طراحی مدل ترکیبی شبیه‌سازی با به‌کارگیری پارادایم‌های شبیه‌سازی پیشامد گسسته و سیستم‌های پویا در راستای تحلیل شرکت بیمه

محورهای موضوعی : مدیریت صنعتی

Reza Shakerin ¹ (Industrial Management, Faculty of Educational Sciences and Consulting (Management and Accounting), Islamic Azad University of Roodehen, Tehran, Iran.)
Abbass Toloie Eshlaghy ² (Professor of Industrial Management, Science and Research Branch, Islamic Azad University, Tehran, Iran)
Reza Radfar ³ (Professor of Industrial Management, Science and Research Branch, Islamic Azad University, Tehran, Iran.)

تاریخ دریافت : 1399/08/02 تاریخ پذیرش : 1399/09/17 تاریخ انتشار : 1399/11/01

کلید واژه: شبیه‌سازی ترکیبی, فرایند خدمت‌دهی, شبیه‌سازی پیشامد گسسته, شبیه‌سازی سیستم‌های پویا,

چکیده مقاله :

با توجه به نقش فزاینده رقابت بین شرکت‌های بیمه در زمینۀ به‌دست‌آوردن سهم بیشتری از بازار، شرکت‌های بیمه ناگزیر به دنبال روش‌هایی برای کسب رضایت و وفاداری بیشتر مشتریانشان هستند. شرکت‌های بیمه‌ای توجه ویژه‌ای به کیفیت خدمات ارائه‌شده دارند و بنابراین مدت‌زمان دریافت خدمت نقش مهمی در درک مشتری از کیفیت ایفا می‌کنند. هدف پژوهش حاضر طراحی مدلی برای افزایش میزان رضایت مشتریان و بررسی تأثیر آن بر بازار فروش بیمه با به‌کارگیری پارادایم‌های شبیه‌سازی می‌باشد. مطالعۀ حاضر به لحاظ هدف، توصیفی است زیرا توصیف ارتباط بین متغیرهایی که در طول مدت پژوهش شناسایی‌شده‌اند را در جامعۀ خاص مورد آزمون قرار می‌دهد و از حیث روش، مطالعۀ کمی مبتنی بر رویکرد مدل‌سازی ریاضی با پارادایم‌های ترکیبی شبیه‌سازی با استفاده از نرم‌افزار Anylogic است. یافته‌های پژوهش نشان می‌دهد که مدل طراحی‌شده با دنیای واقعی انطباق داشته است به این معنا که داده‌های خروجی صحیح و معتبری ارائه می‌دهد. پس از ارتباط مدل شبیه‌سازی پیشامد گسسته از طریق شناسایی نقاط تعامل با مدل شبیه‌سازی سیستم‌های پویا، سناریوی ترکیبی افزایش تعداد فروش، پیاده‌سازی ‌شده است. با توجه به اینکه مدل شبیه‌سازی پیشامد گسسته و سیستم‌های پویا در ابتدای مدل شبیه‌سازی به‌صورت تعاملی با یکدیگر مشغول اجرا شدن هستند، لذا در ابتدا به دلیل نرخ رضایت بالای مشتریان نرخ ورود توسط مدل سیستم‌های پویا بالا می‌رود و درصد مواقع مشغول به کار بودن کارکنان افزایش می‌یابد. به دلیل اینکه از یک بازه‌ای به بعد تعداد مشتریان ناراضی بیشتر می‌شوند، نرخ ورود توسط مدل سیستم‌های پویا پایین می‌آید تا درنهایت سیستم به حالت تعادل می‌رسد.

چکیده انگلیسی:

According to the increasing role of competition among insurance companies in achieving greater market share, insurance companies are forced to seek methods for increasing customer satisfaction and loyalty. The insurance companies pay special attention to the provided service quality and thus the duration of service delivery plays an important role in customer perception of service quality.This study attempts to design a model for increasing customer satisfaction and its effect on the market of insurance sales by using simulation paradigms. The present study is descriptive in terms of purpose because it tests the relationship between the declared variables during the research in a particular community and in terms of method, a quantitative study based on the mathematical modeling approach with combined simulation paradigms using Anylogic software. The research findings show the designed model is matched with the real world, which means the output data is accurate and valid. After relating the Discrete Event Simulation Model by identifying the interaction points with the System Dynamics Simulation Model, the mixed scenario in increasing sales has been implemented. Considering the Discrete Event Simulation Model and System Dynamics Model are implemented interactively with each other at the beginning of the simulation model, so due to high rate of customer satisfaction, the input rate is raised by the System Dynamics Model and percentage of employee performance is increased. As the number of dissatisfied customers increases over a period of time, the input rate is reduced by the System Dynamics Model until the system is finally balanced.

منابع و مأخذ:

1. Abolhassani, Zahra. (2008). Measuring the internal gap of seven service quality and prioritizing the dimensions of internal service quality in the insurance industry. Master Thesis. University of Isfahan, Faculty of Economics and Administrative Sciences. (in Persian)


2. Abtahi, A., Rashnavadi, Y., & Ghadimi, R. (2019). Designing a Supplemental Health Insurance Plane and Analysing the Related Risk Factors Faced by insurance Companies. Iranian journal of insurance Reaserch, 33(4), 21-42. (in Persian)


3. Ahmadzadeh, A., & Soluki, S. (2019). Life Insurance Claim Management Process Vulnerability using FMEA: an Iranian Insurance Company Case study. Iranian Journal of Insurance Research, 34(2), 30-51. (in Persian)


4. Ataee Gortolmesh, A., Toloie Eshlaghy, A. & Pourebrahimi, A. (2020). Business Process Modeling through Hybrid Simulation Approach (Case Study: One of the Iranian Banks). Industrial Management Journal, 11(4), 600-620. (in Persian)


5. Chahal, K., Eldabi, T. & Young, T. (2013). A conceptual framework for hybrid system dynamics and discrete event simulation for healthcare. Journal of Enterprise Information Management, 26(1/2), 50-74.


