Araştırma Makalesi
PDF Zotero Mendeley EndNote BibTex Kaynak Göster

Endüstriyel Dönüşüm Sürecinde AHP Yöntemi ile Performans Kriterlerinin Belirlenmesi

Yıl 2019, Cilt 8, Sayı 16, 105 - 117, 31.07.2019

Öz

Teknolojik gelişmelerin ışığında mekanik yapılar, büyük oranda dijital sistemlere dönüşmüş ve bu dönüşüm günümüzde endüstri 4.0 devrimi olarak tanımlanmıştır. Endüstri 4.0 dijital devriminin sağladığı imkanlar ile işletmeler, üretim ve hizmet yeteneklerini arttırarak rakiplerine oranla daha rekabetçi hale gelmişlerdir. Fakat özellikle üretim sektöründe olan birçok firma, henüz bu baş döndürücü dijital dönüşüme nasıl entegre olunacağı konusunda sıkıntı yaşadıkları anlaşılmaktadır. Bu kapsamda işletmelerin kendilerini değerlendirebilmesi ve endüstri 4.0 dönüşümüne katılabilmeleri için bir yol haritasına ihtiyaç olduğu görülmektedir. Bu çalışma ile Endüstri 4.0 süreci analiz edilerek işletmelerde doğru dönüşümün gerçekleştirilmesini sağlayacak bir yol haritasının oluşturulması amaçlanmıştır. Endüstriyel dönüşüm sürecinde alana özgü yeni parametrelerin belirlenmesi ve bu parametrelere bağlı değerlendirmelerin yapılması gerekmektedir. Bu amaçla mümkün olduğunca geniş bir literatür incelenmiş ve sonrasında uzmanlar yardımı ile temel kriterler Analitik Hiyerarşi Yöntemi (AHP) kullanılarak belirlenmiştir. Ayrıca işletmeler için endüstri 4.0 stratejilerini belirlemelerinde yardımcı olabilecek bir yol haritası ve öz değerlendirme çalışması yapılmıştır.

