Journal of Distribution Science (유통과학연구)

Korea Distribution Science Association (한국유통과학회)
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Identification of Supply Chain Management Performance Assessment Criteria for Textile and Apparel Enterprises in Distribution Science

  • Nhu-Mai Thi NONG (Faculty of Commerce, University of Finance - Marketing) ;
  • Duc-Son HA (Faculty of Commerce, University of Finance - Marketing)
  • Received : 2024.04.24
  • Accepted : 2024.07.05
  • Published : 2024.07.30
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Abstract

Purpose: This study aims to identify the assessment criteria on textile and apparel supply chain management performance. Research design, data, and methodology: An integrated method of Delphi, quantitative survey, and ANP, in which Delphi with Kamet principle was applied to define the set of criteria, quantitative survey with reliability and validity test was utilized to ensure the match between the set of criteria and the whole textile and apparel industry, and ANP was used to derive weights of these criteria. Results: The set of supply chain management performance evaluation criteria composes of seven criteria namely order fulfillment quality, agility, costs, asset management, information sharing, innovation, and product development and 19 sub-criteria. Conclusions: This study theoretical contribution is the proposition of the set of evaluation criteria on supply chain performance. Regarding practical contribution, the study findings are guidelines for T&A companies in assessing and improving their supply chain capability. However, the findings are only for Vietnamese T&A context. Future research, therefore, may be expanded to other regions or countries' T&A industry. Additionally, future step to this study may be the utilization of other techniques of MCDM or methodological approaches like multiple regression, PLSSEM in defining weights of criteria or performance evaluation.

Keywords

Acknowledgement

The authors express their appreciation to the colleagues who reviewed this paper and offered useful recommendations for improving it. The author greatly appreciates the time and useful suggestions from guest editors and reviewers.

