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Yıl 2020, Cilt: 16 Sayı: 2, 93 - 107, 30.12.2020

Zeynep Begüm Kurt Aytaç Yıldız

Öz

Teşekkür

Bu çalışmaya büyük katkı sağlayan Toksan Otomotiv A.Ş. Ar-Ge Merkezi Müdürü Sayın Berna Başak MIŞIL'a teşekkür ederiz.

Kaynakça

  • [1] Mikkola, J. H. (2001). Portfolio management of R&D projects: implications for innovation management. Technovation, 21(7), 423-435.
  • [2] Klintong, N., Vadhanasindhu, P., & Thawesaengskulthai, N. (2012, February). Artificial intelligence and successful factors for selecting product innovation development. In 2012 Third International Conference on Intelligent Systems Modelling and Simulation (pp. 397-402). IEEE.
  • [3] Tohidi, H., & Jabbari, M. M. (2012). The important of innovation and its crucial role in growth, survival and success of organizations. Procedia Technology, 1, 535-538.
  • [4] Huvaj, M. N., & Johnson, W. C. (2019). Organizational complexity and innovation portfolio decisions: Evidence from a quasi-natural experiment. Journal of Business Research, 98, 153-165.
  • [5] Hall, D. L., & Nauda, A. (1990). An interactive approach for selecting IR&D projects. IEEE Transactions on Engineering Management, 37(2), 126-133.
  • [6] Dodgson, M. (1997). Systematic integration of the innovation press within the firm. Australian-Asia Management Center.
  • [7] Tohidi, H., Jafari, A., & Afshar, A. A. (2010). Using balanced scorecard in educational organizations. Procedia-Social and Behavioral Sciences, 2(2), 5544-5548.
  • [8] Calantone, R. J., Cavusgil, S. T., & Zhao, Y. (2002). Learning orientation, firm innovation capability, and firm performance. Industrial marketing management, 31(6), 515-524.
  • [9] Wei, C. C., & Chang, H. W. (2011). A new approach for selecting portfolio of new product development projects. Expert Systems with Applications, 38(1), 429-434.
  • [10] Mavrotas, G., & Makryvelios, E. (2020). Combining multiple criteria analysis, mathematical programming and Monte Carlo simulation to tackle uncertainty in Research and Development project portfolio selection: A case study from Greece. European Journal of Operational Research, in press.
  • [11] Souza, D. G., Silva, C. E., & Soma, N. Y. (2020). Selecting projects on the Brazilian R&D energy sector: a fuzzy-based approach for criteria selection. IEEE Access, 8, 50209-50226.
  • [12] Mitchell, R., Phaal, R., & Athanassopoulou, N. (2014, July). Scoring methods for prioritizing and selecting innovation projects. In Proceedings of PICMET'14 Conference: Portland International Center for Management of Engineering and Technology; Infrastructure and Service Integration (pp. 907-920). IEEE.
  • [13] Bhattacharyya, R. (2015). A grey theory based multiple attribute approach for R&D project portfolio selection. Fuzzy Information and Engineering, 7(2), 211-225.
  • [14] Cheng, C. H., Liou, J. J., & Chiu, C. Y. (2017). A consistent fuzzy preference relation based ANP model for R&D project selection. Sustainability, 9(8), 1352.
  • [15] Martino, J. P. (1995). Research and development project selection. Wiley.
  • [16] Loch, C. H., Pich, M. T., Terwiesch, C., & Urbschat, M. (2001). Selecting R&D projects at BMW: A case study of adopting mathematical programming models. IEEE Transactions on Engineering Management, 48(1), 70-80.
  • [17] Ali, A., Kalwani, M. U., & Kovenock, D. (1993). Selecting product development projects: Pioneering versus incremental innovation strategies. Management Science, 39(3), 255-274.
  • [18] Cooper, R., Edgett, S., & Kleinschmidt, E. (2001). Portfolio management for new product development: results of an industry practices study. R&D Management, 31(4), 361-380.
  • [19] Bellman, R. E., & Zadeh, L. A. (1970). Decision-making in a fuzzy environment. Management science, 17(4), B-141.
  • [20] Huang, C. C., & Chu, P. Y. (2011). Using the fuzzy analytic network process for selecting technology R&D projects. International journal of technology management, 53(1), 89-115.
  • [21] Liberatore, M. J., & Titus, G. J. (1983). The practice of management science in R&D project management. Management Science, 29(8), 962-974.
  • [22] Abdel-Basset, M., Atef, A., & Smarandache, F. (2019). A hybrid Neutrosophic multiple criteria group decision making approach for project selection. Cognitive Systems Research, 57, 216-227.
  • [23] Tavana, M., Khosrojerdi, G., Mina, H., & Rahman, A. (2020). A new dynamic two-stage mathematical programming model under uncertainty for project evaluation and selection. Computers & Industrial Engineering, 149, 106795.
  • [24] Hwang, C. L., & Yoon, K. (1981). Methods for multiple attribute decision making. In Multiple attribute decision making (pp. 58-191). Springer, Berlin, Heidelberg.
  • [25] Rashidi, K., & Cullinane, K. (2019). A comparison of fuzzy DEA and fuzzy TOPSIS in sustainable supplier selection: Implications for sourcing strategy. Expert Systems with Applications, 121, 266-281.
  • [26] Yayla, A. Y., Yildiz, A., & Ozbek, A. (2012). Fuzzy TOPSIS method in supplier selection and application in the garment industry. Fibres & Textiles in Eastern Europe, 20, 4(93): 20-23.
  • [27] Yıldız, A., & Demir, Y. (2019). Bulanık TOPSIS yöntemiyle Türkiye’nin yerli otomobili için en uygun fabrika yerinin seçimi. Business & Management Studies: An International Journal, 7(4), 1427-1445.
  • [28] Han, H., & Trimi, S. (2018). A fuzzy TOPSIS method for performance evaluation of reverse logistics in social commerce platforms. Expert Systems with Applications, 103, 133-145.
  • [29] Hatami-Marbini, A., & Kangi, F. (2017). An extension of fuzzy TOPSIS for a group decision making with an application to Tehran stock exchange. Applied Soft Computing, 52, 1084-1097.
  • [30] Gupta, H. (2018). Assessing organizations performance on the basis of GHRM practices using BWM and Fuzzy TOPSIS. Journal of environmental management, 226, 201-216.
  • [31] Chen, C. T. (2000). Extensions of the TOPSIS for group decision-making under fuzzy environment. Fuzzy sets and systems, 114(1), 1-9.
  • [32] Yildiz, A., & Yayla, A. (2017). Application of fuzzy TOPSIS and generalized Choquet integral methods to select the best supplier. Decision Science Letters, 6(2), 137-150.
  • [33] Sirisawat, P., & Kiatcharoenpol, T. (2018). Fuzzy AHP-TOPSIS approaches to prioritizing solutions for reverse logistics barriers. Computers & Industrial Engineering, 117, 303-318.
  • [34] Solangi, Y. A., Tan, Q., Mirjat, N. H., & Ali, S. (2019). Evaluating the strategies for sustainable energy planning in Pakistan: An integrated SWOT-AHP and Fuzzy-TOPSIS approach. Journal of Cleaner Production, 117655.
  • [35] Mitchell, R., Phaal, R., & Athanassopoulou, N. (2018). Scoring methods for evaluating and selecting early stage technology and innovation projects. Centre for Technology Management working paper series, 2, 1-19.

