İNOVASYONDAN İMALATA GEÇİŞTE TÜRK SANAYİNDE ÖNEMLİ BİR BİLEŞEN OLAN KALIPÇILIK SEKTÖRÜNÜN ULUSLARARASI REKABETÇİLİK ANALİZİ

Abstract

Amaç - Söz konusu makalenin amacı, ileri zaman serisi analizi ve optimizasyon teknikleri yardımıyla kalıpçılık sektöründeki işletmelerin gelecek 10 yıllık dönemde rekabet gücünün nasıl geliştireceğine yönelik alternatif modellerin geliştirilmesi ve ilgili karar yapıcılarının hizmetine sunulabilir hale getirilmesidir. Yöntem - Doğrusal olmayan ARDL (NARDL) modeli yardımıyla kalıp ihracatının bileşenleri asimetrik etkileri hesaba katacak biçimde ele alınmıştır. EURO/TRY döviz kuru modelde hesaba katılırken, Almanya, Fransa ve İtalya’nın GSYİH’ları da NARDL modelinde bulunmaktadır. Asimetrik etkilerin hesaba katılması hususunda ise kümülatif toplamlar yaklaşımı dikkate alınmıştır. Bulgular - İtalya’nın GSYHİ’nın düşmesinin ülkede kalıpçılık sektörünü olumsuz etkileyeceği ve yerli üreticilere kauçuk ve plastik ürünlerin imalatı ve ihracatı kapsamında bir fırsat doğurabileceği öne sürülebilmektedir. Söz konusu bulgulara uygun olarak, kauçuk ve plastik ürünlerin imalatı ve ihracatı ile modelin diğer değişkenleri arasında genel anlamda herhangi bir asimetrik etki kısa ve uzun vade için geçerli değildir. Sonuç - Kalıp üretimi ve ihracatı kapsamında talebin önemli belirsizlikler taşıyor olması, işletmelerin bu süreçte üretim fonksiyonundaki toplam faktör verimliliklerini artırmaya odaklanmaları gerektiğinin altını çizmektedir.

Keywords

• NARDL
• asimetri
• kümülatif toplamlar yaklaşımı
• kalıpçılık sektörü
• rekabet analizi

INTERNATIONAL COMPETITIVENESS ANALYSIS OF THE MOLDING INDUSTRY: AN IMPORTANT COMPONENT OF TURKISH INDUSTRY IN THE TRANSITION FROM INNOVATION TO MANUFACTURING

Abstract

Purpose – The purpose of this article is to develop alternative models for how to improve the competitiveness of enterprises in the molding sector within the next 10 years by using advanced time series analysis and optimization techniques and to make them available to the relevant policy makers. Methodology – With the help of the non-linear ARDL (NARDL) model, the components of the mold export are analyzed to take into account the asymmetric effects. While the EURO/TRY exchange rate is considered in the model, the GDPs of Germany, France and Italy are also included in the NARDL model. The cumulative sums approach is taken into consideration in the context of the asymmetric effects. Findings – It can be argued that the decrease in the GDP of Italy will adversely affect the molding industry in the country and may create an opportunity for domestic producers within the scope of the manufacture and export of rubber and plastic products. In accordance with the findings, any asymmetric effect in general terms between the manufacture and export of rubber and plastic products and other variables of the model is not present for the short- and long-run. Conclusion – The fact that the demand has significant uncertainties within the scope of mold production and export underlines the need for enterprises to focus on increasing their total factor productivity in this process.

Keywords

• NARDL
• asymmetry
• cumulative sums approach
• mold sector
• competitiveness analysis

