahaplar A. (2018). “Turnaround Time Prediction for a Medical Laboratory Using Artificial Neural Networks”, International Journal of Informatics Technologies, Vol.11 No. 4, pp: 357–368. Google Scholar öffnen doi.org/10.5771/9783828872301
- Fieri, M., Ranney, N. F., Schroeder, E. B., Van Aken, E. M., & Stone, A. H. (2010). “Analysis and improvement of patient turnaround time in an emergency department”, in Proceedings of the 2010 IEEE Systems and Information Engineering Design Symposium, University of Virginia Charlottesville, VA, USA, 2010, pp. 239–244. Google Scholar öffnen doi.org/10.5771/9783828872301
- Goodfellow, A., Bengio, Y., & Courville, A. (2017). Deep Learning, The MIT Press, USA. Google Scholar öffnen doi.org/10.5771/9783828872301
- Goswami, B., Singh, B., Chawla, R., Gupta, V. K., & Mallika, V. (2010). “Turnaround time (TAT) as a benchmark of laboratory performance”, Indian Journal of Clinical Biochemistry, Vol. 25 No. 4, pp. 376–379. Google Scholar öffnen doi.org/10.5771/9783828872301
- Graves, A. (2012). Supervised Sequence Labelling with Recurrent Neural Networks, Springer-Verlag., Berlin. Google Scholar öffnen doi.org/10.5771/9783828872301
- Han, J., & Kamber, M. (2006). Data Mining: Concepts and Techniques, 2nd edition, Morgan Kaufmann Publishers, San Francisco. Google Scholar öffnen doi.org/10.5771/9783828872301
- Hand, C., Mannila, H., & Smyth P. (2001). Principles of Data Mining, the MIT Press, London. Google Scholar öffnen doi.org/10.5771/9783828872301
- Hassanpour, M., Vaferi, B., & Masoumi, M. E. (2018). “Estimation of pool boiling heat transfer coefficient of alumina water-based nanofluids by various artificial intelligence (AI) approaches”, Applied Thermal Engineering, Vol. 128, pp. 1208–1222. Google Scholar öffnen doi.org/10.5771/9783828872301
- Hastie, T., Tibshirani, R., & Friedman, J. (2009). The Elements of Statistical Learning, Data Mining, Inference and Prediction, 2nd edition, Springer, New York. Google Scholar öffnen doi.org/10.5771/9783828872301
- Haykin, S. (2009). Neural Networks and Learning Machines, 3rd edition, Pearson Education, Inc., New Jersey. Google Scholar öffnen doi.org/10.5771/9783828872301
- He, C., Fan, X., & Li, Y. (2013). “Toward Ubiquitous Healthcare Services with a Novel Efficient Cloud Platform”, IEEE Transactions on Biomedical Engineering, Vol. 60 No. 1, pp. 230–234. Google Scholar öffnen doi.org/10.5771/9783828872301
- Hendrickx, I., & Antal, V. B. (2005). "Hybrid algorithms with Instance-Based Classification", in Proceedings of 16th European Conference on Machine Learning Machine Learning: ECML2005, Porto, Portugal, 2005, pp. 158–169. Google Scholar öffnen doi.org/10.5771/9783828872301
- Hope, T., Yehezkel, S. R., & Lieder, I. (2017). Learning TensorFlow: A Guide to Building Deep Learning Systems, O’Reilly Media, Inc., USA. Google Scholar öffnen doi.org/10.5771/9783828872301
- Huang, W., Lai, K. K., Nakamori, Y., & Wang, S. (2004). “Forecasting Foreign Exchange Rates with Artificial Neural Networks: A Review”, International Journal of Information Technology & Decision Making, Vol. 3 No. 1, pp. 145–165. Google Scholar öffnen doi.org/10.5771/9783828872301
- Köksal, H., Eminağaoğlu, M., & Türkoğlu, B. (2016) “An Adaptive Network-Based Fuzzy Inference System for Estimating the Duration of Medical Services: A Case Study”, in Proceedings of 10th IEEE International Conference on Application of Information and Communication Technologies, Baku, Azerbaijan, pp: 801–806. Google Scholar öffnen doi.org/10.5771/9783828872301
- Kumar, S. (2017). Neural Networks – A Classroom Approach, 2nd ed., McGraw-Hill, New Delhi. Google Scholar öffnen doi.org/10.5771/9783828872301
- Larose, D. T. (2005). Discovering Knowledge in Data – An Introduction to Data Mining, John Wiley & Sons Inc., New Jersey. Google Scholar öffnen doi.org/10.5771/9783828872301
- Larose, D. T. (2006). Data Mining Methods and Models, John Wiley & Sons Inc., New Jersey. Google Scholar öffnen doi.org/10.5771/9783828872301
- Lyon, A. R., Wasse, J. K., Ludwig, K., Zachry, M., Bruns, E. J., Unutzer, J., & McCauley, E. (2016). “The Contextualized Technology Adaptation Process (CTAP): Optimizing Health Information Technology to Improve Mental Health Systems”, Administration and Policy in Mental Health and Mental Health Services Research, Vol. 43 No. 3, pp. 394–409. Google Scholar öffnen doi.org/10.5771/9783828872301
- Mitchell, T. M. (2017). Machine Learning, McGraw-Hill, India. Google Scholar öffnen doi.org/10.5771/9783828872301
- Nedjah, N., Luiza, M. M., & Kacprzyk, J. (2009). Innovative Applications in Data Mining, Springer-Verlag, Berlin. Google Scholar öffnen doi.org/10.5771/9783828872301
- Ng, X. W., & Chung, W. Y. (2012). “VLC-based Medical Healthcare Information System”, Biomedical Engineering: Applications, Basis and Communications, Vol. 24 No. 2, pp. 155–163. Google Scholar öffnen doi.org/10.5771/9783828872301
- Patterson, J., & Gibson, A. (2017). Deep Learning: A Practitioner’s Approach, O’Reilly Media, Inc., USA. Google Scholar öffnen doi.org/10.5771/9783828872301
- Python, (2019). Programming language. Retrieved from https://www.python.org/downloads/ windows/ Google Scholar öffnen doi.org/10.5771/9783828872301
- Olivas, E. S., Guerrero, J. D. M., Sober, M. M., Benedito, J. R. M., & Lopez, A. J. S. (2009). Handbook of Research on Machine Learning Applications and Trends: Algorithms, Methods, and Techniques, IGI Publishing, USA. Google Scholar öffnen doi.org/10.5771/9783828872301
- Quinlan, R. J. (1992). “Learning with Continuous Classes”, in Proceedings of the 5th Australian Joint Conference on Artificial Intelligence, Singapore, 1992, pp. 343–348. Google Scholar öffnen doi.org/10.5771/9783828872301
- Ravinesh, C. D., & Şahin, M. (2015). “Application of the extreme learning machine algorithm for the prediction of monthly Effective Drought Index in eastern Australia”, Applied Soft Computing, Vol. 153, pp. 512–525. Google Scholar öffnen doi.org/10.5771/9783828872301
- Reyes, J., Morales-Esteban, A., & Martinez-Alvarez, F. (2013). “Neural networks to predict earthquakes in Chile”, Applied Soft Computing, Vol. 13 No. 2, pp. 1314–1328. Google Scholar öffnen doi.org/10.5771/9783828872301
- Samudrala, S. (2019). Machine Intelligence: Demystifying Machine Learning, Neural Networks and Deep Learning, Notion Press, Chennai. Google Scholar öffnen doi.org/10.5771/9783828872301
- Scagliarini, M., Apreda, M., Wienand, U., & Valpiani, G. (2016). "Monitoring operating room turnaround time: a retrospective analysis", International Journal of Health Care Quality Assurance, Vol. 29 No. 3, pp. 351–359. Google Scholar öffnen doi.org/10.5771/9783828872301
- Sinreich, D., & Marmor, Y. (2005). "Ways to reduce patient turnaround time and improve service quality in emergency departments", Journal of Health Organization and Management, Vol. 19 No. 2, pp. 88–105. Google Scholar öffnen doi.org/10.5771/9783828872301
- Söderholm, H.M., & Sonnenwald, D. H. (2010). "Visioning Future Emergency Healthcare Collaboration: Perspectives from Large and Small Medical Centers", Journal of The American Society for Information Science and Technology, Vol. 61 No. 9, pp. 1808–1823. Google Scholar öffnen doi.org/10.5771/9783828872301
