An approach that combines multi-criteria decision making and simulation in new product selection

Year 2024, Volume: 39 Issue: 2, 1193 - 1208, 30.11.2023

Yasemin Yavuz Güzeler Gökhan Akyüz

https://doi.org/10.17341/gazimmfd.1183811

Abstract

The decision to determine the new product or products to be added to the product mix is one of the important strategic decisions for businesses. In taking this decision, besides the conditions such as the demand for the product at the desired level in the competitive environment, the production process and costs are also effective. The application was carried out in a company operating in the cosmetics and cleaning products sector. In the first stage of the two-stage application, a pre-selection was made with multi-criteria decision-making methods (AHP and TOPSIS) among the natural products that the company thought to include in the product mix. In the second stage, a simulation model was created and run in Arena Rockwell program for the demand and production processes of the first two alternatives. With this, it is possible to see the status of the manufacturing process before the products are produced in the company. Then with the Process Analyzer tool of the Arena program, by trying different values of controllable variables, it was tried to reach values that would reduce the cost and the amount of lost sales, while increasing the amount of sales at the same time. After determining the most suitable variable values, the final selection was made by selecting the product due to the sales revenue and cost advantage. The study aims to contribute to the literature by carrying out a study that considers both the selection criteria and the manufacturing process in the selection of new products.

Keywords

New Product Selection, Multi-Criteria Decision Making, Simulation

Yeni ürün seçiminde çok kriterli karar verme ile simülasyonu birleştiren yaklaşım

Abstract

Ürün karmasına eklenecek yeni ürün veya ürünleri belirleme kararı işletmeler için önemli stratejik kararlardan biridir. Bu kararın alınmasında rekabet ortamında ürüne olan talebin istenilen düzeyde olması gibi koşulların yanında üretim süreci ve maliyetleri de etkilidir. Uygulama kozmetik ve temizlik ürünleri sektöründe faaliyet gösteren bir firmada gerçekleştirilmiştir. İki aşamalı uygulamanın ilk aşamasında, firmanın ürün gamına dahil etmeyi düşündüğü doğal ürünler arasından çok kriterli karar verme yöntemleri (AHP ve TOPSIS) ile ön seçim yapıldı. İkinci aşamada, ilk iki alternatifin talep ve üretim süreçleri için bir simülasyon modeli oluşturulmuş ve Arena Rockwell programında çalıştırılarak ürünler firmada üretilmeden önce üretim sürecinin durumunu görmek mümkün olmuştur. Daha sonra Arena programının Process Analyzer aracı ile kontrol edilebilir değişkenlerin farklı değerleri denenerek, aynı anda satış miktarını artırırken, maliyeti ve kayıp satış miktarını azaltacak değerlere ulaşılmaya çalışılmıştır. En uygun değişken değerleri belirlendikten sonra satış geliri ve maliyet avantajından dolayı ürün seçilerek nihai seçim yapılmıştır. Çalışma, yeni ürün seçiminde hem seçim kriterlerini hem de üretim sürecini dikkate alan bir çalışma gerçekleştirerek literatüre katkı sağlamayı amaçlamaktadır.

