Araştırma Makalesi
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Müşteri İlişkileri Yönetimi Uygulamasında Gösterge Paneli Kullanımı ile Kestirimci Bakım Yaklaşımı

Yıl 2021, Cilt: 33 Sayı: 3, 390 - 397, 01.09.2021

Samet Gürsev

https://doi.org/10.7240/jeps.784267

Öz

Yazılım süreçlerinde log mesajlarının doğru anlaşılması sistem ve network devamlılığı için büyük önem taşımaktadır. Log mesajlarının doğru analiz edilebilmesi için log indeks ve pattern yapılarının doğru kurgulanması gerekmektedir. Günümüzde pek çok bankacılık işlemi ve resmi işlemler online platformlar üzerinden yazılımlar ile sağlanmaktadır. Bu uygulamalardaki kesintiler büyük maddi zararlara ve müşteri kayıplarına yol açmaktadır. Kurumların prestijlerinin korunması ve müşteri memnuniyeti sağlanması için kestirimci bakım uygulamaları önem taşımaktadır. Çalışmada yer alan çok sayıda müşterinin tahsilat ve iş süreçlerini takip ettiği uygulamaların anlık log bilgilerinin grafikler ve dashboardlar yardımı ile takip edilmesi ve tekrar eden hataların önceden incelenip olası bir kesintinin engellenmesi konusunda yapılan araştırma ve çalışmaları incelemektedir.

