Engineering and Technology Journal

Investigation of the Level of Adoption of the Lean Philosophy in Small and Medium Enterprises Using integrated Fuzzy Assessment Model Based on Fuzzy DEMATEL / Fuzzy TOPSIS

Document Type : Research Paper

Authors

Production Engineering and Metallurgy Dept., University of Technology-Iraq, Alsina’a street, 10066 Baghdad, Iraq.

Abstract

Lean Production is a continuous improvement philosophy derived from the Toyota Production System (TPS) aimed at improving the operational efficiency and performance of companies. Although the Lean philosophy has been widely applied in large enterprises, its implementation and adoption in small and medium-sized enterprises (SMEs) has remained relatively underexplored. In this paper, a fuzzy assessment model has been proposed that integrates Fuzzy Decision-Making Trial and Evaluation Laboratory (Fuzzy DEMATEL) and Fuzzy Technique for Order of Preference by Similarity to Ideal Solution (Fuzzy TOPSIS) to aim to investigate the level of implementation and adoption of the lean philosophy in SMEs through five lean dimensions, namely, management, process, supplier, customer, and employee. Fuzzy DEMATEL is used to identify the weight of influence of each lean dimension on SMEs leanness and identify the cause and effect lean dimensions, while Fuzzy TOPSIS is used to investigate and assess the level of adoption of lean philosophy related to these five dimensions in SMEs. The main contribution of this research lies in providing a comprehensive framework for; assessing the level of influence of lean dimensions on SMEs leanness which is an important issue in the improvement process and identifying the level of adoption of lean philosophy in SMEs. The proposed model has been applied in five Iraqi SMEs for producing healthy water and juice. The results show that although the management, employee, and process have the highest weights of influence on SMEs leanness, management, and employee are cause lean dimensions that have a high influence on improving the effect dimensions, process, and customer. The level of adoption of lean philosophy of the assessed SMEs related to the five lean dimensions is in the mid-level so this refers to acceptable implementation of lean philosophy.

Graphical Abstract

Highlights

  • The adoption of lean philosophy in SMEs, which is important for economies yet under-explored, was assessed
  • FMCDM methods were used to efficiently assess SMEs' lean level
  • A fuzzy assessment model integrating FDEMATEL and FTOPSIS was used to investigate SMEs' lean adoption
  • A cause-effect diagram depicted and distinguished lean dimension types
  • Radar maps visually illustrated SMEs' lean level