- Storrow, A. B., Zhou, C., Gaddis, G., Han, J. H., Miller, K., Klubert, D., Laidig A., & Aronsky, D. (2008). “Decreasing Lab Turnaround Time Improves Emergency Department Throughput and Decreases Emergency Medical Services Diversion: A Simulation Model”, Academic Emergency Medicine, Vol. 15 No. 11, pp. 1130–1135. Google Scholar öffnen doi.org/10.5771/9783828872301
- Stvilia, B., Mon, L., & Yi, Y. J. (2009). "A Model for Online Consumer Health Information Quality", Journal of the American Society for Information Science and Technology, Vol. 60 No. 9, pp. 1781–1791. Google Scholar öffnen doi.org/10.5771/9783828872301
- Tensorflow (2019). An open source machine learning framework for everyone. Retrieved from https://github.com/tensorflow/tensorflow/blob/master/RELEASE.md Google Scholar öffnen doi.org/10.5771/9783828872301
- Wang, Y. & Witten, I. H. (1997). “Induction of model trees for predicting continuous classes”, in Poster papers of the 9th European Conference on Machine Learning, Prague, Czech Republic, 1997. Google Scholar öffnen doi.org/10.5771/9783828872301
- Weka (2019). Data Mining Software in Java. Retrieved from http://www.cs.waikato.ac.nz/ ml/weka/ Google Scholar öffnen doi.org/10.5771/9783828872301
- Willoughby, K. A., Chan, B. T. B., & Strenger, M. (2010). "Achieving wait time reduction in the emergency department", Leadership in Health Services, Vol. 23 No. 4, pp. 304–319. Google Scholar öffnen doi.org/10.5771/9783828872301
- Witten, I. H., Frank, E., & Hall, M. A. (2011). Data Mining: Practical Machine Learning Tools and Techniques, 3rd edition, The Morgan Kaufmann Series in Data Management Systems, Burlington. Google Scholar öffnen doi.org/10.5771/9783828872301
- Wold, S, Sjöström, M., & Eriksson, L. (2001). "PLS-regression: a basic tool of chemometrics", Chemometrics and Intelligent Laboratory Systems, Vol. 58 No. 2, pp. 109–130. Google Scholar öffnen doi.org/10.5771/9783828872301
- Yeh, I-C., & Lien, C. (2009). “The comparisons of data mining techniques for the predictive accuracy of probability of default of credit card clients”, Expert Systems with Applications, Vol. 36, pp. 2473–2480. Google Scholar öffnen doi.org/10.5771/9783828872301
- Yu, L., Lai, K. K., & Wang, S. Y. (2006). “Currency Crisis Forecasting with General Regression Neural Networks”, International Journal of Information Technology & Decision Making, Vol. 5 No. 3, pp. 437–454. Google Scholar öffnen doi.org/10.5771/9783828872301
- Application of Artificial Neural Networks in Growth Models by Semra Benzer, Recep Benzer Google Scholar öffnen doi.org/10.5771/9783828872301
- Acatech, “Acatech: Recommendations for Implementing the Strategic Initiative Industrie 4.0”, Final Report of the Industry 4.0 Working Group, http://www.acatech.de/fileadmin/user_upload/Baumstruktur_nach_Website/Acatech/root/de/Material_fuer_Sonderseiten/Industrie_4.0/Final_report__Industrie_4.0_accessible.pdf (15.12.2018). 2013. Google Scholar öffnen doi.org/10.5771/9783828872301
- Altındağ, A., Shah, S.L., Yigit, S., The growth features of tench (Tinca tinca L., 1758) in Bayındır Dam Lake, Ankara, Turkey. Turk J Zool, 26, 385–391. 2002. Google Scholar öffnen doi.org/10.5771/9783828872301
- Altındağ, A., Yiğit, S., Ahıska, S., Özkurt, Ş., The Growth Features of Tench (Tinca tinca L., 1758) in the Kesikköprü Dam Lake. Turk J Zool, 22, 311- 318. 1998. Google Scholar öffnen doi.org/10.5771/9783828872301
- Andrade, H.A. and Campos, R.O., Allometry coefficient variations of the length-weight relationship of skipjack tuna (Katsuwonus pelamis) caught in the southwest South Atlantic. Fish.Res., 55: 307–312. 2002. Google Scholar öffnen doi.org/10.5771/9783828872301
- Andrews, R., Diederich, J., & Tickle, A. B. Survey and critique of techniques for extracting rules from trained artificial neural networks. Knowledge-based systems, 8(6), 373–389. 1995. Google Scholar öffnen doi.org/10.5771/9783828872301
- Bahçecitapar, M., and Aktaş, S. Use of linear mixed model in multicollinearity and an application. Sakarya University Journal of Science, 21(6), 1349–1359. 2017. Google Scholar öffnen doi.org/10.5771/9783828872301
- Balık, İ., Çubuk, H., Çınar, Ş., Özkök, R., Population structure, growth, mortality and estimated stock size of the introduced tench, Tinca tinca (L.), population in Lake Beyşehir, Turkey. J Appl Ichthyol, 25, 206–210. 2009. Google Scholar öffnen doi.org/10.5771/9783828872301
- Balık, S., Sarı, H.M., Ustaoğlu, M.R., Ilhan, A., The structure, mortality and growth of the tench (Tinca tinca L., 1758) in Çivril Lake, Denizli, Turkey. Turk J Vet Anim Sci, 28, 973–979. 2004. Google Scholar öffnen doi.org/10.5771/9783828872301
- Banger, G. Industry 4.0 and Smart Business, Dorlion Press., Ankara (In Turkish). 2016. Google Scholar öffnen doi.org/10.5771/9783828872301
- Benzer S. and Benzer R., Evaluation of growth in pike (Esox lucius L., 1758) using traditional methods and artificial neural networks. Appl Ecol Environ Res, 14(2): 543–54. 2016. Google Scholar öffnen doi.org/10.5771/9783828872301
- Benzer S., Benzer R. and Günay A.Ç., Artificial neural networks approach in morphometric analysis of crayfish (Astacus leptodactylus) in Hirfanlı Dam Lake. Biologia, 72(5): 527–35. 2017. Google Scholar öffnen doi.org/10.5771/9783828872301
- Benzer, R. and Benzer, S., Application of artificial neural network into the freshwater fish caught in Turkey. International Journal of Fisheries and Aquatic Studies, 2(5), 341–346. 2015. Google Scholar öffnen doi.org/10.5771/9783828872301
- Benzer, R., Population Dynamics Forecasting Using Artificial Neural Networks. Fresenius Environmental Bulletin, 12:1–15. 2015. Google Scholar öffnen doi.org/10.5771/9783828872301
- Benzer, R., and Benzer, S. Alternative approaches for growth models: Artificial neural networks. 26th Signal Processing and Communications Applications Conference (SIU) (pp. 1–4). IEEE. 2018. Google Scholar öffnen doi.org/10.5771/9783828872301
- Benzer, R., and Benzer, S., Growth and length–weight relationships of Pseudorasbora parva (Temminck & Schlegel, 1846) in Hirfanlı Dam Lake: Comparison with traditional and artificial neural networks approaches. Iranian Journal of Fisheries Sciences. DOI:10.22092/ijfs.2018.119889. 2019. Google Scholar öffnen doi.org/10.5771/9783828872301
- Benzer, S. and Benzer, R., Comparative growth models of big-scale sand smelt (Atherina boyeri Risso, 1810) sampled from Hirfanlı Dam Lake, Kırşehir, Ankara, Turkey. Computational Ecology and Software 7(2): 82–90. 2017. Google Scholar öffnen doi.org/10.5771/9783828872301
- Benzer, S., and Benzer, R. New Perspectives for Predicting Growth Properties of Crayfish (Astacus leptodactylus Eschscholtz, 1823) in Uluabat Lake. Pakistan Journal of Zoology, 50(1), 35–35. 2018. Google Scholar öffnen doi.org/10.5771/9783828872301
- Benzer, S., Gül, A., Yılmaz, M., Growth Properties of Tench (Tinca tinca, L., 1758) Living in Kapulukaya Dam Lake, Turkey. Kastamonu Educ J, 18, 839–848. 2010. Google Scholar öffnen doi.org/10.5771/9783828872301