Anahtar Kelimeler

kestirimci bakım Yönetim Bilişim Sistemleri Log Yönetimi

Kaynakça

  • Ante, G., Facchini, F., Mossa, G. and Digiesi, S. (2018), “Developing a key performance indicators tree for lean and smart production systems”, IFAC-PapersOnLine, Vol. 51 No. 11, pp. 13-18.
  • Azapagic, A. and Perdan, S. (2000), “Indicators of sustainable development for industry: a general framework”, Process Safety and Environmental Protection, Vol. 78 No. 4, pp. 243-261.
  • Barney, J. (1991), “Firm resources and sustained competitive advantage”, Journal of management, Vol. 17 No. 1, pp. 99-120.
  • Bengtsson, M. and Salonen, A. (2016), “Requirements and needs – a foundation for reducing maintenance-related waste”, in Koskinen K. et al. (Eds) Proceedings of the 10th World Congress on Engineering Asset Management (WCEAM 2015), Springer, Cham, pp. 105-112.
  • Bititci, U.S., Carrie, A.S. and McDevitt, L. (1997), “Integrated performance measurement systems: a development guide”, International Journal of Operations and Production Management, Vol. 17 No. 5, pp. 522-534.
  • Bocken, N., Morgan, D. and Evans, S. (2013), “Understanding environmental performance variation in manufacturing companies”, International Journal of Productivity and Performance Management, Vol. 62 No. 8, pp. 856-870.
  • Bokrantz, J., Skoogh, A., Berlin, C. and Stahre, J. (2017), “Maintenance in digitalised manufacturing: Delphi-based scenarios for 2030”, International Journal of Production Economics, Vol. 191, pp. 154-169.
  • Braz, R.G.F., Scavarda, L.F. and Martins, R.A. (2011), “Reviewing and improving performance measurement systems: an action research”, International Journal of Production Economics, Vol. 133 No. 2, pp. 751-760.
  • Brundage, M.P., Morris, K.C., Sexton, T., Moccozet, S. and Hoffman, M. (2018). “Developing maintenance key performance indicators from maintenance work order data”, Paper Presented at the ASME 2018 13th International Manufacturing Science and Engineering Conference, MSEC.
  • Campbell, J.D. (1995), Uptime: Strategies for Excellence in Maintenance Management, Productivity Press, New York, NY.
  • Campbell, J.L., Quincy, C., Osserman, J. and Pedersen, O.K. (2013), “Coding in-depth semi structured interviews: problems of unitization and intercoder reliability and agreement”, Sociological Methods and Research, Vol. 42 No. 3, pp. 294-320.
  • Carnero, M.C. (2005), “Selection of Diagnostic techniques and instrumentation in a predictive maintenance program. A case study”, Decision Support Systems, Vol. 38, pp. 539-555.
  • Dalenogare, L.S., Benitez, G.B., Ayala, N.F. and Frank, A.G. (2018), “The expected contribution of Industry 4.0 technologies for industrial performance”, International Journal of Production Economics, Vol. 204, pp. 383-394. De Toni, A. and Tonchia, S. (2001), “Performance measurement systems-models, characteristics and measures”, International Journal of Operations and Production Management, Vol. 21 Nos 1-2, pp. 46-71.
  • Ehrenfeld, J.R. (2009), “Understanding of complexity expands the reach of industrial ecology”, Journal of Industrial Ecology, Vol. 13 No. 2, pp. 165-167.
  • Fangucci, A., Galante, G.M., Inghilleri, R. and La Fata, C.M. (2017), “Structured methodology for selection of maintenance key performance indicators: application to an oil refinery plant”, International Journal of Operations and Quantitative Management, Vol. 23 No. 2, pp. 89-113.
  • Hamooni, B. Debnath, J. Xu, H. Zhang, G. Jiang, A. Mueen, LogMine: fast pattern recognition for log analytics, Assoc. Comput. Mach. (2016) 1573–1582.
  • He P., Zhu J., Zheng Z., Lyu, M. (2017), Drain: an online log parsing approach with fixed depth Tree, IEEE Xplore 33–40.
  • Kang, N., Zhao, C., Li, J. and Horst, J.A. (2016), “A Hierarchical structure of key performance indicators for operation management and continuous improvement in production systems”, International Journal of Production Research, Vol. 54 No. 21, pp. 6333-6350.
  • Kans, M., Galar, D. and Thaduri, A. (2016), “Maintenance 4.0 in railway transportation industry”, in Koskinen K. et al. (Eds), Proceedings of the 10th World Congress on Engineering Asset Management (WCEAM 2015), Springer, Cham, pp. 317-331.
  • Kennerley, M. and Neely, A. (2003), “Measuring performance in a changing business environment”, International Journal of Operations and Production Management, Vol. 23 No. 2, pp. 213-229.
  • Ketokivi, M. (2016), “Point–counterpoint: resource heterogeneity, performance, and competitive advantage”, Journal of Operations Management, Vol. 41 No. 1, pp. 75-76. Krippendorff, K. (2004), Content Analysis: An Introduction to its Methodology, Sage Publications, Thousand Oaks.
  • Kumar, U., Galar, D., Parida, A., Stenstr€om, C. and Berges, L. (2013), “Maintenance performance metrics: a state-of-the-art review”, Journal of Quality in Maintenance Engineering, Vol. 19 No. 3, pp. 233-277.
  • Lee, J., Ni, J., Djurdjanovic, D., Qiu, H. and Liao, H. (2006), “Intelligent prognostics tools and e-maintenance”, Computers in Industry, Vol. 57 No. 6, pp. 476-489.
