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Ranking of productivity improvement strategies in Iran mineral sector based on integrated SWOT-FAHP-FTOPSIS analysis

  • R. Shakoor Shahabi
  • M. H. Basiri
  • Mahdi Rashidi Kahag
Original Paper
  • 112 Downloads

Abstract

The Iran’s mineral sector, as a major supplier of mineral industries, plays a central role on Iran economy. Promoting the productivity in this area leads to the enhancement of the business environment. Also, it can influence the production chain and minerals value added. In this study, a comprehensive study was conducted on the major barriers/drivers factors affecting the efficiency in the mining sector. Furthermore, 16 key factors were identified using the expert views. These parameters are divided into four main categories: strengths, weaknesses, opportunities, and threats. In addition, a hierarchy model was designed in which the mentioned four efficient strategic parameters were implemented and the SWOT analysis was applied, as well as the nine macro strategies were determined. In order to prioritize these strategies, multiple steps were carried out. First, the subfactors were weighted by implementing the fuzzy multi attribute decision-making technique by combining the experts’ views and triangular fuzzy numbers. Subsequently, the strategies were prioritized by employing the group Fuzzy Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) model. The sensitive analysis of the results presents some minor changes in score of the alternatives, but no alteration was affected in the prioritized strategies. The results revealed the importance of resource allocation strategies related to the existing investments as well as supporting and facilitating the new technology in the mining sector.