- Benzer, S., Karasu Benli, Ç. and Benzer R., The comparison of growth with length-weight relation and artificial neural networks of crayfish, Astacus leptodactylus, in Mogan Lake. J Black Sea/Mediter Environ, 21(2): 208–23. 2015. Google Scholar öffnen doi.org/10.5771/9783828872301
- Benzer, Ş.S., Gül, A., Yılmaz, M. Growth Properties of Tench (Tinca tinca, L., 1758) Living in Hirfanlı Reservoir (Kırşehir, Turkey). Iran J Fish Sci, 8, 219–224. 2009. Google Scholar öffnen doi.org/10.5771/9783828872301
- Beyer, J.E., On length˗weight relationships: Part II. Computing mean weights from length statistics. Fishbyte, 9: 50˗54. 1991. Google Scholar öffnen doi.org/10.5771/9783828872301
- Bon, A.T., Hui, H.S. Industrial Engineering Solution in the Industry: Artificial Neural Network Forecasting Approach. Proceedings of the International Conference on Industrial Engineering and Operations Management Rabat, Morocco, April 11–13, 2017. 2017. Google Scholar öffnen doi.org/10.5771/9783828872301
- Brosse, S., Guegan, J., Tourenq, J. and Lek S., The Use of Artificial Neural Networks to Assess Fish Abundance and spatial occupancy in the littoral zone of a mesotrophic lake. Ecol Model., 120(2–3):299–311. 1999. Google Scholar öffnen doi.org/10.5771/9783828872301
- Cabreira, A. G., Tripode, M., Madirolas, A. Artificial neural networks for fish-species identification. ICES Journal of Marine Science, 66(6), 1119–1129. 2009. Google Scholar öffnen doi.org/10.5771/9783828872301
- Demirsoy, A., Basic Rules of Life, Vertebrates, (in Turkish). Hacettepe University Publication. III A/55: pp 684. 1998. Google Scholar öffnen doi.org/10.5771/9783828872301
- Deval, M. C., Bök, T., Ateş, C., & Tosunoğlu, Z.. Length-based estimates of growth parameters, mortality rates, and recruitment of Astacus leptodactylus (Eschscholtz, 1823) (Decapoda, Astacidae) in unexploited inland waters of the northern Marmara region, European Turkey. Crustaceana, 80(6), 655–665. 2007 Google Scholar öffnen doi.org/10.5771/9783828872301
- Erguden Alagoz, S., Goksu, M.Z.L., Age, growth and sex ratio of tench Tinca tinca (L., 1758) in Seyhan Dam Lake, Turkey. J Appl Ichthyol, 26, 546 -549. 2010. Google Scholar öffnen doi.org/10.5771/9783828872301
- Etchison, L., Jacquemin, S. J., Allen, M., Pyron, M. Morphological variation of rusty crayfish Orconectes rusticus (Cambaridae) with gender and local scale spatial gradients. International Journal of Biology 4 (2): 163˗171. 2012. Google Scholar öffnen doi.org/10.5771/9783828872301
- Fish, K. E., Barnes, J. H., & AikenAssistant, M. W. Artificial neural networks: a new methodology for industrial market segmentation. Industrial Marketing Management, 24(5), 431–438. 1995. Google Scholar öffnen doi.org/10.5771/9783828872301
- Geldiay, R. and Balık, S., Freshwater Fishes of Turkey, 3. Edition. Ege University press, No: 46, Izmir, 532 p. 1996. Google Scholar öffnen doi.org/10.5771/9783828872301
- Gentry, T.W., Wiliamowski B.M. and Weatherford L.R., A comparison of traditional forecasting techniques and neural networks. Intelligent Engineering Systems Through Artificial Neural Networks, 5, 765–770. 1995. Google Scholar öffnen doi.org/10.5771/9783828872301
- Gillet, C. and Laurent, P.J., Tail length variations among noble crayfish (Astacus astacus (L)) populations. Freshwater Crayfish, 10: 31–36. 1995. Google Scholar öffnen doi.org/10.5771/9783828872301
- Goethals, P. L., Dedecker, A. P., Gabriels, W., Lek, S., & De Pauw, N. Applications of artificial neural networks predicting macroinvertebrates in freshwaters. Aquatic Ecology, 41(3), 491–508. 2007. Google Scholar öffnen doi.org/10.5771/9783828872301
- Hopgood A.A. Intelligent Systems for Engineers and Scientists. CRC Press, Florida, 461 pp. 2000. Google Scholar öffnen doi.org/10.5771/9783828872301
- Innal, D., Population Structures and Some Growth Properties of Three Cyprinid Species [Squalius cephalus (Linnaeus, 1758); Tinca tinca (Linnaeus, 1758) and Alburnus escherichii Steindachner, 1897] Living in Camkoru Pond (Ankara-Turkey), Kafkas Univ Vet Fak Derg, 16, 297–304. 2010. Google Scholar öffnen doi.org/10.5771/9783828872301
- ITRE Industry 4.0, European Parliament’s Committee on Industry, Research and Energy”,http://www.europarl.europa.eu/RegData/etudes/STUD/2016/570007/IPOL_STU(2016)570007_EN.pdf (15.12.2018) 2016. Google Scholar öffnen doi.org/10.5771/9783828872301
- Joy, K.M. and Death, R.G., Predictive modelling and spatial mapping of freshwater fish and decapod assemblages using GIS and neural Networks. Freshwater Biol., 49(8):1306–1052. 2004. Google Scholar öffnen doi.org/10.5771/9783828872301
- Kagermann, H., Lukas, W. and Wahlster, W., Industrie 4.0 -Mit dem Internet der Dinge auf dem Weg zur 4. Industriellen Revolution, Inhalte der Ausgabe Nr. 13/2011,VDI Nachrichten, Berlin. 2011. Google Scholar öffnen doi.org/10.5771/9783828872301
- Kılıç, S., Becer, Z. A. Some Growth Characters of Tench (Tinca tinca L., 1758) in Lake Yeniçaga, Bolu, Turkey. Journal of Applied Biological Sciences, 7(3). 2013. Google Scholar öffnen doi.org/10.5771/9783828872301
- Krenker, A., BešTer, J. and Kos, A., Introduction to the Artificial Neural Networks, Artificial Neural Networks – Methodological Advances and Biomedical Applications, Prof. Kenji Suzuki (Ed.)., ISBN: 978–953–307–243–2. 2011. Google Scholar öffnen doi.org/10.5771/9783828872301
- Lagler, K.F., Freshwater fishery biology. W.M.C. Brown Company, Dubuque, IA. 421. 1966. Google Scholar öffnen doi.org/10.5771/9783828872301
- Lek, S., and Guégan, J.F. Artificial neural networks as a tool in ecological modelling, an introduction. Ecological modelling, 120(2–3), 65–73. 1999. Google Scholar öffnen doi.org/10.5771/9783828872301
- Lewis, C.D. Industrial and business forecasting methods. London: Butterworths. 1982. Google Scholar öffnen doi.org/10.5771/9783828872301
- Lindqvist, O.V. and Lahti, E., On the sexual dimorphism and condition index in the crayfish Astacus astacus L. in Finland. Freshwater Crayfish., 5: 3–11. 1983. Google Scholar öffnen doi.org/10.5771/9783828872301
- Maravelias, C.D., Haralabous, J. and Papaconstantinou, C., Predicting demersal fish species distributions in the Mediterranean Sea using artificial neural networks. Mar Ecol., 255:249–258. 2003. Google Scholar öffnen doi.org/10.5771/9783828872301
- Mastrorillo, S., Lek, S., Dauba, F. and Belaud, A., The use of artificial neural networks to predict the presence of small-bodied fish in river. Freshwater Biol., 38: 237–246. 1997. Google Scholar öffnen doi.org/10.5771/9783828872301
- Mendes, B., Fonseca, P. and Campos, A., Weight length relationships for 46 fish species of the Portuguese west coast. J. Appl. Ichthyol., 20: 355–361. 2004. Google Scholar öffnen doi.org/10.5771/9783828872301
- Moutopoulos, D. K., Stergiou, K. I. Length˗weight and length˗length relationships of fish species from Aegean Sea (Greece). J. Appl. Ichthyol. 18: 200˗203. 2002. Google Scholar öffnen doi.org/10.5771/9783828872301
- Nikolsky, G.V., The ecology of fishes (translated by L. Birkett). Academic Press, London, pp 352. 1963. Google Scholar öffnen doi.org/10.5771/9783828872301
