Identifying Agility Assessment Factors using a mixed approach of qualitative and quantitative methods (case study: Automotive Industry)

Subject Areas : Industrial Management

Eisa Roghani Mamaghani ¹ , Jala Haghighat monfared ² , Ahmad Jafarnejad ³

1 - Department of Industrial Management , Faculty of Management. Central Tehran Branch, Islamic Azad University, Tehran, Iran
2 - Department of Industrial Management, Central Tehran Branch, Islamic Azad University, Tehran, Iran
3 - Department of Industrial Management, University of Tehran, Tehran, Iran

Received: 2020-12-05 Accepted : 2021-03-06 Published : 2021-05-15

Keywords: fuzzy Delphi, Meta-synthesis, cognitive mapping, factor analysis, Agility Assessment of Automotive Industry,

Abstract :

To achieve agile manufacturing in any manufacturing industry, needed to measure agility in that industry. The purpose of article is to identify and explain the main factors for measuring agility in the automotive industry as the most important non-oil industry. This research is an applied research in terms of purpose and a mixed qualitative-quantitative research in terms of method. The statistical population of the research in qualitative section is the academics and the automotive industry managers, and in the quantitative section is the experts of the mentioned industry. The method of collecting information has been through a questionnaire. In the qualitative section, First, by researching related articles by meta-synthesis method, 89 sources were selected and 27 criteria for measuring agility were identified. Then the criteria were generalized to the automotive industry by surveying 16 experts using fuzzy Delphi method. Then these criteria were categorized into 4 general clusters of agility drivers, agility enablers, agility strategies and agility capabilities using cognitive mapping method and with the help of 5 industry and university experts. In the quantitative part, by distributing questionnaires and surveys of 286 experts in the automotive industry, the extracted factors were validated by confirmatory factor analysis. These factors and criteria can be a good tool for managers of manufacturing industries to assess and gap analysis of the agility of their organizations. Agility enablers, agility strategies and agility capabilities measure the current level of agility of the organization and agility drivers measure the level of agility required by the organization.

References:

1. Ayatollah Khamenei, S.A. (2003 ~ 2020). Bulletin, communications, statements and messages. Official website of grand ayatollah khamenei (www.khamenei.ir).

2. Aravindraj, S. & Vinodh, S. (2014). Forty criteria based agility assessment using scoring approach in an Indian relays manufacturing organization. Journal of Engineering, Design and Technology, 12 (4): 507 – 518.

3. Azar, A., Khosravani, F. & Jalali, R. (2016). Soft Operational Research: Problem structuring approaches. Tehran: Industrial management organization publications, second edition. (in Persian)

4. Azar, A. & Pishdar, M. (2011). Identification and measurement of organizational agility indicators (case study). Management researches, 4 (11): 5-20. (in Persian)

5. Balaji, M., Velmurugan, V. & Subashree, C. (2015). An assessment methodology for agile supply chains. Journal of Applied Research and Technology, 13: 504-509.

6. Bottani, E. (2010). Profile and enablers of agile companies: An empirical investigation. International Journal of Production Economics, 125: 251–261.

7. Bouzon, M., Kannan, G. & Rodriguez, C.M.T. (2016). Identification and analysis of reverse logistics barriers using fuzzy Delphi method and AHP. Resources, Conservation and Recycling, 108: 182–197.

8. Bui, T.D., Tsai, F.M., Tseng, M.L. & Ali, M.H. (2020). Identifying sustainable solid waste management barriers in practice using the fuzzy Delphi method. Resources, Conservation & Recycling, 154, 104625.

9. Christopher, M. (2000). The Agile Supply Chain. Industrial Marketing Management, 29: 37-44.

1. Conover, W.J. (1999). Practical Nonparametric Statistics, 3rd edition. New York: Wiley, NY.


2. Conradsen, N., Lystlund, M. (2003). The vision of next generation manufacturing- how a company can start. Integrated Manufacturing Systems, 14 (4): 324-333.


3. Danaeifard, H. (2013). Strategies of Theory Building, 2^th edition. Tehran: Samt Publication. (in Persian)


4. Danaeifard, H. & Mozafari, Z. (2008). Promoting Validity and Reliability in Qualitative Management Research: A Reflection on Research Auditing Strategies. Management researches, 1 (1): 131-162. (in Persian)


5. Dehghani P., H., Shafaghat, A., Ebnoreza, S.M. & Pashaei H., A. (2018). Identifying the factors affecting organizational agility in one of the armed forces organizations with a fuzzy approach. Journal of Strategic Knowledge Interdisciplinary Studies, 8 (31): 7-48. (in Persian)


6. Dove, R. (1992). The 21s Century Manufacturing Enterprise Strategy or What Is All This Talk About Agility? Paradigm Shift International, Agility Forum, Bethlehem, PA, USA.