Kaynakça

  • Adeyeri, S., Kanisuru, M., Khumbulani, M., & Olukorede, T. (2015). Integration of agent technology into manufacturing enterprise: A review and platform for industry 4.0. In: Proceedings of the 2015 International Conference on Industrial Engineering and Operations Management Dubai, United Arab Emirates, 1625-1635.
  • Amatoa, F., & Moscato, F. (2017), Exploiting cloud and workflow patterns for the analysis of composite cloud services. Future Generation Computer Systems, 67, 255-265.
  • Angeles, R. (2005). RFID technologies: Supply-chain applications and implementation issues. Information systems management, 22, 51-65.
  • Ângelo, A., Barata, J., da Cunha, P. R., & Almeida, V. (2017). Digital transformation in the pharmaceutical compounds supply chain: Design of a service ecosystem with e-labeling. In European, Mediterranean, and Middle Eastern Conference on Information Systems, 307-323.
  • ARIZ (2017). Human–machine cooperation in Industry 4.0. (Erişim: 30.08.2018), https://www.festo.com/group/en/ cms/12690.htm
  • Badawi, H., Dong, H., & El Saddika, A. (2017). Mobile cloud-based physical activity advisory system using biofeedback sensors. Future Generation Computer Systems, 66, 59-70.
  • Bagheri, B., Yang S., Kao, H. A., & Lee, J. (2015). Cyber-physical systems architecture for selfaware machines in industry 4.0 environment. IFAC-PapersOnLine, 48(3), 1622-1627.
  • Baheti, R., & Gill, H. (2011). Cyber-physical systems. The impact of control technology, 12, 161-166.
  • Baygin, M., Yetis, H., Karakose, M., & Akin, E. (2016). An effect analysis of industry 4.0 to higher education. 15th International Conference on Information Technology Based Higher Education and Training (ITHET), July 10-12, 2017, Ohrid, Macedonia.
  • Bellini, P., Bruno, I., Cenni, D., & Nesi, P. (2017). Managing cloud via smart cloud engine and knowledge base. IEEE 8th International Conference on Cloud Computing, 27 June-2 July 2015, New York, USA.
  • Biral, A., Centenaro, M., Zanella, A., Vangelista, L., & Zorzi, M. (2015). The challenges of M2M massive access in wireless cellular networks. Digital Communications and Networks, 1(1), 1-19.
  • Bourke, R., & Mentis, M. (2014). An assessment framework for inclusive education: integrating assessment approaches. Assesment in Education, 21(4), 384-397.
  • Chen, T., & Chiu, M. (2017). Development of a cloud-based factory simulation system for enabling ubiquitous factory simulation. Robotics and Computer-Integrated Manufacturing, 45, 133–143.
  • Elmangousha, A., Coricib, A., Steinkeb, R., Coricib, M., & Magedanz, T. (2015). A framework for handling heterogeneous M2M traffic. Procedia Computer Science, 63, 112-119.
  • ENTOC (2017). Behaviour models of components for virtual commissioning. (Erişim: 30.08.2018), https://www.festo.com/group/en/cms/12827.htm
  • Erol, S., Jäger, A., Hold P., Ott, K., & Sihn, W. (2016). Tangible industry 4.0: a scenario-based approach to learning for the future of production. Procedia CIRP, 54, 13-18.
  • Fallera, C., & Feldmüllera, D. (2015). Industry 4.0 learning factory for regional SMEs. The 5th Conference on Learning Factories, 32, 88-91.
  • Filippi, S., & Barattin, D. (2012). Classification and selection of prototyping activities for interaction design. Intelligent Information Management, 4, 147-156.
  • Foehr, M., Vollmar, J., Calà, A., Leitão, P., Karnouskos, S., & Colombo, A., W. (2017). Engineering of next generation cyber-physical automation system architectures. In MultiDisciplinary Engineering for Cyber-Physical Production Systems, 185-206. https://doi.org/10.1007/978-3-319-56345-9_8.
  • Forti, T., & Munteanub, V. (2017). Topics in cloud incident management. Future Generation Computer Systems, 72, 163–164.
  • Giasiranis, S., & Sofos, L. (2016). Production and evaluation of educational material using augmented reality for teaching the module of “Representation of the information on computers” in junior high school. Creative Education, 7, 1270-1291.