- Obach, M., Wagner, R., Werner, H. and Schmidt, H.H., Modelling population dynamics of aquatic insects ith artificial neural networks. Ecol Model., 146:207–217. 2001. Google Scholar öffnen doi.org/10.5771/9783828872301
- Olden, J. D., & Jackson, D. A. Fish–habitat relationships in lakes: gaining predictive and explanatory insight by using artificial neural networks. Transactions of the American Fisheries Society, 130(5), 878–897. 2001. Google Scholar öffnen doi.org/10.5771/9783828872301
- Olsson, K., Dynamics of omnivorous crayfish in freshwater ecosystems. Ph.D. thesis. Department of Ecology, Limnology, Lund Univ., 119 pp. 2008. Google Scholar öffnen doi.org/10.5771/9783828872301
- Ouali, D., Chebana, F., & Ouarda, T. B. Fully nonlinear statistical and machine‐learning approaches for hydrological frequency estimation at ungauged sites. Journal of Advances in Modeling Earth Systems, 9(2), 1292–1306. 2017. Google Scholar öffnen doi.org/10.5771/9783828872301
- Park, Y.S., Verdonschot, P.F.M., Chon, T.S. and Lek, S., Patterning and predicting aquatic macro invertabrate diversities using artificial neural network. Water Res., 37: 1749–1758. 2003. Google Scholar öffnen doi.org/10.5771/9783828872301
- Pimpica, E., Pinos, B., Growth of Female Tench, Tinca tinca (L.,1758) in Lake Dgal Wielki, NE Poland. Folia Zool, 48, 143–148. 1999. Google Scholar öffnen doi.org/10.5771/9783828872301
- Pompei, L., Franchi, E., Giannetto, D., Lorenzoni, M., Growth and reproductive properties of tench, Tinca tinca Linnaeus, 1758 in Trasimeno Lake (Umbria, Italy), Knowl Manag Aquat Ec, 406, 1–13. 2012. Google Scholar öffnen doi.org/10.5771/9783828872301
- Primavera, J.H., Parado-Estepa, F.D. and Lebata, J.L., Morphometric relationship of length and weight of giant tiger prawn Penaeus monodon according to life stage, sex and source. Aquaculture, 164: 67–75. 1998. Google Scholar öffnen doi.org/10.5771/9783828872301
- Ricker, W.E., Linear regressions in fishery research. J Fish Res Board Can., 30:409–434. 1973. Google Scholar öffnen doi.org/10.5771/9783828872301
- Rocha, J. C., Passalia, F. J., Matos, F. D., Takahashi, M. B., de Souza Ciniciato, D., Maserati, M. P., ... Nogueira, M. F. G. A method based on artificial intelligence to fully automatize the evaluation of bovine blastocyst images. Scientific Reports, 7(1), 7659. 2017. Google Scholar öffnen doi.org/10.5771/9783828872301
- Rosa, H., A synopsis of the biological data on the tench, Tinca tinca (L., 1758). FAO 58, 951. 1958. Google Scholar öffnen doi.org/10.5771/9783828872301
- Rumelhart, D.E., Hinton, G.E. and Williams, R.J., Learning internal representations by error propagation in Parallel Distributed Processing. Explorations in the Microstructure of Cognition MIT Press, 1:318–362. 1986. Google Scholar öffnen doi.org/10.5771/9783828872301
- Sarı, M. Artificial Neural Networks And Sales Demand Forecasting Application In The Automotive Industry. Msc Thesis. Sakarya University. 2016. Google Scholar öffnen doi.org/10.5771/9783828872301
- Saygı B.Y, Demirkalp, F.Y. Trophic status of shallow Yeniçağa Lake (Bolu, Turkey) in relation to physical and chemical environment. Fresenius Environmental Bulletin. 13:385 -393. 2004. Google Scholar öffnen doi.org/10.5771/9783828872301
- Sholahuddin, A., Ramadhan, A. P., & Supriatna, A. K. The Application of ANN-Linear Perceptron in the Development of DSS for a Fishery Industry. Procedia Computer Science, 72, 67–77. 2015. Google Scholar öffnen doi.org/10.5771/9783828872301
- Sinis, A.I., Meunier, F.J., Vieillot, H.F., Comparision of sclaes, opercular bones, and Vertabrae to Determine Age and Population Structure in Tench, Tinca tinca (L., 1758) (Pisces, teleostei), Israel J Zool, 45, 453–465.1999. Google Scholar öffnen doi.org/10.5771/9783828872301
- Skurdal, J. and Qvenild, T. Growth, maturity and fecundity of Astacus astacus in Lake Steinsfjorden, In: Freshwater Crayfish (Eds., J. Skurdal and T. TougbØl), Norway, pp. 182–186. 1986. Google Scholar öffnen doi.org/10.5771/9783828872301
- Sun, L., Xiao, H., Li, S. and Yang, D., Forecating fish stock recruitment and planning optimal harvesting strategies by using neural network. Journal of Computers, 4(11):1075–1082. 2009. Google Scholar öffnen doi.org/10.5771/9783828872301
- Suryanarayana, I., Braibanti, A., Rao, R.S., Ramamc, V.A., Sudarsan, D. and Rao, G.N., Neural networks in fisheries research. Fish Res., 92:115–139. 2008. Google Scholar öffnen doi.org/10.5771/9783828872301
- Tekin, M. Numerical Methods (Computer Analysis). (Updated 6. Edition). Konya: Günay Ofset. (Turkish). 2008. Google Scholar öffnen doi.org/10.5771/9783828872301
- Teles, L. O., Vasconcelos, V., Pereira, E., & Saker, M. Time series forecasting of cyanobacteria blooms in the Crestuma Reservoir (Douro River, Portugal) using artificial neural networks. Environmental Management, 38(2), 227–237. 2006. Google Scholar öffnen doi.org/10.5771/9783828872301
- Tosunoğlu, Z., Aydın, C., Özaydın, O. and Leblebici, S., Trawl codend mesh selectivity of braided PE material for Parapenaeus longirostris (Lucas, 1846) (Decapoda, Penaeidae). Crustaceana, 80: 1087–1094. 2007. Google Scholar öffnen doi.org/10.5771/9783828872301
- Tureli Bilen, C, Kokcu, P. and Ibrikci, T., Application of Artificial Neural Networks (ANNs) for Weight Predictions of Blue Crabs (Callinectes sapidus Rathbun, 1896) Using Predictor Variables. Mediterranean Marine Science, 12(2):439–446. 2011. Google Scholar öffnen doi.org/10.5771/9783828872301
- Verdiell-Cubedo, D., Oliva-Paterna, F.J. and Torralva, M., Length-weight relationships for 22 fish species of the Mar Menor coastal lagoon (Western Mediterranean Sea). Journal of Applied Ichthyology, 22: 293–294. 2006. Google Scholar öffnen doi.org/10.5771/9783828872301
- Westman, K., Savolainen, R., Growth of the signal crayfish Pacifastacus leniusculus, in a small forest lake in Finland. Boreal Environ. Res. 7, 53–61. 2002. Google Scholar öffnen doi.org/10.5771/9783828872301
- Witt, S.F. and Witt C.A. Modeling and Forecasting Demand in Tourism. Londra: Academic Press. 1992. Google Scholar öffnen doi.org/10.5771/9783828872301
- Wright, R.M., Giles, N. The population biology of tench, Tinca tinca (L.) in two gravel pit lakes. J Fish Biol, 38, 17–28. 1991. Google Scholar öffnen doi.org/10.5771/9783828872301
- Alternative approaches to traditional methods for growth parameters of fisheries industry: Artificial Neural Networks by Recep Benzer, Semra Benzer Google Scholar öffnen doi.org/10.5771/9783828872301
- Aksu, Ö. and Harlioğlu, M.M., The Effect of Placing Hides into the Natural Habitat on Astacus Leptodactylus (Eschscholtz, 1823) Harvest. Ecological Life Sciences, 11(2), 1–10. 2016. Google Scholar öffnen doi.org/10.5771/9783828872301
- Andrade, H.A. and Campos, R.O., Allometry coefficient variations of the length-weight relationship of skipjack tuna (Katsuwonus pelamis) caught in the southwest South Atlantic. Fish.Res., 55: 307–312. 2002. Google Scholar öffnen doi.org/10.5771/9783828872301
- Andrews, R., Diederich, J., & Tickle, A. B. Survey and critique of techniques for extracting rules from trained artificial neural networks. Knowledge-based systems, 8(6), 373–389. 1995. Google Scholar öffnen doi.org/10.5771/9783828872301