6. Haghighi Kafash, M., Mousavi Moradi, S., Bharam, A. & Akbari, M. (2015). Developing a Model to Measure Level of Insured Satisfaction among Active Insurance Companies of Iranian Insurance Industry. Iranian Journal of Insurance Research, 29(4), 189-211. (in Persian)


7. Helal, M. & Rabelo, L. (2017). Synchronizing Discrete Event Simulation Models and System Dynamics Models. International Conference on Industrial Engineering and Operations Management (IEOM) Bristo.


8. Khan, M.M. & Fasih, M. (2014). Impact of service quality on customer satisfaction and sustomer loyalty: Evidence from banking sector. Pakistan Journal of Commerce and Social Sciences, 8(2).


9. Kuster, I., Vila, N. & Canales, P. (2016). How does the online service level influence consumers’ purchase intentions before a transaction? A formative approach. European journal of management and business economics, 25(3), 111-120


10. Law, A. M. & Kelton, W. D. (2000). Simulation modeling and analysis. (Vol. 3). New York: McGraw-Hill.


11. Mashayekhi, A., (2017). Systems Dynamics.Tehran, Ariana Qalam Publications. First Edition, 264-265. (in Persian)


12. Mashayekhi, A., Azar, A. & Zangoueinezhad, A. (2014). A Dynamic Modelling for Decreasing of Loss Time Average in Insurance Companies: System Dynamics Approach. Managment, 5(10), 45-64. (in Persian)


13. Mielczarek, B. e. (2014). Simulation modelling for contracting hospital emergency services at the regional level. European Journal of Operational Research, 235(1), 287-299.


14. Mohtashami, A. & Khatami Firoozabadi, M., (2012). Provide a methodology for allocating equipment and facilities to ports using multi-criteria decision making and simulation tools. International Journal of Industries and Production Management. (2), 22, 163-170. (in Persian)


15. Nasirzadeh, F., Khanzadi, M. & Mir, M. (2018). A hybrid simulation framework for modelling construction projects using agent-based modelling and system dynamics: an application to model construction workers' safety behavior. International Journal of Construction Management, 18(2), 132-143. (in Persian)


16. Nouraei, M., Asghari, F. & Masoumi, F. (2013). The Relationship Between the Use of Internet with Life Insurance Marketing (Case Study: Iran Insurance in Zanjan Province). Quantitative Researches in Management, 4(3) 3, 89-100. (in Persian)


17. Rabelo, L., Sarmiento, A., Helal, M. & Jones, A. (2015). Supply chain and hybrid simulation in the hierarchical enterprise. International Journal of Computer Integrated Manufacturing, 28 (5), 488-500.


18. Ranjbarfard, M. & Bakhtiari, S. (2014). Insured Satisfaction of Third-Party Insurance Policy Services. Iranian Journal of Insurance Research, 29(1), 79-104. (in Persian)


19. Robinson, S. (2014). Simulation: The Practice of Model Development and Use. Palgrave Macmillan: Ontario.


20. Sadeghi, M. A. A., Firozabadi, A. K. & Rabbani, Y. (2011). Using combined method of SD and SSM for solving unstructured social problems. (in Persian)


21. Sepehri, M., Pedram, Y., Teimoor Poor, B. & Matlabi, M. (2013). Measurement and Analysis of Strategies to Reduce Referral’s Waiting Time to Public Health Centers Based on Simulation. Journal of Health System Research, 9(5), 550-60. (in Persian)


22. Sogunro, A.B. &Abiola, B. (2014). Measuring customer satisfaction on life insurance products case study: Lagos state, Nigeria. International Journal of Management business research, 4(2), 73-80.


23. Sterman, J. (2000). Business Dynamics: Systems Thinking and Modeling for a Complex World. Irwin: Boston.


24. Suryani, E., Chou, S. Y. & Chen, C. H. (2010). Air passenger demand forecasting and passenger terminal capacity expansion: A system dynamics framework. Expert Systems with Applications, 37(3), 2324-2339.


25. Taghavi Fard, M.T., Dadvand, A. & Aghaei, M. (2017). Improving the Banking Service Process with Simulation and Scenario Making Approach (Case Study: Pasargad Bank), 2nd International Conference on Industrial Management, Babolsar, Mazandaran University. (in Persian)


26. Taghavifard, M., Habibi, R. & Aghaei, M. (2018). Determining Retention and Profitability of Bank Customers Using Extended Decision Tree and Forest Regression. Modern Research in Decision Making, 2(4), 57-79. (in Persian)


27. Talebi, H., Amiri, M. & Azimi, P. (2018). Analysis of a Bank Queuing System and Reducing the Waiting Time for Customers by Simulation and Design of Experiment Approach. IQBQ,22 (1),95-118. (in Persian)


28. Sadeghi Moghaddam,A.,  Khatami Firoozabadi,A., & Rabbani,Y. ( 2011).Using the combined SD and SSM approach to solve unstructured social problems. Industrial Management, 3(2), 55-76. (in Persian)