  • Higashinoa, W., Capretz, M., & Bittencourt, L. (2017). CEPSim: Modelling and simulation of complex event processing systems in cloud environments. Future Generation Computer Systems, 65, 122–139.
  • I4MTS (2016). From Industry 4.0 to Digitising Manufacturing An End User Perspective. (Erişim: 30.08.2018), http://www.the-mtc.org/pdf/Industry-4-Report-2016-e.pdf
  • Jararweha, Y., Al-Ayyoub, M., Darabseh, A., Benkhelifa, E., Vouk, M., & Rindos, A. (2017). Software defined cloud: Survey, system and evaluation. Future Generation Computer Systems, 58, 56-74.
  • Kagermann, H., Wahlster, W., & Helbig, J. (2013). Recommendations for implementing the strategic initiative industry 4.0: Final report of the industry 4.0 working Group. Cloud based health system, Computer Science, 18, 989-1000.
  • Koseleva, N., & Ropaite, G. (2017). Big data in building energy efficiency: understanding of big data and main challenges. Procedia Engineering, 172, 544–549.
  • Kurth, M., & Syleyer, C. (2016). Smart factory and education, IEEE 11th Conference on Industrial Electronics and Applications (ICIEA) , 5-7 June 2016, Hefei, China, 110-119.
  • Lee, J., Bagheri, B., & Kao, H. (2015). A cyber systems architecture for Industry 4.0 based manufacturing systems. Manufacturing Letters, 3, 18–23.
  • Li, Z., Shen, H., Li, H., Xia, G., Gamba, P., & Zhang, L. (2017). Multi-feature combined cloud and cloud shadow detection in GaoFen-1 wide field of view imagery. Remote Sensing of Environment, 191, 342–358.
  • MetamoFAB (2017). Metamorphosis to intelligent and networked factory. (Erişim: 30.08.2018), https://www.festo.com/group/en/cms/10275.htm
  • Nawrocki, P., & Reszelewski, W. (2017). Resource usage optimization in mobile cloud computing. Journal Computer Communications, 99, 1-12.
  • Nuñez, D., Fernández, G., & Luna, J. (2017). Cloud System. Procedia Computer Engineering, 62, 149-164.
  • Ojha, T., Misra, S., & Raghuwanshi, N. (2017). Sensing-cloud: Leveraging the benefits for agricultural applications. Computers and Electronics in Agriculture, 135, 96–107.
  • Özkan, M., Al, A., & Yavuz, S. (2018). Uluslararası politik ekonomi açısından dördüncü sanayi-endüstri devrimi’nin etkileri ve Türkiye. International Journal of Political Science & Urban Studies, 6(2).
  • Pan, M., & Kraft, M. (2015). Applying industry 4.0 to the Jurong Island Eco-Park. Energy Procedia, 75, 1536-1541.
  • ParsiFAI (2017). Intelligent foils for Industry 4.0. (Erişim: 30.08.2018), https://www.festo.com/group/en/cms/12002.htm
  • Peres, R., Parreira-Rocha, M., Rocha, A., Barbosa, J., Leit˜ao, P., & Barata, J. (2016). Selection of a data exchange format for industry 4.0 manufacturing systems. In: Industrial Electronics Society, 42nd Annual Conference of the IEEE, 23-26 Oct. 2016, Florence, Italy.
  • Qin, J., Liu, Y., & Grosvenora, R. (2007). A categorical framework of manufacturing for industry 4.0 and beyond. Virtual Production, 52, 173-178.
  • Rashid, M., Riaz, Z., Turan, E., Haskilic, V., Sunje, A., & Khan, N. (2012). Smart factory: e-business perspective of enhanced ERP in aircraft manufacturing industry. In: Proceedings of Technology Management for Emerging Technologies (PICMET’12), 29 July-2 Aug. 2012, Vancouver, BC, Canada, 3262-3275.
  • Rosendahl, R., Schmidt, N., Lüder, A., & Ryashentseva, D., (2016). Industry 4.0 value networks in legacy systems. In: Emerging Technologies & Factory Automation (ETFA), IEEE 20th Conference on 8-11 Sept. 2015, Luxembourg.
  • Ruivo, P., Oliveira, T., & Neto, M. (2014), ERP post-adoption: value impact on firm performance. In: Information Systems and Technologies (CISTI), 7th Iberian Conference on 20-23 June 2012, Madrid, Spain.
  • Saaty, T.L., & Niemira, M.P. (2006). A framework for making a better decision. Research Review, 13, 1-100.
  • Schouh, G., Gartzen, T., & Marks, A. (2015). Promoting work-based learning through Industry 4.0. CIRP Conference on Learning Factorie, 32, 82-87.