- Aydin, H., Harlioğlu, M.M. and Deniz, T., An investigation on the population parameters of freshwater crayfish (Astacus leptodactylus Esch., 1823) in Lake İznik (Bursa). Turkish Journal of Zoology, 39(4), 660–668. 2015. Google Scholar öffnen doi.org/10.5771/9783828872301
- Azari, M.A., Seidgar, M. and Mohebbi, F., Population dynamics of freshwater crayfish (Astacus leptodactylus) in Aras reservoir, Iran. Environ. Resourc. Res., 3: 15–26. 2015. Google Scholar öffnen doi.org/10.5771/9783828872301
- Azari, S., Jhin, G., Papini, M. and Spelt, J.K., Fatigue threshold and crack growth rate of adhesively bonded joints as a function of load/displacement ratio. Composites Part A 57, 59–66. 2014. Google Scholar öffnen doi.org/10.5771/9783828872301
- Balık, S., Ustaoğlu, M.R., Sarı H.M., Berber, S., Determination of traits some growth and morphometric of crayfish (Astacus leptodactylus Eschscholtz, 1823) at Demirköprü Dam Lake (Manisa). EU Journal of Fisheries & Aquatic Sciences, 22(1–2): 83–89. 2005. Google Scholar öffnen doi.org/10.5771/9783828872301
- Baran İ, Soylu E., Crayfish plague in Turkey (short communication). J Fish Dis 12: 193–197. 1989. Google Scholar öffnen doi.org/10.5771/9783828872301
- Benzer, R. Population Dynamics Forecasting Using Artificial Neural Networks. Fresenius Environmental Bulletin, 24(2), 460–466. 2015. Google Scholar öffnen doi.org/10.5771/9783828872301
- Benzer, S and Benzer, R., Determine Some Morphological Characteristics of Crayfish (Astacus Leptodactylus Eschscholtz, 1823) with Tradional Methods and Artificial Neural Networks in Dikilitas Pond, Ankara, Turkey. Fresenius Environmental Bulletin, 24 (11A):3727–3735. 2015. Google Scholar öffnen doi.org/10.5771/9783828872301
- Benzer, S., Karasu Benli, Ç., Benzer, R., The comparison of growth with length-weight relation and artificial neural networks of crayfish, Astacus leptodactylus, in Mogan Lake. J. Black Sea/Mediterranean Environment, 21(2):208–223. 2015. Google Scholar öffnen doi.org/10.5771/9783828872301
- Benzer, S and Benzer, R., Evaluation of growth in pike (Esox lucius L., 1758) using traditional methods and artificial neural networks. Applied Ecology and Environmental Research; 14(2), 543–554. 2016. Google Scholar öffnen doi.org/10.5771/9783828872301
- Benzer, S, Benzer, R and Gül A., Developments in Science and Engineering. St. Kliment Ohridski University Presssofia, Chaper 5: Artificial Neural Networks Application for Biological Systems: The Case Study of Pseudorasbora parva.; A. ISBN 978–954–07–4137–6. 2016. Google Scholar öffnen doi.org/10.5771/9783828872301
- Benzer, S., Benzer, R., and Günal, A. Ç. Artificial Neural Networks approach in morphometric analysis of crayfish (Astacus leptodactylus) in Hirfanlı Dam Lake. Biologia, 72(5), 527–535. 2017. Google Scholar öffnen doi.org/10.5771/9783828872301
- Benzer, S. and Benzer, R. New Perspectives for Predicting Growth Properties of Crayfish (Astacus leptodactylus Eschscholtz, 1823) in Uluabat Lake. Pakistan J. Zool. 50(1):35–45. 2018. Google Scholar öffnen doi.org/10.5771/9783828872301
- Berber, S. and Balık, S., Determination of traits some growth and morphometric of crayfish (Astacus leptodactylus Eschscholtz, 1823) at Manyas Lake (Balıkesir), EU Journal of Fisheries & Aquatic Sciences, 23(1–2):83–91. 2006. Google Scholar öffnen doi.org/10.5771/9783828872301
- Berber, S. and Balık, S., The length-weight relationships, and meat yield of crayfish (Astacus leptodactylus Eschcholtz, 1823) population in Apolyont Lake (Bursa, Turkey). J Fish Sci., 3(2):86–99. 2009. Google Scholar öffnen doi.org/10.5771/9783828872301
- Beyer, J.E., On length˗weight relationships: Part II. Computing mean weights from length statistics. Fishbyte, 9: 50˗54. 1991. Google Scholar öffnen doi.org/10.5771/9783828872301
- Bolat, Y., Mazlum, Y., Demirci, A., & Koca, H. U. Estimating the population size of Astacus leptodactylus (Decapoda: Astacidae) by mark-recapture technique in Egirdir lake, Turkey. African Journal of Biotechnology, 10(55), 11778. 2011. Google Scholar öffnen doi.org/10.5771/9783828872301
- Bon, A.T., Hui, H.S. Industrial Engineering Solution in the Industry: Artificial Neural Network Forecasting Approach. Proceedings of the International Conference on Industrial Engineering and Operations Management Rabat, Morocco, April 11–13, 2017. Google Scholar öffnen doi.org/10.5771/9783828872301
- Bök, T. D., Aydın, H., & Ateş, C. A study on some morphological characteristics of Astacus leptodactylus (Eschscholtz 1823) in seven different inland waters in Turkey. Journal of Black Sea/Mediterranean Environment, 19(2). 2013. Google Scholar öffnen doi.org/10.5771/9783828872301
- Brosse, S., Guegan, J., Tourenq, J. and Lek S., The Use of Artificial Neural Networks to Assess Fish Abundance and spatial occupancy in the littoral zone of a mesotrophic lake. Ecol Model., 120(2–3):299–311. 1999. Google Scholar öffnen doi.org/10.5771/9783828872301
- Cabreira, A. G., Tripode, M., Madirolas, A. Artificial neural networks for fish-species identification. ICES Journal of Marine Science, 66(6), 1119–1129. 2009. Google Scholar öffnen doi.org/10.5771/9783828872301
- Demirol, F., Gündüz, F., Yüksel, F., Çoban, M.Z., Abdulmutalip, B.E.R.İ., Kurtoğlu, M. and Küçükyilmaz, M., The Investigation of By-catch and Discard Rates in Crayfish (Astacus leptodactylus Eschscholtz, 1823) Catching in the Keban Dam Lake. Journal of Limnology and Freshwater Fisheries Research, 1(2), 69–74. 2015. Google Scholar öffnen doi.org/10.5771/9783828872301
- Deniz, T.B., Aydın, C. and Ateş, C., A study on some morphological characteristics of Astacus leptodactylus (Eschscholtz 1823) in seven different inland waters in Turkey. J Black Sea/Mediterranean Environment., 19(2):190˗205. 2013. Google Scholar öffnen doi.org/10.5771/9783828872301
- Deval, M. C., Bök, T., Ateş, C., & Tosunoğlu, Z. Length-based estimates of growth parameters, mortality rates, and recruitment of Astacus leptodactylus (Eschscholtz, 1823)(Decapoda, Astacidae) in unexploited inland waters of the northern Marmara region, European Turkey. Crustaceana, 80(6), 655–665. 2007. Google Scholar öffnen doi.org/10.5771/9783828872301
- Dopico, M., Gomez, A., De la Fuente, D., García, N., Rosillo, R., & Puche, J. A vision of industry 4.0 from an artificial intelligence point of view. In Proceedings on the International Conference on Artificial Intelligence (ICAI) (p. 407). The Steering Committee of The World Congress in Computer Science, Computer Engineering and Applied Computing (WorldComp). 2016. Google Scholar öffnen doi.org/10.5771/9783828872301
- Ekici, B.B. and Aksoy, U.T., Prediction of building energy consumption by using artificial neural networks, Advances in Engineering Software, 40: 356–362. 1993. Google Scholar öffnen doi.org/10.5771/9783828872301
- Etchison, L., Jacquemin, S. J., Allen, M., Pyron, M. Morphological variation of rusty crayfish Orconectes rusticus (Cambaridae) with gender and local scale spatial gradients. International Journal of Biology 4 (2): 163˗171. 2012. Google Scholar öffnen doi.org/10.5771/9783828872301