References

1. Alexandrov, I.A., Mikhailov, M.S. & Oleinik, A.V. (2020). Application of neural simulation methods for technological parameters identification of composite products injection molding process. Journal of Applied Engineering Science, 18(2), 165-172. DOI: 10.5937/jaes18-25912
2. Alp, S. (2005). Kalıpçılık Sektör Araştırması. İstanbul Ticaret Odası, İstanbul.
3. Butler, M.J. (1973). Mold costs and how to estimate accurately. Plastic Polymers, 41, 60-61.
4. Chen, Y.M. & Liu, J.J. (1999). Cost-effective design for injection molding. Robotics and Computer-Integrated Manufacturing, 15(1), 1-21. DOI: 10.1016/S0736-5845(99)00005-8
5. Chin, K.S. & Wong, T.N. (1995). An expert system for injection mold cost estimation. Advances in Polymer Technology, 14(4), 303-314. DOI: 10.1002/adv.1995.060140404
6. Chin, K.S. & Wong, T.N. (1996). Developing a knowledge-based injection mould cost estimation system by decision tables. The International Journal of Advanced Manufacturing Technology, 11, 353-365. DOI: 10.1007/BF01845694
7. Fagade, A.A. & Kazmer, D. (2000). Early cost estimation for injection molded components. Journal of Injection Molding Technology, 4(3), 97-106.
8. Hatemi-J, A. (2012). Asymmetric Causality Tests with an Application. Empirical Economics. 43(1): 447-456. DOI: 10.1007/s00181-011-0484-x

9. Kang, K.S., Kim, T.H. & Rhee, I.K. (1994). The establishment of standard time in die manufacturing process using standard data. Computers & Industrial Enginering, 26, 539-542. DOI: 10.1016/0360-8352(94)90353-0
10. Kuzman, K. & Nardin, B. (2004). Determination of manufacturing Technologies in mould manufacturing. Journal of Materials Processing Technology, 157-158, 573-577. DOI: 10.1016/j.jmatprotec.2004.07.116
11. Kwak, K., Kim, W. & Kim, K. (2018). Latecomer firms' combination of strategies in a specialized suppliers sector: A comparative case study of the Korean plastic injection molding machine industry. Technological Forecasting and Social Change, 133, 190-205. DOI: 10.1016/j.techfore.2018.04.004
12. Pilani, R., Narasimhan, K., Maiti, S.K., Singh, U.P. & Date, P.P. (2000). A hybrid intelligent systems approach for die design in sheet metal forming. The International Journal of Advanced Manufacturing Technology, 16, 370-375. DOI: 10.1007/s001700050168
13. Raviwongse, R. & Allada, V. (1997). Artificial neural network based model for computation of injection mould complexity. The International Journal of Advanced Manufacturing Technology, 13, 577-586. DOI: 10.1007/BF01176302
14. Schützer, K., Helleno, A.L. & Pereira, S.C. (2006). The influence of the manufacturing strategy on the production of molds and dies. Journal of Materials Processing Technology, 179(1-3), 172-177. DOI: 10.1016/j.jmatprotec.2006.03.098
15. Tiengtavaj, S., Phimonsathienand, T. & Fongsuwan, W. (2017). Ensuring competitive advantage through innovation capability and clustering in the thai automotive parts molding industry: a SEM approach. Management and Production Engineering Review, 8(1), 89-100. DOI: 10.1007/s00170-018-2762-7
16. Tosello, G., Charalambis, A., Kerbache, L., Mischkot, M., Pedersen, D.B., Calaon, M. & Hansen, H.N. (2019). Value chain and production cost optimization by integrating additive manufacturing in injection molding process chain. The International Journal of Advanced Manufacturing Technology, 100, 783-795. DOI: 10.1007/s00170-018-2762-7
17. Vogelsang, T.J. (1993). Unpublished computer program.
18. Wang, H., Ruan, X.Y. & Zhou, X.H. (2003). Research on Injection Mould Intelligent Cost Estimation System and Key Technologies. The International Journal of Advanced Manufacturing Technology, 21, 215-222. DOI: 10.1007/s001700300024
19. Yao, D., Kim, B., Choi, J. & Brown, R. (2008). Optimizing Injection Molding Toward Multiple Quality and Cost Issues. Polymer-Plastics Technology and Engineering, 38(5), 955-966. DOI: 10.1080/03602559909351624
20. Zheng, J., Wang, Q., Zaho, P. & Wu, C. (2009). Optimization of high-pressure die-casting process parameters using artificial neural network. The International Journal of Advanced Manufacturing Technology, 44, 667-674. DOI: 10.1007/s00170-008-1886-6