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Verim Rassallığı ve Gecikme Yaptırım Maliyetleri: Ülke Dışında Üretim ve Komşu Ülkede Üretim Stratejilerinin Karşılaştırılması

Year 2025, Volume: 24 Issue: 53, 693 - 736, 29.09.2025
https://doi.org/10.46928/iticusbe.1654814

Abstract

Pandeminin ulaşım ve sevkiyat süreçlerinde yol açtığı yoğunluk, müşteri sipariş teslim sürelerindeki dalgalanmaların küresel tedarik zincirlerinde yol açabileceği potansiyel zararları ortaya koymuştur. Ayrıca, gecikmenin yanında teslim edilen ürün sayısının beklenenden az olması durumunda, tedarik zincirindeki hâlihazırda bozulmuş ürün akışı daha da kötüleşmektedir. Bu nedenle, özellikle sipariş teslim sürelerindeki dalgalanmalarla birlikte tecrübe edildiğinde, verim rassallığı, üzerinde durulması gereken bir husustur. Dolayısıyla, tedarikçi seçim mekanizmaları, farklı dış faktör kombinasyonları altında verim ve teslim süresi rassallığını beraber ele alarak tedarikçi performanslarını karşılaştırırsa, daha başarılı seçimler yapılacaktır. Bu etmenler doğrultusunda, tedarikçi seçiminin ortaya koyduğu performansı göstermek amacıyla, Seviye 1 tedarikçisinin Seviye 2 tedarikçisine verdiği bir sipariş için stokastik bir optimizasyon modeli oluşturulmuş; 2ᵏ faktör temsili ile senaryolar üretilerek, üretim veriminin ve ortalama teslim süresinin tedarik zinciri maliyetleri üzerindeki etkisi hesaplanmıştır. Bulgularımız, teslim süresinin artışının toplam tedarik zinciri maliyetleri üzerindeki etkisinin, üretim veriminin artışının etkisinden çok daha büyük olduğunu göstermektedir. Ayrıca, ele aldığımız diğer üç dış faktörden en az ikisi — teslimatta eksik olan her ürün başına müşteri tarafından katlanılan maliyet, sipariş verilen toplam ürün miktarı ve ürün başına katlanılan gecikme yaptırım maliyeti — yüksek olduğunda, bu iki tedarikçi performans kriterinin (teslim süresi ve üretim verimi) toplam maliyetler üzerindeki etkilerinin daha baskın hale geldiği tespit edilmiştir. Sayısal analizimiz ayrıca, teslim süresinin rassallığını betimleyen parametrelerin ve olasılık fonksiyonunun doğru tahmin edilmesine yönelik çaba göstermenin çok önemli olduğunu ortaya koymuştur. Çünkü hatalı varsayımlar, tedarik zinciri maliyetlerinin ciddi şekilde azımsanmasına yol açarak yanıltıcı sonuçlara sebep olabilmektedir.

Ethical Statement

Bu çalışma, bilimsel etik kurallarına uygun olarak hazırlanmıştır. Araştırma sürecinde intihal, sahtecilik, çarpıtma, tekrar yayın, bölerek yayınlama, haksız yazarlık gibi etik dışı davranışlardan kaçınılmıştır. Makalede yer alan tüm kaynaklar doğru biçimde atıf yapılmış ve akademik dürüstlük ilkelerine riayet edilmiştir. Çalışmada herhangi bir etik kurul onayı gerektiren insan veya hayvan deneklerine yönelik veri bulunmamaktadır.

Supporting Institution

Yoktur

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Yield Uncertainty and Tardiness Penalties: A Comparison of Offshoring and Nearshoring

Year 2025, Volume: 24 Issue: 53, 693 - 736, 29.09.2025
https://doi.org/10.46928/iticusbe.1654814

Abstract

The extreme congestion caused by the pandemic at shipment routes demonstrated the potential damage fluctuations in customer order lead time could cause in global supply chains. Furthermore, if the order arrives with missing items, it will exacerbate the already disrupted flow of goods in the supply chain. Hence, yield uncertainty needs to be considered, especially when coupled with lead time fluctuations. Thus, supplier selection mechanisms could benefit from a comparison of supplier performances combining yield and lead time uncertainty under different combinations of other factors. To demonstrate the consequences of supplier selection based on these effects, we developed a stochastic optimization model for an order placed by a Tier 1 supplier with a Tier 2 supplier. We generated scenarios via 2k factor representation to calculate the effect of increasing production yield and mean lead time on supply chain costs. We have found that the magnitude of the effect of increasing the lead time on total supply chain costs was much higher than the magnitude of the effect of increasing the production yield. We further found that the magnitude of the effects of these two supplier characteristics was more pronounced if at least two of the other three factors -unit underage cost, order amount, and unit procurement or tardiness cost- were high. Our numerical analysis also demonstrated that it was very important to put effort into estimating lead time parameters and distribution correctly, as making incorrect assumptions could cause severe underestimation of supply chain costs.