- Fish, K. E., Barnes, J. H., & AikenAssistant, M. W. Artificial neural networks: a new methodology for industrial market segmentation. Industrial Marketing Management, 24(5), 431–438. 1995. Google Scholar öffnen doi.org/10.5771/9783828872301
- Furst, M., Future perspectives for Turkish crayfish fishery. I Unv J Fish Aquat Sci 2: 139–147. 1988. Google Scholar öffnen doi.org/10.5771/9783828872301
- Füreder, L., Oberkofler, B., Hanel, R., Leiter J. and Thaler, B., The freshwater crayfish Austropotamobius pallipes in South Tyrol: Heritage species and bioindicator. B Fr Peche Piscic., 370–371:79−95. 2003. Google Scholar öffnen doi.org/10.5771/9783828872301
- Gentry, T.W., Wiliamowski B.M. and Weatherford L.R., A comparison of traditional forecasting techniques and neural networks. Intelligent Engineering Systems Through Artificial Neural Networks, 5, 765–770. 1995. Google Scholar öffnen doi.org/10.5771/9783828872301
- Gillet, C. and Laurent, P.J., Tail length variations among noble crayfish (Astacus astacus (L)) populations. Freshwater Crayfish, 10: 31–36. 1995. Google Scholar öffnen doi.org/10.5771/9783828872301
- Goethals, P. L., Dedecker, A. P., Gabriels, W., Lek, S., & De Pauw, N. Applications of artificial neural networks predicting macroinvertebrates in freshwaters. Aquatic Ecology, 41(3), 491–508. 2007. Google Scholar öffnen doi.org/10.5771/9783828872301
- Gutiérrez-Yurrita, P.J., Martínez, J.M., Bravo-Utrera, M.A., Montes, C., Ilhéu M. and Bernardo, J.M., The status of crayfish populations in Spain and Portugal. Pages 161–192 in F. Gherardi, and D. M. Holdich, editors. Crayfish in Europe as alien species: how to make the best of a bad situation? A. A. Balkema, Rotterdam, Netherlands. 1999. Google Scholar öffnen doi.org/10.5771/9783828872301
- Harlioğlu, M. M. Comparative biology of the signal crayfish, Pacifastacus leniusculus (Dana), and the narrow-clawed crayfish, Astacus leptodactylus Eschscholtz (Doctoral dissertation, University of Nottingham). 1996. Google Scholar öffnen doi.org/10.5771/9783828872301
- Harlioğlu, M.M., The Relationships between Length-Weight, and Meat Yield of Freshwater Crayfish, Astacus leptodactylus Eschscholtz, in the Ağın Region of Keban Dam Lake. Turk J Zool., 23: 949–958. 1999. Google Scholar öffnen doi.org/10.5771/9783828872301
- Harlioğlu, M.M., The present situation of freshwater crayfish, Astacus leptodactylus (Eschscholtz, 1823) in Turkey. Aquaculture, 230:181–187. 2004. Google Scholar öffnen doi.org/10.5771/9783828872301
- Harlioğlu, M.M. and Harlioğlu, A.G., The comparison of morphometric analysis and meat yield contents of freshwater crayfish, Astacus leptodactylus (Esch 1823) caught from İznik, Eğirdir Lakes and Hirfanlı Dam Lake, Science and Engineering Journal of Fırat University, 17(2):412–423. 2005. Google Scholar öffnen doi.org/10.5771/9783828872301
- Hogger, J. B., Ecology, population biology and behaviour, In: D.M. Holdich and R.S. Lowery (eds.), Freshwater Crayfish, Biology, Menagement and Exploitation, Cambridge, 114–144. 1988. Google Scholar öffnen doi.org/10.5771/9783828872301
- Holdich, D.M. and Lowery, R.S., Freshwater Crayfish – Biology, Management and Exploitation. Chapman and Hall, London. 498 p. 1988. Google Scholar öffnen doi.org/10.5771/9783828872301
- Hopgood A.A. Intelligent Systems for Engineers and Scientists. CRC Press, Florida, 461 pp. 2000. Google Scholar öffnen doi.org/10.5771/9783828872301
- Joy, K.M. and Death, R.G., Predictive modelling and spatial mapping of freshwater fish and decapod assemblages using GIS and neural Networks. Freshwater Biol., 49(8):1306–1052. 2004. Google Scholar öffnen doi.org/10.5771/9783828872301
- Kaastra, I., Boyd, M. Designing a neural network for forecasting financial and economic time series. Neurocomputing, 10(3), 215–236. 1996. Google Scholar öffnen doi.org/10.5771/9783828872301
- Kılıç, S., Becer, Z. A. Some Growth Characters of Tench (Tinca tinca L., 1758) in Lake Yeniçaga, Bolu, Turkey. Journal of Applied Biological Sciences, 7(3). 2013. Google Scholar öffnen doi.org/10.5771/9783828872301
- Krenker, A., BešTer, J. and Kos, A., Introduction to the Artificial Neural Networks, Artificial Neural Networks – Methodological Advances and Biomedical Applications, Prof. Kenji Suzuki (Ed.)., ISBN: 978–953–307–243–2. 2011. Google Scholar öffnen doi.org/10.5771/9783828872301
- Lek, S., & Guégan, J. F. Artificial neural networks as a tool in ecological modelling, an introduction. Ecological modelling, 120(2–3), 65–73. 1999. Google Scholar öffnen doi.org/10.5771/9783828872301
- Lewis, C.D. Industrial and business forecasting methods. London: Butterworths. 1982. Google Scholar öffnen doi.org/10.5771/9783828872301
- Lindqvist, O.V. and Lahti, E., On the sexual dimorphism and condition index in the crayfish Astacus astacus L. in Finland. Freshwater Crayfish., 5: 3–11. 1983. Google Scholar öffnen doi.org/10.5771/9783828872301
- Maravelias, C.D., Haralabous, J. and Papaconstantinou, C., Predicting demersal fish species distributions in the Mediterranean Sea using artificial neural networks. Mar Ecol., 255:249–258. 2003. Google Scholar öffnen doi.org/10.5771/9783828872301
- Mastrorillo, S., Lek, S., Dauba, F. and Belaud, A., The use of artificial neural networks to predict the presence of small-bodied fish in river. Freshwater Biol., 38: 237–246. 1997. Google Scholar öffnen doi.org/10.5771/9783828872301
- Mendes, B., Fonseca, P. and Campos, A., Weight length relationships for 46 fish species of the Portuguese west coast. J. Appl. Ichthyol., 20: 355–361. 2004. Google Scholar öffnen doi.org/10.5771/9783828872301
- Momot, W. T. Annual production and production/biomass of the crayfish, Orconectes virilis in two northern Ontario Lakes. Trans. Am. Fish. Soc., 96: 202–209. 1978. Google Scholar öffnen doi.org/10.5771/9783828872301
- Moutopoulos, D. K., Stergiou, K. I. Length˗weight and length˗length relationships of fish species from Aegean Sea (Greece). J. Appl. Ichthyol. 18: 200˗203. 2002. Google Scholar öffnen doi.org/10.5771/9783828872301
- Nystrom, P. Ecology. In: Biology of Freshwater Crayfish (ed. D. M. Holdich), pp. 192–235. Blackwell Science, Oxford. 2002. Google Scholar öffnen doi.org/10.5771/9783828872301
- Obach, M., Wagner, R., Werner, H. and Schmidt, H.H., Modelling population dynamics of aquatic insects ith artificial neural networks. Ecol Model., 146:207–217. 2001. Google Scholar öffnen doi.org/10.5771/9783828872301
- Olden, J. D., & Jackson, D. A. Fish–habitat relationships in lakes: gaining predictive and explanatory insight by using artificial neural networks. Transactions of the American Fisheries Society, 130(5), 878–897. 2001. Google Scholar öffnen doi.org/10.5771/9783828872301
- Olsson, K., Dynamics of omnivorous crayfish in freshwater ecosystems. Ph.D. thesis. Department of Ecology, Limnology, Lund Univ., 119 pp. 2008. Google Scholar öffnen doi.org/10.5771/9783828872301
- Ouali, D., Chebana, F., & Ouarda, T. B. Fully nonlinear statistical and machine‐learning approaches for hydrological frequency estimation at ungauged sites. Journal of Advances in Modeling Earth Systems, 9(2), 1292–1306. 2017. Google Scholar öffnen doi.org/10.5771/9783828872301