Keywords

Yeni Ürün Seçimi, Çok Kriterli Karar Verme, Simülasyon

References

• 1. Kaya, N., Stok Yönetimi, İksad Publisher, Ankara ,2020.
• 2. Bozyiğit, S. and Kılınç Doğan, G., The Marketıng Problems of Natural and Organıc Product Producers In Turkey: An Exploratory Study, Afyon Kocatepe University Journal of Economics and Administrative Sciences, Volume: XVII Issue: 1, Pages:33-47, 2015.
• 3. Uludağ, A. S., and Doğan, H., A Service Quality Application Focusing on the Comparison of Multi-Criteria Decision Making Methods, Journal of the Faculty of Economics and Administrative Sciences, 6(2), 17-48, 2016.
• 4. Roy, B., Multicriteria Methodology for Decision Aiding. Kluwer, Dordrecht, The Netherlands, 1996.
• 5. Mostafa, A. M., An MCDM Approach for Cloud Computing Service Selection Based on Best-Only Method. In IEEE Access, vol. 9, pp. 155072-155086, 2021.
• 6. Li, S. M., Chan, F. T., Tsang, Y. P., & Lam, H. Y., New Product Idea Selection in the Fuzzy Front End of Innovation: A Fuzzy Best-Worst Method and Group Decision-Making Process, Mathematics, 9(4), 337, 2021.
• 7. Goswami, S. S. S., Behera, D. K. K., Afzal, A., Razak Kaladgi, A., Khan, S. A. A., Rajendran, P., & Asif, M., Analysis of a Robot Selection Problem Using Two Newly Developed Hybrid MCDM Models of TOPSIS-ARAS and COPRAS-ARAS. Symmetry, 13(8), 1331, 2021.
• 8. Chawla, S., and Singari, R. M., Integrated TOPSIS-PROMETHEE-MOORA model for material selection of crankcase cover, Indian Journal of Engineering and Materials Sciences (IJEMS), 28(5), 454-461, 2021.
• 9. Ardil, C. Fighter Aircraft Selection Using Technique for Order Preference by Similarity to Ideal Solution with Multiple Criteria Decision Making Analysis, International Journal of Transport and Vehicle Engineering, 13(10), 649-657, 2021.
• 10. Ransikarbum, K., and Khamhong, P., Integrated Fuzzy Analytic Hierarchy Process and Technique for Order of Preference by Similarity to Ideal Solution for Additive Manufacturing Printer Selection. Journal of Materials Engineering and Performance, 30(9), 6481–6492, 2021.
• 11. Agrawal, R. Sustainable material selection for additive manufacturing technologies: A critical analysis of rank reversal approach, Journal of Cleaner Production, 296, 126500, 2021.
• 12. Nasution, N., Febriadi, B., Mahalisa, G., Hijriana, N., Rasyidan, M., Sinaga, D. M., Dewi, S. M., Windarto, A. P., Aswan, N & Raharjo, M. R., Application of ELECTRE Algorithm in Skincare Product Selection, In Journal of Physics: Conference Series (Vol. 1471, No. 1, p. 012066). IOP Publishing, 2020.
• 13. Karmarkar, A. U., and Gilke, N. R., An application of AHP for parameter importance evaluation for farm machinery selection. In IOP Conference Series: Materials Science and Engineering, 810(1): p. 012022. IOP Publishing, 2020.
• 14. Kumar, R., Bhattacherjee, A., Singh, A. D., Singh, S., & Pruncu, C. I. Selection of portable hard disk drive based upon weighted aggregated sum product assessment method: A case of Indian market, Measurement and Control, 53(7-8), 1218-1230, 2020.
• 15. Yörükoğlu, M., & Aydın, S., Wind turbine selection by using MULTIMOORA method, Energy Systems, 12 (4), 863-876, 2020.
• 16. Koç Ustalı, N. and Tosun, N., New Product Selection with Fuzzy AHP and Fuzzy WASPAS Methods, Studies on Marketing Insights, 3 (2), 25-34, 2019.
• 17. Wu, C., Zhang, Z. & Zhong, W., A Group Decision-Making Approach Based on DST and AHP for New Product Selection Under Epistemic Uncertainty, Mathematical Problems in Engineering, 1-16, 2019.
• 18. Kusakci, A. O., A Decision Support System for Product Selection Using Hybridized Fuzzy-AHP TOPSIS Methods, International Journal of Engineering Research and Development, 11(1), 99-108, 2019.
• 19. Atalay, K.D. and Can, G.F., A New Hybrid İntuitionistic Approach For New Product Selection, Soft Comput 22(8), 2633–2640, 2018.
• 20. Karadağ, İ and Kılıç Delice, E., A New Fuzzy Hybrıd Mcdm-Zogp Approach For Product Selectıon Problem: A Case Study Of The Optıcal Fıber Cable, International Journal of Industrial Engineering, 25(4), 507-525, 2018.
• 21. Adalı, E. A., and Işık, A. T., The multi-objective decision making methods based on MULTIMOORA and MOOSRA for the laptop selection problem, Journal of Industrial Engineering International, 13(2), 229-237, 2017.
• 22. Elahi, F., Muqtadir, A., Anam, S., & Mustafiz, K., Pharmaceutical Product Selection: Application of AHP, International Journal of Business and Management, 12(8), 193-200, 2017.