Keywords

Ranking Productivity SWOT Fuzzy AHP Fuzzy TOPSIS Iran mineral sector 

References

  1. Adar E, Karatop B, İnce M, Bilgili MS (2016) Comparison of methods for sustainable energy management with sewage sludge in Turkey based on SWOT-FAHP analysis. Renew Sustain Energy Rev 62:429–440CrossRefGoogle Scholar
  2. Azimi R, Yazdani-Chamzini A, Fouladgar MM, Zavadskas EK, Basiri MH (2011) Ranking the strategies of mining sector through ANP and TOPSIS in a SWOT framework. J Bus Econ Manag 12:670–689CrossRefGoogle Scholar
  3. Buckley JJ (1985) Fuzzy hierarchical analysis. Fuzzy Set Syst 17:233–247CrossRefGoogle Scholar
  4. Chang D-Y (1996) Applications of the extent analysis method on fuzzy AHP. Eur J Oper Res 95:649–655CrossRefGoogle Scholar
  5. Chen S-JJ, Hwang C-L, Beckmann MJ, Krelle W (1992) Fuzzy multiple attribute decision making: methods and applications. Springer-Verlag Inc, New YorkCrossRefGoogle Scholar
  6. Cheng C-H, Mon D-L (1994) Evaluating weapon system by analytical hierarchy process based on fuzzy scales. Fuzzy Set Syst 63:1–10CrossRefGoogle Scholar
  7. Ediger VŞ, Berk I, Ersoy M (2015) An assessment of mining efficiency in Turkish lignite industry. Resour Policy 45:44–51CrossRefGoogle Scholar
  8. Ghorbani M (2013) The position of Iranian mining industry in the world, The economic geology of Iran: mineral deposits and natural resources. Springer Netherlands, Dordrecht, pp 297–332CrossRefGoogle Scholar
  9. Gogus O, Boucher TO (1997) A consistency test for rational weights in multi-criterion decision analysis with fuzzy pairwise comparisons. Fuzzy Set Syst 86:129–138CrossRefGoogle Scholar
  10. Gwo-Hshiung T, Tzeng GH, Huang JJ (2011) Multiple attribute decision making: methods and applications. CRC Press, Boca RatonGoogle Scholar
  11. Hill T, Westbrook R (1997) SWOT analysis: it’s time for a product recall. Long Range Plann 30:46–52CrossRefGoogle Scholar
  12. Hwang C-L, Yoon K (1981) Methods for multiple attribute decision making, Multiple attribute decision making. Springer, Berlin, pp 58–191Google Scholar
  13. Jozi SA, Firouzei M (2013) Environmental impacts of Tehran poultry slaughterhouse of nemone using TOPSIS technique. J Kermanshah Univ Med Sci 17:520–530Google Scholar
  14. Keivani FS, Sourkouhi ZK (2014) A survey on supply side of mines: a case study of Iran. Eur Online J Nat Soc Sci 3:589Google Scholar
  15. Krugman P (2014) Productivity growth, and the survival of the US. copper industry. Productivity in natural resource industries: improvement through innovation. 109Google Scholar
  16. Kulshreshtha M, Parikh JK (2000) A study of productivity in the Indian coal sector. Energy Policy 29:701–713CrossRefGoogle Scholar
  17. Lumaksono H (2014) Implementation of SWOT-FAHP method to determine the best strategy on development of traditional shipyard in Sumenep. Acad Res Int 5:56Google Scholar
  18. MacKay DB, Bowen WM, Zinnes JL (1996) A Thurstonian view of the analytic hierarchy process. Eur J Oper Res 89:427–444CrossRefGoogle Scholar
  19. Mahadevan R, Asafu-Adjaye J (2005) The productivity–inflation nexus: the case of the Australian mining sector. Energy Econ 27:209–224CrossRefGoogle Scholar
  20. Mining M (2002) Breaking new ground: the report of the mining, minerals and sustainable development project. EarthscanGoogle Scholar
  21. Nikolaou IE, Evangelinos KI (2010) A SWOT analysis of environmental management practices in Greek mining and mineral industry. Resour Policy 35:226–234Google Scholar
  22. Pahl N, Richter A (2009) SWOT analysis—idea, methodology and a practical approach. BoD–Books on Demand.Google Scholar
  23. Qu L, Chen Y (2009) An integrated Fuzzy AHP-SWOT method and its application in the analysis of China’s software industry basesGoogle Scholar
  24. Rodríguez XA, Arias C (2008) The effects of resource depletion on coal mining productivity. Energy Econ 30:397–408CrossRefGoogle Scholar
  25. Saaty TL, Vargas LG (1987) Uncertainty and rank order in the analytic hierarchy process. Eur J Oper Res 32:107–117CrossRefGoogle Scholar
  26. Shahabi RS (2009) System synamics modeling of mine sector management to preferment on economy, Faculty of Mine, Petroleum and Geophysics. Shahrood University of Technology, Shahrud, p 315Google Scholar
  27. Shakoor Shahabi R, Basiri MH, Rashidi Kahag M, Ahangar Zonouzi S (2014) An ANP–SWOT approach for interdependency analysis and prioritizing the Iran’s steel scrap industry strategies. Resour Policy 42:18–26CrossRefGoogle Scholar
  28. Sun C-C (2010) A performance evaluation model by integrating fuzzy AHP and fuzzy TOPSIS methods. Expert Syst Appl 37:7745–7754CrossRefGoogle Scholar
  29. Tahernejad M, Ataei M, Khalokakaie R (2012) Selection of the best strategy for Iran’s quarries: SWOT-FAHP method. J Min Environ 3:1–13Google Scholar
  30. Tang Y-C, Lin TW (2010) Application of the fuzzy analytic hierarchy process to the lead-free equipment selection decision. Int J Bus Syst Res 5:35–56CrossRefGoogle Scholar
  31. Tilton JE, Landsberg HH (1999) Innovation, productivity growth, and the survival of the US copper industry. Productivity in Natural Resource Industries; Improvement through Innovation, pp 109–139Google Scholar
  32. Van Laarhoven P, Pedrycz W (1983) A fuzzy extension of Saaty’s priority theory. Fuzzy Set Syst 11:199–227CrossRefGoogle Scholar
  33. Wang Y-M, Elhag TMS, Hua Z (2006) A modified fuzzy logarithmic least squares method for fuzzy analytic hierarchy process. Fuzzy Set Syst 157:3055–3071CrossRefGoogle Scholar
  34. Yazdani-Chamzini A, Fouladgar MM, Zavadskas EK (2011) An integrated model for prioritizing strategies of the Iranian mining sector. Technological and Economic Development of Economy, pp 459–483Google Scholar
  35. Zhu K-J, Jing Y, Chang D-Y (1999) A discussion on extent analysis method and applications of fuzzy AHP. Eur J Oper Res 116:450–456CrossRefGoogle Scholar

Copyright information

© Saudi Society for Geosciences 2018

Authors and Affiliations

  • R. Shakoor Shahabi
    • 1
  • M. H. Basiri
    • 2
  • Mahdi Rashidi Kahag
    • 3
  1. 1.Faculty of EngineeringImam Khomeini International UniversityQazvinIran
  2. 2.Faculty of EngineeringTarbiat modares UniversityTehranIran
  3. 3.Young Researchers and Elite Club Qazvin Branch, Islamic Azad UniversityQazvinIran

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