- Park, Y.S., Verdonschot, P.F.M., Chon, T.S. and Lek, S., Patterning and predicting aquatic macro invertabrate diversities using artificial neural network. Water Res., 37: 1749–1758. 2003. Google Scholar öffnen doi.org/10.5771/9783828872301
- Pârvulescu L., Schrimpf A., Kozubíková E., Cabanillas Resino S., Vrålstad T., Petrusek A & Schulz R. Invasive crayfish and crayfish plague on the move: first detection of the plague agent Aphanomyces astaci in the Romanian Danube. Diseases of Aquatic Organisms 98: 85–94. 2012. Google Scholar öffnen doi.org/10.5771/9783828872301
- Primavera, J.H., Parado-Estepa, F.D. and Lebata, J.L., Morphometric relationship of length and weight of giant tiger prawn Penaeus monodon according to life stage, sex and source. Aquaculture, 164: 67–75. 1998. Google Scholar öffnen doi.org/10.5771/9783828872301
- Rahe R, Soylu E. Identification of the pathogenic fungus causing destruction to Turkish crayfish stocks (Astacus leptodactylus). J Invertebr Pathol 54: 10–15. 1989. Google Scholar öffnen doi.org/10.5771/9783828872301
- Rhodes, C.P. and Holdich, D.M., On size and sexual dimorphism in Austropotamobius pallipes (Lereboullet) – A step in assessing the commercial exploitation potential of the native British freshwater crayfish. Aquaculture, 17: 345–358. 1979. Google Scholar öffnen doi.org/10.5771/9783828872301
- Ricker, W.E., Linear regressions in fishery research. J Fish Res Board Can., 30:409–434. 1973. Google Scholar öffnen doi.org/10.5771/9783828872301
- Rocha, J. C., Passalia, F. J., Matos, F. D., Takahashi, M. B., de Souza Ciniciato, D., Maserati, M. P., ... Nogueira, M. F. G. A method based on artificial intelligence to fully automatize the evaluation of bovine blastocyst images. Scientific Reports, 7(1), 7659. 2017. Google Scholar öffnen doi.org/10.5771/9783828872301
- Romaire, R.P., Forester J.S. and Avault, J.W., Length˗weight relationships of two commercially important crayfishes of the genus Procambarus. Freshwater Crayfish, 3:463˗470. 1977. Google Scholar öffnen doi.org/10.5771/9783828872301
- Rumelhart, D.E., Hinton, G.E. and Williams, R.J., Learning internal representations by error propagation in Parallel Distributed Processing. Explorations in the Microstructure of Cognition MIT Press, 1:318–362. 1986. Google Scholar öffnen doi.org/10.5771/9783828872301
- Sari, B. G., Lúcio, A. D. C., Santana, C. S., Krysczun, D. K., Tischler, A. L., & Drebes, L. Sample size for estimation of the Pearson correlation coefficient in cherry tomato tests. Ciência Rural, 47(10):1–6. 2017. Google Scholar öffnen doi.org/10.5771/9783828872301
- Saygı B.Y, Demirkalp, F.Y. Trophic status of shallow Yeniçağa Lake (Bolu, Turkey) in relation to physical and chemical environment. Fresenius Environmental Bulletin. 13:385 -393. 2004. Google Scholar öffnen doi.org/10.5771/9783828872301
- Sharda, R., Patil, R. B. Connectionist approach to time series prediction: an empirical test. Journal of Intelligent Manufacturing, 3(5), 317–323. 1992. Google Scholar öffnen doi.org/10.5771/9783828872301
- Sholahuddin, A., Ramadhan, A. P., & Supriatna, A. K. The Application of ANN-Linear Perceptron in the Development of DSS for a Fishery Industry. Procedia Computer Science, 72, 67–77. 2015. Google Scholar öffnen doi.org/10.5771/9783828872301
- Skurdal, J. and Qvenild, T. Growth, maturity and fecundity of Astacus astacus in Lake Steinsfjorden, In: Freshwater Crayfish (Eds., J. Skurdal and T. TougbØl), Norway, pp. 182–186. 1986. Google Scholar öffnen doi.org/10.5771/9783828872301
- Skurdal, J. and Taugbol, T., Crayfish of commercial importance-Astacus. In: D.M. Holdich (Ed.), Biology of Freshwater Crayfish. Blackwell Science, Oxford: 467–510. 2001. Google Scholar öffnen doi.org/10.5771/9783828872301
- Souty-Grosset, C., Holdrich, D.M., Noel, P.Y., Reynolds, J.D. and Haffner, P., Atlas of crayfish in Europe. Publications Scientifiques du MNHN-Paris. 2006. Google Scholar öffnen doi.org/10.5771/9783828872301
- Sun, L., Xiao, H., Li, S. and Yang, D., Forecating fish stock recruitment and planning optimal harvesting strategies by using neural network. Journal of Computers, 4(11):1075–1082. 2009. Google Scholar öffnen doi.org/10.5771/9783828872301
- Suryanarayana, I., Braibanti, A., Rao, R.S., Ramamc, V.A., Sudarsan, D. and Rao, G.N., Neural networks in fisheries research. Fish Res., 92:115–139. 2008. Google Scholar öffnen doi.org/10.5771/9783828872301
- Tekin, M. Numerical Methods (Computer Analysis). (Updated 6. Edition). Konya: Günay Ofset. (Turkish). 2008. Google Scholar öffnen doi.org/10.5771/9783828872301
- Teles, L. O., Vasconcelos, V., Pereira, E., & Saker, M. Time series forecasting of cyanobacteria blooms in the Crestuma Reservoir (Douro River, Portugal) using artificial neural networks. Environmental Management, 38(2), 227–237. 2006. Google Scholar öffnen doi.org/10.5771/9783828872301
- Tesch, F.W., Age and growth. In: Methods for Assessment of Fish Production in Fresh Waters (ed., W. E. Ricker), Blackwell Scientific Publications, Oxford, 99˗130. 1971. Google Scholar öffnen doi.org/10.5771/9783828872301
- Tosunoğlu, Z., Aydın, C., Özaydın, O. and Leblebici, S., Trawl codend mesh selectivity of braided PE material for Parapenaeus longirostris (Lucas, 1846) (Decapoda, Penaeidae). Crustaceana, 80: 1087–1094. 2007. Google Scholar öffnen doi.org/10.5771/9783828872301
- Tureli Bilen, C., Kokcu, P. and Ibrikci, T., Application of Artificial Neural Networks (ANNs) for Weight Predictions of Blue Crabs (Callinectes sapidus Rathbun, 1896) Using Predictor Variables. Medit Mar Sci., 12(2):439–446. 2011. Google Scholar öffnen doi.org/10.5771/9783828872301
- TÜİK Aquaculture Statistics. Ankara, Turkey: Turkey Statistical Institute Publications (in Turkish). www.tuik.gov.tr. 2018. Google Scholar öffnen doi.org/10.5771/9783828872301
- TÜİK, Aquaculture Statistics (1984–1991). Ankara, Turkey: Turkey Statistical Institute Publications (in Turkish). 1984–1991. Google Scholar öffnen doi.org/10.5771/9783828872301
- Verdiell-Cubedo, D., Oliva-Paterna, F.J. and Torralva, M., Length-weight relationships for 22 fish species of the Mar Menor coastal lagoon (Western Mediterranean Sea). Journal of Applied Ichthyology, 22: 293–294. 2006. Google Scholar öffnen doi.org/10.5771/9783828872301
- Westman, K., Savolainen, R., Growth of the signal crayfish Pacifastacus leniusculus, in a small forest lake in Finland. Boreal Environ. Res. 7, 53–61. 2002. Google Scholar öffnen doi.org/10.5771/9783828872301
- Witt, S.F. and Witt C.A. Modeling and Forecasting Demand in Tourism. Londra: Academic Press. 1992. Google Scholar öffnen doi.org/10.5771/9783828872301
- Yüksel, F., and Duman, E. The investigation of the crayfish (Astacus leptodactylus Eschscholtz, 1823) population amplitude in Keban Dam Lake. Journal of FisheriesSciences. com, 5(3), 226. 2011. Google Scholar öffnen doi.org/10.5771/9783828872301
- Would the Benefits Created by Industry 4.0 Via Innovations Set the Consumers Free of Planned Obsolescence? by Sinem Zeliha Dalak, Cagla Seneler Google Scholar öffnen doi.org/10.5771/9783828872301