Keywords

Main Subjects

References
  1. P. I. R. Prasanna, J. M. S. B. Jayasundara, S. K. N. Gamage, E. M. S. Ekanayake, P. S. K. Rajapakshe, and G. A. K. N. J. Abeyrathne, Sustainability of SMEs in the competition: A systemic review on technological challenges and SME performance, J. Open Innov. Technol. Mark. Complex., 5 (2019) 1–18, https://doi.org/10.3390/joitmc5040100.
  2. Saini and D. Singh, Investigating the perceptions of Lean manufacturing practices in Northern India SMEs: an empirical study, Ind. Eng. J., 13 ( 2020) https://doi.org/10.26488/iej.13.3.1218.
  3. Medonos, Leanness Level of Manufacturing Companies - a Survey on Lean Manufacturing Implementation, Acta Acad. karviniensia, 21 (2021) 54–65. https://doi.org/10.25142/aak.2021.012.
  4. Kaplanog, Z. D. U. Durmus, and S. Cenk, Fuzzy DEMATEL and fuzzy hierarchical TOPSIS methods for truck selection, Expert Syst. Appl., 40 (2013) 899–907. https://doi.org/10.1016/j.eswa.2012.05.046.
  5. Nazir and N. Cavus, Fuzzy DEMATEL method for identifying LMS evaluation criteria, Procedia Comput. Sci., 120 (2017) 742–749.https://doi.org/10.1016/j.procs.2017.11.304.
  6. Altuntas and M. K. Yİlmaz, FUZZY DEMATEL METHOD TO EVALUATE THE DIMENSIONS OF MARKETING RESOURCES : AN APPLICATION IN SMEs, J. Bus. Econ. Manag., 17 (2016) 347–364. https://doi.org/ 10.3846/16111699.2015.1068220.
  7. X. 1 and X. D. 1 Xiaoyong Zhu, Yu Liang, Yongmao Xiao, Identification of Key Brittleness Factors for the Lean–Green Manufacturing System in a Manufacturing Company in the Context of Industry 4.0, Based on the DEMATEL-ISM-MICMAC Method, Processes, 11 ( 2023).
  8. S. Kilic, P. Yurdaer, and C. Aglan, A leanness assessment methodology based on neutrosophic DEMATEL, J. Manuf. Syst., 59 (2019) 320–344. https://doi.org/10.1016/j.jmsy.2021.03.003.
  9. -Y. L. & Y.-H. H. Cathay Kuo-Tai Kang, Key Factors to Increasing Free Cash Flow for Manufacturers Utilizing Lean Production: An AHP-DEMATEL Approach, Inf. Manag. Bus. Rev., 14 (2022) 28–45.
  10. Tayaksi, M. Sagnak, and Y. Kazancoglu, A new holistic conceptual framework for leanness assessment, Int. J. Math. Eng. Manag. Sci., 5 (2020) 567–590. https://doi.org/10.33889/IJMEMS.2020.5.4.047.
  11. S. A. R. D. M. A. Qadrir, Empirical assessment of the causal relationships among lean criteria using DEMATEL method, Benchmarking An Int. J., 23 ( 2016).
  12. Azadeh, M. Zarrin, M. Abdollahi, S. Noury, and S. Farahmand, Leanness assessment and optimization by fuzzy cognitive map and multivariate analysis, Expert Syst. Appl., 42 (2015) 6050–6064 https://doi.org/ 10.1016/j.eswa.2015.04.007.
  13. Senthilkannan and R. Parameshwaran, Performance analysis and quality improvement using fuzzy MCDM and lean tools in a paper industry, Int. J. Integr. Supply Manag., 12 (2019) 205–229.https://doi.org/ 10.1504/IJISM.2019.099715.
  14. NǍdǍban, S. Dzitac, and I. Dzitac, Fuzzy TOPSIS: A General View, Procedia Comput. Sci., 91 (2016) 823–831. https://doi.org/10.1016/j.procs.2016.07.088.
  15. Li and P. Yuan, Evaluation of Enterprise Lean Level Based on Fuzzy Multiple Subjective and Objective Weights, Open J. Bus. Manag., 10 (2022) 1290–1309. https://doi.org/10.4236/ojbm.2022.103070.
  16. Devnath, M. S. Islam, S. Rashid, An integrated QFD-TOPSIS method for prioritization of major lean tools: a case study, Int. J., 9 (2020) 65–76. https://doi.org/10.22105/riej.2020.213445.1110.
  17. Seyed Vahid Mirnoori, Prioritizing Lean Techniques by Employing Multi- Criteria Decision-Making (MCDM): The Case of MCoutinho, 2020.
  18. Rajpurohit, A. Verma, and M. Singh, Comparative Study of Leanness Index Methods for Manufacturing Organizations, Int. J. Adv. Eng. Res. Dev., 4 (2017). https://doi.org/10.21090/ijaerd.29189.
  19. Kumar, B. Singh, M. A. Qadri, Y. V. S. Kumar, and A. Haleem, A framework for comparative evaluation of lean performance of firms using fuzzy TOPSIS, Int. J. Product. Qual. Manag., 11(2013) 371–392. https://doi.org/10.1504/IJPQM.2013.054267.
