Enterprise &amp; Business Management: A Handbook for Educators, Consultants, and Practitioners

Prof. Dr. Alptekin Erkollar

doi:10.5771/9783828872301

Summary

Organizations have always been dependent on communication, information, technology and their management. The development of information technology has sped up the importance of management information systems, which is an emerging discipline combining various aspects of informatics, information technology, and business management. Understanding the impact of information on today’s organizations requires technological and managerial views, which are both offered by management information systems.

Business management is not only about generating greater returns and using new technologies for developing businesses to reach future goals. Business management also means generating better revenue performance if plans are diligently followed.

It is part of business management to have an ear to the ground of global economic trends, changing environmental conditions and preferences, as well as the behavior of value chain partners. While, until now, business management and management information systems are mostly treated as independent fields, this publication takes an interest in the cooperation of the two. Its contributions focus on both research areas and practical approaches, in turn showing novelties in the area of enterprise and business management.

Main topics covered in this book are technology management, software engineering, knowledge management, innovation management and social media management.

This book adopts an international view, combines theory and practice, and is authored for researchers, lecturers, students as well as consultants and practitioners.

Search publication

Bibliographic data

Edition: 1/2020
Copyright Year: 2020
ISBN-Print: 978-3-8288-4255-7
ISBN-Online: 978-3-8288-7230-1
Publisher: Tectum, Baden-Baden
Series: Enterprise & Business Management
Language: English
Pages: 412
Product Type: Edited Book

