• صفحه اصلی
  • تاب آوری زنجیره تأمین در حمل ونقل پستی با رویکرد تحلیل عاملی

اشتراک گذاری

آدرس مقاله


کد مقاله : IMJ-2008-1420 (R1) بازدید : 201 صفحه: 175 - 196

نوع مقاله: پژوهشی

تاب آوری زنجیره تأمین در حمل ونقل پستی با رویکرد تحلیل عاملی

محورهای موضوعی : مدیریت صنعتی

Mansour Jangizehi 1 , Mohammad Reza Maleki 2

1 - PhD candidate in Industrial Engineering, Eyvanekey University, Semnan, Iran
2 - Department of Industrial Engineering, Golpayegan University of Technology, Golpayegan, Iran

تاریخ دریافت : 1399/05/12 تاریخ پذیرش : 1399/09/26 تاریخ انتشار : 1399/12/03

کلید واژه: حمل و نقل پستی, تاب آوری, تحلیل عاملی, تحلیل شبکه ای, زنجبره تامین,

چکیده مقاله :

هدف این پژوهش، بررسی وضعیت تاب آوری زنجیره تامین در حمل و نقل کالا توسط شرکتهای پستی در شرایط بحرانی است. برای این منظور پس از بررسی ادبیات و پیشینه تحقیق و مصاحبه با خبرگان، 14 شاخص تاب آوری استخراج و پرسشنامه بین 98 نفر از مشتریان پستی توزیع و جمع آوری شد. بررسی و تحلیل اولیه داده ها از طریق نرم افزار spss و روش تحلیل مولفه های اصلی و تحلیل عاملی اکتشافی انجام شد. پس از چرخش واریماکس، متغیرهایی که دارای مقادیر ویژه بزرگتر از یک و بارهای عاملی بزرگتر از 55/0 بودند و بیشترین همبستگی و واریانس را تبیین می کردند، مشخص شدند. درنهایت شش عامل پنهان بدست آمد. همچنین بمنظور بدست آوردن ارتباط بین شاخص ها و عامل ها و ارتباط درونی بین شاخص ها، از طریق نرم افزار متلب تمام محاسبات سوپر ماتریس، ماتریس های موزون و ماتریس حدی از روش فرآیند تحلیل شبکه ای انجام شده است. در پایان، ارزش اهمیت هر عامل و اهمیت ضریب نسبی هر شاخص محاسبه شده ، که بیمه بودن کالا، مهمترین عامل و تحویل به موقع کالا، به عنوان مهمترین شاخص بدست آمده است.

چکیده انگلیسی:

The purpose of this study is to investigate the resilience of the supply chain in the transportation of goods by postal companies in critical situations. For this purpose, after reviewing the literature and research background and interviewing experts, 14 resilience indices were extracted and questionnaires were distributed and collected among 98 postal customers. Preliminary data analysis was performed through SPSS software and principal component analysis and exploratory factor analysis. After varimax rotation, the variables with eigenvalues greater than one and factor loads greater than 0.55 were identified and explained the highest correlation and variance. Finally, six hidden factors were obtained. Also, in order to obtain the relationship between indicators and factors and the internal relationship between indicators, through MATLAB software, all super matrix calculations, weighted matrices and partial matrices have been performed through the network analysis process method. Finally, the value of the importance of each factor and the importance of the relative coefficient of each index are calculated, in which the insurance of the goods is the most important factor and the timely delivery of the goods is the most important indicator.

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    53.
  53. Wilson, M.C. (2007). The impact of transportation disruptions on supply chain performance. Transportation Research Part E: Logistics and Transportation Review, 43 (4): 295–320.
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  54. Xia, D., and B. Chen. (2011). A Comprehensive Decision Making Model for Risk Management of Supply Chain.Expert Systems with Applications, 38 (5): 4957–4966.
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  55. Zebardast, E. (2014). Application of F'ANP in Urban Planning, Journal of Fine Arts (Architecture and Urban Planning), 19(2): 38-23.
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  56. Zebardast, E. (2013). Constructing a Social Vulnerability Index to Earthquake Hazards using a Hybrid Factor Analysis and Analytic Network Process (F’ANP) Model. Natural Hazards, 65(1): 1331-1359.
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    58.
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    59.
_||_