  • Schumacher, A., Erol, S., & Sihna, W. (2016). A maturity model for assessing industry 4.0 readiness and maturity of manufacturing enterprises. Reconfigurable & Virtual Production, 52, 161-166.
  • Sharma, A., & Gupta, S. (2014). Identifying the role of ERP in enhancing operational efficiency and supply chain mobility in aircraft manufacturing industry. In: Issues and Challenges in Intelligent Computing Techniques International Conference on, 7-8 Feb. 2014, Ghaziabad, India, 330-333.
  • Shi, Y., Lin, L., Zhou, C., Zhu, M., Xie, L., & Chai, G. (2017). A study of an assisting robot for mandible plastic surgery based on augmented reality. Minimally Invasive Therapy and Allied Technologies, 26(1), 23–30.
  • Shrimali, R., Shah, H., & Chauhan, R. (2017). Proposed caching scheme for optimizing trade-off between freshness and energy consumption in name data networking based IoT. Advances in Internet of Things, 7, 11-24.
  • Singh, A., & Chatterjee, K. (2017). Cloud security issues and challenges: A survey. Journal of Network and Computer Applications, 79, 88–115.
  • SMT (2017). Smart Factory. (Erişim: 30.08.2018), http://www.asm-smt.com/en/asm-smt/smart-factory
  • Sogoti (2014). Industry 4.0 report. (Erişim: 30.08.2018), https://www.fr.sogeti.com/globalassets/global/downloads/ reports/vint-research-3-the-fourth-industrial-revolution
  • Stock, T., & Seliger, G. (2016), Opportunities of sustainable manufacturing in industry 4.0. School of Economy, 40, 536-541.
  • Strozzi, F., Colicchia, C., Creaazza, A., & Noe, C. (2017). Literature review on the smart factory concept using bibliometric tools. International Journal of Production Research, 1-20.
  • Sun, C. (2012). Application of RFID technology for logistics on internet of things. Procedia Computer Science, 1, 106-111.
  • Tao, C., & Gao, J. (2017). On building a cloud based mobile testing infrastructure service system. Journal of Systems and Software, 124, 39-55.
  • Tekez, E., & Taşdeviren, G. (2016). A model to assess leanness capability of enterprises, Procedia Computer Science, 100, 776-781.
  • TUBİTAK (2016). Endüstri 4.0 yeni sanayi devrimi yol haritası, 3-4 Kasım 2016. Ankara: Tubitak.
  • Tuncel, C., & Polat, A. (2016). Sectoral system of innovation and sources of technological change in machinery industry: an investigation on Turkish machinery industry. Innovation and Business Management, 229, 214-225.
  • TUSIAD (2016). Tusiad industry 4.0 in Turkey as an imperative for global competitiveness an ermerging market perspective. (Erişim: 23.05.2017), http://tusiad.org/tr/yayinlar/raporlar/item/download/7848_180faab86b5ec60d04ec929643ce6e45
  • Vallsa, M., Calva, C., Puenteb, J., & Alonsob, A. (2017). Adjusting middleware knobs to assess scalability limits of distributed cyber-physical systems. Computer Standards, 51, 95–103.
  • Wang, X., Zhu, Y., Ha, Y., Qui, M., Huang, T., Si, X., & Wu, J. (2017). An energy-efficient system on a programmable chip platform for cloud applications. Journal of Systems Architecture, 76, 117-132.
  • Weyer, S., Schmitt, M., Ohmer, M., & Gorecky, D. (2015). Towards industry 4.0 - Standardization as the crucial challenge for highly modular multi-vendor. Production Systems, 48(3), 579-584.
  • Xinga, Y., Malcolm, R., Hornera, W., El-Harama, M., & Bebbingtonb, J. (2009). A framework model for assessing sustainability impacts of urban development. Accounting Forum, 33(3), 209-224.
  • Yusof, M., Othman, M., Omar, Y., & Yusof, M. (2013). The study on the application of business intelligence in manufacturing: A review. IJCSI International Journal of Computer Science Issues, 10(1), 317-324.
  • Zarte, M., & Pechmann, A. (2016). Building an Industry 4.0-compliant lab environment to demonstrate connectivity between shopfloor and IT levels of an enterprise. Industrial Electronics Society, IECON 2016 - 42nd Annual Conference of the IEEE, 23-26 Oct. 2016, Florence, Italy.