- Accenture.com. (2018). Airbus | Wearable Technology | Accenture. [online] Available at: https://www.accenture.com/gb-en/success-airbus-wearable-technology Google Scholar öffnen doi.org/10.5771/9783828872301
- Adamson, G. and Stevens, B. (2003). Industrial strength design. Milwaukee, Wis.: Milwaukee Art Museum. Google Scholar öffnen doi.org/10.5771/9783828872301
- AM Sub-Platform. (2014). Additive Manufacturing: Strategic Research Agenda. [online] Available at: http://www.rm-platform.com/linkdoc/AM%20SRA%20-%20February%202014.pdf Google Scholar öffnen doi.org/10.5771/9783828872301
- Amankwah-Amoah, J. (2017). Integrated vs. add-on: A multidimensional conceptualisation of technology obsolescence. Technological Forecasting and Social Change, 116, pp.299 – 307. Google Scholar öffnen doi.org/10.5771/9783828872301
- Auschitzky, E., Hammer, M. and Rajagopaul, A. (2018). How big data can improve manufacturing. [online] McKinsey & Company. Available at: https://www.mckinsey.com/business-functions/operations/our-insights/how-big-data-can-improve-manufacturing Google Scholar öffnen doi.org/10.5771/9783828872301
- Bertolucci, J. (2018). Intel Cuts Manufacturing Costs With Big Data – InformationWeek. [online] InformationWeek. Available at: https://www.informationweek.com/software/information-management/intel-cuts-manufacturing-costs-with-big-data/d/d-id/1109111 [Accessed 25 Nov. 2018]. Google Scholar öffnen doi.org/10.5771/9783828872301
- Bidgoli, H. (2010). Supply chain management, marketing and advertising, and global management. Hoboken, NJ: Wiley. Google Scholar öffnen doi.org/10.5771/9783828872301
- Bokhari, M., Shallal, Q. and Tamandani, Y. (2016). Cloud computing service models: A comparative study. IEEE. Google Scholar öffnen doi.org/10.5771/9783828872301
- Bulow, J. (1986). An Economic Theory of Planned Obsolescence. The Quarterly Journal of Economics, 101(4), p.729. Google Scholar öffnen doi.org/10.5771/9783828872301
- Burgess, J. (2018). 4 Big Data Use Cases in the Manufacturing Industry. [online] Ingrammicroadvisor.com. Available at: http://www.ingrammicroadvisor.com/data-center/4-big-data-use-cases-in-the-manufacturing-industry Google Scholar öffnen doi.org/10.5771/9783828872301
- Butt, T., Camilleri, M., Paul, P. and Jones, K. (2015). Obsolescence types and the built environment – definitions and implications. International Journal of Environment and Sustainable Development, 14(1), p.20. Google Scholar öffnen doi.org/10.5771/9783828872301
- Castells, M. (2014). The Impact of the Internet on Society: A Global Perspective. [online] MIT Technology Review. Available at: https://www.technologyreview.com/s/530566/the-impact-of-the-internet-on-society-a-global-perspective/ Google Scholar öffnen doi.org/10.5771/9783828872301
- Credit Suisse. (2015). Global Wealth Report. [online] Available at: http://publications.credit-suisse.com/tasks/render/file/index.cfm?fileid=F2425415-DCA7-80B8-EAD989AF9341D47E Google Scholar öffnen doi.org/10.5771/9783828872301
- Deloitte. (2017). Using autonomous robots to drive supply chain innovation. [online] Available at: https://www2.deloitte.com/content/dam/Deloitte/us/Documents/manufacturing/us-supply-chain-of-the-autonomous-robots.pdf Google Scholar öffnen doi.org/10.5771/9783828872301
- Erol, S., Jäger, A., Hold, P., Ott, K. and Sihn, W. (2016). Tangible Industry 4.0: A Scenario-Based Approach to Learning for the Future of Production. Procedia CIRP, 54, pp.13 – 18. Google Scholar öffnen doi.org/10.5771/9783828872301
- Fishman, A., Gandal, N. and Shy, O. (1993). Planned Obsolescence as an Engine of Technological Progress. The Journal of Industrial Economics, 41(4), p.361. Google Scholar öffnen doi.org/10.5771/9783828872301
- Frey, C. and Osborne, M. (2013). THE FUTURE OF EMPLOYMENT: HOW SUSCEPTIBLE ARE JOBS TO COMPUTERISATION?. [online] Available at: https://www.oxfordmartin.ox.ac.uk/downloads/academic/The_Future_of_Employment.pdf Google Scholar öffnen doi.org/10.5771/9783828872301
- Gartner.com. (2018). Gartner Says 6.4 Billion Connected. [online] Available at: https://www.gartner.com/newsroom/id/3165317 Google Scholar öffnen doi.org/10.5771/9783828872301
- Ge.com. (2018). Everything you need to know about IIoT | GE Digital. [online] Available at: https://www.ge.com/digital/blog/everything-you-need-know-about-industrial-internet-things Google Scholar öffnen doi.org/10.5771/9783828872301
- Ge.com. (2018). What is Edge Computing? | GE Digital. [online] Available at: https://www.ge.com/digital/blog/what-edge-computing#to-section-index=section-8 GE Additive. (2018). What is Additive Manufacturing?. [online] Available at: https://www.ge.com/additive/additive-manufacturing Google Scholar öffnen doi.org/10.5771/9783828872301
- Guiltinan, J. (2008). Creative Destruction and Destructive Creations: Environmental Ethics and Planned Obsolescence. Journal of Business Ethics, 89(S1), pp.19 – 28. Google Scholar öffnen doi.org/10.5771/9783828872301
- Grattan, L. (2016). Populism's power. Oxford: Oxford University Press. Google Scholar öffnen doi.org/10.5771/9783828872301
- Grieves, M. (2014). [online] Innovate.fit.edu. Available at: http://innovate.fit.edu/plm/documents/doc_mgr/912/1411.0_Digital_Twin_White_Paper_Dr_Grieves.pdf Google Scholar öffnen doi.org/10.5771/9783828872301
- Hawking, S., Russell, S., Tegmark, M. and Wilczek, F. (2014). Stephen Hawking: 'Transcendence looks at the implications of artificial intelligence – but are we taking AI seriously enough?'. [online] The Independent. Available at: https://www.independent.co.uk/news/science/stephen-hawking-transcendence-looks-at-the-implications-of-artificial-intelligence-but-are-we-taking-9313474.html Google Scholar öffnen doi.org/10.5771/9783828872301
- Hawking, S., Russell, S., Tegmark, M. and Wilczek, F. (2014). Stephen Hawking: 'Transcendence looks at the implications of artificial intelligence – but are we taking AI seriously enough?'. [online] The Independent. Available at: https://www.independent.co.uk/news/science/stephen-hawking-transcendence-looks-at-the-implications-of-artificial-intelligence-but-are-we-taking-9313474.html [Accessed 30 Dec. 2018]. Google Scholar öffnen doi.org/10.5771/9783828872301
- Hozdić, E. (2015). MANUFACTURING FOR INDUSTRY 4.0. Google Scholar öffnen doi.org/10.5771/9783828872301
- Hozdić, Elvis. (2015). Smart factory for industry 4.0: A review. International Journal of Modern Manufacturing Technologies. 7. 28–35. Google Scholar öffnen doi.org/10.5771/9783828872301
- Huelsman, T., Powers, E., Peasley, S. and Robinson, R. (2016). Cyber risk in advanced manufacturing. [online] Available at: https://www.nist.gov/sites/default/files/documents/2016/12/28/cyberrisk_manu_fullstudy_landscape_brochure_lpxcic07_06_17_7101_finalfor.pdf Google Scholar öffnen doi.org/10.5771/9783828872301
- Kadir, B. (2017). The nine pillars of Industry 4.0. [online] 4th Post | Industry 4.0 | Smart Manufacturing | Research. Available at: https://www.4thpost.com/single-post/2017/07/23/The-nine-pillars-of-Industry-40 Google Scholar öffnen doi.org/10.5771/9783828872301