Ethical Statement

This study has been conducted in accordance with the principles of scientific ethics. Throughout the research process, unethical practices such as plagiarism, fabrication, falsification, redundant publication, salami slicing, and unjustified authorship have been strictly avoided. All sources cited in the article have been properly referenced, and academic integrity has been upheld. The study does not involve any data requiring ethical committee approval, such as human or animal subjects.

Supporting Institution

None

References

  • Baker, K. R., & Trietsch, D. (2013). Principles of sequencing and scheduling. John Wiley & Sons.
  • Barad, M., & Braha, D. (1996). Control limits for multi-stage manufacturing processes with binomial yield (Single and multiple production runs). The Journal of the Operational Research Society, 47(1), 98-112. https://doi.org/10.1057/jors.1996.9
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  • Barkho, G. (2021, December 16). Chain reactions: Exploding Kittens’ Carly McGinnis on diversifying manufacturing. Modern Retail. https://www.modernretail.co/retailers/chain-reactions-exploding-kittens-carly-mcginnis-on-diversifying-manufacturing/. Accessed July 23, 2025.
  • Boute, R. N., Disney, S. M., Gijsbrechts, J., & Van Mieghem, J. A. (2022). Dual sourcing and smoothing under nonstationary demand time series: Reshoring with SpeedFactories. Management Science, 68(2), 1039–1057. https://doi.org/10.1287/mnsc.2020.3951
  • Cakanyildirim, M., Bookbinder, J. H., & Gerchak, Y. (2000). Continuous review inventory models where random lead time depends on lot size and reserved capacity. International Journal of Production Economics, 68(3), 217-228. https://doi.org/10.1016/S0925-5273(99)00112-7
  • Chambers, S. (2022, January 12). Maersk provides a snapshot of growing port congestion around the world. Splash247. https://splash247.com/maersk-provides-a-snapshot-of-growing-port-congestion-around-the-world/. Accessed July 23, 2025.
  • Chen, H., Hsu, C. W., Shih, Y. Y., & Caskey, D. A. (2022). The reshoring decision under uncertainty in the post-COVID-19 era. Journal of Business & Industrial Marketing, 37(10), 2064–2074. https://doi.org/10.1108/JBIM-01-2021-0066
  • Chen, L., & Hu, B. (2017). Is reshoring better than offshoring? The effect of offshore supply dependence. Manufacturing & Service Operations Management, 19(2), 166–184. https://doi.org/10.1287/msom.2016.0604
  • Choi, S., Jeon, S., Kim, J., & Park, K. (2019). A newsvendor analysis of a binomial yield production process. European Journal of Operational Research, 273(3), 983-991. https://scholar.korea.ac.kr/handle/2021.sw.korea/26229
  • Clemens, J., & Inderfurth, K. (2015). Supply chain coordination by contracts under binomial production yield. Business Research, 8(2), 301-332. https://doi.org/10.1007/s40685-015-0023-2
  • Cohen, M. A., & Lee, H. L. (1988). Strategic analysis of integrated production-distribution systems: Models and methods. Operations Research, 36(2), 216-228. https://doi.org/10.1287/opre.36.2.216
  • da Rocha, A., da Fonseca, L. N. M., & Kogut, C. S. (2025). Deciphering relocation paths: A systematic literature review of near-shoring and friend-shoring. Journal of International Management, 31(5), Article 101282. https://doi.org/10.1016/j.intman.2025.101282
  • Dettenbach, M. (2015). The value of supply chain visibility when yield is random [Doctoral dissertation, University of Cologne]. Logos Verlag Berlin GmbH. https://kups.ub.uni-koeln.de/6100/1/Dissertation_Marcus_Dettenbach_-_Druckexemplar.pdf. Accessed July 23, 2025.
  • Dunning, J. H. (1988). Explaining international production (Routledge Revivals). Routledge.
  • Dunning, J. H. (1998). Location and the multinational enterprise: A neglected factor? Journal of International Business Studies, 29(1), 45–66.