  1. Barad, M. and Sapir, D.E. (2003). Flexibility in logistic systems-modelling and performance evaluation. International Journal of Production Economics, 85 (2): 155–170.
    2.
  2. Carvalho, H., A. P. Barroso, V. H. Machado, S. Azevedo, and V. Cruz-Machodo. (2012). Supply chain Redesign for Resilience Using Simulation. Computers and Industrial Engineering 62 (1): 329–341.
    3.
  3. Chang, S.E. and Nojima, N. (2001). Measuring post-disaster transportation system performance: the 1995 Kobe earthquake in comparative perspective. Transportation Research Part A: Policy and Practice, 35 (6): 475–494.
    4.
  4. Chopra, S. and Meindl, P. (2004). Supply chain management strategy. planning and operation. 7th ed. Upper Saddle River, NJ: Prentice Hall.
    5.
  5. Chopra, S., and M. Sodhi. (2004). Managing Risk to Avoid Supply Chain Breakdown. MIT Sloan Management Review 46 (1): 53–62.
    6.
  6. Christopher, M. and Peck, H. (2004). Building the resilient supply chain. The International Journal of Logistics Management, 15 (2): 1–14.
    7.
  7. Christopher, M. and Lee, H. (2004). Mitigating supply chain risk through improved confidence. International Journal of Physical Distribution & Logistics Management, 34(5): 388-396.
    8.
  8. Cowles, J. (2012). Logistics Management, World Tehnologies, New Delhi.Daniel, H. and Fredrik, N. (2011), Logistics-driven packaging innovation: a case study at IKEA. International Journal of Retail & Distribution Management, 39(9): 638-657
    9.
  9. Davis, T. (1993). Effective supply chain management. Sloan Management Review, 34(4) 35-46.
    10.
  10. Day, G.S. (1994). The capabilities of market driven organisations.Journal of Marketing, 58(4): 37-52.
    11.
  11. Duclos, L.K., Vokurka, R.J., and Lummus, R.R. (2003). A conceptual model of supply chain lexibility. IndustrialManagement and Data Systems, 103 (6): 446–456.
    12.
  12. Elkins, D., R. B. Handfield, J. Blackhurst, and C. W. Craighead. (2005). 18 Ways to Guard against Disruption .Supply Chain Management Review, 9 (1): 46–53.
    13.
  13. Fawcett, S.E. and Stanley, L.L. (1997). Developing a logistics capability to improve the performance of international operations.  Journal of Business Logistics, 18(2): 101-27.
    14.
  14. Feitelson, E. and Salomon, I. (2000). The implications of differential network flexibility for spatial structures. Transportation Research Part A: Policy and Practice, 34 (6): 459–479.
    15.
  15. Faizi, A., Sadeh, E., Amini, Z. and Ehtesham Rathi, R. (2O19). Identifying the components of a resilient economy in the resilient supply chain of the Iranian automotive industry. Journal of Industrial Management, Faculty of Humanities, Islamic Azad University, Sanandaj Branch, 49(14): 1-18.
    16.
  16. Gligor, D.M. and Holcomb, M.C. (2012). Understanding the role of logistics capabilities in achieving supply chain agility: a systematic literature review. Supply Chain Management: An International Journal, 17(4) 38-453.
    17.
  17. Gordon, P., Richardson, H., and Davis, B. (1998). Transport-related impacts of the Northridge earthquake. Journal of Transportation and Statistics, 1 (2): 21–36.
    18.
  18. Guevara, M, Moinuddin, M, and Abqari, R. (2016). The Impact of Data Reduction Using Factor Analysis on the Accuracy of Bankruptcy Prediction Models, Journal of Accounting Advances, 8(2): 189-151.
    19.
  19. Juttner, U. (2005). Supply Chain Risk Management: Understanding the Business Requirements from a Practitioner Perspective.The International Journal of Logistics Management, 16 (1): 120–141.
    20.
  20. Kerivin, H. and Mahjoub, A.R. (2005). Design of survivable networks: a survey. Networks, 46 (1): 1–21.
    21.
  21. Knemeyer, A. M., W. Zinn, and C. Eroglu. (2009). Proactive Planning for Catastrophic Events in Supply Chains. Journal of Operations Management, 27 (2): 141–153.
    22.
  22. Lai, K.-H., Ngai, E.W.T., and Cheng, T.C.E. (2004). An empirical study of supply chain performance in transport logistics. International Journal of Production Economics, 87 (3): 321–331.
    23.
  23. Lawson, B. and Samson, D. (2001). Developing innovation capability in organisations, a dynamic.
    24.
  24. Lee, H. L. (2004). The Triple-A Supply Chain. Harvard Business Review, 82 (10): 102–112.
    25.
  25. Lu, C.-S. and Yang, C. C. (2010). Logistics service capabilities and firmperformance of international distribution center operators.The Service Industries Journal, 30(2): 281-298.
    26.
  26. Major, J. A. (2002). Advanced Techniques for Modeling Terrorism Risk. white paper. www.guycarp.com.
    27.
  27. Mark, S. and Martin, S. (2007). Flexibility from a supply chain perspective: definition and review.  International Journal of Operations & Production Management, 27(7): 685-713.
    28.
  28. Morash, E.A. (2001). Supply chain strategies, capabilities, and performance. Transportation Journal, 41(1): 37-51.
    29.
  29. Mentzer, J.T. and Flint, D.J. (1999). Developing a logistics service quality scale. Journal of Business Logistics, 20 (1): 9-32.
    30.
