Türk Silahlı Kuvvetleri ve Özel Sektör Lojistiğinin Entegrasyonu İçin SWOT ve Bulanık Tercih İlişkileri Analizi

Yıl 2024, , 653 - 664, 27.03.2024

Atilla Gürdere Ahmet İlbaş Fatih Emre Boran

https://doi.org/10.2339/politeknik.1193146

Cited By: 1

Öz

Günümüz ülke silahlı kuvvetlerinin başarısını etkileyen önemli unsurlardan bir tanesi de ihtiyaç duyulan makine, teçhizat ve silah sistemlerinin doğru yer ve zamanda, uygun miktar ve finansal koşullarla tedarik edilmesini sağlayan lojistik faaliyetlerdir. Eğer lojistik faaliyetler etkin ve sürekli bir şekilde planlanıp icra edilemez ise ön cephede ne kadar başarılı olunursa olunsun, başarıyı sürekli kılmak mümkün olmayacaktır. Dünyada silahlı kuvvetlerin lojistik desteği, 2000’li yılların başına kadar geleneksel yöntemlerle, yani Silahlı Kuvvetlerin kendi olanakları ile karşılanmaya çalışılmıştır. Fakat 2000’li yıllardan itibaren, özel sektör lojistik destek firmalarının kendilerini geliştirmeye başlamaları ile birlikte geleneksel yöntem terk edilmeye başlanmış ve Performansa Dayalı Lojistik Sistem (PDL) ile silahlı kuvvetlerinin lojistik desteğini sağlamaya başlamıştır. Bu kapsamda Türk Silahlı Kuvvetleri (TSK) bünyesinde lojistik faaliyetlerin icrasında bu yeni döneme yönelik süreçler başlatılmıştır. Bu çalışmada TSK bünyesinde yürütülen lojistik faaliyetler SWOT analizi ile detaylı olarak incelenmiştir. Yapılan değerlendirme ile güçlü, zayıf yönler, fırsatlar ve tehditler kendi içerisinde bulanık tercih ilişkisi yöntemi kullanılarak önceliklendirilmiştir. Çalışma ile elde edilen bulgular hangi faaliyetlere önem verilerek sürdürülmesi, zayıf yönlerden hangisinin öncelikli olarak giderilmesi gerektiği ile ilgili bilgiler sunmuştur.

Anahtar Kelimeler

Lojistik, performansa dayalı lojistik, SWOT analizi, bulanık kümeler, bulanık tercih ilişkisi, performance-based logistics, SWOT analysis, fuzzy sets, fuzzy preference relation

SWOT and Fuzzy Preference Relations Analysis for the Integration of Turkish Armed Forces and Private Sector Logistics

Öz

One of the most important factors affecting the success of today’s armed forces of countries is the logistics activities that ensure the supply of the required machinery, equipment and weapon systems at the right place and time, with the appropriate quantity and financial conditions. If the logistics activities are not planned and executed effectively and continuously, it would not be possible to sustain success no matter how successful it is on the front line. Until the beginning of the 2000s, the logistics support of the armed forces in the world was tried to be met with traditional methods, namely the armed forces own resources. However, since the 2000’s, as the private sector logistics support companies have started to develop themselves, the traditional methods begun to be abandoned and replaced with the performance-based logistics system for the support of the armed forces’ logistics. In this context, new processes have been initiated for the execution of the logistics activities within Turkish Armed Forces regarding this new era. This study examines the logistic activities carried out in Turkish Armed Forces with SWOT analysis in detail. In this evaluation, strengths, weaknesses, opportunities and threats were prioritized with fuzzy preference relationship method. The findings obtained in this study provided such information as which activities to be pursued with emphasis, and which weaknesses to be eliminated primarily.

