تحلیل روابط میان اقدامات پارادایم‌های مدیریت زنجیره تأمین و معیارهای عملکردی با رویکرد مدلسازی ساختاری تفسیری

نوع مقاله : مقاله پژوهشی

نویسندگان

1 استاد، دانشکده مدیریت، دانشگاه تهران.

2 دانشیار، دانشگاه تهران.

3 دانشجوی دکتری، پردیس البرز دانشگاه تهران.

چکیده

امروزه، شرکت‌ها برای دستیابی به عملکرد بهتر و بهبود رقابت‌پذیری، به دنبال یافتن پارادایم‌های مناسب زنجیره تأمین خود هستند؛ زیرا رقابت بین زنجیره‌های تأمین جایگزین رقابت بین شرکت‌ها شده است. در میان پارادایم‌های مختلف مدیریت زنجیره تأمین، ترکیب پارادایم‌های ناب، چابک و تاب­آور ایده‌ای جدید برای دستیابی به عملکرد بهتر و رقابت‌پذیری به‌شمار می‌رود. هدف اصلی این مقاله، شناسایی اقدامات مهم ناب، چابک و تاب‌آوری است که مدیران ارشد باید برای بهبود عملکرد زنجیره‌های تأمین بر آنها تمرکز کنند؛ ازاین‌رو رویکرد مدلسازی ساختاری تفسیری به‌عنوان روش‌شناسی مناسب برای تعیین روابط میان اقدامات ناب، چابک و تاب آور و عملکرد زنجیره تأمین و نیز طبقه‌بندی آنها بر اساس قدرت نفوذ و وابستگی در نظر گرفته شده است. با توجه به نتایج این پژوهش، اقدام ارتباط با تأمین‌کننده در پایین­ترین سطح با قدرت نفوذ بالا و معیار عملکردی چرخه نقدینگی با قدرت نفوذ ضعیف و وابستگی بالا است؛ به این معنا که به­شدت تحت­تأثیر سایر متغیرهای مورد­مطالعه قرار دارد؛ اما بر آنها تأثیری ندارد.

کلیدواژه‌ها

عنوان مقاله [English]

Analysis Relationships among Practices of Supply Chain Management Paradigms and Performance Measures by Interpretive Structural Modeling Approach (ISM)

نویسندگان [English]

  • Ahmad Jafarnejad 1
  • Hossein Safari 2
  • Maryam Mohseni 3
1 Professor, Tehran University.
2 Associate Professor, Tehran University.
3 Ph.D Student, Alborz Campous of Tehran University.
چکیده [English]

Nowadays, companies are seeking to find suitable supply chain paradigms to gain better performance and improve their competitiveness. Because competition between supply chains has been replaced by competition between companies. Among different paradigms in supply chain management, integrating lean, agile and resilient paradigms are considered as a new idea to gain better performance and competitiveness. The main purpose of this paper is to determine the importance practices of lean, agile and resilient that top managers should focus on them to improve their supply chain's performance. To this end, interpretive structural modeling (ISM) approach is used to analyze relationships among lean, agile and resilient practices and supply chain performance measures. This approach classifies variables according to their driving or dependence power. As the results shows, the practice "supplier relationship" is with strong driving power and also, performance measure "cash-to-cash cycle" is with weak driving power and strong dependence power. It means that, this measure is strongly influenced by the other variables but does not affect them.

کلیدواژه‌ها [English]

  • Lean
  • Agile
  • Resilient
  • Supply Chain Performance
  • Interpretive Structural Modeling

