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





