Determining the Effects of the Use of Information Technologies on International Cold Chain Logistics

Abstract

The use of Information Technologies (IT) in International Cold Chain Logistics (CCL) plays an important role in delivering goods, particularly food and pharmaceutical products, to the consumer without losing their final characteristics. In this study, it is aimed to find out which criteria and how strongly the use of IT is effective in international CCL operations. For this purpose, findings obtained from a comprehensive literature review were divided into main criteria and sub-criteria in accordance with the theoretical background of the study, and the research model of the study was structured. In the methodology part of the study, the Analytical Hierarchy Process (AHP), which is one of the multi-criteria research methods, was used. Questionnaire forms prepared in accordance with the research model and the method were sent to international CCL experts by e-mail, and six of them, which were filled in completely, included in the analysis. Results of the study revealed that IT applications were most effective in Technology-oriented criteria in CCL, and among these criteria, temperature and humidity measurement, and product shelf-life sub-criteria had the highest importance weights, respectively. In addition, it was found that the importance weights of the Resource-oriented and Cost-oriented main criteria were close to each other, but they were significantly lower than the Technology-oriented main criteria. It is considered that the findings obtained from this study can be applied in different samples and regions and will contribute to future studies on this subject.

Keywords

• Cold chain logistics
• Information technologies
• AHP

Bilgi Teknolojileri Kullanımının Uluslararası Soğuk Zincir Lojistiği Üzerine Etkilerinin Belirlenmesi

Öz

Uluslararası Soğuk Zincir Lojistiğinde (SZL) Bilgi Teknolojilerinin (BT) kullanımı başta gıda ve ilaç ürünleri olmak üzere eşyanın nihai özelliklerini kaybetmeden tüketiciye ulaştırılmasında önemli bir rol oynamaktadır. Bu çalışmada BT kullanımının uluslararası SZL faaliyetlerinde hangi kriterler üzerinde ve ne kuvvetle etkili olduğunun ortaya çıkarılması amaçlanmıştır. Bu amaç doğrultusunda kapsamlı bir şekilde yapılan literatür taraması sonucunda elde edilen bulgular çalışmanın teorik altyapısına uygun şekilde ana kriterler ve alt kriterlere ayrılmış ve çalışmanın araştırma modeli oluşturulmuştur. Çalışmanın metodoloji bölümünde ise çok kriterli karar verme yöntemlerinden biri olan Analitik Hiyerarşi Süreci (AHS) kullanılmıştır. Araştırma modeli ve yöntemine uygun şekilde hazırlanan anket formları uluslararası SZL konusunda uzman profesyonellere e-posta ile gönderilmiş ve bunlardan eksiksiz doldurulan altı tanesi analize dahil edilmiştir. Çalışmanın sonunda BT uygulamalarının SZL’de en fazla Teknoloji yönlü kriterlerde etkili olduğu, bu kriterler arasında ise sırasıyla sıcaklık ve nem ölçümü ile ürün raf ömrü alt kriterlerinin en yüksek önem ağırlıklarına sahip olduğu ortaya çıkmıştır. Ayrıca Kaynak yönlü ve Maliyet yönlü ana kriterlerinin önem ağırlıklarının birbirine yakın olduğu ancak Teknoloji yönlü ana kriterinin önem ağırlığından belirgin bir şekilde düşük oldukları sonucuna ulaşılmıştır. Bu çalışmadan elde edilen bulguların farklı örneklem ve bölgelerde uygulanabileceği ve ileride bu konuda yapılacak çalışmalara katkı sağlayacağı düşünülmektedir.

Anahtar Kelimeler

• Soğuk zincir lojistiği
• Bilgi teknolojileri
• AHS

References

1. Abad, E., Palacio, F., Nuin, M., De Zarate, A. G., Juarros, A., Gómez, J. M., & Marco, S. (2009). RFID Smart Tag for Traceability and Cold Chain Monitoring of Foods: Demonstration in an Intercontinental Fresh Fish Logistic Chain. Journal of Food Engineering, 93(4), 394-399.
2. Alfian, G., Syafrudin, M., Farooq, U., Ma'arif, M. R., Syaekhoni, M. A., Fitriyani, N. L., Lee, J. & Rhee, J. (2020). Improving Efficiency of RFID-Based Traceability System for Perishable Food by Utilizing IoT Sensors and Machine Learning Model. Food Control, 110, 107016.
3. Barney, J. (1991). Firm Resources and Sustained Competitive Advantage. Journal of Management, 17 (1), 99–120. Cil, A. Y., Abdurahman, D., & Cil, I. (2022). Internet of Things Enabled Real Time Cold Chain Monitoring in A Container Port. Journal of Shipping and Trade, 7, 9.
4. Coase, R. H. (1937). The Nature of the Firm. Economica. 4 (16): 386-405.
5. Davis, F. D. (1986). A Technology Acceptance Model for Empirically Testing New End-User Information Systems: Theory and Results, Doctoral Dissertation, MIT Sloan School of Management, Cambridge, MA.
6. Davis, F. D. (1989). Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology. MIS Quarterly, 13 (3), 319-340.
7. Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). User Acceptance of Computer Technology: A Comparison of Two Theoretical Models. Management Science, 35(8), 982-1003.
8. Dini, A. & Çeken, C. (2019). Simulation Modeling of an Iot Based Cold Chain Logistics Management System. Sakarya University Journal of Computer and Information Sciences, 2(2), 89-100.