Determination of Performance Criteria with AHP Method in Industrial 4.0 Transformation

Yıl 2019, Cilt 8, Sayı 16, 105 - 117, 31.07.2019

Öz

In the light of technological advances, mechanical structures are transformed into digital systems and this transformation has been defined as the industry 4.0 revolution. With the opportunities provided by the Industry 4.0 digital revolution, businesses can become more powerful than their competitors by increasing their production and service capabilities. However, many companies, especially in the manufacturing sector, have yet to understand how to integrate into this dizzying digital transformation. In this context, it is seen that a road map is needed for enterprises to evaluate themselves and to participate with the industry 4.0 transformation. In this study, it is aimed to analyze the Industry 4.0 process and to create a road map that will ensure the correct transformation in enterprises. In the process of industrial transformation, it is necessary to determine new site-specific parameters and make evaluations based on these parameters. For this purpose, as wide a possible literature has been examined and then, with the help of experts, basic criteria have been determined using Analytical Hierarchy Process (AHP). In addition, a roadmap and self-assessment study was conducted to help to determine the industry 4.0 strategies for both production and service sector.

Kaynakça

  • Adeyeri, S., Kanisuru, M., Khumbulani, M., & Olukorede, T. (2015). Integration of agent technology into manufacturing enterprise: A review and platform for industry 4.0. In: Proceedings of the 2015 International Conference on Industrial Engineering and Operations Management Dubai, United Arab Emirates, 1625-1635.
  • Amatoa, F., & Moscato, F. (2017), Exploiting cloud and workflow patterns for the analysis of composite cloud services. Future Generation Computer Systems, 67, 255-265.
  • Angeles, R. (2005). RFID technologies: Supply-chain applications and implementation issues. Information systems management, 22, 51-65.
  • Ângelo, A., Barata, J., da Cunha, P. R., & Almeida, V. (2017). Digital transformation in the pharmaceutical compounds supply chain: Design of a service ecosystem with e-labeling. In European, Mediterranean, and Middle Eastern Conference on Information Systems, 307-323.
  • ARIZ (2017). Human–machine cooperation in Industry 4.0. (Erişim: 30.08.2018), https://www.festo.com/group/en/ cms/12690.htm
  • Badawi, H., Dong, H., & El Saddika, A. (2017). Mobile cloud-based physical activity advisory system using biofeedback sensors. Future Generation Computer Systems, 66, 59-70.
  • Bagheri, B., Yang S., Kao, H. A., & Lee, J. (2015). Cyber-physical systems architecture for selfaware machines in industry 4.0 environment. IFAC-PapersOnLine, 48(3), 1622-1627.
  • Baheti, R., & Gill, H. (2011). Cyber-physical systems. The impact of control technology, 12, 161-166.
  • Baygin, M., Yetis, H., Karakose, M., & Akin, E. (2016). An effect analysis of industry 4.0 to higher education. 15th International Conference on Information Technology Based Higher Education and Training (ITHET), July 10-12, 2017, Ohrid, Macedonia.
  • Bellini, P., Bruno, I., Cenni, D., & Nesi, P. (2017). Managing cloud via smart cloud engine and knowledge base. IEEE 8th International Conference on Cloud Computing, 27 June-2 July 2015, New York, USA.
  • Biral, A., Centenaro, M., Zanella, A., Vangelista, L., & Zorzi, M. (2015). The challenges of M2M massive access in wireless cellular networks. Digital Communications and Networks, 1(1), 1-19.
  • Bourke, R., & Mentis, M. (2014). An assessment framework for inclusive education: integrating assessment approaches. Assesment in Education, 21(4), 384-397.
  • Chen, T., & Chiu, M. (2017). Development of a cloud-based factory simulation system for enabling ubiquitous factory simulation. Robotics and Computer-Integrated Manufacturing, 45, 133–143.
  • Elmangousha, A., Coricib, A., Steinkeb, R., Coricib, M., & Magedanz, T. (2015). A framework for handling heterogeneous M2M traffic. Procedia Computer Science, 63, 112-119.
  • ENTOC (2017). Behaviour models of components for virtual commissioning. (Erişim: 30.08.2018), https://www.festo.com/group/en/cms/12827.htm
  • Erol, S., Jäger, A., Hold P., Ott, K., & Sihn, W. (2016). Tangible industry 4.0: a scenario-based approach to learning for the future of production. Procedia CIRP, 54, 13-18.