Titelei/Inhaltsverzeichnis No access Pages I - XIV
Authors:
1. Learning Objectives No access
  Authors:
2. Chapter Outline No access
  Authors:
3. Authors:
  1. 1 Introduction No access
    Authors:
  2. Authors:
    1. 2.1 The Role of Reverse Logistics in Sustainable Supply Chain Operations No access
      Authors:
  3. Authors:
    1. 3.1 Impacts of Industry 4.0 on Supply Chain Operations towards Sustainability No access
      Authors:
  4. Authors:
    1. 4.1 A Qualitative Research Approach No access
      Authors:
  5. 5 Conclusions No access
    Authors:
  6. 6 References No access
    Authors:
  7. 7 Key Terms No access
    Authors:
  8. 8 Questions for Further Study No access
    Authors:
  9. 9 Exercises No access
    Authors:
  10. 10 Further Reading No access
    Authors:
Authors:
1. Learning Objectives No access
  Authors:
2. Chapter Outline No access
  Authors:
3. Authors:
  1. Authors:
    1. 1.1 Problem Statement and Company Background No access
      Authors:
    2. 1.2 Motivation No access
      Authors:
  2. Authors:
    1. 2.1 Retail Applications No access
      Authors:
    2. 2.2 SKU Segmentation No access
      Authors:
    3. 2.3 Inventory Management No access
      Authors:
  3. Authors:
    1. Authors:
      1. 3.1.1 Current System Design No access
        Authors:
      2. 3.1.2 Interviews and Business Context No access
        Authors:
    2. 3.2 Decision Frame No access
      Authors:
    3. Authors:
      1. 3.3.1 Ordering Flow No access
        Authors:
      2. 3.3.2 Decision Frame in Safety Stock Calculation No access
        Authors:
    4. Authors:
      1. 3.4.1 System Dynamics Model Parameters No access
        Authors:
      2. 3.4.2 DPS Simulation Parameters No access
        Authors:
  4. Authors:
    1. 4.1 Standard Deviation of Demand or Forecast Error No access
      Authors:
    2. 4.2 Fixed vs. Dynamic Cycle Service Levels No access
      Authors:
    3. 4.3 Improvements on Dynamic Cycle Service Levels No access
      Authors:
  5. Authors:
    1. Authors:
      1. 5.1.1 Operations of System Dynamics Model No access
        Authors:
    2. 5.2 Benefits of Using Dynamic Cycle Service Levels No access
      Authors:
  6. 6 Conclusion No access
    Authors:
  7. 7 References No access
    Authors:
  8. 8 Key Terms No access
    Authors:
  9. 9 Questions for Further Study No access
    Authors:
  10. 10 Exercises No access
    Authors:
  11. 11 Further Reading No access
    Authors:
Authors:
1. Learning Objectives No access
  Authors:
2. Chapter Outline No access
  Authors:
3. Authors:
  1. 1 Introduction No access
    Authors:
  2. 2 Readiness for Industry 4.0 No access
    Authors:
  3. 3 A Roadmap for Industry 4.0 No access
    Authors:
  4. 4 Case Study: Current Situation in Turkey No access
    Authors:
  5. Conclusion No access
    Authors:
  6. References No access
    Authors:
  7. Key Terms No access
    Authors:
  8. Questions for Further Study No access
    Authors:
  9. Exercises No access
    Authors:
  10. Further Reading No access
    Authors:
Authors:
1. Learning Objectives No access
  Authors:
2. Chapter Outline No access
  Authors:
3. Authors:
  1. 1 Introduction No access
    Authors:
  2. 2 Effects of Industry 4.0 on the Shop-Floor No access
    Authors:
  3. 3 Literature Review No access
    Authors:
  4. 4 Research Methodology No access
    Authors:
  5. Authors:
    1. 5.1 Pre-Industry 4.0 Stage No access
      Authors:
    2. 5.2 Industry 4.0 Initiation Stage No access
      Authors:
    3. 5.3 Industry 4.0 Implementation Phase No access
      Authors:
  6. 6 Conclusion No access
    Authors:
  7. References No access
    Authors:
  8. Key Terms No access
    Authors:
  9. Questions for Further Study No access
    Authors:
  10. Exercises No access
    Authors:
  11. Further Reading No access
    Authors:
Authors:
1. Learning Objectives No access
  Authors:
2. Chapter Outline No access
  Authors:
3. Authors:
  1. 1 Introduction No access
    Authors:
  2. Authors:
    1. 2.1 The Principles of Industry 4.0 Technology No access
      Authors:
    2. 2.2 Factors Affecting Industry 4.0 No access
      Authors:
  3. Authors:
    1. 3.1 Milk Supply Process of Sample Company No access
      Authors:
    2. 3.2 Acceptance of Milk and Milk Processing No access
      Authors:
    3. Authors:
      1. 3.3.1 CAN Bus (Controller Area Network Bus) System No access
        Authors:
      2. 3.3.2 GPS (Global Positioning System) No access
        Authors:
      3. 3.3.3 Temperature and Moisture Sensors No access
        Authors:
  4. 4 Current Situation Assessment No access
    Authors:
  5. Authors:
    1. 5.1 Traceability of Product and Service Using RFID No access
      Authors:
    2. 5.2 Interoperability with IoT and Cyber Physical Systems No access
      Authors:
    3. 5.3 Intelligent Systems No access
      Authors:
    4. 5.4 Robots, Automatic Machines and Unmanned Transportation Vehicles No access
      Authors:
    5. 5.5 Customized Services and Products No access
      Authors:
    6. 5.6 Globalizing Systems No access
      Authors:
  6. 6 Threats Coming with Industry 4.0 No access
    Authors:
  7. 7 Conclusion No access
    Authors:
  8. References No access
    Authors:
  9. Key Terms No access
    Authors:
  10. Questions for Further Study No access
    Authors:
  11. Exercises No access
    Authors:
  12. Further Reading No access
    Authors:
Authors:
1. Learning Objectives No access
  Authors:
2. Chapter Outline No access
  Authors:
3. Authors:
  1. 1 Introduction No access
    Authors:
  2. Authors:
    1. 2.1 Cyber physical systems No access
      Authors:
    2. 2.2 Industry 4.0 and Industrial Internet of Things (IIoT) difference No access
      Authors:
  3. 3 Smart Factories No access
    Authors:
  4. 4 Relation between Industry 4.0 and Smart Factory No access
    Authors:
  5. 5 Conclusions No access
    Authors:
  6. 6 References No access
    Authors:
  7. Key Terms No access
    Authors:
  8. Questions for Further Study No access
    Authors:
  9. Exercises No access
    Authors:
  10. Further Reading No access
    Authors:
Authors:
1. Classification of technology No access
  Authors:
2. Relationship between business and technology No access
  Authors:
3. Business view on managing technologies No access
  Authors:
4. Technology management and innovation No access
  Authors:
5. How to review technological innovation? No access
  Authors:
6. Relationship between technology and market No access
  Authors:
7. How to choose technology management methodologies? Which factors have to be considered? No access
  Authors:
8. Prerequisites for a successful methodology? No access
  Authors:
9. Benefits of using a methodology No access
  Authors:
10. When you have chosen a methodology review it consequently No access
  Authors:
11. Strategic technology lifecycle No access
  Authors:
Authors:
1. Learnign Objectives No access
  Authors:
2. Chapter Outline No access
  Authors:
3. Authors:
  1. 1 Introduction No access
    Authors:
  2. Authors:
    1. 2.1 The First Industrial Revolution No access
      Authors:
    2. 2.2 The Second Industrial Revolution No access
      Authors:
    3. 2.3 The Third Industrial Revolution No access
      Authors:
    4. 2.4 The Fourth Industrial Revolution No access
      Authors:
  3. Authors:
    1. 3.1 Technologies of Industry 4.0 No access
      Authors:
  4. 4 Techno Parks No access
    Authors:
  5. Authors:
    1. Authors:
      1. 5.1.1 The level of awareness about Industry 4.0 No access
        Authors:
      2. 5.1.2 Technologies of Industry 4.0 No access
        Authors:
      3. 5.1.3 Application areas No access
        Authors:
      4. 5.1.4 Effect of Size and Establishment Year No access
        Authors:
  6. Conclusion No access
    Authors:
  7. References No access
    Authors:
  8. Key Terms No access
    Authors:
  9. Questions for Further Study No access
    Authors:
  10. Exercises No access
    Authors:
  11. Further Reading No access
    Authors:
Authors:
1. Learning Objectives No access
  Authors:
2. Chapter Outline No access
  Authors:
3. Authors:
  1. Authors:
    1. 1.1 Why is Industry 4.0 Important? No access
      Authors:
    2. 1.2 What is Outsourcing? No access
      Authors:
    3. 1.3 Why do Organizations Outsource? No access
      Authors:
    4. 1.4 Some Samples for the Outsourcing Reasons in Different Countries No access
      Authors:
