The Comparison of Push and Pull Systems in Machine Made Carpet Industry: A Case Study from Türkiye

Abstract

A multitude of successive processes and systems have been developed in order to meet customer needs. Two of these systems are classified as push and pull systems. The mechanisms of production and distribution are subject to a constant paradigm shift between "production for supply (push)" and "production on demand (pull)”. The present study measured and compared the values of different outputs resulting from the application of both push-based and pull-based systems in a company in Türkiye. Furthermore, the financial performance of the relevant systems was evaluated using the ENTROPY-COPRAS method. Despite the inherent advantages and disadvantages of both systems, the findings indicate that the pull system-based model is particularly well-suited to the relevant sector. This model is characterized by its fast, transparent, predictable and sustainable nature, while also ensuring the more sensitive and efficient use of resources. This study is pioneering in its approach, as it is the first to compare these two systems using different parameters for the machine-made carpet industry. In this context, it is hoped that it will contribute to the strategy development of industry players in terms of supply chain management, as well as to researchers.

Keywords

• Supply chain management
• Push-pull systems
• Machine-made carpet industry.

Makine Halısı Sektöründe İtme ve Çekme Sistemlerinin Karşılaştırılması: Türkiye'den Bir Vaka Analizi

Öz

Müşteri ihtiyaçlarını karşılamak amacıyla çok sayıda ardışık süreç ve sistem geliştirilmiştir. Bu sistemlerden ikisi, itme ve çekme sistemleri olarak sınıflandırılmaktadır. Üretim ve dağıtım mekanizmaları, sürekli olarak "arz için üretim (itme)" ile "talep üzerine üretim (çekme)" yaklaşımları arasında paradigmal bir değişim geçirmektedir. Bu çalışma, Türkiye’de faaliyet gösteren bir şirkette hem itme temelli hem de çekme temelli sistemlerin uygulanması sonucunda elde edilen farklı çıktıların değerlerini ölçmüş ve karşılaştırmıştır. Ayrıca, ilgili sistemlerin finansal performansı ENTROPI-COPRAS hibrid yöntemi kullanılarak değerlendirilmiştir. Her iki sistemin doğasında bulunan avantaj ve dezavantajlara rağmen, bulgular çekme sistemi temelli modelin ilgili sektör için özellikle uygun olduğunu göstermektedir. Bu model; hızlı, şeffaf, öngörülebilir ve sürdürülebilir bir yapıya sahip olmasının yanı sıra, kaynakların daha duyarlı ve verimli bir şekilde kullanılmasını da sağlamaktadır. Bu çalışma, makine halıcılığı sektöründe söz konusu iki sistemin farklı parametreler temelinde karşılaştırıldığı ilk araştırma olması bakımından öncü niteliktedir. Bu bağlamda, hem sektörde faaliyet gösteren aktörlerin tedarik zinciri yönetimi stratejilerine katkı sağlaması hem de araştırmacılara yeni bir perspektif sunması öngörülmektedir.

Anahtar Kelimeler

• Tedarik Zinciri Yönetimi
• İtme-Çekme Sistemleri
• Makine Halısı Sektörü.