مراجع

1. Agarwal, A., Shankar, R., & Tiwari, M. (2007). Modeling agility of supply chain. Industrial Marketing Management, 36(4), 443-457.
2. Anand, G., & Kodali, R. (2008). A conceptual framework for lean supply chain and its implementation. Int J Value Chain Manage, 2, 313–357.
3. Andersson, P., Aronsson, H., & Storhagen, NG. (1989). Measuring Logistics Performance. Eng Costs Prod Econ, 17, 253–262.
4. Azevedo, S., Carvalho, H., Cruz-Machado, V., & Grilo, F. (2010) The influence of agile and resilient practices on supply chain performance: an innovative conceptual model proposal. In: Innovative process optimization methods in logistics: emerging trends, concepts and technologies. Erich Schmidt Verlag GmbH & Co. KG, Hamburg, Germany, 265–281.
5. Azevedo, S., Carvalho, H., & Cruz-Machado, V. (2013). Using interpretive structural modeling to identify and rank performance measures: an application in the automotive supply chain. Baltic J Manage, 8(2), 208–230.
20.
6. Beamon, BM. (1999). Measuring supply chain performance. Int J Oper Prod Manage, 19(3), 275–292.
7. Bolan˜os, R., Fontela, E., Nenclares, A., & Pastor, P. (2005).Using interpretive structural modelling in strategic decision-making groups. Manage Decis 43(6), 877–895.
8. Cabral, I, Grilo, A., & Cruz-Machado, V. (2012). A decision-making model for Lean, Agile, Resilient and Green supply chain management. Int J Prod Res, 50(17), 4830–4845.
9. Carvalho, H., Azevedo, S., & Cruz-Machado., V. (2010). Supply chain performance management: lean and green paradigms. Int J Bus Perform Supply Chain Model 2(3/4), 151–179.
10. Carvalho, H., Duarte, S., & Cruz-Machado, V. (2011). Lean, agile, resilient and green: divergences and synergies. Int J Lean Six Sigma, 2(2), 151–179.
11. Carvalho, H., Maleki, M., & Cruz-Machado, V. (2012).The links between supply chain disturbances and resilience strategies. Int J Agile Syst Manage, 5(3), 203–234.
12. Carvalho, H., Azevedo, S., & Cruz-Machado, V. (2014). Trade-offs among lean, agile, resilient and green paradigms in supply chain management: a case study approach. In: Xu J, Fry JA, Lev B, Hajiyev A (eds) Proceedings of the seventh international conference on management science and engineering management. Springer Berlin, 953–968.
13. Chan, F. (2003).Performance measurement in a supply chain”, International Journal of Advanced Manufacturing Technology, 21(7), 534-48.
14. Charan, P., Shankar, R., Baisya, R. (2008). Analysis of interactions among the variables of supply chain performance measurement system implementation. Bus Process Manage J, 14(4), 512–529.
15. Christopher, M., & Towill, D. (2002).The supply chain strategy conundrum: to be lean or agile or to be lean and agile?. International Journal of Logistics, 5(3), 299-309.
16. Christopher, M., & Peck, H. (2004). Building the resilient supply chain. Int J Logist Manage, 15(2), 1–14.
17. Cumbo, D., Kline, D., & Bumgardner, M. M. (2006). Benchmarking performance measurement and lean manufacturing in the rough mill. Forest Products Journal, 56(6), 25-30.
18. Duarte, S., Carvalho, H., & Cruz-Machado, V. (2010). Exploring relationships between supply chain performance measures. The Fourth International Conference on Management Science and Engineering Management, Chungli, Taiwan, 3-7.
19. Farris, T., & Hutchison, P. (2002).Cash-to-cash: the new supply chain management metric, International Journal of Physical Distribution & Logistics Management, 32(3/4), 288-98.
20. Ghalayini, A., & Noble, J. (1996).The changing basis of performance measurement. Int J Operat Prod Manage, 16(8), 63–80.
21. Glickman, T., & White, S. (2006).Security, visibility and resilience: the keys to mitigating supply chain vulnerabilities. Int J Logist Syst Manage, 2, 107–119.
22. Goldsby, T. J., Griffis, S. E., & Roath, A. S. (2006). Modeling lean, agile, and leagile supply chain strategies. Journal of Business Logistics, 27(1), 57-80.
23. Gunasekaran, A., Patel, C., & Tirtiroglu, E. (2001). Performance measures and metrics in a supply chain environment. Int J Operat Prod Manage, 21(1/2), 71–87.
24. Holweg, M. (2007). The genealogy of lean production. Journal of Operations Management, 25(3), 420-437.
25. Lambert, D. & Pohlen, L. (2001). Supply chain metrics, The International Journal of Logistics Management, 12 (1), 1-19.
26. Lin, C., Chiu, H., & Chu, P. (2006). Agility index in the supply chain. International Journal of Production Economics, 100(2), 285-299.
27. Naylor, B., Naim, M., & Berry, D. (1999). Leagility: integrating the lean and agile manufacturing paradigms in the total supply chain. Int J Prod Econ, 62(1–2), 107–118.
28. Schroer, B. (2004).Simulation as a tool in understanding the concepts of lean manufacturing, Simulation, 80(3), 171-175.
29. Tang, C. (2006). Robust strategies for mitigating supply chain disruptions. Int J Logist Res Appl, 9(1), 33–45.
30. Zobel, C. (2011).Representing perceived tradeoffs in defining disaster resilience. Decis Support Syst, 50(2), 394–403.
31. Zsidisin, G., Ragatz, G., & Melnyk, S. (2005). The dark side of supply chain management. Supply Chain Manage Rev, 9(2), 46–52.

فایل ها

هم رسانی

ارجاع به این مقاله

آمار