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SİMÜLASYON VE MATEMATİKSEL MODEL YAKLAŞIMLARI İLE MÜŞTERİ ŞİKAYETLERİNİ ÇÖZME SÜRESİ VE MALİYETİNİN ARAŞTIRILMASI

Year 2024, Volume: 27 Issue: 2, 426 - 446, 03.06.2024

Vedat Çekici

https://doi.org/10.17780/ksujes.1392121

Abstract

Şirketler katma değerli ürün ve hizmetler üreterek müşterilerinin beklentilerini karşılamayı ve onları mutlu etmeyi amaçlamaktadır. Müşteri memnuniyeti ise birçok faktörden etkilenmektedir. Şikayetin hızlı ve beklenen kalitede çözülmesi bu faktörlerden biridir. Bunun için şikayet sistemine optimum kaynakların sağlanması gerekir. Bu çalışmada, oluşturulan simülasyon modeli sayesinde şikâyetlerin ulaşmasından kapatılmasına kadar geçen aşamalar değerlendirilerek, şikayet sisteminde kullanılan kişi sayısı ve maliyeti belirlenmiştir. Ayrıca çalışmada şikayet gelişinden kapanışına kadar olan servis süreleri üstel dağılım alınarak matematiksel model kurulmuş ve çözülmüştür. Sonrasında aynı koşullardaki simülasyon sonuçlarıyla karşılaştırılmıştır. Karşılaştırmanın sonucunda, simülasyon ve matematiksel model sonuçlarının birbirine çok yakın olduğu tespit edilmiştir. Servis süreleri üstel dağılımlı olan matematiksel model ve servis süreleri üstel dağılımlı olan simülasyon model çözümleri arasındaki şikayet kapatma süresi farkı sadece % 0,381 olmuştur. Ayrıca servis süreleri üstel dağılım olarak alındığında, elde edilen şikayet kapatma süresi, servis sürelerinin fiili dağılım değeri ile elde edilenden %6,749 daha düşük çıkmıştır. Sonuçlara göre servis sürelerini üstel dağılım olarak almanın şirketler için uygulanabilir bir seçenek olduğu gösterilmiştir. Optimum eleman kullanımı ile, müşteri memnuniyeti artacak ve elemanların daha verimli kullanılmasına katkı sağlanacaktır.

Keywords

Şikayeti değerlendirme kuyruk sistemi simülasyon optimizasyonu matematiksel modelleme

Ethical Statement

Bu makalenin yazarı olarak herhangi bir çıkar çatışması olmadığını beyan ederim.

Supporting Institution

Çukurova Üniversitesi Bilimsel Araştırma Projeleri Koordinatörlüğü

Project Number

MMF2011D1

Thanks

Çalışmayı MMF2011D1 numaralı proje ile destekleyen Çukurova Üniversitesi Bilimsel Araştırma Projeleri Koordinatörlüğü’ne teşekkürlerimi sunarım.