  2. 2 Convergence Point of These Two Phenomena „Outsourcing and Industry 4.0“: Technology No access
    Authors:
  3. 3 The Relationship between the Institutional Logic, Pragmatism, Industry 4.0 and Outsourcing No access
    Authors:
  4. Conclusion No access
    Authors:
  5. References No access
    Authors:
  6. Key Terms No access
    Authors:
  7. Questions for Further Studies in the Field No access
    Authors:
  8. Exercises No access
    Authors:
  9. Further Reading No access
    Authors:
Authors:
1. Definitions No access
  Authors:
2. Stakeholders No access
  Authors:
3. Software products No access
  Authors:
4. Software product evaluation criteria No access
  Authors:
5. Attributes of good software No access
  Authors:
6. Classification of software process models No access
  Authors:
7. Generic software process models No access
  Authors:
8. Engineering process model No access
  Authors:
9. Hybrid process models No access
  Authors:
10. Spiral model No access
  Authors:
11. Potential problems of process models No access
  Authors:
12. Process visibility No access
  Authors:
13. Questions on Software engineering No access
  Authors:
Authors:
1. Learning Objectives No access
  Authors:
2. Chapter Outline No access
  Authors:
3. Authors:
  1. 1 Introduction No access
    Authors:
  2. 2 Definition and Scope of ERP No access
    Authors:
  3. 3 Development of ERP System No access
    Authors:
  4. 4 Fundamental Features of ERP System No access
    Authors:
  5. 5 Components of ERP System No access
    Authors:
  6. 6 ERP Software in Turkey No access
    Authors:
  7. Authors:
    1. 7.1 Foreign ERP Providers No access
      Authors:
    2. 7.2 Turkey ERP Providers No access
      Authors:
  8. 8 Definition of Information and Information Safety No access
    Authors:
  9. Authors:
    1. 9.1 ERP Information Safety Gaps No access
      Authors:
  10. 10 Conclusions No access
    Authors:
  11. 11 References No access
    Authors:
  12. 12 Key Terms No access
    Authors:
  13. 13 Questions for Further Study No access
    Authors:
  14. 14 Exercises No access
    Authors:
  15. 15 Further Reading No access
    Authors:
Authors:
1. Learning Objectives No access
  Authors:
2. Chapter Outline No access
  Authors:
3. Authors:
  1. 1 Introduction No access
    Authors:
  2. 2 Background No access
    Authors:
  3. 3 Artificial Neural Networks No access
    Authors:
  4. 4 Materials and Methods No access
    Authors:
  5. 5 Design and Implementation No access
    Authors:
  6. 6 Results and Discussion No access
    Authors:
  7. 7 Conclusions and Recommendations No access
    Authors:
  8. 8 References No access
    Authors:
  9. 9 Key TErms No access
    Authors:
  10. 10 Questions for Further Study No access
    Authors:
  11. 11 Exercises No access
    Authors:
  12. 12 Further Reading No access
    Authors:
Authors:
1. Learning Objectives No access
  Authors:
2. Chapter Outline No access
  Authors:
3. Authors:
  1. 1 Introduction No access
    Authors:
  2. Authors:
    1. 2.1 Study area No access
      Authors:
    2. 2.2 Data collection No access
      Authors:
    3. 2.3 Length–weight relationship (LWR) equation No access
      Authors:
    4. 2.4 Artificial Neural Networks (ANNs) No access
      Authors:
    5. 2.5 Normalization No access
      Authors:
    6. 2.6 Estimation Accuracy Validation No access
      Authors:
    7. 2.7 Statistics No access
      Authors:
    8. 2.8 Data Editing for MATLAB No access
      Authors:
  3. Authors:
    1. 3.1 Tinca tinca No access
      Authors:
    2. 3.2 LENGTH˗WEIGHT RELATIONSHIPS (LWR) No access
      Authors:
    3. 3.3 ARTIFICIAL NEURAL NETWORKS (ANNs) No access
      Authors:
  4. 4 Results and Discussion No access
    Authors:
  5. 5 References No access
    Authors:
  6. 6 Key Terms No access
    Authors:
  7. 7 Questions for Further Study No access
    Authors:
  8. 8 Exercises No access
    Authors:
  9. 9 Further Reading No access
    Authors:
Authors:
1. Definitions No access
  Authors:
2. Knowledge generation No access
  Authors:
3. Knowledge classification No access
  Authors:
4. Transforming knowledge No access
  Authors:
5. Tacit to Tacit No access
  Authors:
6. Explicit to Tacit No access
  Authors:
7. Tacit to Explicit No access
  Authors:
8. Explicit to Explicit No access
  Authors:
9. Knowledge management components No access
  Authors:
10. Strategies, processes and metrics No access
  Authors:
11. How to develop a knowledge strategy? No access
  Authors:
12. Knowledge management architecture No access
  Authors:
13. Aspects of Secure Knowledge Management No access
  Authors:
14. Security Strategies No access
  Authors:
15. Security processes No access
  Authors:
16. Metrics No access
  Authors:
17. Techniques No access
  Authors:
18. Knowledge management cycle No access
  Authors:
19. Knowledge management technologies No access
  Authors:
20. People and systems No access
  Authors:
21. People No access
  Authors:
22. Systems No access
  Authors:
23. Two ways to generate and use knowledge No access
  Authors:
24. Knowledge cycle No access
  Authors:
25. Levers No access
  Authors:
26. Principles of effective learning No access
  Authors:
27. understanding No access
  Authors:
28. skills No access
  Authors:
29. processes No access
  Authors:
30. The goal of knowledge management metrics No access
  Authors:
Authors:
1. Learning Objectives No access
  Authors:
2. Chapter Outline No access
  Authors:
3. Authors:
  1. 1 Introduction No access
    Authors:
  2. Authors:
    1. 2.1 Study area No access
      Authors:
    2. 2.2 Data collection No access
      Authors:
    3. 2.3 Length–weight relationship (LWR) equation No access
      Authors:
    4. 2.4 Artificial Neural Networks (ANNs) No access
      Authors:
    5. 2.5 Normalization No access
      Authors:
    6. 2.6 Estimation Accuracy Validation No access
      Authors:
    7. 2.7 Statistics No access
      Authors:
    8. 2.8 Data Editing for MATLAB No access
      Authors:
  3. 3 Literature Review No access
    Authors:
  4. 4 Results No access
    Authors:
  5. 5 Discussion No access
    Authors:
  6. 6 References No access
    Authors:
  7. 7 Key Terms No access
    Authors:
  8. 8 Questions for Further Study No access
    Authors:
  9. 9 Exercises No access
    Authors:
  10. 10 Further Reading No access
    Authors:
Authors:
1. Learning Objectives No access
  Authors:
2. Chapter Outline No access
  Authors:
3. Authors:
  1. Authors:
    1. 1.1 Definition and Evolution of Industry 4.0 No access
      Authors:
  2. Authors:
    1. 2.1 Autonomous Robots No access
      Authors:
    2. 2.2 Big Data and Analytics No access
      Authors:
    3. 2.3 Simulation No access
      Authors:
    4. 2.4 System Integration No access
      Authors:
    5. 2.5 Cybersecurity No access
      Authors:
    6. 2.6 The Industrial Internet of Things (IIoT) No access
      Authors:
    7. 2.7 The Cloud No access
      Authors:
    8. 2.8 Additive Manufacturing No access
      Authors:
    9. 2.9 Augmented Reality No access
      Authors:
  3. Authors:
    1. 3.1 Companies and Overall Economy No access
      Authors:
    2. 3.2 Managers and Employees No access
      Authors:
    3. 3.3 Countries, Regions, Cities and Transnational Relations No access
      Authors:
    4. 3.4 Individual and the Society No access
      Authors:
  4. Authors:
    1. 4.1 Definition and History of Planned Obsolescence No access
      Authors:
    2. Authors:
      1. 4.2.1 Obsolescence of Function No access
        Authors:
      2. 4.2.2 Obsolescence of Quality No access
        Authors:
      3. 4.2.3 Obsolescence of Desirability No access
        Authors:
  5. 5 Conclusions No access
    Authors:
  6. 6 References No access
    Authors:
  7. Key Terms No access
    Authors:
  8. Questions for Further Study No access
    Authors:
  9. Exercises No access
    Authors:
  10. Further Reading No access
    Authors:
Authors:
1. Learning Objectives No access
  Authors:
2. Chapter Outline No access
  Authors:
3. Authors:
  1. 1 Introduction No access
    Authors:
  2. Authors:
    1. 2.1 Literature Review No access
      Authors:
  3. 3 Conclusion No access
    Authors:
  4. References No access
    Authors:
  5. Key Terms No access
    Authors:
  6. Questions for Further Study No access
    Authors:
  7. Exercises No access
    Authors:
  8. Further Reading No access
    Authors:
About the Chapter Contributors No access Pages 407 - 412