Fuzzy TOPSIS Based Decision Model for Evaluating and Prioritizing R&D / Innovation Projects, Ar-Ge/İnovasyon Projelerinin Değerlendirilmesi ve Önceliklendirilmesi İçin Fuzzy TOPSIS Tabanlı Karar Modeli

Yıl 2020, Cilt: 16 Sayı: 2, 93 - 107, 30.12.2020

Zeynep Begüm Kurt Aytaç Yıldız

Öz

İnovasyon, firmaların yenilikçilik kabiliyetlerini ortaya çıkararak müşterilere fayda yaratan ürün ve hizmetleri sunmaktadır. Bu yönüyle firmaların can damarı olma özelliğini kendinde toplayan ve aynı zamanda yönetilmesi zor olan süreçlerden biridir. Bu bağlamda, firma içerisinde yürütülen inovasyon süreçlerinin başarıya ulaşabilmesi için, firmanın gerçekten ihtiyacı olan doğru projelere odaklanması ve bunlar arasından en fazla fayda sağlayacak projeyi seçmesi gerekmektedir. Ancak, proje seçimi karmaşık koşullarda çok sayıda birbirine bağlı karar içerebileceğinden, yüksek düzeyde yapılandırılmış ve belli bir modele dayalı yöntemlerle yapılması gerekmektedir.
Bu çalışmada, otomotiv yan sanayinde faaliyet gösteren Toksan Otomotiv A.Ş firmasının Ar-Ge Merkezine sunulan Ar-Ge/inovasyon proje önerilerinin değerlendirilmesi ve önceliklendirilmesi yapılarak proje portföyü için bir karar modeli oluşturmak amaçlanmıştır. Model için projelerin değerlendirilmesinde kullanılan 23 kriter ve bu kriterlere ait 97 ölçek belirlenmiştir. Belirlenen bu kriterler ve ölçeklerin sözel ifadeleri bulanık ortamlarda hem nitel hem de nicel kriterlerin değerlendirilmesini sağlayan fuzzy TOPSIS yöntemine göre değerlendirilerek sabitlenmiş ve böylelikle karar vericilerin sübjektif değerlendirmeleri en aza indirilmiştir. Sonrasında, fuzzy TOPSIS yönteminin algoritmasına göre Microsoft Excel’de makro yazılmış ve karar modeli oluşturulmuştur. Firmanın daha önce kullandığı yönteme göre değerlendirdiği ve önceliklendirdiği, beş adet Ar-Ge/İnovasyon proje önerisi oluşturulan modele göre değerlendirilmiştir. Değerlendirme sonunda yapılan önceliklendirmelerin aynı olduğu tespit edilmiş ve projeleri çok yönlü değerlendiren ve sübjektif değerlendirmeleri en aza indiren bu modelin, firmada proje portföyü oluşturmak için uygulanmasına karar verilmiştir.