7. Dove, R. (1995). Best Agile Practice Reference Base- 1994: Challenge Model and Benchmarks. Proceedings: 4th Annual Agility Conference, Agility Forum, Bethlehem, PA., www.parshift.com.


8. Dove, R. (1996). An Agile Enterprise Reference Model with a Case Study. Agility Forum - AR96-04 - December 1996.


9. Dove, R. (1999). Knowledge management, response ability, and the agile enterprise. Journal of Knowledge Management, 3 (1): 18–35.


10. Faisal, M.N., Banwet, D.K. & Shankar, R. (2007). An approach to measure supply chain agility. International Journal of Industrial and Systems Engineering, 2 (1): 79-98. 


11. Goldman, S.L., Nagel, R.N.,& Preiss, K.P. (1995). Agile Competitors and Virtual Organizations: Strategies for Enriching the Customer. New York, Van Nostrand Reinhold.


12. Gunasekaran, A. (2001). Agile Manufacturing: The 21st Century Competitive Strategy. Elsevier Science: Oxford, UK.


13. Habibi, A., Firouzi Jahantigh, F., Sarafrazi, A. (2015). Fuzzy Delphi Technique for Forecasting and Screening Items. Asian Journal of Research in Business Economics and Management, 5 (2): 130-143.


14. Hormozi, A. M. (2001). Agile manufacturing: the next logical step. Benchmarking: An International Journal, 8 (2): 132 – 143.


15. Hooman, H. (2008). Structural Equation Modeling. Tehran: Samt Publication. (in Persian)


16. Jafarnejad, A. (2015). Modern Production and Operations Management (Concepts, Systems, Models and Supply Chain). Tehran: University of Tehran Press, 5th edition. (in Persian)


17. Jafarnejad, A., Shahaei, B. (2010). Introduction to Organizational Agility and Agile Manufacturing. Tehran: Mehraban Nashr Pub, 2th edition. (in Persian)


18. Kazazi, A. & Sohrabi, R. (2010). Presenting the components and indicators of supply chain agility assessment of the National Iranian Oil Company (Case study: Southern Oilfields Company). Journal of Transformation Management, 2(4): 141-165. (in Persian)


19. Khatami F., S.M., Bamdad S., J. & Bigdeli, E. (2013). Identification and Classification of Factors Affecting Agility with QFD Approach (Case Study: Auto Parts Industry). Management researches in Iran, 17 (4): 119-138. (in Persian)


20. Khiyabani, H.M. (2018). Provide an approach of fuzzy inference system to evaluate motor supply chain agility (MS Thesis). Industrial Management Dept., Management Faculty, Amol Higher Education Institute. (in Persian)


21. Khoshsima, G. (2008). A Strategic Model for Measuring Agility with Fuzzy Logic. ISICA 2008: Advances in Computation and Intelligence, pp 258-268.


22. Khosravi, A., Abtahi, S.H., Ahmadi, R. & Salimi, H. (2012). Identification of human resource agility enablers by Delphi method in electronic industries. Improvement management, 6 (4): 129-153. (in Persian)


23. Kidd, P.T. (1994). Agile manufacturing: forging new frontiers. Addison-Wesley, Reading, MA.


24. Komijani, A. & Naghdi, Y. (2009). Investigating the Correlation between Production and Inflation in Iran's Economy (with Emphasis on Sectoral Production). Journal of Economic Sciences, 9 (32): 99-124. (in Persian)


25. Kumar, A. & Motwani, J. (1995). A methodology for assessing time-based competitive advantage of manufacturing firms. International Journal of Operations & Production Management, 15(2): 36-53.


26. Kumar, A., Kaviani, M.A., Hafezalkotob A. & Zavadskas, E.K. (2017). Evaluating innovation capabilities of real estate firms: a combined fuzzy Delphi and DEMATEL approach, International Journal of Strategic Property Management, 21(4): 401-416.


27. Kumar, A., Zavadskas, E.K., Mangla, S.K., Agrawal, V., Kartik Sharma, K. & Gupta, D. (2018). When risks need attention: adoption of green supply chain initiatives in the pharmaceutical industry, International Journal of Production Research, https://doi.org/10.1080/00207543.2018.1543969.


28. Lin, C.T., Chiu, H. & Tseng, Y.H. (2006), Agility evaluation using fuzzy logic, International Journal of Production Economics, 101: 353–368.


29. Ma, Z., Shao, C., Ma, S. & Ye, Z. (2011). Constructing road safety performance indicators using Fuzzy Delphi Method and Grey Delphi Method. Expert Systems with Applications, 38: 1509–1514.


30. Meade, L.M., Sarkis, J. (1999). Analyzing organizational project alternatives for agile manufacturing processes: an analytical network approach. International Journal of Production Research, 37 (2): 241-261.