References

  • Akdeniz, H.A. & Tatar, B. (2009). Hava limanında Kuyruk Simülasyonu: İzmir-Gaziemir Adnan Menderes Havalimanı Uygulaması. Dokuz Eylül Üniversitesi Sosyal Bilimler Enstitüsü Dergisi; 11(3): 03-12.
  • Akın, H.K, Ordu M. (2022). A Novel Simulation-Based Two Stage Optimization Approaches For Nurse Planning, International Journal of Simulation Modelling, 21(4): 591-601.
  • Altıok, T., & Melamed, B. (2007). Simulation Modelling and Analysis With Arena. Academic Press, Elsevier, USA, 440s.
  • Armaneri, Ö. (2005). Bir Montaj Hattı Üretim Sisteminde Optimal İşgücü Dağılımının Arena Proses Analyzer (PAN) ve OptQuest Kullanılarak Belirlenmesi. Uygulamalı Bilimler ve Mühendislik Dergisi, 11(1), 1-16.
  • Atalan, A. (2022). A cost analysis with the discrete‐event simulation application in nurse and doctor employment management. Journal of Nursing Management, 30(3), 733-741.
  • Atalan, A., Şahin, H., & Atalan, Y. A. (2022, September). Integration of Machine Learning Algorithms and Discrete-Event Simulation for the Cost of Healthcare Resources. In Healthcare (Vol. 10, No. 10, p. 1920). MDPI
  • Atalan, A., & Dönmez, C.C. (2020), “Optimizing experimental simulation design for the emergency departments”, Brazilian Journal of Operations & Production Management, 17(4). https://doi.org/10.14488/BJOPM.2020.02
  • Atalan, A., & Donmez, C. C. (2019). Employment of emergency advanced nurses of Turkey: A discrete-event simulation application. Processes, 7(1), 48.
  • Bahari, A., & Asadi, F. (2020). A Simulation Optimization Approach for Resource Allocation in an Emergency Department Healthcare Unit. Global Heart, 15(1): 14. DOI: https://doi.org/10.5334/gh.528
  • Baş, İ., Tosun Ö., & Bayram, V. (2021). Robot yer seçimi ve işçi-istasyon ataması düşünceleri altında hat dengeleme optimizasyonu. Bir bulaşık makinesi fabrikası vaka analizi. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi, 27(4), 495-503.
  • Baykoç, Ö.F., Abacı, S., & Duyar, M. (2002). Tam Zamanında Üretim Sisteminin Servis Sistemlerine Uygulanabilirliği. Journal of the Faculty of Engineering and Architecture of Gazi University , 17(4), 139-155.
  • Belgin Ö. (2019). Hybrid approach in a production line for multi-objective simulation optimization. Journal of the Faculty of Engineering and Architecture of Gazi University, 34(4): 1847-1859.
  • Carson, Y., & Maria, A. (1997). Simülation Optimization. Methods & Applications. Proceedings of the 1997 Winter Simulation Conference ed.
  • Çekici, V., & Yüregir O.H., (2021). Process optimization of the customer complaints handling system and a new customer oriented model proposal, Journal of the Faculty of Engineering and Architecture of Gazi University, 36:2, 855-869.
  • Çekici, V., & Yüregir O.H., (2020). Investigation and Analysis of Customer Complaints Handling System of the Companies in Turkey, Çukurova University Journal of the Faculty of Engineering and Architecture, 35 (3), 753-768.
  • Çekici, V (2013). A Conceptual Model For Customer Complaint Handling Processes And Evaluation With Simulation Optimisation Method, Cukurova University, Faculty of Engineering and Architechture, Industrial Engineering Doctoral Thesis, Adana p247.
  • Davidow, M. (2003). Organisational Responses to Costomer Complaints. What Works and What Doesn’t. Journal of Service Research, 5(3), 225-250.
  • Düzgit, Z., Toy, A.Ö, Çoban, S., Alibaşoğlu, Z., Tok, Ö., Özkeskin, Ö.T., Karakaya, M., & Bayrak, Y. (2019. Hizmet lojistiğinde iş atama ve rotalama politikaları tasarımı. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi, 25(9), 1071-1079.
  • Estelami, H. (2000). The Profit Impact of Consumer Complaint Solicitation Across Market Conditions. Journal of Professional Services Marketing, 20 (1): 165- 195.
  • Faed, A., Chang, E., Saberki, M., Hussain, O.K., & Azadeh, A. (2016). Intelligent Customer Complaint Handling Utilising Principal Component and Data Envelopment Analysis (PDA). Applied Soft Computing, 47 (1), 614-630.
