Process Analysis in Maritime Logistic Pricing Activities with Monte Carlo Simulation in Fuzzy Environment

Öz

Nowadays, increasing competition conditions show their effect in logistics sector as in all sectors. Logistics processes contain many activities, including pricing. Although competition is not only a price-oriented process in logistics companies, when a holistic approach is taken, businesses will provide customer satisfaction in a more competitive environment. One of the most important functions of pricing is the supply of the most affordable price and the most suitable service to the sales department's price requests in the shortest time. In addition, considering the logistics sector as a service sector, it further increases the importance of pricing in logistics. In this study, the analysis of the pricing process in logistics was performed by applying Monte Carlo simulation in the fuzzy time environment. Firstly, the pricing process was breakdown structure into activities, and fuzzy times were assigned to these activities, and fuzzy times were defuzzied and analysed with the Monte Carlo simulation. The analysis of the subject and the proposed method reveals the originality of the study.

Anahtar Kelimeler

• Logistics Process
• Logistics Activities
• Monte Carlo Simulation
• Pricing Process

Bulanık Çevre Ortamında Monte Carlo Simülasyonu ile Denizyolu Lojistiği Fiyatlandırma Süreç Analizi

Öz

Günümüzde artan rekabet koşullar tüm sektörlerde olduğu gibi lojistik sektöründe de etkisini göstermektedir. Lojistik süreçleri fiyatlandırma dahil olmak üzere pek çok aktiviteyi barındırmaktadır. Lojistik şirketlerinde rekabet sadece fiyat odaklı bir süreç olmamakla beraber bütüncül bir yaklaşım sergilendiğinde işletmeler daha rekabetçi bir ortamda müşteri memnuniyeti sağlayacaklardır. Fiyatlandırmanın en önemli fonksiyonlarından biri satış departmanının fiyat taleplerine en kısa sürede, en uygun fiyat ve en uygun servis hizmetinin tedarikidir. Bunun yanında bir hizmet sektörü olarak lojistik sektörü dikkate alındığında, lojistikte fiyatlandırmanın önemini daha da arttırmaktadır. Bu çalışmada bulanık süre ortamında Monte Carlo simülasyonu uygulanarak lojistikte fiyatlandırma sürecinin analizi yapılmıştır. Öncelikle fiyatlandırma süreci faaliyetlere ayrıştırılarak bu faaliyetlere bulanık süreler atanmış ve bulanık süreler durulaştırılarak Monte Carlo simülasyonu ile analizi yapılmıştır. Ele alınan konu ve önerilen yöntemle analizi, çalışmanın özgünlüğünü ortaya koymaktadır.

Anahtar Kelimeler

• Lojistik Süreci
• Lojistik Faaliyetler
• Monte Carlo Simülasyonu
• Fiyatlandırma Süreci

Kaynakça

1. Akbudak, K., (2006), Tekstil Sektöründe Fiyatlandırma Yöntemleri ve Bir Uygulama, Gazi Üniversitesi Sosyal Bilimler Enstitüsü Yüksek Lisans Tezi, Ankara.
2. Avlijas, G., (2019), Examining the Value of Monte Carlo Simulationfor Project Time Management, Management: Journal of Sustainable Business and Management Solutions in Emerging Economies, 2019/24(1),11-21.
3. Bellman. R.E. &Zadeh. L.A., (1970), Decision-making in a fuzzy environment, Management Science. vol.17. no.4. pp.141–164.
4. Bonato, F.K., Albuquerque, A.A., Paixao, M.A.S., (2019), An application of Earned Value Management (EVM) with Monte Carlo simulation in engineering Project management, Gest. Prod., São Carlos, v. 26, n. 3, e4641.
5. Chopra S. &Meindl, P., (2015), Supply Chain Management Strategy, Planning, and Operation, Pearson, 6 edt.
6. Coşgun, Ö., Ekinci Y., Yanık, S., (2014), Fuzzy rule-based demand forecasting for dynamic pricing of a maritime company, Knowledge- Based Systems, 70 (2014) 88-96.
7. Demir, E., Huang, Y., Scholts, S., Woensel T. V., (2015), A selectedreview on the negativeexternalities of the freight transportation: Modeling and pricing, Transportation Research Part E, 77 (2015), 95-114.
8. Elbert, R., Scharf, K., Reinhardt, D., (2017), Simulation of the order process in maritime hinterland transportation: the impact of order realese times, Proceedings of the 2017 Winter Simulation Conference, p:3471 -3482.