Bibliography (643 entries)

Kenton, W. (2018). Functional Obsolescence. [online] Investopedia. Available at: https://www.investopedia.com/terms/f/functional-obsolescence.asp Open Google Scholar DOI: 10.5771/9783828872301
Kenton, W. (2018). Vertical Integration. [online] Investopedia. Available at: https://www.investopedia.com/terms/v/verticalintegration.asp Open Google Scholar DOI: 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 Open Google Scholar DOI: 10.5771/9783828872301
Kessler, T. and Brendel, J. (2016). Planned Obsolescence and Product-Service Systems: Linking Two Contradictory Business Models. Open Google Scholar DOI: 10.5771/9783828872301
Keynes, J. (1931). Economic Possibilities for our Grandchildren. Open Google Scholar DOI: 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 Open Google Scholar DOI: 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 Open Google Scholar DOI: 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 Open Google Scholar DOI: 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. Open Google Scholar DOI: 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. Open Google Scholar DOI: 10.5771/9783828872301
London, B. (1932). Ending the Depression Through Planned Obsolescence. Open Google Scholar DOI: 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 Open Google Scholar DOI: 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 Open Google Scholar DOI: 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. Open Google Scholar DOI: 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]. Open Google Scholar DOI: 10.5771/9783828872301
OECD. (2011). Divided We Stand: Why Inequality Keeps Rising. [online] Available at: http://www.oecd.org/els/soc/49499779.pdf Open Google Scholar DOI: 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 Open Google Scholar DOI: 10.5771/9783828872301
Orbach, B. (2004). The Durapolist Puzzle: Monopoly Power in Durable-Goods Market. Yale Journal on Regulation, 21, pp.67 – 118. Open Google Scholar DOI: 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 Open Google Scholar DOI: 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. Open Google Scholar DOI: 10.5771/9783828872301
Packard, V. (1960). The Waste Makers. Great Britain, London: Longmans. Open Google Scholar DOI: 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. Open Google Scholar DOI: 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 Open Google Scholar DOI: 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. Open Google Scholar DOI: 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. Open Google Scholar DOI: 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. Open Google Scholar DOI: 10.5771/9783828872301
Rodič, B. (2017). Industry 4.0 and the New Simulation Modelling Paradigm. Organizacija, 50(3), pp.193 – 207. Open Google Scholar DOI: 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 Open Google Scholar DOI: 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 Open Google Scholar DOI: 10.5771/9783828872301
Savu, L. (2011). Cloud Computing: Deployment Models, Delivery Models, Risks and Research Challenges. 2011 International Conference on Computer and Management (CAMAN). Open Google Scholar DOI: 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. Open Google Scholar DOI: 10.5771/9783828872301
Schwab, K. (2016). The Fourth Industrial Revolution. World Economic Forum. Open Google Scholar DOI: 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 Open Google Scholar DOI: 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. Open Google Scholar DOI: 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. Open Google Scholar DOI: 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. Open Google Scholar DOI: 10.5771/9783828872301
Stock, T. and Seliger, G. (2016). Opportunities of Sustainable Manufacturing in Industry 4.0. Procedia CIRP, 40, pp.536 – 541. Open Google Scholar DOI: 10.5771/9783828872301
Strausz, R. (2009). Planned Obsolescence as an Incentive Device for Unobservable Quality. The Economic Journal, 119(540), pp.1405 – 1421. Open Google Scholar DOI: 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. Open Google Scholar DOI: 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. Open Google Scholar DOI: 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. Open Google Scholar DOI: 10.5771/9783828872301
Utaka, A. (2000). Planned obsolescence and marketing strategy. Managerial and Decision Economics, 21(8), pp.339 – 344. Open Google Scholar DOI: 10.5771/9783828872301
Vaidya, S., Ambad, P. and Bhosle, S. (2018). Industry 4.0 – A Glimpse. Procedia Manufacturing, 20, pp.233 – 238. Open Google Scholar DOI: 10.5771/9783828872301
Waldman, M. (1996). Planned Obsolescence and the R&D Decision. The RAND Journal of Economics, 27(3), p.583. Open Google Scholar DOI: 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 Open Google Scholar DOI: 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/ Open Google Scholar DOI: 10.5771/9783828872301
White, L. (1969). The American Automobile Industry in the Post War Period. Open Google Scholar DOI: 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 Open Google Scholar DOI: 10.5771/9783828872301
Zissis, D. and Lekkas, D. (2012). Addressing cloud computing security issues. Future Generation Computer Systems, 28(3), pp.583–592. Open Google Scholar DOI: 10.5771/9783828872301
Industry 4.0 and Big Data Literature Review by Burcu OZCAN, Cevher HİLAL AYTAC Open Google Scholar DOI: 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. Open Google Scholar DOI: 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. Open Google Scholar DOI: 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. Open Google Scholar DOI: 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. Open Google Scholar DOI: 10.5771/9783828872301
Arslantekin, S. and Doğan, K. (2016). Big Data: Its Importance, Structure and Current Status. DTCF Journal, 56, 15–36. Open Google Scholar DOI: 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. Open Google Scholar DOI: 10.5771/9783828872301
Bartevyan, L. Industry 4.0 – Summary Report. (2015). DLG: Expert Report. (Report No:5), 1–8. Open Google Scholar DOI: 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. Open Google Scholar DOI: 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. Open Google Scholar DOI: 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. Open Google Scholar DOI: 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. Open Google Scholar DOI: 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 Open Google Scholar DOI: 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. Open Google Scholar DOI: 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. Open Google Scholar DOI: 10.5771/9783828872301
Chang, V. (2018). A Proposed Social Network Analysis Platform for Big Data Analytics. Technological Forecasting and Social Change, 130, 57–68. Open Google Scholar DOI: 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. Open Google Scholar DOI: 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. Open Google Scholar DOI: 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. Open Google Scholar DOI: 10.5771/9783828872301
Chung, M.K. (2018). Statistical Challenge of Big Brain Network Data. Statistics & Probability Letters, 136, 78–82. Open Google Scholar DOI: 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. Open Google Scholar DOI: 10.5771/9783828872301
Davenport, T.H. (2014). Big Data at Work: Dispelling the Myths, Uncovering the Opportunities. Boston Massachusetts: Harvard Business Press. Open Google Scholar DOI: 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. Open Google Scholar DOI: 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. Open Google Scholar DOI: 10.5771/9783828872301
EBSO,Aegean Region Chamber of Industry (2015). Industry 4.0. Research Directorate. Open Google Scholar DOI: 10.5771/9783828872301
Elragal, A. (2014). ERP and Big Data: The Inept Couple. Procedia Technology,16, 242–249. Open Google Scholar DOI: 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. Open Google Scholar DOI: 10.5771/9783828872301
Fessele, K.L. (2018). The Rise of Big Data in Oncology. Seminars in Oncology Nursing, 34 (2), 168–176. Open Google Scholar DOI: 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. Open Google Scholar DOI: 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 Open Google Scholar DOI: 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. Open Google Scholar DOI: 10.5771/9783828872301
Golzer, P., Simon, L., Cato, P., et al. (2015). Designing Global Manufacturing Networks Using Big Data. Procedia CIRP, 33,191–196. Open Google Scholar DOI: 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. Open Google Scholar DOI: 10.5771/9783828872301
Gursakal, N. (2014). Big Data. Bursa: Dora Publishing. Open Google Scholar DOI: 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. Open Google Scholar DOI: 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. Open Google Scholar DOI: 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. Open Google Scholar DOI: 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. Open Google Scholar DOI: 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. Open Google Scholar DOI: 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. Open Google Scholar DOI: 10.5771/9783828872301
Hurwitz, J., Nugent, A., Halper, F., et al. (2013). Big Data for Dummies. Hoboken, NJ: For Dummies, sa Wiley Brand Open Google Scholar DOI: 10.5771/9783828872301
Iafrate, F. (2015). From Big Data to Smart Data. Hoboken, NJ: ISTE Ltd, John Wiley and Sons Inc., Open Google Scholar DOI: 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 Open Google Scholar DOI: 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. Open Google Scholar DOI: 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. Open Google Scholar DOI: 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. Open Google Scholar DOI: 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 Open Google Scholar DOI: 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. Open Google Scholar DOI: 10.5771/9783828872301
Kobusinska, A., Pawluczuk, K., Brzezinski, J. (2018). Big Data Fingerprinting Information Analytics for Sustainability. Future Generation Computer Systems, 86, 1321–1337. Open Google Scholar DOI: 10.5771/9783828872301
Laney, D. (2001). 3D Data Management: Controlling Data Volume, Velocity, and Variety (). META Group. Open Google Scholar DOI: 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. Open Google Scholar DOI: 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. Open Google Scholar DOI: 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. Open Google Scholar DOI: 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. Open Google Scholar DOI: 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 Open Google Scholar DOI: 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. Open Google Scholar DOI: 10.5771/9783828872301
Mayer-Schönberger, V., Cukier, K. (2013). Big Data – A Revolution to Transform Your Life, Work and Thinking. İstanbul: Paloma Publisher. Open Google Scholar DOI: 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. Open Google Scholar DOI: 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 Open Google Scholar DOI: 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. Open Google Scholar DOI: 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 Open Google Scholar DOI: 10.5771/9783828872301
Morabito, V. (2015). Big Data and Analytics. Berlin: Springer International Publishing. Open Google Scholar DOI: 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. Open Google Scholar DOI: 10.5771/9783828872301
Pries, K.H., Dunnigan, R. (2015). Big Data Analytics: A Practical Guide for Managers. New York: CRC Press.Taylor & Francis Group. Open Google Scholar DOI: 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. Open Google Scholar DOI: 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 Open Google Scholar DOI: 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. Open Google Scholar DOI: 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. Open Google Scholar DOI: 10.5771/9783828872301
Sayer, S., Ulker, A. (2014). Product Lifecycle Management. Engineer & the Machinery Magazine,55 (657), 65–72. Open Google Scholar DOI: 10.5771/9783828872301
Schwab, K. (2016). The Fourth Industrial Revolution. İstanbul: Optimist Publications. Open Google Scholar DOI: 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. Open Google Scholar DOI: 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. Open Google Scholar DOI: 10.5771/9783828872301
Siegel, E. (2013). Predictive Analytics: The Power to Predict Who Will Click, buy, Lie, or Die. Hoboken, N.J: Wiley. Open Google Scholar DOI: 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 Open Google Scholar DOI: 10.5771/9783828872301
Sinan, A. (2016). A New Theme for Production: Industry 4.0. Journal of Life Economics, 3 (2), 19–30. Open Google Scholar DOI: 10.5771/9783828872301
Smith, S.M. and Nichols, T.E. (2018). Statistical Challenges in Big Data Human Neuroimaging, Neuroview, 97 (2), 263–268. Open Google Scholar DOI: 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. Open Google Scholar DOI: 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. Open Google Scholar DOI: 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. Open Google Scholar DOI: 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. Open Google Scholar DOI: 10.5771/9783828872301