Anahtar Kelimeler

R&D innovation project selection fuzzy TOPSIS

Kaynakça

  • [1] Mikkola, J. H. (2001). Portfolio management of R&D projects: implications for innovation management. Technovation, 21(7), 423-435.
  • [2] Klintong, N., Vadhanasindhu, P., & Thawesaengskulthai, N. (2012, February). Artificial intelligence and successful factors for selecting product innovation development. In 2012 Third International Conference on Intelligent Systems Modelling and Simulation (pp. 397-402). IEEE.
  • [3] Tohidi, H., & Jabbari, M. M. (2012). The important of innovation and its crucial role in growth, survival and success of organizations. Procedia Technology, 1, 535-538.
  • [4] Huvaj, M. N., & Johnson, W. C. (2019). Organizational complexity and innovation portfolio decisions: Evidence from a quasi-natural experiment. Journal of Business Research, 98, 153-165.
  • [5] Hall, D. L., & Nauda, A. (1990). An interactive approach for selecting IR&D projects. IEEE Transactions on Engineering Management, 37(2), 126-133.
  • [6] Dodgson, M. (1997). Systematic integration of the innovation press within the firm. Australian-Asia Management Center.
  • [7] Tohidi, H., Jafari, A., & Afshar, A. A. (2010). Using balanced scorecard in educational organizations. Procedia-Social and Behavioral Sciences, 2(2), 5544-5548.
  • [8] Calantone, R. J., Cavusgil, S. T., & Zhao, Y. (2002). Learning orientation, firm innovation capability, and firm performance. Industrial marketing management, 31(6), 515-524.
  • [9] Wei, C. C., & Chang, H. W. (2011). A new approach for selecting portfolio of new product development projects. Expert Systems with Applications, 38(1), 429-434.
  • [10] Mavrotas, G., & Makryvelios, E. (2020). Combining multiple criteria analysis, mathematical programming and Monte Carlo simulation to tackle uncertainty in Research and Development project portfolio selection: A case study from Greece. European Journal of Operational Research, in press.
  • [11] Souza, D. G., Silva, C. E., & Soma, N. Y. (2020). Selecting projects on the Brazilian R&D energy sector: a fuzzy-based approach for criteria selection. IEEE Access, 8, 50209-50226.
  • [12] Mitchell, R., Phaal, R., & Athanassopoulou, N. (2014, July). Scoring methods for prioritizing and selecting innovation projects. In Proceedings of PICMET'14 Conference: Portland International Center for Management of Engineering and Technology; Infrastructure and Service Integration (pp. 907-920). IEEE.
  • [13] Bhattacharyya, R. (2015). A grey theory based multiple attribute approach for R&D project portfolio selection. Fuzzy Information and Engineering, 7(2), 211-225.
  • [14] Cheng, C. H., Liou, J. J., & Chiu, C. Y. (2017). A consistent fuzzy preference relation based ANP model for R&D project selection. Sustainability, 9(8), 1352.
  • [15] Martino, J. P. (1995). Research and development project selection. Wiley.
  • [16] Loch, C. H., Pich, M. T., Terwiesch, C., & Urbschat, M. (2001). Selecting R&D projects at BMW: A case study of adopting mathematical programming models. IEEE Transactions on Engineering Management, 48(1), 70-80.
  • [17] Ali, A., Kalwani, M. U., & Kovenock, D. (1993). Selecting product development projects: Pioneering versus incremental innovation strategies. Management Science, 39(3), 255-274.
  • [18] Cooper, R., Edgett, S., & Kleinschmidt, E. (2001). Portfolio management for new product development: results of an industry practices study. R&D Management, 31(4), 361-380.
  • [19] Bellman, R. E., & Zadeh, L. A. (1970). Decision-making in a fuzzy environment. Management science, 17(4), B-141.