References

  1. Bukhori, I. B., Widodo, K. H., & Ismoyowati, D. (2015). Evaluation of poultry supply chain performance in XYZ slaughtering house Yogyakarta using SCOR and AHP method. Agriculture and Agricultural Science Procedia, 3, 221-225. https://doi.org/10.1016/j.aaspro.2015.01.043
  2. Charkha, P. G., & Jaju, S. B. (2014). Designing innovative framework for supply chain performance measurement in textile industry. International Journal of Logistics Systems and Management 1, 18(2), 216-230. https://doi.org/10.1504/IJLSM.2014.062327
  3. Chu, H. C., & Hwang, G. J. . (2008). A Delphi-based approach to developing expert systems with the cooperation of multiple experts. Expert systems with applications, 34(4), 2826-2840. doi:https://doi.org/10.1016/j.eswa.2007.05.034
  4. Dissanayake, C. K., & Cross, J. A. (2018). Systematic mechanism for identifying the relative impact of supply chain performance areas on the overall supply chain performance using SCOR model and SEM. International Journal of Production Economics, 201, 102-115. https://doi.org/10.1016/j.ijpe.2018.04.027
  5. Haque, K. M. A., Hossain, M. M., Hossain, M. S. J., Islam, M. A., Hassan, M., Shamshuzzaman, M., & Hossain, M. D. (2011). Performance evaluation of Bangladeshi apparel and textile supply chain network, a case study. Performance Evaluation, 1(1), 211-218
  6. He, J., & Zhu, J. (2022). Evaluation on risk factors of elderly services from the perspective of integrated SCOR model. International Journal of Information Systems in the Service Sector, 14(1), 1-18. https://doi.org/10.4018/IJISSS.2022010102
  7. Jagan Mohan Reddy. K, Neelakanteswara Rao. A, & Krishnanand. L. (2019). A review on supply chain performance measurement systems. Procedia Manufacturing, 30, 40-47. https://doi.org/10.1016/j.promfg.2019.02.007
  8. Khan, M. M., Bashar, I., Minhaj, G. M., Wasi, A. I., & Hossain, N. U. I. (2023). Resilient and sustainable supplier selection: an integration of SCOR 4.0 and machine learning approach. Sustainable and Resilient Infrastructure, 8(5), 453-469. https://doi.org/10.1080/23789689.2023.2165782
  9. Kusrini, E., Caneca, V. I., Helia, V. N., & Miranda, S. (2019, December). Supply Chain Performance Measurement Using Supply Chain Operation Reference (SCOR) 12.0 Model: A Case Study in AA Leather SME in Indonesia. In IOP Conference Series: Materials Science and Engineering (Vol 697(1), p.012023). IOP Publishing.
  10. Lee, H. L. & Ng, S. M. (1997). Introduction to the special issue on global supply chain management. Production and Operations Management, 6(3), 191-192. https://doi.org/10.1111/j.1937-5956.1997.tb00425.x
  11. Mankins, M. (2017). Great companies obsess over productivity, not efficiency. Harvard Business Review, 3
  12. Moazzam, M., Akhtar, P., Garnevska, E., & Marr, N. E. (2018). Measuring agri-food supply chain performance and risk through a new analytical framework: a case study of New Zealand dairy. Production Planning & Control, 29(15), 1258-1274. https://doi.org/10.1080/09537287.2018.1522847
  13. Nong, N. M. T., & Ho, P. T. (2019). Criteria for supplier selection in textile and apparel industry: a case study in vietnam. The Journal of Asian Finance, Economics, and Business, 6(2), 213-221. https://doi.org/10.13106/jafeb.2019.vol6.no2.213
  14. Nong, N. M. T. (2022). An application of delphi and dea to performance efficiency assessment of retail stores in fashion industry. The Asian Journal of Shipping and Logistics, 38(3), 135-142. https://doi.org/10.1016/j.ajsl.2022.05.001
  15. Nong, T. N. M. (2023). Performance efficiency assessment of Vietnamese ports: An application of Delphi with Kamet principles and DEAmodel. The Asian Journal of Shipping and Logistics, 39(1), 1-12. https://doi.org/10.1016/j.ajsl.2022.10.002
  16. Oztaysi, B., Surer, O. (2014). Supply Chain Performance Measurement Using a SCOR Based Fuzzy VIKOR Approach. In: Kahraman, C., Oztaysi, B. (eds) Supply Chain Management Under Fuzziness. Studiesin Fuzziness and Soft Computing, 313. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-53939-89
  17. Prasetya, W., Natalia, C., Kristiana, S. P. D., Aldi, B. E., Silalahi, A., Monique, K., & Oktavia, C. W. (2017). Performance measurement and analysis of coffee supply chain with scor method (case study of north sumatera coffee). Ijrdo-journal of business management, 3(11), 01-17.
  18. Romano, P. & Vinelli, A. (2001). Quality management in a supply chain perspective: strategic and operative choices in a textile-apparel network. International Journal of Operations & Production Management, 21(4), 446-460. https://doi.org/10.1108/01443570110381363
  19. Saaty, T. (2005). Theory and Applications of the Analytic Network Process. Decision Making with Benefits, Opportunities, Costs and Risks. Pittsburgh, PA: RWS Publications.
  20. Schorr, J. E. (1998), Purchasing in the 21st Century. John Wiley & Sons, Inc., New York
  21. Sellitto, M. A., Pereira, G. M., Borchardt, M., Da Silva, R. I., & Viegas, C. V. (2015). A SCOR-based model for supply chain performance measurement: application in the footwear industry. International Journal of Production Research, 53(16), 4917-4926. https://doi.org/10.1080/00207543.2015.1005251
  22. Tongzon, J. L. (2009). Port choice and freight forwarders. Transportation Research Part E: Logistics and Transportation Review, 45(1), 186-195. doi:https://doi.org/10.1016/j.tre.2008.02.004
  23. Wibowo, M. A., & Sholeh, M. N. (2015). The analysis of supply chain performance measurement at construction project. Procedia Engineering, 125, 25-31. https://doi.org/10.1016/j.proeng.2015.11.005
  24. Wiegmans, B., & Dekker, S. (2016). Benchmarking deep-sea port performance in the Hamburg-Le Havre range. Benchmarking: An International Journal, 23(1), 96-112. doi:https://doi.org/10.1108/BIJ-04-2013-0050