Torrecilla, J.L. and Romo, J. (2018). Data Learning From Big Data. Statitics & Probability Letters, 136, 15–19. Open Google Scholar DOI: 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. Open Google Scholar DOI: 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. Open Google Scholar DOI: 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. Open Google Scholar DOI: 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. Open Google Scholar DOI: 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 Open Google Scholar DOI: 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. Open Google Scholar DOI: 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. Open Google Scholar DOI: 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. Open Google Scholar DOI: 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. Open Google Scholar DOI: 10.5771/9783828872301
Young, S.D. (2015). A “Big Data” Approach to HIV Epidemiology and Prevention. Preventive Medicin, 70, 17–18. Open Google Scholar DOI: 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. Open Google Scholar DOI: 10.5771/9783828872301
Zeide, E. (2017). The Structural Consequences of Big Data-Driven Education. Big Data, 5 (2), 165–172. Open Google Scholar DOI: 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. Open Google Scholar DOI: 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 Open Google Scholar DOI: 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. Open Google Scholar DOI: 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. Open Google Scholar DOI: 10.5771/9783828872301
Bartodziej, C. J. 2016. The Concept Industry 4.0: An Empirical Analysis Of Technologies And Applications In Production Logistics, Springer. Open Google Scholar DOI: 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. Open Google Scholar DOI: 10.5771/9783828872301
Cascadealliance 2017. The State Of The Mattress Recycling Industry. Open Google Scholar DOI: 10.5771/9783828872301
Chopra, S. & Meindl, P. 2007. Supply Chain Management. Strategy, Planning & Operation. Das Summa Summarum Des Management. Springer. Open Google Scholar DOI: 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. Open Google Scholar DOI: 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. Open Google Scholar DOI: 10.5771/9783828872301
Gbce. 2018. Bye Bye Mattress Recycling Program [Online]. Greater Community Bridgeport Enterprises. Available: Https://Greenteambpt.Com/Bye-Bye-Mattress-Recycling-Program/. Open Google Scholar DOI: 10.5771/9783828872301
Handfield, R. B. & Nichols, E. L. 1999. Introduction To Supply Chain Management, Upper Saddle River, Nj: Prentice Hall. Open Google Scholar DOI: 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. Open Google Scholar DOI: 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. Open Google Scholar DOI: 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. Open Google Scholar DOI: 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. Open Google Scholar DOI: 10.5771/9783828872301
Porter, M. E. 1985. Competitive Advantage: Creating And Sustaining Superior Performance. 1985. New York: Free Press. Open Google Scholar DOI: 10.5771/9783828872301
Stock, T. & Seliger, G. 2016. Opportunities Of Sustainable Manufacturing In Industry 4.0. Procedia Cirp, 40, 536–541. Open Google Scholar DOI: 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. Open Google Scholar DOI: 10.5771/9783828872301
Tuck. 2018. Mattresses [Online]. Tuck Advancing Better Sleep. Available: Https://Www.Tuck.Com/Mattresses/. Open Google Scholar DOI: 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. Open Google Scholar DOI: 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 Open Google Scholar DOI: 10.5771/9783828872301
Armstrong, David J. "Sharpening inventory management." Harvard Business Review 63.6 (1985): 42–58. Open Google Scholar DOI: 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. Open Google Scholar DOI: 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. Open Google Scholar DOI: 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. Open Google Scholar DOI: 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. Open Google Scholar DOI: 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. Open Google Scholar DOI: 10.5771/9783828872301
Koottatep, Pakawkul, and Jinqian Li. Promotional forecasting in the grocery retail business. Diss. Massachusetts Institute of Technology, 2006. Open Google Scholar DOI: 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. Open Google Scholar DOI: 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. Open Google Scholar DOI: 10.5771/9783828872301
Ng, Wan Lung. "A simple classifier for multiple criteria ABC analysis." European Journal of Operational Research 177.1 (2007): 344–353. Open Google Scholar DOI: 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. Open Google Scholar DOI: 10.5771/9783828872301
Ramanathan, Ramakrishnan. "ABC inventory classification with multiple-criteria using weighted linear optimization." Computers & Operations Research 33.3 (2006): 695–700. Open Google Scholar DOI: 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. Open Google Scholar DOI: 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. Open Google Scholar DOI: 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. Open Google Scholar DOI: 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. Open Google Scholar DOI: 10.5771/9783828872301
Thomopoulos, Nick T. "Promotion Forecasts" Demand Forecasting for Inventory Control. Springer International Publishing, 2015. 71–87. Open Google Scholar DOI: 10.5771/9783828872301
Timofeev, Roman. "Classification and regression trees (cart) theory and applications." Humboldt University, Berlin (2004). Open Google Scholar DOI: 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. Open Google Scholar DOI: 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. Open Google Scholar DOI: 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. Open Google Scholar DOI: 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. Open Google Scholar DOI: 10.5771/9783828872301
Industry 4.0: Is Your Country Ready? by Serpil Erol, Gül Didem Batur Sir Open Google Scholar DOI: 10.5771/9783828872301
Cabinet Office. “Report on the 5th science and technology basic plan”, Cabinet Office of Japan, Tokyo, 2015. Open Google Scholar DOI: 10.5771/9783828872301
Conseil national de l’industrie. “The new face of industry in France”, French National Industry Council, Paris, 2013. Open Google Scholar DOI: 10.5771/9783828872301
European Commission, “Factories of the Future PPP: Towards Competitive EU Manufacturing”, European Commission, Bruxelles, 2016. Open Google Scholar DOI: 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. Open Google Scholar DOI: 10.5771/9783828872301
Evans, P.C. & Annunziata, M. “Industrial internet: pushing the boundaries of minds and machines”, General Electric, Boston, 2012. Open Google Scholar DOI: 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. Open Google Scholar DOI: 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. Open Google Scholar DOI: 10.5771/9783828872301
https://www.statista.com/statistics/667634/leading-countires-industry-40-worldwide/ Open Google Scholar DOI: 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. Open Google Scholar DOI: 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. Open Google Scholar DOI: 10.5771/9783828872301
Li, K. “Made in China 2025”, State Council of China, Beijing, 2015. Open Google Scholar DOI: 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. Open Google Scholar DOI: 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. Open Google Scholar DOI: 10.5771/9783828872301
National Research Foundation. “Research, innovation and enterprise (RIE) 2015 plan” Prime Minister’s Office of Singapore, Singapore, 2016. Open Google Scholar DOI: 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. Open Google Scholar DOI: 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. Open Google Scholar DOI: 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. Open Google Scholar DOI: 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. Open Google Scholar DOI: 10.5771/9783828872301
Transformation of Shop Floor with Industry 4.0: Guidelines for Manufacturing Companies by Fatma DEMIRCAN KESKIN, Haluk SOYUER, Hakan OZKARA Open Google Scholar DOI: 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. Open Google Scholar DOI: 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. Open Google Scholar DOI: 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. Open Google Scholar DOI: 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. Open Google Scholar DOI: 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. Open Google Scholar DOI: 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. Open Google Scholar DOI: 10.5771/9783828872301
Industrie 4.0 Reifegrad – Selbstcheck f¨ur Unternehmen. 2016. URL:https://ihk-industrie40.de/selbstcheck/. Open Google Scholar DOI: 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. Open Google Scholar DOI: 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). Open Google Scholar DOI: 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. Open Google Scholar DOI: 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. Open Google Scholar DOI: 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. Open Google Scholar DOI: 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. Open Google Scholar DOI: 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. Open Google Scholar DOI: 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). Open Google Scholar DOI: 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. Open Google Scholar DOI: 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. Open Google Scholar DOI: 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. Open Google Scholar DOI: 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. Open Google Scholar DOI: 10.5771/9783828872301
Porter, M.E., and Heppelmann, J.E. (2015). How Smart, Connected Products Are Transforming Companies, Harvard Business Review, 1–9. Open Google Scholar DOI: 10.5771/9783828872301
PricewaterhouseCoopers: The Industry 4.0 / Digital Operations Self Assessment, (2016). Open Google Scholar DOI: 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). Open Google Scholar DOI: 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. Open Google Scholar DOI: 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. Open Google Scholar DOI: 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. Open Google Scholar DOI: 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. Open Google Scholar DOI: 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. Open Google Scholar DOI: 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. Open Google Scholar DOI: 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. Open Google Scholar DOI: 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. Open Google Scholar DOI: 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. Open Google Scholar DOI: 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. Open Google Scholar DOI: 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. Open Google Scholar DOI: 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. Open Google Scholar DOI: 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. Open Google Scholar DOI: 10.5771/9783828872301
Zainal, Z. (2007). Case study as a research method. Jurnal Kemanusiaan, (9), 1–6. Open Google Scholar DOI: 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. Open Google Scholar DOI: 10.5771/9783828872301
A Review on Cold Chain Management for Industry 4.0 by Cagla Ediz Open Google Scholar DOI: 10.5771/9783828872301
Atzori, L., Iera, A., & Morabito, G. (2010). The Internet Of Things: A Survey. Computer Networks, 54(15), 2787–2805. Open Google Scholar DOI: 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. Open Google Scholar DOI: 10.5771/9783828872301
Barreto, L., Amaral, A., & Pereira, T. (2017). Industry 4.0 Implications In Logistics: An Overview. Procedia Manufacturing, 13, 1245–1252. Open Google Scholar DOI: 10.5771/9783828872301
Benešová, A., & Tupa, J. (2017). Requirements For Education And Qualification Of People In Industry 4.0. Procedia Manufacturing, 11, 2195–2202. Open Google Scholar DOI: 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. Open Google Scholar DOI: 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). Open Google Scholar DOI: 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. Open Google Scholar DOI: 10.5771/9783828872301
Drath, R., & Horch, A. (2014). Industrie 4. 0: Hit Or Hype? IEEE Ind Electron Mag, 8(2):56–58. Open Google Scholar DOI: 10.5771/9783828872301
Erkollar, A. & Oberer, B. (2017). Endüstri 4.0 Ve Ulaşımda Kullanımı. Transist 2017, 493–498. Open Google Scholar DOI: 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. Open Google Scholar DOI: 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). Open Google Scholar DOI: 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. Open Google Scholar DOI: 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. Open Google Scholar DOI: 10.5771/9783828872301