  • Ellram, L. M., Tate, W. L., & Petersen, K. J. (2013). Offshoring and reshoring: An update on the manufacturing location decision. Journal of Supply Chain Management, 49(2), 14–22. https://doi.org/10.1111/jscm.12019
  • Eppen, G. D., & Martin, R. K. (1988). Determining safety stock in the presence of stochastic lead time and demand. Management Science, 34(11), 1380-1390. https://doi.org/10.1287/mnsc.34.11.1380
  • Foster, P. (2024, December 3). UK chemicals sector doubts Keir Starmer’s ‘reset’ will end Brexit blues. Financial Times. https://www.ft.com/content/401fca38-d156-4128-b46e-7682a30a3d66#comments-anchor. Accessed July 23, 2025.
  • Fratocchi, L., Di Mauro, C., Barbieri, P., Nassimbeni, G., & Zanoni, A. (2014). When manufacturing moves back: Concepts and questions. Journal of Purchasing and Supply Management, 20(1), 54–59. https://doi.org/10.1016/j.pursup.2014.01.004
  • Grandinetti, R., & Tabacco, R. (2015). A return to spatial proximity: Combining global suppliers with local subcontractors. International Journal of Globalisation and Small Business, 7(2), 139–161. https://doi.org/10.1504/IJGSB.2015.071189
  • Grosfeld-Nir, A., & Gerchak, Y. (2004). Multiple lotsizing in production to order with random yields: Review of recent advances. Annals of Operations Research, 126(1), 43-69. https://doi.org/10.1023/B:ANOR.0000012275.01260.f5
  • Hekimoğlu, M., van der Laan, E., & Dekker, R. (2018). Markov-modulated analysis of a spare parts system with random lead times and disruption risks. European Journal of Operational Research, 269(3), 909-922. https://doi.org/10.1016/j.ejor.2018.02.040
  • Hilletofth, P., Sequeira, M., & Adlemo, A. (2019). Three novel fuzzy logic concepts applied to reshoring decision-making. Expert Systems with Applications, 126, 133–143. https://doi.org/10.1016/j.eswa.2019.02.018
  • Hilletofth, P., Sequeira, M., & Tate, W. (2021). Fuzzy-logic-based support tools for initial screening of manufacturing reshoring decisions. Industrial Management & Data Systems, 121(5), 965–992. https://doi.org/10.1108/IMDS-05-2020-0290
  • Ivănescu, V. C., Fransoo, J. C., & Bertrand, J. W. M. (2006). A hybrid policy for order acceptance in batch process industries. OR Spectrum, 28(2), 199-222. https://doi.org/10.1007/s00291-005-0015-2
  • Jakšič, M., & Fransoo, J. C. (2018). Dual sourcing in the age of near-shoring: Trading off stochastic capacity limitations and long lead times. European Journal of Operational Research, 267(1), 150-161. https://doi.org/10.1016/j.ejor.2017.11.030
  • Kaivo-Oja, J., Knudsen, M. S., & Lauraéus, T. (2018). Reimagining Finland as a manufacturing base: The nearshoring potential of Finland in an Industry 4.0 perspective. Business, Management and Economics Engineering, 16, 65–80. DOI: 10.3846/bme.2018.2480
  • Kang, Y., Albey, E., & Uzsoy, R. (2018). Rounding heuristics for multiple product dynamic lot-sizing in the presence of queueing behavior. Computers & Operations Research, 100, 54-65. https://doi.org/10.1016/j.cor.2018.07.019
  • Law, A. M. (2017, December). A tutorial on design of experiments for simulation modeling. In W. K. V. Chan, A. D’Ambrogio, G. Zacharewicz, N. Mustafee, G. Wainer, & E. Page (Eds.), 2017 Winter Simulation Conference (WSC) (pp. 550-564). IEEE. DOI: 10.1109/WSC.2017.8247814
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There are 55 citations in total.

Details

Primary Language English
Subjects Production and Operations Management
Journal Section Research Article
Authors

Orkun Bayram 0000-0001-9958-7822

Publication Date September 29, 2025
Submission Date March 10, 2025
Acceptance Date September 13, 2025
Published in Issue Year 2025 Volume: 24 Issue: 53

Cite

APA Bayram, O. (2025). Yield Uncertainty and Tardiness Penalties: A Comparison of Offshoring and Nearshoring. İstanbul Ticaret Üniversitesi Sosyal Bilimler Dergisi, 24(53), 693-736. https://doi.org/10.46928/iticusbe.1654814