  • Fallera, C., & Feldmüllera, D. (2015). Industry 4.0 learning factory for regional SMEs. The 5th Conference on Learning Factories, 32, 88-91.
  • Filippi, S., & Barattin, D. (2012). Classification and selection of prototyping activities for interaction design. Intelligent Information Management, 4, 147-156.
  • Foehr, M., Vollmar, J., Calà, A., Leitão, P., Karnouskos, S., & Colombo, A., W. (2017). Engineering of next generation cyber-physical automation system architectures. In MultiDisciplinary Engineering for Cyber-Physical Production Systems, 185-206. https://doi.org/10.1007/978-3-319-56345-9_8.
  • Forti, T., & Munteanub, V. (2017). Topics in cloud incident management. Future Generation Computer Systems, 72, 163–164.
  • Giasiranis, S., & Sofos, L. (2016). Production and evaluation of educational material using augmented reality for teaching the module of “Representation of the information on computers” in junior high school. Creative Education, 7, 1270-1291.
  • Higashinoa, W., Capretz, M., & Bittencourt, L. (2017). CEPSim: Modelling and simulation of complex event processing systems in cloud environments. Future Generation Computer Systems, 65, 122–139.
  • I4MTS (2016). From Industry 4.0 to Digitising Manufacturing An End User Perspective. (Erişim: 30.08.2018), http://www.the-mtc.org/pdf/Industry-4-Report-2016-e.pdf
  • Jararweha, Y., Al-Ayyoub, M., Darabseh, A., Benkhelifa, E., Vouk, M., & Rindos, A. (2017). Software defined cloud: Survey, system and evaluation. Future Generation Computer Systems, 58, 56-74.
  • Kagermann, H., Wahlster, W., & Helbig, J. (2013). Recommendations for implementing the strategic initiative industry 4.0: Final report of the industry 4.0 working Group. Cloud based health system, Computer Science, 18, 989-1000.
  • Koseleva, N., & Ropaite, G. (2017). Big data in building energy efficiency: understanding of big data and main challenges. Procedia Engineering, 172, 544–549.
  • Kurth, M., & Syleyer, C. (2016). Smart factory and education, IEEE 11th Conference on Industrial Electronics and Applications (ICIEA) , 5-7 June 2016, Hefei, China, 110-119.
  • Lee, J., Bagheri, B., & Kao, H. (2015). A cyber systems architecture for Industry 4.0 based manufacturing systems. Manufacturing Letters, 3, 18–23.
  • Li, Z., Shen, H., Li, H., Xia, G., Gamba, P., & Zhang, L. (2017). Multi-feature combined cloud and cloud shadow detection in GaoFen-1 wide field of view imagery. Remote Sensing of Environment, 191, 342–358.
  • MetamoFAB (2017). Metamorphosis to intelligent and networked factory. (Erişim: 30.08.2018), https://www.festo.com/group/en/cms/10275.htm
  • Nawrocki, P., & Reszelewski, W. (2017). Resource usage optimization in mobile cloud computing. Journal Computer Communications, 99, 1-12.
  • Nuñez, D., Fernández, G., & Luna, J. (2017). Cloud System. Procedia Computer Engineering, 62, 149-164.
  • Ojha, T., Misra, S., & Raghuwanshi, N. (2017). Sensing-cloud: Leveraging the benefits for agricultural applications. Computers and Electronics in Agriculture, 135, 96–107.
  • Özkan, M., Al, A., & Yavuz, S. (2018). Uluslararası politik ekonomi açısından dördüncü sanayi-endüstri devrimi’nin etkileri ve Türkiye. International Journal of Political Science & Urban Studies, 6(2).
  • Pan, M., & Kraft, M. (2015). Applying industry 4.0 to the Jurong Island Eco-Park. Energy Procedia, 75, 1536-1541.
  • ParsiFAI (2017). Intelligent foils for Industry 4.0. (Erişim: 30.08.2018), https://www.festo.com/group/en/cms/12002.htm
  • Peres, R., Parreira-Rocha, M., Rocha, A., Barbosa, J., Leit˜ao, P., & Barata, J. (2016). Selection of a data exchange format for industry 4.0 manufacturing systems. In: Industrial Electronics Society, 42nd Annual Conference of the IEEE, 23-26 Oct. 2016, Florence, Italy.
  • Qin, J., Liu, Y., & Grosvenora, R. (2007). A categorical framework of manufacturing for industry 4.0 and beyond. Virtual Production, 52, 173-178.
  • Rashid, M., Riaz, Z., Turan, E., Haskilic, V., Sunje, A., & Khan, N. (2012). Smart factory: e-business perspective of enhanced ERP in aircraft manufacturing industry. In: Proceedings of Technology Management for Emerging Technologies (PICMET’12), 29 July-2 Aug. 2012, Vancouver, BC, Canada, 3262-3275.
  • Rosendahl, R., Schmidt, N., Lüder, A., & Ryashentseva, D., (2016). Industry 4.0 value networks in legacy systems. In: Emerging Technologies & Factory Automation (ETFA), IEEE 20th Conference on 8-11 Sept. 2015, Luxembourg.