  • Fornell. C., & Wernerfelt, B. (1998). A Model for Customer Complaint Management Source. Marketing Science, Published by INFORMS, 7(3), 287-298.
  • Greasley, A., & Barlow, S. (1998). Using simulation modelling for BPR: resource allocation in a police custody process. International Journal of Operations & Production Management, 18(9/10), 978-988.
  • Güleryüz, S.S., Koyuncu M. (2023). Simulation of Intensive Care Bed Capacity Based On Mixture Distribution. International Journal of Simulation modelling, 22(2), 221-232.
  • Hamad, W. A., & Arisha, A. (2013). Simulation-based framework to improve patient experience in an emergency department. European Journal of Operational Research, 224, 154–166.
  • Hillier, F.S., & Lieberman, G.J. (2010). Introduction to Operations Research Nineth Edition, The McGraw-Hill Companies, page: 1075.
  • İbiş, S., Kızıldemir, Ö., & Çöp, S. (2019). Evaluation of comments and e-complaints for five star hotel enterprises in Afyonkarahisar, Electronic Journal of Social, 18 (71), 1315-1324.
  • Kim, C., Kim, S., Im, S., & Shin, C. (2003). The effect of attitude and perception on consumer complaint intentions. Journal of Consumer Marketing, 20 (4), 352-371.
  • Koruca, H.İ., & Kocaer, E.R.(2024). Simulation software development and workforce optimization for service systems: QSSim software. Journal of the Faculty of Engineering and Architecture of Gazi University, 39(1), 77-89.
  • Lin, S.Y., & Horng, S.C. (2006). Ordinal Optimization Approach To Stochastic Simulation Optimization Problems And Applications. Proceeding of the 15th Lasted International Conference. Applied Simülation and Modeling. 26-28.
  • Liu, W.K., & Yen, C.C. (2016). Optimizing Bus Passenger, Complaint Service through Big Data Analysis. Systematized Analysis for Improved Public Sector Management. Sustainability, 8 (12), 1-21.
  • Mattila, A.S., & Mount, D.J. (2003). The impact of selected customer characteristics and response time on E-complaint satisfaction and return intent. Hospitality Management, 22, 135–145.
  • Mutlu, Ö., Karagül, K., & Şahin, Y. (2022). Ulaştırma probleminin başlangıç uygun çözümünün belirlenmesi için en büyük maliyetten kaçınma yöntemi. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi, 28(4), 569-576.
  • Ordu, M. (2022). A Simulation-Based Decision-Making Approach to Evaluate the Returns on Investments. International Journal of Simulation Modelling, 2022, 21(3), 441-452. https://doi.org/10.2507/IJSIMM21-3-609.
  • Ordu, M. & Korhan, E. (2022). Simülasyon Destekli Tesis Yerleşim Tasarımı ve İyileştirme Çalışmaları: Bir Tekstil Firması Örneği. Osmaniye Korkut Ata Üniversitesi Fen Bilimleri Enstitüsü Dergisi, 5(Özel Sayı), 26-39. https://doi.org/10.47495/okufbed.1034177
  • Ordu, M., Eren, D., TOFALLIS, C. (2020). Simulation-Based Outpatient Clinic Capacity Management Integrated With Population Growth Projection, Journal of Industrial Engineering 31(3), 411-429.
  • Rossetti, M.D. (2010). Simulation Modeling and Arena. Editor: Repasky, N. And Ruel, C, John Wiley &Sons, Inc., Danvers, USA, 573.
  • Şenses, S., Gölbaşı, O., & Bakal, İ.S. (2022). Madencilikte bir yedek parça envanter optimizasyonu çalışması. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi, 28(1), 128‐138.
  • Tian, X., Vertommen, I., Tsiami, L., Thienen, P., & Paraskevopoulos, S. (2022). Automated Customer Complaint Processing for Water Utilities Based on Natural Language Processing. Case Study of a Dutch Water Utility Water, 14, 674.
  • Timur, M.N., & Sarıyer, N. (2004). Kayseri’deki Otomobil Bayilerinde Müşteri Tatmin Aracı Olarak Şikâyet Toplama Yöntemlerine İlişkin Bir Uygulama. Erciyes Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, 1 (17) , 9-32.
  • Uncu, & Koyuncu, M. (2023). An improved emergency medical service system simulation-optimization model with Poisson mixture distribution, Journal of Engineering Research, 11(3), 100-111.