  • Lee, J., Wu, F., Zhao, W., Ghaffari, M., Liao, L. and Siegel, D. (2014), “Prognostics and health management design for rotary machinery systems – reviews, methodology and applications”, Mechanical Systems and Signal Processing, Vol. 42, pp. 314-334.
  • Lu, Y. (2017), “Industry 4.0: a survey on technologies, applications and open research issues”, Journal of Industrial Information Integration, Vol. 6, pp. 1-10.
  • Messaoudi S., Panichella A., Bianculli D., Briand L., Sasnauskas R. (2018) A Search-based approach for accurate identification of log message formats, Assoc. Comput. Mach. 167–H.
  • Muchiri, P., Pintelon, L., Gelders, L. and Martin, H. (2011), “Development of maintenance function performance measurement framework and indicators”, International Journal of Production Economics, Vol. 131 No. 1, pp. 295-302.
  • Muller, A., Marquez, A.C. and Iung, B. (2008), “On the concept of E-maintenance: review and current research”, Reliability Engineering and System Safety, Vol. 93, pp. 1165-1187.
  • Munzinger, C., Fleischer, J., Broos, A., Hennrich, H., Wieser, J., Ochs, A. and Schopp, M. (2009), “Development and implementation of smart maintenance activities for machine tools”, CIRP Journal of Manufacturing Science and Technology, Vol. 1, pp. 237-246.
  • Neely, A., Mills, J., Platts, K., Richards, H., Gregory, M., Bourne, M. and Kennerley, M. (2000), “Performance measurement system design: developing and testing a process-based approach”, International Journal of Operations and Production Management, Vol. 20 No. 10, pp. 1119-1145.
  • Parida, A. and Chattopadhyay, G. (2007), “Development of a multi-criteria hierarchical framework for maintenance performance measurement (MPM)”, Journal of Quality in Maintenance Engineering, Vol. 13 No. 3, pp. 241-258.
  • Parida, A. and Kumar, U. (2006), “Maintenance performance measurement (MPM): issues and challenges”, Journal of Quality in Maintenance Engineering, Vol. 12 No. 3, pp. 239-251.
  • Parida, A., Kumar, U., Galar, D. and Stenstr€om, C. (2015), “Performance measurement and management for maintenance: a literature review”, Journal of Quality in Maintenance Engineering, Vol. 21 No. 1, pp. 2-33.
  • Pintelon, L. and Van Puyvelde, F. (1997), “Maintenance performance reporting systems: some experiences”, Journal of Quality in Maintenance Engineering, Vol. 3 No. 1, pp. 4-15.
  • Rouse, P. and Putterill, L. (2003), “An integral framework for performance measurement”, Management Decision, Vol. 41 No. 8, pp. 791-805.
  • Salloum, M. (2013), “Explaining the evolution of performance measures – a dual case-study approach”, Journal of Engineering, Project, and Production Management, Vol. 3, p. 99.
  • Schneiderman, A. (1999), “Why balanced scorecards fail”, Journal of Strategic Performance Measurement, Vol. 2 No. 11, pp. 6-11.
  • Stefanovic, M., Nestic, S., Djordjevic, A., Djurovic, D., Macuzic, I., Tadic, D. and Gacic, M. (2017), “An assessment of maintenance performance indicators using the fuzzy sets approach and genetic algorithms”, Proceedings of the Institution of Mechanical Engineers, Part B, Journal of Engineering Manufacture, Vol. 231 No. 1, pp. 15-27, available at: https://doi.org/10.1177/ 0954405415572641.
  • Tangen, S. (2004), “Performance measurement: from philosophy to practice”, International Journal of Productivity and Performance Management, Vol. 53 No. 8, pp. 726-737.
  • Tirabeni, L., De Bernardi, P., Forliano, C. and Franco, M. (2019), “How can organisations and business models lead to a more sustainable society? A framework from a systematic review of the industry 4.0”, Sustainability, Vol. 11 No. 22, p. 6363.
  • Vaarandi R, Blumbergs B., Kont M. (2018), An unsupervised framework for detecting anomalous messages from syslog log files, IEEE Xplore 1–6.
  • Vaarandi R. (2003), A data clustering algorithm for mining patterns from event logs, IEEE Xplore 119–126.
  • Vaarandi R., M. Kont, Pihelgas M., (2016) Event log analysis with the LogCluster tool, IEEE Xplore 982–987.
  • Vaarandi R., Zhuge C. (2017), Efficient Event Log Mining with LogClusterC, IEEE Xplore 261–266.
  • Veleva, V. and Ellenbecker, M. (2001), “Indicators of sustainable production: framework and methodology”, Journal of Cleaner Production, Vol. 9 No. 6, pp. 519-549.
  • Wijesinghe, D. and Mallawarachchi, H. (2019), “A systematic approach for maintenance performance measurement: apparel industry in Sri Lanka”, Journal of Quality in Maintenance Engineering, Vol. 25 No. 1, pp. 41-53.
  • Winroth, M., Almstr€om, P. and Andersson, C. (2016), “Sustainable production indicators at factory level”, Journal of Manufacturing Technology Management, Vol. 27 No. 6, pp. 842-873.
  • Wireman, T. (2005), Developing Performance Indicators for Managing Maintenance, Industrial Press, New York, NY. Xu, L.D., Xu, E.L. and Li, L. (2018), “Industry 4.0: state of the art and future trends”, International Journal of Production Research, Vol. 56 No. 8, pp. 2941-2962