Kara, İ. (2009). CAN Haberleşme Protokolünün İncelenmesi Ve Bir Sıcaklık Kontrol Sistemine Uygulanması (Doctoral Dissertation). Open Google Scholar DOI: 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. Open Google Scholar DOI: 10.5771/9783828872301
Olsen, P., & Borit, M. (2013). How to define traceability. Trends in food science & technology, 29(2), 142–150. Open Google Scholar DOI: 10.5771/9783828872301
Onat, O. (2018). Sürücüsüz Otomobil de Kaza Yapar, CNN Turk, 20.03.2018. Open Google Scholar DOI: 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. Open Google Scholar DOI: 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. Open Google Scholar DOI: 10.5771/9783828872301
Oberer, B., & Erkollar, A. (2017), Sustainable Cities Need Smart Transportation: The Industry 4.0 Transportation Matrix. Transist 2017, 188–197. Open Google Scholar DOI: 10.5771/9783828872301
Ozgüven, M. M. (2016), Radyo Frekansli (Rf) Pedometre Tasarimi. (Master Thesis), Gaziosmanpaşa University, Tokat. Open Google Scholar DOI: 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. Open Google Scholar DOI: 10.5771/9783828872301
Sung, T. K. (2018). Industry 4.0: A Korea perspective. Technological Forecasting and Social Change, 132, 40–45. Open Google Scholar DOI: 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. Open Google Scholar DOI: 10.5771/9783828872301
TC Milli Eğitim Bakanlığı (2013). Gıda Teknolojisi, Sütü İşletmeye Alma, Ankara,. Open Google Scholar DOI: 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. Open Google Scholar DOI: 10.5771/9783828872301
Thames, L., & Schaefer, D. (2016). Software-Defined Cloud Manufacturing For Industry 4.0. Procedia CIRP, 52, 12–17. Open Google Scholar DOI: 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. Open Google Scholar DOI: 10.5771/9783828872301
Tupa, J., Simota, J., & Steiner, F. (2017). Aspects Of Risk Management Implementation For Industry 4.0. Procedia Manufacturing, 11, 1223–1230. Open Google Scholar DOI: 10.5771/9783828872301
Connection between industry 4.0 and smart factories by Elif Nurten, Cagla Seneler Open Google Scholar DOI: 10.5771/9783828872301
Alcin, S. (2016). ÜRETİM İÇİN YENİ BİR İZLEK: SANAYİ 4.0. Journal of Life Economics, 3(8), pp.19 – 19. Open Google Scholar DOI: 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 Open Google Scholar DOI: 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 Open Google Scholar DOI: 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. Open Google Scholar DOI: 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/ Open Google Scholar DOI: 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 Open Google Scholar DOI: 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 Open Google Scholar DOI: 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. Open Google Scholar DOI: 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). Open Google Scholar DOI: 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. Open Google Scholar DOI: 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. Open Google Scholar DOI: 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. Open Google Scholar DOI: 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. Open Google Scholar DOI: 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 Open Google Scholar DOI: 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 Open Google Scholar DOI: 10.5771/9783828872301
Rojko, A. (2017). Industry 4.0 Concept: Background and Overview. International Journal of Interactive Mobile Technologies (iJIM), 11(5), p.77. Open Google Scholar DOI: 10.5771/9783828872301
Rghioui, A. (2017). Internet of Things: Visions, Technologies, and Areas of Application. Automation, Control and Intelligent Systems, 5(6), p.83. Open Google Scholar DOI: 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. Open Google Scholar DOI: 10.5771/9783828872301
Schwab, K. (n.d.). The fourth industrial revolution. Open Google Scholar DOI: 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. Open Google Scholar DOI: 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 Open Google Scholar DOI: 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 Open Google Scholar DOI: 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 Open Google Scholar DOI: 10.5771/9783828872301
Techno-Parks on the Digital Transformation by Gizem ATAK, Ferhan ÇEBİ Open Google Scholar DOI: 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. Open Google Scholar DOI: 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. Open Google Scholar DOI: 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 Open Google Scholar DOI: 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. Open Google Scholar DOI: 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. Open Google Scholar DOI: 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. Open Google Scholar DOI: 10.5771/9783828872301
Gubán, M., & Kovács, G. (2017). Industry 4.0 Conception. Acta Technical Corviniensis-Bulletin of Engineering, 10(1), 111. Open Google Scholar DOI: 10.5771/9783828872301
IASP (2017). Date retrieved 23.10.2017, from https://www.iasp.ws/OurIndustry/Definitions. Open Google Scholar DOI: 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. Open Google Scholar DOI: 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). Open Google Scholar DOI: 10.5771/9783828872301
Kiran, V. (2016). Trends 2016: Big Data, IoT take the plunge. Voice & Data; New Delhi. Open Google Scholar DOI: 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. Open Google Scholar DOI: 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. Open Google Scholar DOI: 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. Open Google Scholar DOI: 10.5771/9783828872301
Official Gazette 24454 (2001). Date retrieved 23.10.2017, from http://www.resmigazete.gov.tr/eskiler/2001/07/20010706.htm#1. Open Google Scholar DOI: 10.5771/9783828872301
Ozdogan, O. (2017). Endüstri 4.0. Ankara: Pusula yayın. Open Google Scholar DOI: 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. Open Google Scholar DOI: 10.5771/9783828872301
Schwab, K. (2017). Dördüncü Sanayi Devrimi. Istanbul: Optimist. Open Google Scholar DOI: 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. Open Google Scholar DOI: 10.5771/9783828872301
Soysal, M., & Pamuk, N. S. (2018). Yeni Sanayi Devrimi Endüstri 4.0 Üzerine Bir İnceleme. Verimlilik Dergisi, (1), 41–66. Open Google Scholar DOI: 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. Open Google Scholar DOI: 10.5771/9783828872301
Türkiye Odalar ve Borsalar Birliği (2016). Akıllı Fabrikalar Geliyor. TOBB Ekonomik Forum Dergisi, 259, 16–27. Open Google Scholar DOI: 10.5771/9783828872301
TÜSİAD & BCG (2016). Türkiye’nin Küresel Rekabetçiliği için Bir Gereklilik Olarak Sanayi 4.0: Gelişmekte Olan Ekonomi Perspektifi. İstanbul: TÜSİAD. Open Google Scholar DOI: 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 Open Google Scholar DOI: 10.5771/9783828872301
Adler, P. S. Making the HR Outsourcing Decision. MIT Sloan Management Review, 45(1), 2003, 53–60. Open Google Scholar DOI: 10.5771/9783828872301
Alexander, M., and D. Young, Strategic Outsourcing. Long Range Planning, 29(1), 1996,116–119. Open Google Scholar DOI: 10.5771/9783828872301
Alford, R. R. and R. Friedland,. Powers of Theory: Capitalism, the State, and Democracy. Cambridge: Cambridge University Press,1985. Open Google Scholar DOI: 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. Open Google Scholar DOI: 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) Open Google Scholar DOI: 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. Open Google Scholar DOI: 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. Open Google Scholar DOI: 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. Open Google Scholar DOI: 10.5771/9783828872301
Bartodziej, C. J. The Concept Industry 4.0: An Empirical Analysis of Technologies and Applications in Production Logistics. Springer. 2016. Open Google Scholar DOI: 10.5771/9783828872301
Battilana, J. 2006. ‘Agency and Institutions: The Enabling Role of Individuals’ Social Position,’ Organization, Forthcoming. Open Google Scholar DOI: 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. Open Google Scholar DOI: 10.5771/9783828872301
Belcourt, M. “Outsourcing — the Benefits and the Risks”, Human Resource Management Review, Vol. 16, 2006, P. 269–279. Open Google Scholar DOI: 10.5771/9783828872301
Berger, P. and T. Luckmann, the Social Construction of Reality. New York: Doubleday Anchor. 1967. Open Google Scholar DOI: 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. Open Google Scholar DOI: 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. Open Google Scholar DOI: 10.5771/9783828872301
Greenwood, R. & C. R. Hinings, Understanding Strategic Change: The Contribution of Archetypes. Academy of Management Journal, 36(5), 1993,1052–1081. Open Google Scholar DOI: 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. Open Google Scholar DOI: 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. Open Google Scholar DOI: 10.5771/9783828872301
Haour, G. Stretching the Knowledge‐Base of the Enterprise Through Contract Research. R&D Management, 22(2), 1992, 177–182. Open Google Scholar DOI: 10.5771/9783828872301
Heng, S. Industry 4.0. Upgrade Des Industriestandorts Deutschland Steht Bevor. In: DB Research Management. Frankfurt A. M., 2014. Open Google Scholar DOI: 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. Open Google Scholar DOI: 10.5771/9783828872301
Howells, J. Research and Technology Outsourcing, Technology Analysis & Strategic Management, 11:1, 1999, 17–29, Open Google Scholar DOI: 10.5771/9783828872301
Huff, S. L. Outsourcing of Information Services. Business Quarterly, 55(4), 1991, 62–65. Open Google Scholar DOI: 10.5771/9783828872301
Jackall, R. Moral Mazes: The World of Corporate Managers. International Journal of Politics, Culture, and Society, 1(4), 1988, 598–614. Open Google Scholar DOI: 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. Open Google Scholar DOI: 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. Open Google Scholar DOI: 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. Open Google Scholar DOI: 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. Open Google Scholar DOI: 10.5771/9783828872301
Li-Jun, Z. “Research on Analysis and Control of Enterprise Logistics Outsourcing Risks”, Energy Procedia, Vol. 17,2012, Pp. 1268–1273. Open Google Scholar DOI: 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. Open Google Scholar DOI: 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. Open Google Scholar DOI: 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. Open Google Scholar DOI: 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. Open Google Scholar DOI: 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. Open Google Scholar DOI: 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. Open Google Scholar DOI: 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. Open Google Scholar DOI: 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. Open Google Scholar DOI: 10.5771/9783828872301
Scott, AND. R. [1995] 2001. Institutions and Organizations, 2nd Edn. Thousand Oaks, CA: Sage,1992. Open Google Scholar DOI: 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. Open Google Scholar DOI: 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. Open Google Scholar DOI: 10.5771/9783828872301
Thornton, P. AND., & AND. Ocasio, Institutional Logics. The Sage Handbook of Organizational Institutionalism, 840, 2008, 99–128.,Pdf Open Google Scholar DOI: 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. Open Google Scholar DOI: 10.5771/9783828872301
Weick, K. E. Educational Organizations as Loosely Coupled Systems. Administrative Science Quarterly, 21, 1–19, 1976. Open Google Scholar DOI: 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. Open Google Scholar DOI: 10.5771/9783828872301
William, J. The Meaning of Truth. Retrieved 5 March, 1909/2015. Open Google Scholar DOI: 10.5771/9783828872301
https://Tez.Yok.Gov.Tr/Ulusaltezmerkezi/Tezsorgusonucyeni.Jsp Open Google Scholar DOI: 10.5771/9783828872301
Usage of Enterprise Resource Planning (ERP) in Turkey and Information Safety by Recep Benzer, Emre Akar Open Google Scholar DOI: 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. Open Google Scholar DOI: 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. Open Google Scholar DOI: 10.5771/9783828872301
Anonymous, Security and threats in ERP. Https://Cpm.Com.Tr/Tr/Erp-Blog/Erpde-Guvenlik-Ve-Tehditler. 2018. Open Google Scholar DOI: 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. Open Google Scholar DOI: 10.5771/9783828872301
Aydoğan, E.. Enterprise Resource Planning, TSA Dergisi Yı l:2 S:2, Ağustos 2008, s.109. 2008 Open Google Scholar DOI: 10.5771/9783828872301
Başaran, A. Cyberspace Arion Press (In Turkish). 2017. Open Google Scholar DOI: 10.5771/9783828872301
Başaran, A. Http://Alperbasaran.Com/Kurumsal-Kaynak-Planlama-Yazilimi-Erp-Guvenligi/. 2018. Open Google Scholar DOI: 10.5771/9783828872301
Braggs, S. ERP: the state of the industry. Arc. Insights 12 ECL, New York. 2005. Open Google Scholar DOI: 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). Open Google Scholar DOI: 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. Open Google Scholar DOI: 10.5771/9783828872301