  30. Movahed, A., Firoozi, M. A., and Roozbeh, H. (2011). Analysis of the degree of development of cities in Khuzestan province using factor analysis and cluster analysis, Journal of Urban Research and Planning, 2(5): 56-43.
    31.
  31. Naim, M. M., Potter, A. T., Mason, R. J., & Bateman, N. (2006). The role of transport flexibility in logistics provision. International Journal of Logistics Management, 17 (3): 297–311.
    32.
  32. Peck, H. (2006). Reconciling supply chain vulnerability, risk and supply chain management. International Journal of Logistics Research and Applications, 9(2): 127-142.
    33.
  33. Ponomarov, S. and Holcomb, M. (2009). Understanding the concept of supply chain resilience. The International Journal of Logistics Management, 20 (1): 124–143.
    34.
  34. Prater, E., Biehl, M. and Smith, M.A. (2001). International supply chain agility – tradeoffs between flexibility and uncertainty. International Journal of Operations & Production Management, 21(5): 823-839.
    35.
  35. Ravanistan, K., Aghajani, H., Safaei,Qadiklayi, A. and Yahya Zadeh Far, M. (2017). Determining and weighting supply chain strategies in the supply chain Iran Khodro. Journal of Industrial Management Perspective of Shahid Beheshti University, 25(1): 145-172.
    36.
  36. Rice, J. and Caniato, F. (2003). Building a secure and resilient supply network. Supply Chain Management Review, 7 (5): 22–30.
    37.
  37. Saaty, T. L. (2003). Decision making in complex environments. Super Decisions.
    38.
  38. Saaty, T. L. (1996). Decision making with dependence and feedback: The analytic network process. RWS Publications.
    39.
  39. Schmitt, A. J., and M. Singh. (2012). A Quantitative Analysis of Disruption Risk in a Multi-Echelon Supply Chain. International Journal of Production Economics, 139 (1): 22–32.
    40.
  40. Sedighpour, A., Zandieh, M., AlamTabriz, A.,and DoriNokourani, B. (2018). Design and Explanation of Resilient Supply Chain Model in Iranian Pharmaceutical Industry, Quarterly Journal of Industrial Management Studies, Year, 16(15): 106-55.
    41.
  41. Sheffi,Y. (2007). The resilient enterprise: overcoming vulnerability for competitive advantage. Cambridge, MA:MIT Press Books.
    42.
  42. Sheffi, Y., and J. B. Rice. (2005). A Supply Chain View of the Resilient Enterprise. MIT Sloan Management Review, 47 (1): 41–48.
    43.
  43. Simangunsong, E., Hendry, L.C. and Stevenson, M. (2012). Supply-chain uncertainty: a review and theoretical foundation for future research. International Journal of Production Research, 50(16): 4493-4523.
    44.
  44. Soni, S., Gupta, R., and Pirkul, H. (1999). Survivable network design: the state of the art. Information Systems Frontiers,1 (3): 303–315.
    45.
  45. Svensson, G. (2002). Dyadic vulnerability in companies’ inbound and outbound logistics flows. International Journal of Logistics: Research and Applications, 5 (1): 13–43.
    46.
  46. Tang, O. and Nurmaya Musa, S. (2011). Identifying risk issues and research advancements in supply chain risk management. International Journal of Production Economics, 133(1): 25-34.
    47.
  47. Tse, Y. K., and K. H. Tan. (2012). Managing Product Quality Risk and Visibility in Multi-layer Supply Chain. International Journal of Production Economics, 139 (1): 49–57.
    48.
  48. Tukamuhabwa, B. R., M. Stevenson, J. Busby, and M. Zorzini. (2015). Supply Chain Resilience: Definition, Review and Theoretical Foundations for Further Study. International Journal of Production Research, 53 (18): 5592–5623.
    49.
  49. Van der Vegt, G. S., P. Essens, M. Wahlstrom, and G. George. (2015). From the Editors: Managing Risk and Resilience. Academy of Management Journal, 58 (4): 971–980.
    50.
  50. Wang, Z. (2005). Business intelligence. Taiwan: DrMaster Culture Limited Company.
    51.
  51. Waters, D. (2011). Supply Chain Risk Management Vulnerability and Resilience in Logistics.2nd ed., Kogan Page, London.
    52.
  52. Wernerfelt, B. (1984). A resource based view of the firm. Strategic Management Journal, 5(2): 171-180.
    53.
  53. Wilson, M.C. (2007). The impact of transportation disruptions on supply chain performance. Transportation Research Part E: Logistics and Transportation Review, 43 (4): 295–320.
    54.
  54. Xia, D., and B. Chen. (2011). A Comprehensive Decision Making Model for Risk Management of Supply Chain.Expert Systems with Applications, 38 (5): 4957–4966.
    55.
  55. Zebardast, E. (2014). Application of F'ANP in Urban Planning, Journal of Fine Arts (Architecture and Urban Planning), 19(2): 38-23.
    56.
  56. Zebardast, E. (2013). Constructing a Social Vulnerability Index to Earthquake Hazards using a Hybrid Factor Analysis and Analytic Network Process (F’ANP) Model. Natural Hazards, 65(1): 1331-1359.
    57.
  57. Zhang, Q.,Vonderembse, M., and Lim, J. (2005). Logistics flexibility and its impact on customer satisfaction. International Journal of Logistics Management, 16 (1): 71–95.
    58.
  58. Zhao, M., Dröge, C. and Stank, T.P. (2001). The effects of logitsics capabilities on firm performance: customer-focused versus informati,on-focused capabilities. Journal of Business Logistics, 22(2): 91-107.
    59.

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