9. Doğu, S. Ö. & Şireli, U. T. (2015). Gıdalarda İzlenebilirlik. Gıda, 40(5), 295-302.
10. Dolgui, A., & Proth, J. M. (2008). RFID Technology in Supply Chain Management: State of The Art and Perspectives. IFAC Proceedings Volumes, 41(2), 4464-4475.
11. Eom, K. H., Hyun, K. H., Lin, S., & Kim, J. W. (2014). The Meat Freshness Monitoring System Using The Smart RFID Tag. International Journal of Distributed Sensor Networks, 10 (7), 591812.
12. Esmer, Ö. K., & Melikoğlu, A. Y. (2015). Gıda Güvenliğinin Sağlanmasında Radyo Frekanslı Tanımlama Teknolojisinin Rolü. Akademik Gıda, 13(1), 72-80.
13. Grunow, M., & Piramuthu, S. (2013). RFID in Highly Perishable Food Supply Chains–Remaining Shelf Life to Supplant Expiry Date?. International Journal of Production Economics, 146(2), 717-727.
14. Jedermann, R., Ruiz-Garcia, L., & Lang, W. (2009). Spatial Temperature Profiling by Semi-Passive RFID Loggers for Perishable Food Transportation. Computers and Electronics in Agriculture, 65(2), 145-154.
15. Joshi, R., Banwet, D. K., & Shankar, R. (2011). A Delphi-AHP-TOPSIS Based Benchmarking Framework for Performance Improvement of A Cold Chain. Expert Systems with Applications, 38(8), 10170-10182.
16. Korucuk, S. (2018). Soğuk Zincir Taşımacılığı Yapan İşletmelerde 3PL Firma Seçimi: İstanbul örneği. Iğdır Üniversitesi Sosyal Bilimler Dergisi, 16, 341-366.
17. Köhler, S., & Pizzol, M. (2020). Technology Assessment of Blockchain-Based Technologies in The Food Supply Chain. Journal of Cleaner Production, 269, 122193.
18. Kumar, N., Tyagi, M., Garg, R. K., Sachdeva, A., & Panchal, D. (2021a). A Framework Development and Assessment for Cold Supply Chain Performance System: A Case of Vaccines. In Operations Management and Systems Engineering: Select Proceedings of CPIE 2019 (pp. 339-353). Springer Singapore.
19. Kumar, N., Tyagi, M., & Sachdeva, A. (2021b). Estimation of Critical Key Performance Factors of Food Cold Supply Chain Using Fuzzy AHP Approach. In Advances in Manufacturing and Industrial Engineering: Select Proceedings of ICAPIE 2019 (pp. 701-711). Springer Singapore.
20. Lin, Q., Wang, H., Pei, X., & Wang, J. (2019). Food Safety Traceability System Based on Blockchain and EPCIS. IEEE Access, 7, 20698-20707.
21. Lu, S., & Wang, X. (2016). Toward an Intelligent Solution for Perishable Food Cold Chain Management. In 2016 7th IEEE International Conference on Software Engineering and Service Science (ICSESS), 852-856.
22. Luo, H., Zhu, M., Ye, S., Hou, H., Chen, Y., & Bulysheva, L. (2016). An Intelligent Tracking System Based on Internet of Things for The Cold Chain. Internet Research. 26 (2): 435-445.
23. Mangun, N., Rombe, E., Taqwa, E., Sutomo, M., & Hadi, S. (2021). AHP Structure for Determining Sustainable Performance of Indonesian Seafood Supply Chain From Stakeholders Perspective. Journal of Management Information and Decision Sciences, 24(7), 1-10.
24. Mejjaouli, S., Babiceanu, R. F., & Nisanci, I. (2014). The Use of RFID Sensor Tags for Perishable Products Monitoring in Logistics Operations. In Proceedings of the Winter Simulation Conference, IEEE, 2001-2012.
25. Özbek, A. & Eren, T. (2013). Üçüncü Parti Lojistik (3PL) Firmanın Analitik Hiyerarşi Süreciyle (AHS) Belirlenmesi. International Journal of Engineering Research and Development, 5(2), 46-54.
26. Peng, Y., Zhang, L., Song, Z., Yan, J., Li, X., & Li, Z. (2018). A QR Code Based Tracing Method for Fresh Pork Quality in Cold Chain. Journal of Food Process Engineering, 41, e12685.
27. Pirtini, S. (2015). Pazarlamada Dağıtım Kanalı Yönetimi Kararları Açısından İşlem Maliyeti Analizi, Yağcı, M. İ & Çabuk, S. (Ed.) Pazarlama Teorileri içinde (s. 373-383). İstanbul: MediaCat.