  20. A. M.A. Alemi, MEASURING THE LEANNESS OF MANUFACTURING SYSTEMS BY USING FUZZY TOPSIS: A CASE STUDY OF THE ‘PARIZAN SANAT’ COMPANY, South African J. Ind. Eng., 24 (2013) 166–174 https://doi.org/10.21608/egjec.2013.94997.
  21. M. Hossein Hojjati and A. Anvary, An integrated SAW, TOPSIS method for ranking the major lean practices based on four attributes, World Appl. Sci. J., 28 (2013) 1862–1871. https://doi.org/10.5829/idosi.wasj.2013.28.11.2079.
  22. Elnadi and E. Shehab, Product-service system leanness assessment model: study of a UK manufacturing company, Int. J. Lean Six Sigma, 12 (2021)1046–1072.https://doi.org/10.1108/IJLSS-03-2020-0036.
  23. D. Harjanto and P. D. Karningsih, Development of Lean Assessment Tools Dimensions and Indicators for MSMEs in Indonesia, Prozima, 5 (2021) 21–29.
  24. S. Dahda, D. Andesta, and A. S. Wicaksono, Measuring leanness index using fuzzy logic approach,J. Phys. Conf. Ser., 1469 ( 2020). https://doi.org/10.1088/1742-6596/1469/1/012040.
  25. P. Bueno, G. B. Benitez, E. D. S. Fernandes, and L. P. Godoy, Fuzzy in Lean To Evaluate the Decision Degree, Rev. Gestão e Desenvolv., 17 ( 2020) https://doi.org/10.25112/rgd.v17i1.1696.
  26. K. Vishal A Wankhede, Ankur Chaurasia, Leanness assessment of an organization using fuzzy logic approach, J. Eng. Pract. Futur. Technol., 2 (2019) 1–6.
  27. Yadav, G. Khandelwal, R. Jain, and M. L. Mittal, Development of leanness index for SMEs, Int. J. Lean Six Sigma, 10(2019) 397–410. https://doi.org/10.1108/IJLSS-09-2017-0109.
  28. Rajpurohit, A Study of Leanness Assessment of Manufacturing Organizations, 2017.
  29. Agrawal, P. Asokan, and S. Vinodh, Benchmarking fuzzy logic and ANFIS approaches for leanness evaluation in an Indian SME: A case study, Benchmarking, 24 (2017) 973–993. https://doi.org/10.1108/BIJ-06-2016-0083
  30. Abreu and C. J. M. F., A Fuzzy Logic Model to Evaluate the Lean Level of an Organization, Int. J. Artif. Intell. Appl., 8 (2017) 59–75. https://doi.org/10.5121/ijaia.2017.8505.
  31. Vidyadhar R. Sudeep Kumar S. Vinodh Jiju Antony, Application of fuzzy logic for leanness assessment in SMEs: a case study, J. Eng. Des. Technol., 14 ( 2016).
  32. K. Balasubramanian and K. Hemamala, Leanness Assessment using Fuzzy Logic Approach: A Case of Indian Horn Manufacturing Company, Int. J. Data Min. Tech. Appl., 5 (2016) 102–109. https://doi.org/ 10.20894/ijdmta.102.005.002.001.
  33. Başhan and H. Demirel, Application of Fuzzy Dematel Technique to Assess Most Common Critical Operational Faults of Marine Boilers, J. Polytech., 22 (2019) 545–555. https://doi.org/10.2339/politeknik.426644.
  34. P. and J. J., An Integrated DEMATEL and AHP approach Multi Criteria Green Supplier Selection Process for Public Procurement, Int. J. Eng. Technol., 9 (2017)113–124 . https://doi.org/10.21817/ijet/2017/v9i1/170901414.
  35. Yunus AYDINEL, Investigating Causal Relationship Among Lean Maintenance Factors Using Fuzzy Dematel Method, 2020.
  36. Falatoonitoosi, Z. Leman, S. Sorooshian, and M. Salimi, Decision-making trial and evaluation laboratory, Res. J. Appl. Sci. Eng. Technol., 5 (2013) 3476–3480. https://doi.org/10.19026/rjaset.5.4475.
  37. M. Torkabadi and R. V. Mayorga, Evaluation of pull production control strategies under uncertainty: An integrated fuzzy ahp-topsis approach, J. Ind. Eng. Manag., 11 (2018) 161–184. https://doi.org/10.3926/jiem.2528.
  38. R. Lima Junior, L. Osiro, and L. C. R. Carpinetti, A comparison between Fuzzy AHP and Fuzzy TOPSIS methods to supplier selection, Appl. Soft Comput. J., 21 (2014) 194–209. https://doi.org/10.1016/j.asoc.2014.03.014.
Engineering and Technology Journal
Volume 41, Issue 12
Production & Metallurgy Engineering
18 articles
December 2023
Page 1638-1652
Files
Share
Export Citation
Statistics