Yıl 2021, Cilt: 33 Sayı: 3, 390 - 397, 01.09.2021

Samet Gürsev

https://doi.org/10.7240/jeps.784267

Öz

Kaynakça

  • Ante, G., Facchini, F., Mossa, G. and Digiesi, S. (2018), “Developing a key performance indicators tree for lean and smart production systems”, IFAC-PapersOnLine, Vol. 51 No. 11, pp. 13-18.
  • Azapagic, A. and Perdan, S. (2000), “Indicators of sustainable development for industry: a general framework”, Process Safety and Environmental Protection, Vol. 78 No. 4, pp. 243-261.
  • Barney, J. (1991), “Firm resources and sustained competitive advantage”, Journal of management, Vol. 17 No. 1, pp. 99-120.
  • Bengtsson, M. and Salonen, A. (2016), “Requirements and needs – a foundation for reducing maintenance-related waste”, in Koskinen K. et al. (Eds) Proceedings of the 10th World Congress on Engineering Asset Management (WCEAM 2015), Springer, Cham, pp. 105-112.
  • Bititci, U.S., Carrie, A.S. and McDevitt, L. (1997), “Integrated performance measurement systems: a development guide”, International Journal of Operations and Production Management, Vol. 17 No. 5, pp. 522-534.
  • Bocken, N., Morgan, D. and Evans, S. (2013), “Understanding environmental performance variation in manufacturing companies”, International Journal of Productivity and Performance Management, Vol. 62 No. 8, pp. 856-870.
  • Bokrantz, J., Skoogh, A., Berlin, C. and Stahre, J. (2017), “Maintenance in digitalised manufacturing: Delphi-based scenarios for 2030”, International Journal of Production Economics, Vol. 191, pp. 154-169.
  • Braz, R.G.F., Scavarda, L.F. and Martins, R.A. (2011), “Reviewing and improving performance measurement systems: an action research”, International Journal of Production Economics, Vol. 133 No. 2, pp. 751-760.
  • Brundage, M.P., Morris, K.C., Sexton, T., Moccozet, S. and Hoffman, M. (2018). “Developing maintenance key performance indicators from maintenance work order data”, Paper Presented at the ASME 2018 13th International Manufacturing Science and Engineering Conference, MSEC.
  • Campbell, J.D. (1995), Uptime: Strategies for Excellence in Maintenance Management, Productivity Press, New York, NY.
  • Campbell, J.L., Quincy, C., Osserman, J. and Pedersen, O.K. (2013), “Coding in-depth semi structured interviews: problems of unitization and intercoder reliability and agreement”, Sociological Methods and Research, Vol. 42 No. 3, pp. 294-320.
  • Carnero, M.C. (2005), “Selection of Diagnostic techniques and instrumentation in a predictive maintenance program. A case study”, Decision Support Systems, Vol. 38, pp. 539-555.
  • Dalenogare, L.S., Benitez, G.B., Ayala, N.F. and Frank, A.G. (2018), “The expected contribution of Industry 4.0 technologies for industrial performance”, International Journal of Production Economics, Vol. 204, pp. 383-394. De Toni, A. and Tonchia, S. (2001), “Performance measurement systems-models, characteristics and measures”, International Journal of Operations and Production Management, Vol. 21 Nos 1-2, pp. 46-71.
  • Ehrenfeld, J.R. (2009), “Understanding of complexity expands the reach of industrial ecology”, Journal of Industrial Ecology, Vol. 13 No. 2, pp. 165-167.
  • Fangucci, A., Galante, G.M., Inghilleri, R. and La Fata, C.M. (2017), “Structured methodology for selection of maintenance key performance indicators: application to an oil refinery plant”, International Journal of Operations and Quantitative Management, Vol. 23 No. 2, pp. 89-113.
  • Hamooni, B. Debnath, J. Xu, H. Zhang, G. Jiang, A. Mueen, LogMine: fast pattern recognition for log analytics, Assoc. Comput. Mach. (2016) 1573–1582.
  • He P., Zhu J., Zheng Z., Lyu, M. (2017), Drain: an online log parsing approach with fixed depth Tree, IEEE Xplore 33–40.
  • Kang, N., Zhao, C., Li, J. and Horst, J.A. (2016), “A Hierarchical structure of key performance indicators for operation management and continuous improvement in production systems”, International Journal of Production Research, Vol. 54 No. 21, pp. 6333-6350.
  • Kans, M., Galar, D. and Thaduri, A. (2016), “Maintenance 4.0 in railway transportation industry”, in Koskinen K. et al. (Eds), Proceedings of the 10th World Congress on Engineering Asset Management (WCEAM 2015), Springer, Cham, pp. 317-331.
  • Kennerley, M. and Neely, A. (2003), “Measuring performance in a changing business environment”, International Journal of Operations and Production Management, Vol. 23 No. 2, pp. 213-229.
  • Ketokivi, M. (2016), “Point–counterpoint: resource heterogeneity, performance, and competitive advantage”, Journal of Operations Management, Vol. 41 No. 1, pp. 75-76. Krippendorff, K. (2004), Content Analysis: An Introduction to its Methodology, Sage Publications, Thousand Oaks.
  • Kumar, U., Galar, D., Parida, A., Stenstr€om, C. and Berges, L. (2013), “Maintenance performance metrics: a state-of-the-art review”, Journal of Quality in Maintenance Engineering, Vol. 19 No. 3, pp. 233-277.
  • Lee, J., Ni, J., Djurdjanovic, D., Qiu, H. and Liao, H. (2006), “Intelligent prognostics tools and e-maintenance”, Computers in Industry, Vol. 57 No. 6, pp. 476-489.