References

1. Abadi, M., Huang, J., Yeow, J., Mohandes, S.R., and Zhang, L. (2025). Towards a complex push-to-pull dynamics in circular construction supply chains: a systematic literature review. Engineering, Construction and Architectural Management, 32(2), 912-938.
2. Alam, M. R. N., Hardi, R., and Sumardi, S. (2022). The utilization of complex proportional assessment (COPRAS) in determining the selection of the best speaker. Multica Science and Technology, 2(1), 30-37.
3. Alnıpak, S. ve Toraman, Y. (2023). Türkiye’deki İlk 10 Konteyner Limanının Toplam Faktör Verimliliğindeki Değişim: Pandemi Dönemi Ve Sonrasına Yönelik Bir Çalışma. Yorulmaz, M. (Ed.), Deniz İşletmeciliği Ve Yönetiminde Güncel Yaklaşımlar. 89 -109. ISBN:978-625-6939-69-1.
4. Anwar, U. A. A., Rahayu, A., Wibowo, L. A., et al. (2025). Supply chain integration as the implementation of strategic management in improving business performance. Discover Sustainability, 6, 101.
5. Ayçin, E. (2019). BIST Menkul Kıymet Yatırım Ortaklıkları Endeksinde (XYORT) Yer Alan İşletmelerin Finansal Performanslarının Entropi ve Gri İlişkisel Analiz Bütünleşik Yaklaşımı İle Değerlendirilmesi. Dokuz Eylül Üniversitesi İktisadi İdari Bilimler Fakültesi Dergisi, 33(2), 595-622.
6. Bahçeci, A., & Belgin, Ö. (2021). Organize Sanayi Bölgesi (OSB) İçinde Kurulması Planlanan Bir Teknopark’ın Gelişimi İçin En Uygun OSB’nin Entropi Tabanlı TOPSIS Yöntemi İle Seçimi. Dumlupınar Üniversitesi Sosyal Bilimler Dergisi(68), 153-163.
7. Bebitoğlu, M. E. (2022). Contemporary aspects of entrepreneurship and innovation management. Nobel Akademik Yayıncılık.
8. Cachon, G. (2004). The allocation of inventory risk in a supply chain: Push, pull and advance-purchase discount contracts. Management Sciences, 50(2), 222–238.