  • Ruivo, P., Oliveira, T., & Neto, M. (2014), ERP post-adoption: value impact on firm performance. In: Information Systems and Technologies (CISTI), 7th Iberian Conference on 20-23 June 2012, Madrid, Spain.
  • Saaty, T.L., & Niemira, M.P. (2006). A framework for making a better decision. Research Review, 13, 1-100.
  • Schouh, G., Gartzen, T., & Marks, A. (2015). Promoting work-based learning through Industry 4.0. CIRP Conference on Learning Factorie, 32, 82-87.
  • Schumacher, A., Erol, S., & Sihna, W. (2016). A maturity model for assessing industry 4.0 readiness and maturity of manufacturing enterprises. Reconfigurable & Virtual Production, 52, 161-166.
  • Sharma, A., & Gupta, S. (2014). Identifying the role of ERP in enhancing operational efficiency and supply chain mobility in aircraft manufacturing industry. In: Issues and Challenges in Intelligent Computing Techniques International Conference on, 7-8 Feb. 2014, Ghaziabad, India, 330-333.
  • Shi, Y., Lin, L., Zhou, C., Zhu, M., Xie, L., & Chai, G. (2017). A study of an assisting robot for mandible plastic surgery based on augmented reality. Minimally Invasive Therapy and Allied Technologies, 26(1), 23–30.
  • Shrimali, R., Shah, H., & Chauhan, R. (2017). Proposed caching scheme for optimizing trade-off between freshness and energy consumption in name data networking based IoT. Advances in Internet of Things, 7, 11-24.
  • Singh, A., & Chatterjee, K. (2017). Cloud security issues and challenges: A survey. Journal of Network and Computer Applications, 79, 88–115.
  • SMT (2017). Smart Factory. (Erişim: 30.08.2018), http://www.asm-smt.com/en/asm-smt/smart-factory
  • Sogoti (2014). Industry 4.0 report. (Erişim: 30.08.2018), https://www.fr.sogeti.com/globalassets/global/downloads/ reports/vint-research-3-the-fourth-industrial-revolution
  • Stock, T., & Seliger, G. (2016), Opportunities of sustainable manufacturing in industry 4.0. School of Economy, 40, 536-541.
  • Strozzi, F., Colicchia, C., Creaazza, A., & Noe, C. (2017). Literature review on the smart factory concept using bibliometric tools. International Journal of Production Research, 1-20.
  • Sun, C. (2012). Application of RFID technology for logistics on internet of things. Procedia Computer Science, 1, 106-111.
  • Tao, C., & Gao, J. (2017). On building a cloud based mobile testing infrastructure service system. Journal of Systems and Software, 124, 39-55.
  • Tekez, E., & Taşdeviren, G. (2016). A model to assess leanness capability of enterprises, Procedia Computer Science, 100, 776-781.
  • TUBİTAK (2016). Endüstri 4.0 yeni sanayi devrimi yol haritası, 3-4 Kasım 2016. Ankara: Tubitak.
  • Tuncel, C., & Polat, A. (2016). Sectoral system of innovation and sources of technological change in machinery industry: an investigation on Turkish machinery industry. Innovation and Business Management, 229, 214-225.
  • TUSIAD (2016). Tusiad industry 4.0 in Turkey as an imperative for global competitiveness an ermerging market perspective. (Erişim: 23.05.2017), http://tusiad.org/tr/yayinlar/raporlar/item/download/7848_180faab86b5ec60d04ec929643ce6e45
  • Vallsa, M., Calva, C., Puenteb, J., & Alonsob, A. (2017). Adjusting middleware knobs to assess scalability limits of distributed cyber-physical systems. Computer Standards, 51, 95–103.
  • Wang, X., Zhu, Y., Ha, Y., Qui, M., Huang, T., Si, X., & Wu, J. (2017). An energy-efficient system on a programmable chip platform for cloud applications. Journal of Systems Architecture, 76, 117-132.
  • Weyer, S., Schmitt, M., Ohmer, M., & Gorecky, D. (2015). Towards industry 4.0 - Standardization as the crucial challenge for highly modular multi-vendor. Production Systems, 48(3), 579-584.
  • Xinga, Y., Malcolm, R., Hornera, W., El-Harama, M., & Bebbingtonb, J. (2009). A framework model for assessing sustainability impacts of urban development. Accounting Forum, 33(3), 209-224.
  • Yusof, M., Othman, M., Omar, Y., & Yusof, M. (2013). The study on the application of business intelligence in manufacturing: A review. IJCSI International Journal of Computer Science Issues, 10(1), 317-324.
  • Zarte, M., & Pechmann, A. (2016). Building an Industry 4.0-compliant lab environment to demonstrate connectivity between shopfloor and IT levels of an enterprise. Industrial Electronics Society, IECON 2016 - 42nd Annual Conference of the IEEE, 23-26 Oct. 2016, Florence, Italy.