  • Lee, J., Wu, F., Zhao, W., Ghaffari, M., Liao, L. and Siegel, D. (2014), “Prognostics and health management design for rotary machinery systems – reviews, methodology and applications”, Mechanical Systems and Signal Processing, Vol. 42, pp. 314-334.
  • Lu, Y. (2017), “Industry 4.0: a survey on technologies, applications and open research issues”, Journal of Industrial Information Integration, Vol. 6, pp. 1-10.
  • Messaoudi S., Panichella A., Bianculli D., Briand L., Sasnauskas R. (2018) A Search-based approach for accurate identification of log message formats, Assoc. Comput. Mach. 167–H.
  • Muchiri, P., Pintelon, L., Gelders, L. and Martin, H. (2011), “Development of maintenance function performance measurement framework and indicators”, International Journal of Production Economics, Vol. 131 No. 1, pp. 295-302.
  • Muller, A., Marquez, A.C. and Iung, B. (2008), “On the concept of E-maintenance: review and current research”, Reliability Engineering and System Safety, Vol. 93, pp. 1165-1187.
  • Munzinger, C., Fleischer, J., Broos, A., Hennrich, H., Wieser, J., Ochs, A. and Schopp, M. (2009), “Development and implementation of smart maintenance activities for machine tools”, CIRP Journal of Manufacturing Science and Technology, Vol. 1, pp. 237-246.
  • Neely, A., Mills, J., Platts, K., Richards, H., Gregory, M., Bourne, M. and Kennerley, M. (2000), “Performance measurement system design: developing and testing a process-based approach”, International Journal of Operations and Production Management, Vol. 20 No. 10, pp. 1119-1145.
  • Parida, A. and Chattopadhyay, G. (2007), “Development of a multi-criteria hierarchical framework for maintenance performance measurement (MPM)”, Journal of Quality in Maintenance Engineering, Vol. 13 No. 3, pp. 241-258.
  • Parida, A. and Kumar, U. (2006), “Maintenance performance measurement (MPM): issues and challenges”, Journal of Quality in Maintenance Engineering, Vol. 12 No. 3, pp. 239-251.
  • Parida, A., Kumar, U., Galar, D. and Stenstr€om, C. (2015), “Performance measurement and management for maintenance: a literature review”, Journal of Quality in Maintenance Engineering, Vol. 21 No. 1, pp. 2-33.
  • Pintelon, L. and Van Puyvelde, F. (1997), “Maintenance performance reporting systems: some experiences”, Journal of Quality in Maintenance Engineering, Vol. 3 No. 1, pp. 4-15.
  • Rouse, P. and Putterill, L. (2003), “An integral framework for performance measurement”, Management Decision, Vol. 41 No. 8, pp. 791-805.
  • Salloum, M. (2013), “Explaining the evolution of performance measures – a dual case-study approach”, Journal of Engineering, Project, and Production Management, Vol. 3, p. 99.
  • Schneiderman, A. (1999), “Why balanced scorecards fail”, Journal of Strategic Performance Measurement, Vol. 2 No. 11, pp. 6-11.
  • Stefanovic, M., Nestic, S., Djordjevic, A., Djurovic, D., Macuzic, I., Tadic, D. and Gacic, M. (2017), “An assessment of maintenance performance indicators using the fuzzy sets approach and genetic algorithms”, Proceedings of the Institution of Mechanical Engineers, Part B, Journal of Engineering Manufacture, Vol. 231 No. 1, pp. 15-27, available at: https://doi.org/10.1177/ 0954405415572641.
  • Tangen, S. (2004), “Performance measurement: from philosophy to practice”, International Journal of Productivity and Performance Management, Vol. 53 No. 8, pp. 726-737.
  • Tirabeni, L., De Bernardi, P., Forliano, C. and Franco, M. (2019), “How can organisations and business models lead to a more sustainable society? A framework from a systematic review of the industry 4.0”, Sustainability, Vol. 11 No. 22, p. 6363.
  • Vaarandi R, Blumbergs B., Kont M. (2018), An unsupervised framework for detecting anomalous messages from syslog log files, IEEE Xplore 1–6.
  • Vaarandi R. (2003), A data clustering algorithm for mining patterns from event logs, IEEE Xplore 119–126.
  • Vaarandi R., M. Kont, Pihelgas M., (2016) Event log analysis with the LogCluster tool, IEEE Xplore 982–987.
  • Vaarandi R., Zhuge C. (2017), Efficient Event Log Mining with LogClusterC, IEEE Xplore 261–266.
  • Veleva, V. and Ellenbecker, M. (2001), “Indicators of sustainable production: framework and methodology”, Journal of Cleaner Production, Vol. 9 No. 6, pp. 519-549.
  • Wijesinghe, D. and Mallawarachchi, H. (2019), “A systematic approach for maintenance performance measurement: apparel industry in Sri Lanka”, Journal of Quality in Maintenance Engineering, Vol. 25 No. 1, pp. 41-53.
  • Winroth, M., Almstr€om, P. and Andersson, C. (2016), “Sustainable production indicators at factory level”, Journal of Manufacturing Technology Management, Vol. 27 No. 6, pp. 842-873.
  • Wireman, T. (2005), Developing Performance Indicators for Managing Maintenance, Industrial Press, New York, NY. Xu, L.D., Xu, E.L. and Li, L. (2018), “Industry 4.0: state of the art and future trends”, International Journal of Production Research, Vol. 56 No. 8, pp. 2941-2962
Toplam 48 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular Mühendislik
Bölüm Araştırma Makaleleri
Yazarlar