9. Ch, M. R., Prasad, K. D., and Subbaiah, K. V. (2024). Application of integrated entropy-COPRAS and ANN approaches for maximizing wire EDM machinability attributes of Al6082-T6/GNP/TiB2 composites. Engineering Research Express, 6(3), 035535.
10. Chiadamrong, N., and Kohly, P. (2005). A comparison of the push and pull production systems at their optimal designs under the economic consideration. ASEAN Journal on Science and Technology for Development, 22 (4), 313-330.
11. Chiang, A.-H., and Huang, M.-Y. (2021), Demand-pull vs supply-push strategy: the effects of organizational structure on supply chain integration and response capabilities. Journal of Manufacturing Technology Management, 32(8), 1493-1514.
12. Choi, T. Y., Netland, T. H., Sanders, N., Sodhi, M. S., and Wagner, S. M. (2023). Just‐in‐time for supply chains in turbulent times. Production and Operations Management, 32(7), 2331-2340
13. Chopra, S., and Meindl, P. (2013). Supply Chain Management: Strategy, Planning and Operation. (5th ed.) Pearson Education.
14. Christopher, M. (2016). Logistics and Supply Chain Management. Pearson. Cochran, J. K., and Kaylani, H. A. (2008). Optimal design of a hybrid push/pull serial manufacturing system with multiple part types. International Journal of Production Research, 46(4), 949-965.
15. Çağlıyan, V., Attar, M., and Abdul-Kareem, A. (2022). Assessing the mediating effect of sustainable competitive advantage on the relationship between organisational innovativeness and firm performance. Competitiveness Review, 32(4), 618-639.
16. Dayı, F., and Adaiem, N. (2024). Profitability analysis of markets traded on Borsa Istanbul. İnsan ve Toplum Bilimleri Araştırmaları Dergisi, 13(1), 130-151.
17. Demir, G. and Arslan, R. (2022). Sensitivity Analysis in Multi-Criterion Decision-Making Problems. Ankara Hacı Bayram Veli Üniversitesi İktisadi Ve İdari Bilimler Fakültesi Dergisi, 24(3), 1025-1056.
18. Dong, Y., and Xu K. (2002). A supply chain model of vendor managed inventory. Transportation Research Part E. Logistics and Transportation Review, 38(2), 75-95.
19. Eliyan, A. F., Haouari, M., & Sleiti, A. (2024). Decarbonizing Public Transportation: A Multi-Criteria Comparative Analysis of Battery Electric Buses and Fuel Cell Electric Buses. Sustainability, 16(21), 9354.
20. Fowler, J.W., Kim, S-H., and Shunk, D-L. (2019). Design for customer responsiveness: Decision support system for push–pull supply chains with multiple demand fulfillment points. Decision Support Systems, 123, 113071.
21. GAIB (Güneydoğu Anadolu İhracatçı Birlikleri). (2022). Halı istatistikleri. https://www.gaib.org.tr/tr/istatistik-ve-raporlar.html
22. Gaur, L., and Tyagi, A. (2022). Lean and green in supply chain management: Push and pull mechanism. In K. Mathiyazhagan, K. E. K. Vimal, H. Kumar, A. Ramesh, & V. Agarwal (Eds.), Lean and green manufacturing: Towards eco-efficiency and business performance (pp. 177–186). Springer. https://doi.org/10.1007/978-981-16-8150-7_14
23. Goswami, S. S., Behera, D. K., Afzal, A., Razak Kaladgi, A., Khan, S. A., Rajendran, P., Subbiah, R., and Asif, M. (2021). Analysis of a robot selection problem using two newly developed hybrid MCDM models of TOPSIS-ARAS and COPRAS-ARAS. Symmetry, 13(8), 1331.
24. Gül, A., and Yerdelen Kaygın, C. (2024). Analysis of the financial performances of real estate investment trusts critic aras and copras methods: the case of BIST. Journal of Business Research-Turk, 16 (4), 2062-2076.
25. Hezer, S., Gelmez, E., and Özceylan,E. (2021). Comparative analysis of TOPSIS, VIKOR and COPRAS methods for the COVID-19 regional safety Assessment. Journal of Infection and Public Health, 14(6). 775-786.
26. Hipólito, T., Nabais, J. L., Carmona-Benítez, R., Botto, M. A., and Negenborn, R. R. (2020). A centralised model predictive control framework for logistics management of coordinated supply chains of perishable goods. International Journal of Systems Science: Operations and Logistics, 9(1), 1–21.
27. Hopp, J.W., and Spearman, M.L. (2004). To pull or not to pull: what is the question?. Manufacturing and Service Operations Management, 6(2), 133-148.
28. Khedr, A.M., and Sheeja Rani, S. (2024). Enhancing supply chain management with deep learning and machine learning techniques: A review. Journal of Open Innovation: Technology, Market and Complexity, 10(4),100379.