INVESTIGATING THE TIME AND THE COST OF SOLVING CUSTOMER COMPLAINTS WITH SIMULATION AND MATHEMATICAL MODEL APPROACHES

Year 2024, Volume: 27 Issue: 2, 426 - 446, 03.06.2024

Vedat Çekici

https://doi.org/10.17780/ksujes.1392121

Abstract

Companies aim to meet their customers' expectations and make them happy by producing value-added products and services. Customer satisfaction is affected by many factors. Resolving the complaint quickly and with the expected quality is one of these factors. For this, optimum resources must be provided to the complaint system. In this study, the number of people used in the complaint system and its cost were determined by evaluating the stages from the arrival of the complaints to their closure, thanks to the simulation model created. In addition, in the study, a mathematical model was established and solved by taking the exponential distribution of service times from complaint arrival to closure. Afterwards, it was compared with the simulation results under the same conditions. As a result of the comparison, it was determined that the simulation and mathematical model results were very close to each other. The difference in complaint closing time between the mathematical model with exponential distribution of service times and the simulation model solutions with exponential distribution of service times was only 0,381%. Additionally, when service times are taken as an exponential distribution, the resulting complaint closing time is 6,749% lower than that obtained with the actual distribution value of service times. The results show that taking service times as an exponential distribution is a viable option for companies. With optimum use of personnel, customer satisfaction will increase, and more efficient use of personnel will be contributed.