Demir, B. Information Security in the Accounting Information Systems. The Journal of Accounting and Finance, (26), 147–156. 2005. Open Google Scholar DOI: 10.5771/9783828872301
Erkan, Turan. Erman. ERP Enterprise Resource Planning. Ankara: Atılım Üniversitesi. (Turkish) Enterprise Resource Planning. 2008. Open Google Scholar DOI: 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 Open Google Scholar DOI: 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. Open Google Scholar DOI: 10.5771/9783828872301
Keçek, G. & Yıldırım, E. Enterprise Resource Planning and The Importance For Company Electronic Journal of Social Sciences 8 :240–258. 2014. Open Google Scholar DOI: 10.5771/9783828872301
Laudon, C. K., & Laudon, P. J. Information Systems in the Enterprise, Managing the Digital Firm, 8/E. Prentice Hall. 2004. Open Google Scholar DOI: 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. Open Google Scholar DOI: 10.5771/9783828872301
Manettı J. How technology is transforming manufacturing. Production and Inventory Management Journal 42(1), 54–64. 2001. Open Google Scholar DOI: 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. Open Google Scholar DOI: 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. Open Google Scholar DOI: 10.5771/9783828872301
Sumner, M., Enterprise resource planning, Upper Saddle River, New Jersey: Prentice-Hall. 2005. Open Google Scholar DOI: 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. Open Google Scholar DOI: 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. Open Google Scholar DOI: 10.5771/9783828872301
Machine learning approaches for prediction of service times in health information systems by Mete Eminağaoğlu Open Google Scholar DOI: 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. Open Google Scholar DOI: 10.5771/9783828872301
Akaike, H. (1981). “Likelihood of a model and information criteria”, Journal of Econometrics, Vol. 16 No. 1, pp. 3–14. Open Google Scholar DOI: 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. Open Google Scholar DOI: 10.5771/9783828872301
Bishop, C. M. (2006). Pattern Recognition and Machine Learning, Springer Science + Business Media LLC, New York. Open Google Scholar DOI: 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. Open Google Scholar DOI: 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. Open Google Scholar DOI: 10.5771/9783828872301
Buduma, N., & Locascio, N. (2017). Fundamentals of Deep Learning, O’Reilly Media, Inc., USA. Open Google Scholar DOI: 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. Open Google Scholar DOI: 10.5771/9783828872301
Dasu T. & Johnson, T. (2003). Exploratory Data Mining and Data Cleaning, John Wiley & Sons Inc., New Jersey. Open Google Scholar DOI: 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 Open Google Scholar DOI: 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. Open Google Scholar DOI: 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. Open Google Scholar DOI: 10.5771/9783828872301
Goodfellow, A., Bengio, Y., & Courville, A. (2017). Deep Learning, The MIT Press, USA. Open Google Scholar DOI: 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. Open Google Scholar DOI: 10.5771/9783828872301
Graves, A. (2012). Supervised Sequence Labelling with Recurrent Neural Networks, Springer-Verlag., Berlin. Open Google Scholar DOI: 10.5771/9783828872301
Han, J., & Kamber, M. (2006). Data Mining: Concepts and Techniques, 2nd edition, Morgan Kaufmann Publishers, San Francisco. Open Google Scholar DOI: 10.5771/9783828872301
Hand, C., Mannila, H., & Smyth P. (2001). Principles of Data Mining, the MIT Press, London. Open Google Scholar DOI: 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. Open Google Scholar DOI: 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. Open Google Scholar DOI: 10.5771/9783828872301
Haykin, S. (2009). Neural Networks and Learning Machines, 3rd edition, Pearson Education, Inc., New Jersey. Open Google Scholar DOI: 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. Open Google Scholar DOI: 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. Open Google Scholar DOI: 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. Open Google Scholar DOI: 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. Open Google Scholar DOI: 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. Open Google Scholar DOI: 10.5771/9783828872301
Kumar, S. (2017). Neural Networks – A Classroom Approach, 2nd ed., McGraw-Hill, New Delhi. Open Google Scholar DOI: 10.5771/9783828872301
Larose, D. T. (2005). Discovering Knowledge in Data – An Introduction to Data Mining, John Wiley & Sons Inc., New Jersey. Open Google Scholar DOI: 10.5771/9783828872301
Larose, D. T. (2006). Data Mining Methods and Models, John Wiley & Sons Inc., New Jersey. Open Google Scholar DOI: 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. Open Google Scholar DOI: 10.5771/9783828872301
Mitchell, T. M. (2017). Machine Learning, McGraw-Hill, India. Open Google Scholar DOI: 10.5771/9783828872301
Nedjah, N., Luiza, M. M., & Kacprzyk, J. (2009). Innovative Applications in Data Mining, Springer-Verlag, Berlin. Open Google Scholar DOI: 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. Open Google Scholar DOI: 10.5771/9783828872301
Patterson, J., & Gibson, A. (2017). Deep Learning: A Practitioner’s Approach, O’Reilly Media, Inc., USA. Open Google Scholar DOI: 10.5771/9783828872301
Python, (2019). Programming language. Retrieved from https://www.python.org/downloads/ windows/ Open Google Scholar DOI: 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. Open Google Scholar DOI: 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. Open Google Scholar DOI: 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. Open Google Scholar DOI: 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. Open Google Scholar DOI: 10.5771/9783828872301
Samudrala, S. (2019). Machine Intelligence: Demystifying Machine Learning, Neural Networks and Deep Learning, Notion Press, Chennai. Open Google Scholar DOI: 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. Open Google Scholar DOI: 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. Open Google Scholar DOI: 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. Open Google Scholar DOI: 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. Open Google Scholar DOI: 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. Open Google Scholar DOI: 10.5771/9783828872301
Tensorflow (2019). An open source machine learning framework for everyone. Retrieved from https://github.com/tensorflow/tensorflow/blob/master/RELEASE.md Open Google Scholar DOI: 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. Open Google Scholar DOI: 10.5771/9783828872301
Weka (2019). Data Mining Software in Java. Retrieved from http://www.cs.waikato.ac.nz/ ml/weka/ Open Google Scholar DOI: 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. Open Google Scholar DOI: 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. Open Google Scholar DOI: 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. Open Google Scholar DOI: 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. Open Google Scholar DOI: 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. Open Google Scholar DOI: 10.5771/9783828872301
Application of Artificial Neural Networks in Growth Models by Semra Benzer, Recep Benzer Open Google Scholar DOI: 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. Open Google Scholar DOI: 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. Open Google Scholar DOI: 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. Open Google Scholar DOI: 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. Open Google Scholar DOI: 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. Open Google Scholar DOI: 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. Open Google Scholar DOI: 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. Open Google Scholar DOI: 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. Open Google Scholar DOI: 10.5771/9783828872301
Banger, G. Industry 4.0 and Smart Business, Dorlion Press., Ankara (In Turkish). 2016. Open Google Scholar DOI: 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. Open Google Scholar DOI: 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. Open Google Scholar DOI: 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. Open Google Scholar DOI: 10.5771/9783828872301
Benzer, R., Population Dynamics Forecasting Using Artificial Neural Networks. Fresenius Environmental Bulletin, 12:1–15. 2015. Open Google Scholar DOI: 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. Open Google Scholar DOI: 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. Open Google Scholar DOI: 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. Open Google Scholar DOI: 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. Open Google Scholar DOI: 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. Open Google Scholar DOI: 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. Open Google Scholar DOI: 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. Open Google Scholar DOI: 10.5771/9783828872301
Beyer, J.E., On length˗weight relationships: Part II. Computing mean weights from length statistics. Fishbyte, 9: 50˗54. 1991. Open Google Scholar DOI: 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. Open Google Scholar DOI: 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. Open Google Scholar DOI: 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. Open Google Scholar DOI: 10.5771/9783828872301
Demirsoy, A., Basic Rules of Life, Vertebrates, (in Turkish). Hacettepe University Publication. III A/55: pp 684. 1998. Open Google Scholar DOI: 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 Open Google Scholar DOI: 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. Open Google Scholar DOI: 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. Open Google Scholar DOI: 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. Open Google Scholar DOI: 10.5771/9783828872301
Geldiay, R. and Balık, S., Freshwater Fishes of Turkey, 3. Edition. Ege University press, No: 46, Izmir, 532 p. 1996. Open Google Scholar DOI: 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. Open Google Scholar DOI: 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. Open Google Scholar DOI: 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. Open Google Scholar DOI: 10.5771/9783828872301
Hopgood A.A. Intelligent Systems for Engineers and Scientists. CRC Press, Florida, 461 pp. 2000. Open Google Scholar DOI: 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. Open Google Scholar DOI: 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. Open Google Scholar DOI: 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. Open Google Scholar DOI: 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. Open Google Scholar DOI: 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. Open Google Scholar DOI: 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. Open Google Scholar DOI: 10.5771/9783828872301
Lagler, K.F., Freshwater fishery biology. W.M.C. Brown Company, Dubuque, IA. 421. 1966. Open Google Scholar DOI: 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. Open Google Scholar DOI: 10.5771/9783828872301
Lewis, C.D. Industrial and business forecasting methods. London: Butterworths. 1982. Open Google Scholar DOI: 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. Open Google Scholar DOI: 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. Open Google Scholar DOI: 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. Open Google Scholar DOI: 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. Open Google Scholar DOI: 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. Open Google Scholar DOI: 10.5771/9783828872301
Nikolsky, G.V., The ecology of fishes (translated by L. Birkett). Academic Press, London, pp 352. 1963. Open Google Scholar DOI: 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. Open Google Scholar DOI: 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. Open Google Scholar DOI: 10.5771/9783828872301
Olsson, K., Dynamics of omnivorous crayfish in freshwater ecosystems. Ph.D. thesis. Department of Ecology, Limnology, Lund Univ., 119 pp. 2008. Open Google Scholar DOI: 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. Open Google Scholar DOI: 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. Open Google Scholar DOI: 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. Open Google Scholar DOI: 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. Open Google Scholar DOI: 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. Open Google Scholar DOI: 10.5771/9783828872301
Ricker, W.E., Linear regressions in fishery research. J Fish Res Board Can., 30:409–434. 1973. Open Google Scholar DOI: 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. Open Google Scholar DOI: 10.5771/9783828872301
Rosa, H., A synopsis of the biological data on the tench, Tinca tinca (L., 1758). FAO 58, 951. 1958. Open Google Scholar DOI: 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. Open Google Scholar DOI: 10.5771/9783828872301