• 23. Özceylan, E., Kabak, M., & Dağdeviren, M. A fuzzy-based decision making procedure for machine selection problem, Journal of Intelligent & Fuzzy Systems, 30(3), 1841-1856, 2016.
• 24. Hanine, M., Boutkhoum, O., Tikniouine, A., & Agouti, T., Application of an integrated multi-criteria decision making AHP-TOPSIS methodology for ETL software selection, SpringerPlus, 5(1), 1-17, 2016.
• 25. Efe, B., Boran, F. & Kurt, M., Ergonomıc Product Concept Selectıon Usıng Intuıtıonıstıc Fuzzy Topsıs Method, Suleyman Demirel University Journal of Engineering Sciences and Design, 3(3), 433-440, 2015.
• 26. Yan, H. B. ve Ma T., A Fuzzy Group Decision Making Approach To New Product Concept Screening At The Fuzzy Front End, International Journal of Production Research, 53(13): 4021-4049, 2015.
• 27. Ertuğrul, İ. and Özçil, A., Air Conditioner Selection with TOPSIS and VIKOR Methods In Multi Criteria Decision Making, Çankırı Karatekin University Journal of the Faculty of Economics and Administrative Sciences, 4(1), 267-282, 2014.
• 28. Song, W., Ming X. & Wu, Z., An İntegrated Rough Number-Based Approach To Design Concept Evaluation Under Subjective Environments, Journal of Engineering Design. 24(5), 320-341, 2013.
• 29. Ishizaka, A., and Labib, A., Selection of new production facilities with the Group Analytic Hierarchy Process Ordering method, Expert Systems with Applications, 38(6), 7317-7325, 2011.
• 30. Ertuğrul, İ. and Tanrıverdi, Y. ABC Method for Stock Controls and the Application of the AHP Analysis to Yarn Company, International Journal of Alanya Faculty of Business, 5(1), 41-52, 2013.
• 31. Sarmah, S. P., and Moharana, U. C., Multi-criteria classification of spare parts inventories–a web based approach, Journal of Quality in Maintenance Engineering, 21(4), 456-477, 2015.
• 32. Noorul Haq, A., and Kannan, G. Design of an integrated supplier selection and multi-echelon distribution inventory model in a built-to-order supply chain environment, International Journal of Production Research, 44(10), 1963-1985, 2006.
• 33. Topçu, Ö. M., Capacity Analysis With Simulation For Continious Production Lines And Buffer Allocation. Master Thesis, Istanbul Technical University Graduate School of Natural and Applied Sciences, İstanbul, 2015.
• 34. Çelikcan, Ş., Sımulatıon-Optımızatıon Approach Based On Multıcrıterıa Decısıon Makıng Methods In Spare Parts Inventory Management. Master Thesis, Cukurova University Graduate School of Natural and Applied Sciences, Adana, 2019.
• 35. Mitrović, M., Popović, D., Vidović, M., & Radivojević, G., Order Level Optimization in Inventory Management Using Arena Simulation Model, International ournal for Traffic and Transport Engineering,11(2), 25, 2021.
• 36. Arani, M., Abdolmaleki, S., Maleki, M., Momenitabar, M., & Liu, X., A simulation-optimization technique for service level analysis in conjunction with reorder point estimation and lead-time consideration: a case study in sea port, arXiv preprint arXiv:2106.00767, 2021.
• 37. Özcan, B., and Yıldırak, E. A Simulation Study on a Production System, Aksaray University Journal of Science and Engineering, 4(2), 172-186, 2020.
• 38. Arat, B., An Applıcatıon In Stock Control And Meltıng Cheese Industry, Master Thesis, Marmara University Institute of Social Sciences, İstanbul, 2020.
• 39. Aslantaş, M., Simulation based optimization of stock control parameters in a transformer company, Master Thesis, Balıkesir University Graduate School of Natural and Applied Sciences, Balıkesir, 2019.
• 40. Kim, J. A., and Jeong, J., Simulation Evaluation for Efficient Inventory Management Based on Demand Forecast, In International Conference on Computational Science and Its Applications (pp. 639-650), Springer, Cham, 2018.
• 41. Göçken, M., Boru, A. & Dosdoğru, A. T. Analyzing Inventory Control and Supplier Selection Simultaneously in Two Echelon Supply Chain by Using Optimization via Simulation Approach, Karaelmas Science and Engineering Journal, 8(1): 1-10, 2017.
• 42. Doğan, İ., Tekkeşin, A. & Kara, A., Supply Chain Modelling of Perishable Products and Simulation Study. Nevsehir Journal of Science and Technology, 6(2): 605-618., 2017.
• 43. Persson, F., Axelsson, M., Edlund, F., Lanshed, C., Lindström, A., & Persson, F., Using simulation to determine the safety stock level for intermittent demand, In 2017 Winter Simulation Conference (WSC) (pp. 3768-3779), IEEE, 2017.