  • [20] Huang, C. C., & Chu, P. Y. (2011). Using the fuzzy analytic network process for selecting technology R&D projects. International journal of technology management, 53(1), 89-115.
  • [21] Liberatore, M. J., & Titus, G. J. (1983). The practice of management science in R&D project management. Management Science, 29(8), 962-974.
  • [22] Abdel-Basset, M., Atef, A., & Smarandache, F. (2019). A hybrid Neutrosophic multiple criteria group decision making approach for project selection. Cognitive Systems Research, 57, 216-227.
  • [23] Tavana, M., Khosrojerdi, G., Mina, H., & Rahman, A. (2020). A new dynamic two-stage mathematical programming model under uncertainty for project evaluation and selection. Computers & Industrial Engineering, 149, 106795.
  • [24] Hwang, C. L., & Yoon, K. (1981). Methods for multiple attribute decision making. In Multiple attribute decision making (pp. 58-191). Springer, Berlin, Heidelberg.
  • [25] Rashidi, K., & Cullinane, K. (2019). A comparison of fuzzy DEA and fuzzy TOPSIS in sustainable supplier selection: Implications for sourcing strategy. Expert Systems with Applications, 121, 266-281.
  • [26] Yayla, A. Y., Yildiz, A., & Ozbek, A. (2012). Fuzzy TOPSIS method in supplier selection and application in the garment industry. Fibres & Textiles in Eastern Europe, 20, 4(93): 20-23.
  • [27] Yıldız, A., & Demir, Y. (2019). Bulanık TOPSIS yöntemiyle Türkiye’nin yerli otomobili için en uygun fabrika yerinin seçimi. Business & Management Studies: An International Journal, 7(4), 1427-1445.
  • [28] Han, H., & Trimi, S. (2018). A fuzzy TOPSIS method for performance evaluation of reverse logistics in social commerce platforms. Expert Systems with Applications, 103, 133-145.
  • [29] Hatami-Marbini, A., & Kangi, F. (2017). An extension of fuzzy TOPSIS for a group decision making with an application to Tehran stock exchange. Applied Soft Computing, 52, 1084-1097.
  • [30] Gupta, H. (2018). Assessing organizations performance on the basis of GHRM practices using BWM and Fuzzy TOPSIS. Journal of environmental management, 226, 201-216.
  • [31] Chen, C. T. (2000). Extensions of the TOPSIS for group decision-making under fuzzy environment. Fuzzy sets and systems, 114(1), 1-9.
  • [32] Yildiz, A., & Yayla, A. (2017). Application of fuzzy TOPSIS and generalized Choquet integral methods to select the best supplier. Decision Science Letters, 6(2), 137-150.
  • [33] Sirisawat, P., & Kiatcharoenpol, T. (2018). Fuzzy AHP-TOPSIS approaches to prioritizing solutions for reverse logistics barriers. Computers & Industrial Engineering, 117, 303-318.
  • [34] Solangi, Y. A., Tan, Q., Mirjat, N. H., & Ali, S. (2019). Evaluating the strategies for sustainable energy planning in Pakistan: An integrated SWOT-AHP and Fuzzy-TOPSIS approach. Journal of Cleaner Production, 117655.
  • [35] Mitchell, R., Phaal, R., & Athanassopoulou, N. (2018). Scoring methods for evaluating and selecting early stage technology and innovation projects. Centre for Technology Management working paper series, 2, 1-19.
Toplam 35 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular Mühendislik
Bölüm Makaleler
Yazarlar

Zeynep Begüm Kurt Bu kişi benim 0000-0002-7166-3130

Aytaç Yıldız 0000-0002-0729-633X

Yayımlanma Tarihi 30 Aralık 2020
Gönderilme Tarihi 24 Kasım 2020
Yayımlandığı Sayı Yıl 2020 Cilt: 16 Sayı: 2

Kaynak Göster

APA Kurt, Z. B., & Yıldız, A. (2020). Fuzzy TOPSIS Based Decision Model for Evaluating and Prioritizing R&D / Innovation Projects, Ar-Ge/İnovasyon Projelerinin Değerlendirilmesi ve Önceliklendirilmesi İçin Fuzzy TOPSIS Tabanlı Karar Modeli. Electronic Letters on Science and Engineering, 16(2), 93-107.
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