28. Qi, L., Jung, G. Y., & Kim, H. H. (2020). Analysis on Influencing Factors of Development of Agricultural Product Cold Chain Logistics in Jilin Province, China. Journal of the Korea Convergence Society, 11(2), 9-15.
29. Saaty, R. W. (1987). The Analytic Hierarchy Process—What It is And How It is Used. Mathematical Modelling, 9 (3-5), 161-176.
30. Saaty, T. L. (1977). A Scaling Method for Priorities in Hierarchical Structures. Journal of Mathematical Psychology, 15(3), 234-281.
31. Saaty, T. L. (1990). How To Make a Decision: The Analytic Hierarchy Process. European Journal of Operational Research, 48, 9-26.
32. Saaty, T. L. (1994a). How to Make a Decision: The Analytic Hierarchy Process. Interfaces, 24(6), 19-43.
33. Saaty, T. L. (1994b). Fundamentals of Decision Making and Priority Theory With The Analytic Hierarchy Process. RWS Publications.
34. Saaty, T. L. (2008). Decision Making with The Analytic Hierarchy Process. International Journal of Services Sciences, 1(1), 83-98.
35. Saaty, T. L. And Vargas, L. G. (2013). “The Analytic Network Process”, In Decision Making with The Analytic Network Process, 1-40, Boston, MA: Springer.
36. Shen, Y., & Liao, K. (2022). An Application of Analytic Hierarchy Process and Entropy Weight Method in Food Cold Chain Risk Evaluation Model. Frontiers in Psychology, 13, 1-13.
37. Singh, R. K., Gunasekaran, A., & Kumar, P. (2018). Third Party Logistics (3PL) Selection for Cold Chain Management: A Fuzzy AHP and Fuzzy TOPSIS Approach. Annals of Operations Research, 267, 531-553.
38. Taş, A. & Gündüz, M. (2021). Gıda Taşımacılığı Sektörünü Etkileyen Kriterlerin Analizi. Erciyes Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 58, 353-366.
39. Tian, Y., Xie, Q., & Wang, Z. (2019). Safety Assessment of Fresh Agricultural Products Cold Chain Logistics Based on AHP-Entropy Weight Method. Storage and Process, 19(5), 185-190.
40. TÜİK (2023). Girişimin Ana Faaliyet Türü ve Ürün Grubuna Göre Dış Ticaret, https://data.tuik.gov.tr/Kategori/GetKategori?p=dis-ticaret-104vedil=1 (Erişim Tarihi: 15.01.2023).
41. Türk, E., & Öztek, M. (2021). Sürdürülebilirlik Açısından Soğuk Zincir Oluşturmanın Önemi ve Bir Araştırma. Econharran, 5 (7), 221-248.
42. Wernerfelt, B. (1984). A Resource‐Based View of The Firm. Strategic Management Journal, 5(2), 171-180.
43. Williamson, O. E. (1975). Markets and Hierarchies: Analysis and Antitrust Implications: A Study in the Economics of Internal Organization. New York: The Free Press.
44. Xiaomeng, C., Huaye, H., & Zhangqiong, W. (2023). Risk Assessment of Wuhan Frozen Food Supply Chain Based on AHP-FCE Method. In Advances in Intelligent Systems, Computer Science and Digital Economics IV (pp. 407-419). Cham: Springer Nature Switzerland.
45. Xiong, Y., Zhao, J., & Lan, J. (2021). Performance Evaluation of Food Cold Chain Logistics Enterprise Based on the AHP and Entropy. In Research Anthology on Food Waste Reduction and Alternative Diets for Food and Nutrition Security (pp. 395-405). IGI Global.
46. Yan, B., & Lee, D. (2009). Application of RFID in Cold Chain Temperature Monitoring System. In 2009 ISECS International Colloquium on Computing, Communication, Control, and Management, IEEE, 2, 258-261.
47. Zhang, J., Liu, L., Mu, W., Moga, L. M., & Zhang, X. (2009). Development of Temperature-Managed Traceability System for Frozen And Chilled Food During Storage and Transportation. Journal of Food, Agriculture & Environment, 7(3/4), 28-31.
48. Zhang, Y., Liu, Y., Jiong, Z., Zhang, X., Li, B. & Chen, E. (2021). Development and Assessment of Blockchain‐Iot‐Based Traceability System for Frozen Aquatic Product. Journal of Food Process Engineering, 44(5), e13669.