• 44. Duong, L. N., Wood, L. C., & Wang, W. Y., A multi-criteria inventory management system for perishable & substitutable products, Procedia Manufacturing, 2, 66-76, 2015.
• 45. İlhan, İ., An application of stock optimization with selection of quantitative demand forecasting methods in supply chain management, Master Thesis Maltepe University Graduate School of Natural and Applied Sciences, İstanbul, 2015.
• 46. Sarıaslan, H. and Uysal, E, Stok Kontrol Sistemlerinde Simülasyon Tekniği: Demir Export AŞ Kangal Kömür İşletmesi Örnek Uygulaması, Ankara University SBF Journal 48(01), 2014.
• 47. Guerrin, F. Simulation Of Stock Control Policies İn A Two-Stage Production System: Application To Pig Slurry Management İnvolving Multiple Farms, Computers and Electronics in Agriculture 45(1-3): 27-50, 2004.
• 48. Vieira, G. E., Ideas for modeling and simulation of supply chains with arena, In Proceedings of the 2004 Winter Simulation Conference, Vol. 2, pp. 1418-1427, IEEE, 2004.
• 49. Kurt, M., and Erol, R., Analysis of price-discounted stochastic inventory policies for perishable items using simulation, Cukurova University Journal of the Faculty of Engineering, 12(1-2), 37-42, 1997.
• 50. Al-Harbi, K. M. A. S. Application of the AHP in project management, International journal of project management, 19(1), 19-27, 2001.
• 51. Saaty, R. W., The analytic hierarchy process—what it is and how it is used, Mathematical modelling, 9(3-5), 161-176, 1987.
• 52. Ömürbek, N. and Tunca, M. Z., A Case Study On Group Decısıon Makıng Stage In Analytıc Hıerarchy Process And Analytıc Network Process Methods, Suleyman Demirel University Journal Of Faculty Of Economics & Administrative Sciences, 18(3), 47-70, 2013.
• 53. Saaty, T. L. Relative Measurement And Its Generalization In Decision Making Why Pairwise Comparisons Are Central In Mathematics For The Measurement Of Intangible Factors The Analytic Hierarchy/Network Process, RACSAM-Revista de la Real Academia de Ciencias Exactas, Fisicas y Naturales, Serie A. Matematicas, 102(2), 251-318, 2008.
• 54. Manap, G., Tourısm Centre Selectıon Wıth Analytıc Hıerarchy Process Abstract, Gazi University Journal of Commerce & Tourism Education Faculty, (2), 157-170, 2006.
• 55. Dağdeviren, M., Diyar, A. & Mustafa, K., Analytıcal Hıerarchy Process For Job Evaluatıon And Applıcatıon, Gazi University Journal of the Faculty of Engineering and Architecture, 19(2), 131-138, 2004.
• 56. Palaz, D., Hakan, U., & Kovancı, A., Evaluate The Selectıon Of Turkısh Navy Submarınes By Usıng Ahp, Journal Of Aeronautics & Space Technologies/Havacilik Ve Uzay Teknolojileri Dergisi, 3(3), 53-60, 2008.
• 57. Timor M., Analitik Hiyearşi Prosesi, Türkmen Bookstore, İstanbul, 2011.
• 58. Hwang, C.L. and Yoon, K., Multiple Attribute Decision Making: Methods and Applications, Springer, Berlin, Heidelberg, New York, 1981.
• 59. Dymova, L., Sevastjanov, P., & Tikhonenko, A., A direct interval extension of TOPSIS method, Expert Systems with Applications, 40(12), 4841-4847, 2013.
• 60. Dymova, L., Sevastjanov, P., & Tikhonenko, A., An approach to generalization of fuzzy TOPSIS method, Information Sciences, 238, 149-162, 2013.
• 61. Özdemir, A., İşletmelerde Karar Verme Teknikleri, T.R. Anadolu University Publication No: 3355, Open Education Faculty Publication No: 2210, Eskişehir, 2019.
• 62. Tsaur, R. C., Decision risk analysis for an interval TOPSIS method, Applied Mathematics and Computation, 218(8), 4295-4304, 2011.
• 63. Özgüner, Z. and Özgüner, M., Analysıs Of Supplıer Evaluatıon And Selectıon Problem Wıth Integrated Entropı-Topsıs Methods, İstanbul Ticaret University Journal of Social Sciences, 19 (37), 551-56, 2019.
• 64. Altıok, T. and Melamed, B., Simulation Modeling and Analysis with ARENA, Amsterdam, The Netherlands: Elsevier, 2007.
• 65. Sarıaslan, H., Yatırım Projelerinin Hazırlanması ve Değerlendirilmesi, Siyasal Bookstore, Ankara, 2014.
• 66. Kahya, A. F., Analysis of waiting times with queue model and labor planning: An application for a dentistry hospital, Master Thesis, Akdeniz University Institute of Social Sciences, Antalya, 2019.
• 67. Yıldırım, A. E., Investigation of Marketing Components Affecting the Evaluation of Female Consumers About Organic Cosmetic Products, Journal Of Busıness Research-Turk, 12(3), 3225-3245, 2020.