Samet Gürsev 0000-0003-2609-4095

Yayımlanma Tarihi 1 Eylül 2021
Yayımlandığı Sayı Yıl 2021 Cilt: 33 Sayı: 3

Kaynak Göster

APA Gürsev, S. (2021). Müşteri İlişkileri Yönetimi Uygulamasında Gösterge Paneli Kullanımı ile Kestirimci Bakım Yaklaşımı. International Journal of Advances in Engineering and Pure Sciences, 33(3), 390-397. https://doi.org/10.7240/jeps.784267
AMA Gürsev S. Müşteri İlişkileri Yönetimi Uygulamasında Gösterge Paneli Kullanımı ile Kestirimci Bakım Yaklaşımı. JEPS. Eylül 2021;33(3):390-397. doi:10.7240/jeps.784267
Chicago Gürsev, Samet. “Müşteri İlişkileri Yönetimi Uygulamasında Gösterge Paneli Kullanımı Ile Kestirimci Bakım Yaklaşımı”. International Journal of Advances in Engineering and Pure Sciences 33, sy. 3 (Eylül 2021): 390-97. https://doi.org/10.7240/jeps.784267.
EndNote Gürsev S (01 Eylül 2021) Müşteri İlişkileri Yönetimi Uygulamasında Gösterge Paneli Kullanımı ile Kestirimci Bakım Yaklaşımı. International Journal of Advances in Engineering and Pure Sciences 33 3 390–397.
IEEE S. Gürsev, “Müşteri İlişkileri Yönetimi Uygulamasında Gösterge Paneli Kullanımı ile Kestirimci Bakım Yaklaşımı”, JEPS, c. 33, sy. 3, ss. 390–397, 2021, doi: 10.7240/jeps.784267.
ISNAD Gürsev, Samet. “Müşteri İlişkileri Yönetimi Uygulamasında Gösterge Paneli Kullanımı Ile Kestirimci Bakım Yaklaşımı”. International Journal of Advances in Engineering and Pure Sciences 33/3 (Eylül 2021), 390-397. https://doi.org/10.7240/jeps.784267.
JAMA Gürsev S. Müşteri İlişkileri Yönetimi Uygulamasında Gösterge Paneli Kullanımı ile Kestirimci Bakım Yaklaşımı. JEPS. 2021;33:390–397.
MLA Gürsev, Samet. “Müşteri İlişkileri Yönetimi Uygulamasında Gösterge Paneli Kullanımı Ile Kestirimci Bakım Yaklaşımı”. International Journal of Advances in Engineering and Pure Sciences, c. 33, sy. 3, 2021, ss. 390-7, doi:10.7240/jeps.784267.
Vancouver Gürsev S. Müşteri İlişkileri Yönetimi Uygulamasında Gösterge Paneli Kullanımı ile Kestirimci Bakım Yaklaşımı. JEPS. 2021;33(3):390-7.