Keywords

Complaint handling queueing system simulation optimization mathematical modeling

Project Number

MMF2011D1

References

  • Akdeniz, H.A. & Tatar, B. (2009). Hava limanında Kuyruk Simülasyonu: İzmir-Gaziemir Adnan Menderes Havalimanı Uygulaması. Dokuz Eylül Üniversitesi Sosyal Bilimler Enstitüsü Dergisi; 11(3): 03-12.
  • Akın, H.K, Ordu M. (2022). A Novel Simulation-Based Two Stage Optimization Approaches For Nurse Planning, International Journal of Simulation Modelling, 21(4): 591-601.
  • Altıok, T., & Melamed, B. (2007). Simulation Modelling and Analysis With Arena. Academic Press, Elsevier, USA, 440s.
  • Armaneri, Ö. (2005). Bir Montaj Hattı Üretim Sisteminde Optimal İşgücü Dağılımının Arena Proses Analyzer (PAN) ve OptQuest Kullanılarak Belirlenmesi. Uygulamalı Bilimler ve Mühendislik Dergisi, 11(1), 1-16.
  • Atalan, A. (2022). A cost analysis with the discrete‐event simulation application in nurse and doctor employment management. Journal of Nursing Management, 30(3), 733-741.
  • Atalan, A., Şahin, H., & Atalan, Y. A. (2022, September). Integration of Machine Learning Algorithms and Discrete-Event Simulation for the Cost of Healthcare Resources. In Healthcare (Vol. 10, No. 10, p. 1920). MDPI
  • Atalan, A., & Dönmez, C.C. (2020), “Optimizing experimental simulation design for the emergency departments”, Brazilian Journal of Operations & Production Management, 17(4). https://doi.org/10.14488/BJOPM.2020.02
  • Atalan, A., & Donmez, C. C. (2019). Employment of emergency advanced nurses of Turkey: A discrete-event simulation application. Processes, 7(1), 48.
  • Bahari, A., & Asadi, F. (2020). A Simulation Optimization Approach for Resource Allocation in an Emergency Department Healthcare Unit. Global Heart, 15(1): 14. DOI: https://doi.org/10.5334/gh.528
  • Baş, İ., Tosun Ö., & Bayram, V. (2021). Robot yer seçimi ve işçi-istasyon ataması düşünceleri altında hat dengeleme optimizasyonu. Bir bulaşık makinesi fabrikası vaka analizi. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi, 27(4), 495-503.
  • Baykoç, Ö.F., Abacı, S., & Duyar, M. (2002). Tam Zamanında Üretim Sisteminin Servis Sistemlerine Uygulanabilirliği. Journal of the Faculty of Engineering and Architecture of Gazi University , 17(4), 139-155.
  • Belgin Ö. (2019). Hybrid approach in a production line for multi-objective simulation optimization. Journal of the Faculty of Engineering and Architecture of Gazi University, 34(4): 1847-1859.
  • Carson, Y., & Maria, A. (1997). Simülation Optimization. Methods & Applications. Proceedings of the 1997 Winter Simulation Conference ed.
  • Çekici, V., & Yüregir O.H., (2021). Process optimization of the customer complaints handling system and a new customer oriented model proposal, Journal of the Faculty of Engineering and Architecture of Gazi University, 36:2, 855-869.
  • Çekici, V., & Yüregir O.H., (2020). Investigation and Analysis of Customer Complaints Handling System of the Companies in Turkey, Çukurova University Journal of the Faculty of Engineering and Architecture, 35 (3), 753-768.
  • Çekici, V (2013). A Conceptual Model For Customer Complaint Handling Processes And Evaluation With Simulation Optimisation Method, Cukurova University, Faculty of Engineering and Architechture, Industrial Engineering Doctoral Thesis, Adana p247.
  • Davidow, M. (2003). Organisational Responses to Costomer Complaints. What Works and What Doesn’t. Journal of Service Research, 5(3), 225-250.
  • Düzgit, Z., Toy, A.Ö, Çoban, S., Alibaşoğlu, Z., Tok, Ö., Özkeskin, Ö.T., Karakaya, M., & Bayrak, Y. (2019. Hizmet lojistiğinde iş atama ve rotalama politikaları tasarımı. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi, 25(9), 1071-1079.
  • Estelami, H. (2000). The Profit Impact of Consumer Complaint Solicitation Across Market Conditions. Journal of Professional Services Marketing, 20 (1): 165- 195.
  • Faed, A., Chang, E., Saberki, M., Hussain, O.K., & Azadeh, A. (2016). Intelligent Customer Complaint Handling Utilising Principal Component and Data Envelopment Analysis (PDA). Applied Soft Computing, 47 (1), 614-630.
  • Fornell. C., & Wernerfelt, B. (1998). A Model for Customer Complaint Management Source. Marketing Science, Published by INFORMS, 7(3), 287-298.