29. Kim, S-H, Fowler, J.W., Shunk, D.L., and Pfund, M.E. (2012). Improving the push–pull strategy in a serial supply chain by a hybrid push–pull control with multiple pulling points. International Journal of Production Research, 50(19), 5651-5668.
30. Lariviere, M., and Porteus, E.L. (2001). Selling to the newsvendor: An analysis of price-only contracts. Manufacturing and Service Operations Management, 3(4), 293-305.
31. Li, J-W. (2003). Simulation-based comparison of push and pull systems in a job-shop environment considering the context of JIT implementation. International Journal of Production Research, 41(3), 427-447.
32. Lim, S.F.W.T., Lawson, B., and Barratt, M. (2017). Revisiting theories of governance for last-mile supply networks in omnichannel retailing. Academy of Management Annual Meeting Proceedings 2017(1), 12330.
33. Lin, J., Zhou, L., Spiegler, V.L.M. Naim, M.M., and Syntetos, A. (2022). Push or pull? The impact of ordering policy choice on the dynamics of a hybrid closed-loop supply chain. European Journal of Operational Research, 300(1), 282-295.
34. Mahapatra, S., Yu, D.Z., and Mahmoodi, F. (2012). Impact of the pull and push-pull policies on the performance of a three-stage supply chain. International Journal of Production Research, 50(16), 4699-4717.
35. Masuchun, W., Davis, S., and Patterson, J.W. (2004). Comparison of push and pull control strategies for supply network management in a make-to-stock environment. International Journal of Production Research, 42(20), 4401-4419.
36. Medetoğlu, B., Kavas, Y. B., and Öztürk, M. (2023). Financial performance determination with COPRAS and MOORA methods: an application in bist paper and paper products printing sector. Sinop Üniversitesi Sosyal Bilimler Dergisi, 7(1), 173-196.
37. Minculete, G., and Olar, P. (2016). Push and pull systems in supply chain management: Correlative approaches in the military field. Journal of Defense Resources Management, 7(2), 165-172.
38. Moh’d Anwer, A.S. (2022). An investigation of transportation logistics strategy on manufacturing supply chain responsiveness in developing countries: the mediating role of delivery reliability and delivery speed. Heliyon 8(12), e11283.
39. Mohanrasu, S.S., Janani, K., and Rakkiyappan, R. (2024). A COPRAS-based approach to multi-label feature selection for text classification. Mathematics and Computers in Simulation, 222. 3-23.
40. Mukhametzyanov, I. (2021). Specific character of objective methods for determining weights of criteria in MCDM problems: Entropy, CRITIC and SD. Decision Making. Applications in Management and Engineering, 4(2), 76–105.
41. Nguyen, H.-Q., Le, X.-H., Nguyen, T.-T., Tran, Q.-H., & Vu, N.-P. (2022). A Comparative Study on Multi-Criteria Decision-Making in Dressing Process for Internal Grinding. Machines, 10(5), 303.
42. Oleskow-Szlapka, O., and Sobic, A. (2008). Manufacturing company activities during transforming push system into pull system: Case study. In Proceedings of the 11th International Symposium on Manufacturing and Applications. Hawaii, USA.
43. Ördek, B., Borgianni, Y., and Coatanea, E. (2024). Machine learning-supported manufacturing: a review and directions for future research. Production and Manufacturing Research, 12(1), 2326526.
44. Podvezko, V. (2011). The comparative analysis of MCDA methods SAW and COPRAS. Engineering Economics, 22, 134-146.
45. Puchkova, A., Romancer, J. L., and McFarlane, D. (2016). Balancing push and pull strategies within the production system. In 7th IFAC Conference on Management and Control of Production and Logistics. Bremen, Germany.
46. Radulescu, C. Z., and Radulescu, M. (2024). A hybrid group multi-criteria approach based on SAW, TOPSIS, VIKOR, and COPRAS methods for complex IoT selection problems. Electronics, 13(4), 789.
47. Rashid T, Ali A and Chu Y-M (2021) Hybrid BW-EDAS MCDM methodology for optimal industrial robot selection. PLoS ONE, 16(2): e0246738.
48. Rashid, A., Rasheed, R., Rahi, S., and Amirah, N. A. (2024). Disruptive factors of vendor-managed inventory in the manufacturing industry. Supply Chain Forum: An International Journal, 25(3), 353–364.
49. Raudeliuniene, J., Trinkuniene, E., Burinskiene, A., and Bubliene, R. (2025). Application of multi-criteria decision-making approach COPRAS for developing sustainable building practices in the European region. Sustainability, 17(8), 3740.