Anahtar Kelimeler

Logistics, performance-based logistics, SWOT analysis, fuzzy sets, fuzzy preference relation

Kaynakça

• [1]. Yalçın, G.D., "Lojistik Sektör İncelemesi Kapsamında İstanbul-Hadımköy Örneği", Yüksek Lisans Tezi, Bahçeşehir Üniversitesi Fen Bilimleri Enstitüsü, (2015).
• [2]. Nowicki, D., Kumar, U.D., Steudel, H., and Verma, D., "Spares provisioning under performance-based logistics contract: profit-centric approach", Journal of the Operational Research Society, 59(3): 342-352, (2008).
• [3]. Mirzahosseinian, H. and Piplani, R., "A study of repairable parts inventory system operating under performance-based contract", European Journal of Operational Research, 214(2): 256-261, (2011).
• [4]. Jin, T., Nalajala, N., and Jimenez, J.A. "A multi-criteria approach for performance based maintenance with variable fleet size", The Proceedings of 2011 9th International Conference on Reliability, Maintainability and Safety. 909-914, (2011).
• [5]. Kim, S.-H., Cohen, M.A., and Netessine, S., "Reliability or inventory? an analysis of performance-based contracts for product support services", Handbook of information exchange in supply chain management, Springer, (2017).
• [6]. Hur, M., Keskin, B.B., and Schmidt, C.P., "End-of-life inventory control of aircraft spare parts under performance based logistics", International Journal of Production Economics, 204: 186-203, (2018).
• [7]. Sharifi, M. and Kwon, R.H., "Performance-based contract design under cost uncertainty: A scenario-based bilevel programming approach", The Engineering Economist, 63(4): 291-318, (2018).
• [8]. Patra, P., Kumar, U.D., Nowicki, D.R., and Randall, W.S., "Effective management of performance-based contracts for sustainment dominant systems", International Journal of Production Economics, 208: 369-382, (2019).
• [9]. Wang, J., Zhao, X., and Guo, X., "Optimizing wind turbine's maintenance policies under performance-based contract", Renewable energy, 135: 626-634, (2019).
• [10]. Agdas, M. and Gencer, C., "A dynamic performance evaluation model suggestion for performance-based logistics", Kybernetes, (2021).
• [11]. Oğultürk, C., "Lojistik hizmetlerin özelleştirilmesi kapsamında Türk Silahlı Kuvvetleri’nde dış kaynak kullanımı ve özel askeri şirketler", Güvenlik Stratejileri Dergisi, 11(22), (2015).
• [12]. Bingöl, O. and Varlık, A.B., "Türk Savunma Sanayinin Geleceği Sürdürülebilir ve Güçlü İhracat İçin Strateji Raporu-1", TASAM, Stratejik Rapor, (71): 1-94, (2015).
• [13]. Zadeh, L.A., "Fuzzy sets", Information and control, 8(3): 338-353, (1965).
• [14]. Saaty, T.L., "What is the analytic hierarchy process?", Mathematical models for decision support, Springer, (1988).
• [15]. Orlovsky, S.A., "Decision-making with a fuzzy preference relation", Fuzzy Sets and Systems, 1(3): 155-167, (1978).
• [16]. Dubois, D.J., "Fuzzy sets and systems: theory and applications", 144, Academic Press, (1980).
• [17]. Tanino, T., "Fuzzy preference orderings in group decision making", Fuzzy Sets and Systems, 12(2): 117-131, (1984).
• [18]. Xu, Z. and Wei, C., "A consistency improving method in the analytic hierarchy process", European Journal of Operational Research, 116(2): 443-449, (1999).
• [19]. Xu, Z., "Method for group decision making with various types of incomplete judgment matrices", Control and Decision, 21: 28-33, (2006).
• [20]. Herrera, F., Herrera-Viedma, E., and Chiclana, F., "Multiperson decision-making based on multiplicative preference relations", European Journal of Operational Research, 129(2): 372-385, (2001).
• [21]. Chiclana, F., Herrera, F., and Herrera-Viedma, E., "Integrating three representation models in fuzzy multipurpose decision making based on fuzzy preference relations", Fuzzy Sets and Systems, 97(1): 33-48, (1998).
• [22]. Chiclana, F., Herrera, F., and Herrera-Viedma, E., "Integrating multiplicative preference relations in a multipurpose decision-making model based on fuzzy preference relations", Fuzzy Sets and Systems, 122(2): 277-291, (2001).
• [23]. Chiclana, F., Herrera-Viedma, E., Alonso, S., and Herrera, F., "Cardinal Consistency of Reciprocal Preference Relations: A Characterization of Multiplicative Transitivity", IEEE Transactions on Fuzzy Systems, 17(1): 14-23, (2009).
• [24]. Klement, E.P., Mesiar, R., and Pap, E., "On the relationship of associative compensatory operators to triangular norms and conorms", International Journal of Uncertainty, Fuzziness and Knowledge- Based Systems, 4(02): 129-144, (1996).