31. Meredith, S. & Francis, D. (2000). Journey towards agility: the agile wheel explored. The TQM Magazine, 12 (2): 137-143.


32. Motadel, M., Toloie-Eshlaghy, A. & Halvachi-Zadeh, D. (2011). Assessment of Supply Chain Agility in the Automotive Industry of Tehran. European Journal of Scientific Research, 61 (2): 210-229.


33. Nagel, R.N. & Dove, R. (1991). 21st Century Manufacturing Enterprise Strategy: An industry-led view of agile manufacturing. Iacocca Institute, Lehigh University, Bethlehem, PA.


34. Noblit, G.W., Hare, R.D. (1988). Meta-Ethnography: Synthesizing Qualitative Studies. Newbury Park, California: SAGE Publications. 


35. Rahiminezhad Galankashi, M., Helmi, S.A., Abdul Rahim, A.R. & Mokhatab Rafiei, F. (2019). Agility assessment in manufacturing companies. Benchmarking: An International Journal, 26 (7): 2081-2104.


36. Ramesh, G., Devadasan, S.R. (2007). Literature review on the agile manufacturing criteria. Journal of Manufacturing Technology Management, 18 (2): 182-201.


37. Sandelowski, M., Barroso, J. (2007). Handbook for Synthesizing Qualitive Research. New York: Springer Publishing Company.


38. Sarkis, J. (2001). Benchmarking for agility. Benchmarking: An International Journal, 8 (2): 88-107.


39. Sharifi, H. & Zhang, Z. (1999). A methodology for achieving agility in manufacturing organizations: An introduction. International Journal of Production Economics, 62: 7-22.


40. Sharifi, H. & Zhang, Z. (2001). Agile manufacturing in practice: Application of a methodology. International Journal of Operations & Production Management, 21 (5/6): 772-794.


41. Sharp, J.M., Irani, Z. & Desai, S. (1999). Working towards agile manufacturing in the UK industry. International Journal of Production Economics, 62: 155-169.


42. Sherehiy, B., Karwowski, W. & Layer, J.K. (2007). A review of enterprise agility: Concepts, frameworks, and attributes, International Journal of Industrial Ergonomics, 37: 445–460.


43. Sindhwani, R., Singh, P.L., Chopra, R., Sharma, K., Basu, A., Prajapati, D.K. & Malhotra, V. (2019). Agility Evaluation in the Rolling Industry: A Case Study. Advances in Industrial and Production Engineering, pp 753-770.


44. Singh, P.K., Sarkar, P. (2019). A framework based on fuzzy Delphi and DEMATEL for sustainable product development: A case of Indian automotive industry. Journal of Cleaner Production, https://doi.org/10.1016/j.jclepro.2019.118991.


45. Tseng, Y.H., Lin, C.T. (2011). Enhancing enterprise agility by deploying agile drivers, capabilities and providers. Information Sciences, 18: 3693–3708.


46. Tiwari, R.K.,Tiwari, J.K. (2019). Measuring Agility of Indian Automotive Small and Medium Sized Enterprises (SMEs). Management and Production Engineering Review, 10 (1): 58-67.  


47. Tsourveloudis, N.C., Valavanis, K.P. (2002), On the Measurement of Enterprise Agility, Journal of Intelligent and Robotic Systems, 33: 329–342.


48. Tehran Chamber of Commerce, Industries, Mines and Agriculture. (2015). An overview of the situation of automotive and parts manufacturing in Iran during the years 2004 to 2014. Tehran: Deputy of Economic Studies - Statistics Collection and Analysis Center. (in Persian)


49. VanHoek, R.I., Harrison, A. & Christopher, M. (2001). Measuring agile capabilities in the supply chain. International Journal of Operations & Production Management, 21 (1/2): 126-147.


50. Vinodh, S. & Aravindraj, S. (2012). Agility evaluation using the IF–THEN approach. International Journal of Production Research, 50 (24): 7100–7109.


51. Vinodh, S., Devadasan, S.R., Vimal, K.E.K. & Deepak, K. (2013). Design of agile supply chain assessment model and its case study in an Indian automotive components manufacturing organization. Journal of Manufacturing Systems, 32: 620–631.


52. Yaghoubi, N.M., Kord, B. & Azadikhah, O. (2011). Assessing Organizational Agility via Fuzzy Logic. International Business Research, 4 (3): 135-144.


53. Yauch, C.A. (2011). Measuring agility as a performance outcome. Journal of Manufacturing Technology Management, 22 (3): 384-404.


54. Zhang, Z. & Sharifi, H. (2007). Towards Theory Building in Agile Manufacturing Strategy- A Taxonomical Approach. IEEE Transactions on Engineering Management, 54 (2): 351 – 370.