Sarı, M. Artificial Neural Networks And Sales Demand Forecasting Application In The Automotive Industry. Msc Thesis. Sakarya University. 2016. Open Google Scholar DOI: 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. Open Google Scholar DOI: 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. Open Google Scholar DOI: 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. Open Google Scholar DOI: 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. Open Google Scholar DOI: 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. Open Google Scholar DOI: 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. Open Google Scholar DOI: 10.5771/9783828872301
Tekin, M. Numerical Methods (Computer Analysis). (Updated 6. Edition). Konya: Günay Ofset. (Turkish). 2008. Open Google Scholar DOI: 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. Open Google Scholar DOI: 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. Open Google Scholar DOI: 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. Open Google Scholar DOI: 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. Open Google Scholar DOI: 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. Open Google Scholar DOI: 10.5771/9783828872301
Witt, S.F. and Witt C.A. Modeling and Forecasting Demand in Tourism. Londra: Academic Press. 1992. Open Google Scholar DOI: 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. Open Google Scholar DOI: 10.5771/9783828872301
Alternative approaches to traditional methods for growth parameters of fisheries industry: Artificial Neural Networks by Recep Benzer, Semra Benzer Open Google Scholar DOI: 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. Open Google Scholar DOI: 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. Open Google Scholar DOI: 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. Open Google Scholar DOI: 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. Open Google Scholar DOI: 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. Open Google Scholar DOI: 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. Open Google Scholar DOI: 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. Open Google Scholar DOI: 10.5771/9783828872301
Baran İ, Soylu E., Crayfish plague in Turkey (short communication). J Fish Dis 12: 193–197. 1989. Open Google Scholar DOI: 10.5771/9783828872301
Benzer, R. Population Dynamics Forecasting Using Artificial Neural Networks. Fresenius Environmental Bulletin, 24(2), 460–466. 2015. Open Google Scholar DOI: 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. Open Google Scholar DOI: 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. Open Google Scholar DOI: 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. Open Google Scholar DOI: 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. Open Google Scholar DOI: 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. Open Google Scholar DOI: 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. Open Google Scholar DOI: 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. Open Google Scholar DOI: 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. Open Google Scholar DOI: 10.5771/9783828872301
Beyer, J.E., On length˗weight relationships: Part II. Computing mean weights from length statistics. Fishbyte, 9: 50˗54. 1991. Open Google Scholar DOI: 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. Open Google Scholar DOI: 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. Open Google Scholar DOI: 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. Open Google Scholar DOI: 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. Open Google Scholar DOI: 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. Open Google Scholar DOI: 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. Open Google Scholar DOI: 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. Open Google Scholar DOI: 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. Open Google Scholar DOI: 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. Open Google Scholar DOI: 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. Open Google Scholar DOI: 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. Open Google Scholar DOI: 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. Open Google Scholar DOI: 10.5771/9783828872301
Furst, M., Future perspectives for Turkish crayfish fishery. I Unv J Fish Aquat Sci 2: 139–147. 1988. Open Google Scholar DOI: 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. Open Google Scholar DOI: 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. Open Google Scholar DOI: 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. Open Google Scholar DOI: 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. Open Google Scholar DOI: 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. Open Google Scholar DOI: 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. Open Google Scholar DOI: 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. Open Google Scholar DOI: 10.5771/9783828872301
Harlioğlu, M.M., The present situation of freshwater crayfish, Astacus leptodactylus (Eschscholtz, 1823) in Turkey. Aquaculture, 230:181–187. 2004. Open Google Scholar DOI: 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. Open Google Scholar DOI: 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. Open Google Scholar DOI: 10.5771/9783828872301
Holdich, D.M. and Lowery, R.S., Freshwater Crayfish – Biology, Management and Exploitation. Chapman and Hall, London. 498 p. 1988. Open Google Scholar DOI: 10.5771/9783828872301
Hopgood A.A. Intelligent Systems for Engineers and Scientists. CRC Press, Florida, 461 pp. 2000. Open Google Scholar DOI: 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. Open Google Scholar DOI: 10.5771/9783828872301
Kaastra, I., Boyd, M. Designing a neural network for forecasting financial and economic time series. Neurocomputing, 10(3), 215–236. 1996. Open Google Scholar DOI: 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. Open Google Scholar DOI: 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. Open Google Scholar DOI: 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. Open Google Scholar DOI: 10.5771/9783828872301
Lewis, C.D. Industrial and business forecasting methods. London: Butterworths. 1982. Open Google Scholar DOI: 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. Open Google Scholar DOI: 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. Open Google Scholar DOI: 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. Open Google Scholar DOI: 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. Open Google Scholar DOI: 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. Open Google Scholar DOI: 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. Open Google Scholar DOI: 10.5771/9783828872301
Nystrom, P. Ecology. In: Biology of Freshwater Crayfish (ed. D. M. Holdich), pp. 192–235. Blackwell Science, Oxford. 2002. Open Google Scholar DOI: 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. Open Google Scholar DOI: 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. Open Google Scholar DOI: 10.5771/9783828872301
Olsson, K., Dynamics of omnivorous crayfish in freshwater ecosystems. Ph.D. thesis. Department of Ecology, Limnology, Lund Univ., 119 pp. 2008. Open Google Scholar DOI: 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. Open Google Scholar DOI: 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. Open Google Scholar DOI: 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. Open Google Scholar DOI: 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. Open Google Scholar DOI: 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. Open Google Scholar DOI: 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. Open Google Scholar DOI: 10.5771/9783828872301
Ricker, W.E., Linear regressions in fishery research. J Fish Res Board Can., 30:409–434. 1973. Open Google Scholar DOI: 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. Open Google Scholar DOI: 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. Open Google Scholar DOI: 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. Open Google Scholar DOI: 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. Open Google Scholar DOI: 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. Open Google Scholar DOI: 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. Open Google Scholar DOI: 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. Open Google Scholar DOI: 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. Open Google Scholar DOI: 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. Open Google Scholar DOI: 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. Open Google Scholar DOI: 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. Open Google Scholar DOI: 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. Open Google Scholar DOI: 10.5771/9783828872301
Tekin, M. Numerical Methods (Computer Analysis). (Updated 6. Edition). Konya: Günay Ofset. (Turkish). 2008. Open Google Scholar DOI: 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. Open Google Scholar DOI: 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. Open Google Scholar DOI: 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. Open Google Scholar DOI: 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. Open Google Scholar DOI: 10.5771/9783828872301
TÜİK Aquaculture Statistics. Ankara, Turkey: Turkey Statistical Institute Publications (in Turkish). www.tuik.gov.tr. 2018. Open Google Scholar DOI: 10.5771/9783828872301
TÜİK, Aquaculture Statistics (1984–1991). Ankara, Turkey: Turkey Statistical Institute Publications (in Turkish). 1984–1991. Open Google Scholar DOI: 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. Open Google Scholar DOI: 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. Open Google Scholar DOI: 10.5771/9783828872301
Witt, S.F. and Witt C.A. Modeling and Forecasting Demand in Tourism. Londra: Academic Press. 1992. Open Google Scholar DOI: 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. Open Google Scholar DOI: 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 Open Google Scholar DOI: 10.5771/9783828872301
Accenture.com. (2018). Airbus | Wearable Technology | Accenture. [online] Available at: https://www.accenture.com/gb-en/success-airbus-wearable-technology Open Google Scholar DOI: 10.5771/9783828872301
Adamson, G. and Stevens, B. (2003). Industrial strength design. Milwaukee, Wis.: Milwaukee Art Museum. Open Google Scholar DOI: 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 Open Google Scholar DOI: 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. Open Google Scholar DOI: 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 Open Google Scholar DOI: 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]. Open Google Scholar DOI: 10.5771/9783828872301
Bidgoli, H. (2010). Supply chain management, marketing and advertising, and global management. Hoboken, NJ: Wiley. Open Google Scholar DOI: 10.5771/9783828872301
Bokhari, M., Shallal, Q. and Tamandani, Y. (2016). Cloud computing service models: A comparative study. IEEE. Open Google Scholar DOI: 10.5771/9783828872301
Bulow, J. (1986). An Economic Theory of Planned Obsolescence. The Quarterly Journal of Economics, 101(4), p.729. Open Google Scholar DOI: 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 Open Google Scholar DOI: 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. Open Google Scholar DOI: 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/ Open Google Scholar DOI: 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 Open Google Scholar DOI: 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 Open Google Scholar DOI: 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. Open Google Scholar DOI: 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. Open Google Scholar DOI: 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 Open Google Scholar DOI: 10.5771/9783828872301
Gartner.com. (2018). Gartner Says 6.4 Billion Connected. [online] Available at: https://www.gartner.com/newsroom/id/3165317 Open Google Scholar DOI: 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 Open Google Scholar DOI: 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 Open Google Scholar DOI: 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. Open Google Scholar DOI: 10.5771/9783828872301
Grattan, L. (2016). Populism's power. Oxford: Oxford University Press. Open Google Scholar DOI: 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 Open Google Scholar DOI: 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 Open Google Scholar DOI: 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]. Open Google Scholar DOI: 10.5771/9783828872301
Hozdić, E. (2015). MANUFACTURING FOR INDUSTRY 4.0. Open Google Scholar DOI: 10.5771/9783828872301
Hozdić, Elvis. (2015). Smart factory for industry 4.0: A review. International Journal of Modern Manufacturing Technologies. 7. 28–35. Open Google Scholar DOI: 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 Open Google Scholar DOI: 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 Open Google Scholar DOI: 10.5771/9783828872301
Kenton, W. (2018). End-To-End. [online] Investopedia. Available at: https://www.investopedia.com/terms/e/end-to-end.asp Open Google Scholar DOI: 10.5771/9783828872301

Similar publications

from the series "Enterprise & Business Management"

Cover of book: Enterprise & Business Management

Edited Book Partial access

Alptekin Erkollar

Enterprise & Business Management