9. Erten, S., (2010), Lojistik süreç yönetimi bir kamu kurumu analizi, T.C. Dokuz Eylül Üniversitesi Sosyal Bilimler Enstitüsü İşletme Anabilim Dalı Sayısal Yöntemler ve Yönetim Bilimi Programı Yüksek Lisans Tezi.
10. Gouri, R.L &Srinivas V.V., (2017), A fuzzy approach to reliability based design of storm water drain network, StochEnvironRes Risk Assess, 31:1091–1106.
11. Gudehus T., Kotzab H., (2012), Comprehensive Logistics, Springer Verlag, Berlin Heidelberg, Second Revised and Enlarged Edition.
12. Guyonnet, D., Bourgine, B., Dubois, D., Fargier, H., Come, B., Chiles, J.P., (2003), A hybrid approach for addressing uncertainty in risk assessments, Journal of EnvironmentalEngineering, January 2003.
13. Hsieh, T. Y., Lu, S. T., &Tzeng, G. H., (2004), Fuzzy MCDM approach for planning and design tender’s selection in public office buildings, International Journal of Project Management, 22(7), 573–584.
14. Kaufmann, A. &Gupta, M.M., (1988), Fuzzy Mathematical Models in Engineering and Management Science, North Holland, Amsterdam.
15. Kaufmann, A. &Gupta, M.M., (1991), Introduction to Fuzzy Arithmetic, Van Nostrand, New York.
16. Kaya, İ. & Kahraman, C., (2010), Fuzzy process capability analyses with fuzzy normal distribution, Expert Systems with Applications, 37 5390–5403.
17. Khalafi, Z., Dehghani, M., Goel L., Li, W., (2015), Observability Evaluation in Power Systems Considering Data Uncertainty,2015 IEEE Eindhoven PowerTech, 29 June-2 July 2015.
18. Kılıç, Ş., Aydınlı, C., (2015), Sağlık Kurumlarında Süreç Yönetimi Uygulamaları, Journal of Business Research Turk, 7/3(2015) 143-172.
19. Kotler P.T., Armstrong G., (2018), Principles of Marketing, Pearson, International Edt., 17th edt.
20. Kong, Z., Zhang, J., Li, C., Zheng, X., Guan, Q., (2015), Risk assessment of plan schedule by Monte Carlo simulation, International Conference on Information Technology and Management Innovation (ICITMI 2015), 509-513.
21. Kwak, Y.H. &Ingall, L., (2007), Exploring Monte Carlo Simulation Applications for Project Management, Risk Management, Vol. 9, No. 1 (Feb., 2007), pp. 44-57.
22. Lambert D.M., Stock J.R., Ellram L.M., (1998), Fundamentals of Logistics Management, McGraw-HillEducation, International edt.
23. Opricovic, S. &Tzeng, G.H., (2003), Defuzzification within a fuzzy multicriteria decision model, International Journal of Uncertainty, Fuzziness and Knowledge-based Systems. 11, 635–652.
24. Prakash, S., &Jokhan, A., (2017), Monte Carlo for selecting risk response strategies, Australasian Transport Research Forum 2017 Proceedings 27 – 29 November 2017, Auckland, New Zealand.
25. Rokseth, B., Utne, I.B., Vinnem, J.E., (2018), Deriving verification objectives and scenarios for maritime systems using the systems-theoretic process analysis, ReliabilityEngineering&SystemSafetyVolume 169, January 2018, Pages 18-31.
26. Rui-mei, L., (2015), Properties of Monte Carlo and Its Application to Risk Management, International Journal of u- and e- Service, Science and Technology Vol.8, No. 9 (2015), pp.381-390.
27. Sadeghi, N., Fayek, A.,R., Pedrycz, W., (2010), Fuzzy Monte Carlo Simulation and Risk Assessment in Construction, Computer-Aided Civil and Infrastructure Engineering, 25 238–252.
28. Seyoum, B., (2009, Export-ImportTheory, Practices, and Procedures, Second Edition, Routledge Taylor& Francis Group New York and London, ISBN: 978-0-7890-3419-9.
29. Traynor B.A. and Mahmoodian, M., (2019), Time and cost contingency management using Monte Carlo simulation, Australian Journal of Civil Engineering 2019, Vol. 17, No. 1, 11–18.
30. Tysiak, W. &Sereseanu, A., (2009), Monte Carlo Simulation in Risk Management in Projects Using Excel, IEEE International Workshop on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications 21-23 September 2009, Rende (Cosenza), Italy.
31. Ueberhuber, C.W. (1997), Monte Carlo Techniques. Numerical Computation 2: Methods, Software, and Analysis. Berlin: Springer-Verlag, pp. 124-125 and 132-138.
32. Uğurlu, S., Coşgun, Ö., Ekinci, Y., (2012), A Dynamic Pricing Model for a Maritime Transportation Service Provider, International Journal of Computational Intelligence Systems, Vol. 5, No. 6 (November 2012),1109-1119.
33. Williams, T., (2004), Why Monte Carlo Simulations of Project Networks Can Mislead, Project Management Journal, September 2004, Vol. 35, No. 3, 53-61.
34. Wyrozębski, P., Wyrozębska, A., (2013), Benefits of Monte Carlo simulation as the extension to the Programe Evaluation and Review Technique, Electronic International Interdisciplinary Conference, September, 2. - 6. 2013, p:95-99.
35. Yin, M., Wan, Z., Kim K.H, Zheng,, S.Y, (2019), An optimal variablepricing model forcontainerlinerevenuemanagementsystems, Marit. Econ. Logist., (2019) 21:173-191.
36. Yıldırım, N., (2015), Fiyatlandırma ve İnternet Ortamında Fiyatlandırma Stratejileri, Dicle Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, C:5 S:8 Yaz 2015 (10-29). Zadeh L.A., (1965), Fuzzy sets,Inform. Control 8 (3) 338–353.