Ayrıntılar

Birincil Dil Türkçe
Konular Planlama ve Gelişim
Yayınlanma Tarihi Temmuz 2009
Bölüm Araştırma Makalesi
Yazarlar

Ercan ÖZTEMEL
MARMARA ÜNİVERSİTESİ, MÜHENDİSLİK FAKÜLTESİ
0000-0003-8488-9991
Türkiye


Semih ÖZEL (Sorumlu Yazar)
MARMARA ÜNİVERSİTESİ, MÜHENDİSLİK FAKÜLTESİ
0000-0001-8281-2704
Türkiye


Samet GÜRSEV
MARMARA ÜNİVERSİTESİ, MÜHENDİSLİK FAKÜLTESİ
0000-0003-2609-4095
Türkiye

Yayımlanma Tarihi 31 Temmuz 2019
Yayınlandığı Sayı Yıl 2019, Cilt 8, Sayı 16

Kaynak Göster

APA Öztemel, E. , Özel, S. & Gürsev, S. (2019). Endüstriyel Dönüşüm Sürecinde AHP Yöntemi ile Performans Kriterlerinin Belirlenmesi . Balkan Sosyal Bilimler Dergisi , 8 (16) , 105-117 . Retrieved from https://dergipark.org.tr/tr/pub/bsbd/issue/47296/429937