50. Raza, S.A., and Govindaluri, S.M. (2022). Pricing and market segmentation coordination in a push–pull supply chain with sequentially observed demand. Computers and Industrial Engineering. 164. 107882.
51. Reyes, J., Mula, J., and Diaz-Madroñero, M. (2024). Quantitative insights into the integrated push and pull production problem for lean supply chain planning 4.0. International Journal of Production Research, 62(17), 6251–6275.
52. Salum, L., and Araz, Ö. U. (2009). Using the when/where rules in dual resource constrained systems for a hybrid push-pull control. International Journal of Production Research, 47(6), 1661-1677.
53. Sarbjit, S. (2017). Study on push/ pull strategy decision taken by organizations for their products and services. Universal Journal of Management, 5(10), 492 – 495.
54. Shukla, R.K., Garg, D., and Agarwal, A. (2011). Understanding of supply chain: A literature review. International Journal of Engineering Science and Technology, 3(3), 2059- 2072.
55. Singh, T. (2024). Entropy weighted WASPAS and MACBETH approaches for optimizing the performance of solar water heating system. Case Studies in Thermal Engineering, 53,103922.
56. Sutee, U., and Yarlagadda, P. K. D. V. (2012). Simulation-based comparison of pull-push systems in motorcycle assembly line. In Applied Mechanics and Materials (Vols. 241–244, pp. 1507–1513). Trans Tech Publications, Ltd.
57. Teeravaraprug, J., and Stapholdecha, S. (2004). Performance analysis of push, pull and mixed systems. Thammasat International Journal of Science and Technology, 9(4), 29–36.
58. Türkiye Exporters Assembly [TİM]. (2025, May). Export figures: Sectoral data [Web page]. Retrieved May 5, 2025, from https://tim.org.tr/tr/ihracat-rakamlari
59. Vrat, P., and Yenradee P. (1991). A Comparative study of push and pull production planning strategies in a hard disk parts manufacturing company. International Journal of Operations and Production Management, 11(9), 41-47.
60. Wankhede, S. V., Pesode, P. A., Pawar, S. K., and Lobo, R. (2023). Comparison study of GRA, COPRAS And MOORA for ranking of phase change material for cooling system. Materials Today: Proceedings.
61. Wee, P.K.P., Wang, C.E., and Wee, H.M. (2004). Business is not what it used to be…and never will be. Technology Forecasting and Social Change, 72(5), 641-644.
62. Wee, H.M, Peng, S.Y., Yang, C.C., and Wee, P.K.P. (2009). The influence of production management practices and systems on business performance: From the perspective of the push-pull production systems. Operations and Supply Chain Management, 2(1), 11- 23.
63. Wei, M., Zheng, J., Zeng, S. and Jin, Y. (2024). Evaluation of high quality and full employment based on CRITIC-entropy-TOPSIS multi-criteria framework. International Journal of Intelligent Computing and Cybernetics, 17(3), 465-485.
64. Więckowski, J., Gajewski, P., Swałdek, K., and Sałabun, W. (2024). Application of COPRAS, PROMETHEE, and EDAS methods in sustainable energy development: A comparative study case. Procedia Computer Science, 246, 5428–5438.
65. Wu, Jinru & Chen, Xiaoling & Lu, Jianzhong. (2022). Assessment of long and short-term flood risk using the multi-criteria analysis model with the AHP-Entropy method in Poyang Lake basin. International Journal of Disaster Risk Reduction. 75. 102968.
66. Yang, L., Cai, G., and Chen J. (2018). Push, pull and supply chain risk-averse attitude. Production and Operations Management, 27(8), 1534–1552.
67. Yazdani, M., Chatterjee, P., Pamučar, D. & Abad, M. D. (2019). A risk-based integrated decision-making model for green supplier selection. Kybernetes, 49(4), 1229–1252.
68. Yeşil, E.Ş., Özturk, P.H., Ozturk, E., and Kitis, M. (2025). Use of COPRAS decision model for the selection of best treatment alternatives in removing micropollutants: Diclofenac example. Chemosphere, 375, 144264.
69. Zagursky, O.M., and Slipukha, T.I. (2019). Comparative Analysis of Production – Logistics Systems That Based on Push and Pull Strategies. Journal of Rural Production Research, 10(3), 69-78.
70. Zhang C., Fang D., Yang X., and Zhang X. (2018). Push and pull strategies by component suppliers when OEMs can produce the component in-house: The roles of branding in a supply chain. Industrial Marketing Management, 72, 99-111.
71. Zheng, N., and Lu, X. (2009). Comparative study on push and pull production system based on Anylogic. Proceedings of the International Conference on Electronic Commerce and Business Intelligence (pp. 455–458). Beijing, China.