  • Greasley, A., & Barlow, S. (1998). Using simulation modelling for BPR: resource allocation in a police custody process. International Journal of Operations & Production Management, 18(9/10), 978-988.
  • Güleryüz, S.S., Koyuncu M. (2023). Simulation of Intensive Care Bed Capacity Based On Mixture Distribution. International Journal of Simulation modelling, 22(2), 221-232.
  • Hamad, W. A., & Arisha, A. (2013). Simulation-based framework to improve patient experience in an emergency department. European Journal of Operational Research, 224, 154–166.
  • Hillier, F.S., & Lieberman, G.J. (2010). Introduction to Operations Research Nineth Edition, The McGraw-Hill Companies, page: 1075.
  • İbiş, S., Kızıldemir, Ö., & Çöp, S. (2019). Evaluation of comments and e-complaints for five star hotel enterprises in Afyonkarahisar, Electronic Journal of Social, 18 (71), 1315-1324.
  • Kim, C., Kim, S., Im, S., & Shin, C. (2003). The effect of attitude and perception on consumer complaint intentions. Journal of Consumer Marketing, 20 (4), 352-371.
  • Koruca, H.İ., & Kocaer, E.R.(2024). Simulation software development and workforce optimization for service systems: QSSim software. Journal of the Faculty of Engineering and Architecture of Gazi University, 39(1), 77-89.
  • Lin, S.Y., & Horng, S.C. (2006). Ordinal Optimization Approach To Stochastic Simulation Optimization Problems And Applications. Proceeding of the 15th Lasted International Conference. Applied Simülation and Modeling. 26-28.
  • Liu, W.K., & Yen, C.C. (2016). Optimizing Bus Passenger, Complaint Service through Big Data Analysis. Systematized Analysis for Improved Public Sector Management. Sustainability, 8 (12), 1-21.
  • Mattila, A.S., & Mount, D.J. (2003). The impact of selected customer characteristics and response time on E-complaint satisfaction and return intent. Hospitality Management, 22, 135–145.
  • Mutlu, Ö., Karagül, K., & Şahin, Y. (2022). Ulaştırma probleminin başlangıç uygun çözümünün belirlenmesi için en büyük maliyetten kaçınma yöntemi. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi, 28(4), 569-576.
  • Ordu, M. (2022). A Simulation-Based Decision-Making Approach to Evaluate the Returns on Investments. International Journal of Simulation Modelling, 2022, 21(3), 441-452. https://doi.org/10.2507/IJSIMM21-3-609.
  • Ordu, M. & Korhan, E. (2022). Simülasyon Destekli Tesis Yerleşim Tasarımı ve İyileştirme Çalışmaları: Bir Tekstil Firması Örneği. Osmaniye Korkut Ata Üniversitesi Fen Bilimleri Enstitüsü Dergisi, 5(Özel Sayı), 26-39. https://doi.org/10.47495/okufbed.1034177
  • Ordu, M., Eren, D., TOFALLIS, C. (2020). Simulation-Based Outpatient Clinic Capacity Management Integrated With Population Growth Projection, Journal of Industrial Engineering 31(3), 411-429.
  • Rossetti, M.D. (2010). Simulation Modeling and Arena. Editor: Repasky, N. And Ruel, C, John Wiley &Sons, Inc., Danvers, USA, 573.
  • Şenses, S., Gölbaşı, O., & Bakal, İ.S. (2022). Madencilikte bir yedek parça envanter optimizasyonu çalışması. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi, 28(1), 128‐138.
  • Tian, X., Vertommen, I., Tsiami, L., Thienen, P., & Paraskevopoulos, S. (2022). Automated Customer Complaint Processing for Water Utilities Based on Natural Language Processing. Case Study of a Dutch Water Utility Water, 14, 674.
  • Timur, M.N., & Sarıyer, N. (2004). Kayseri’deki Otomobil Bayilerinde Müşteri Tatmin Aracı Olarak Şikâyet Toplama Yöntemlerine İlişkin Bir Uygulama. Erciyes Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, 1 (17) , 9-32.
  • Uncu, & Koyuncu, M. (2023). An improved emergency medical service system simulation-optimization model with Poisson mixture distribution, Journal of Engineering Research, 11(3), 100-111.
There are 40 citations in total.

Details

Primary Language Turkish
Subjects Industrial Engineering
Journal Section Industrial Engineering
Authors

Vedat Çekici 0000-0002-6489-5080

Project Number MMF2011D1
Publication Date June 3, 2024
Submission Date November 17, 2023
Acceptance Date January 25, 2024
Published in Issue Year 2024Volume: 27 Issue: 2

Cite

APA Çekici, V. (2024). SİMÜLASYON VE MATEMATİKSEL MODEL YAKLAŞIMLARI İLE MÜŞTERİ ŞİKAYETLERİNİ ÇÖZME SÜRESİ VE MALİYETİNİN ARAŞTIRILMASI. Kahramanmaraş Sütçü İmam Üniversitesi Mühendislik Bilimleri Dergisi, 27(2), 426-446. https://doi.org/10.17780/ksujes.1392121
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