Nothing Special   »   [go: up one dir, main page]
Skip to main content

Advertisement

Springer Nature Link
Log in

Use of Fuzzy Logic for Reconfigurability Assessment in Supply Chain

  • Published:
International Journal of Fuzzy Systems Aims and scope Submit manuscript

Abstract

Nowadays, the high competitiveness of a company depends on its ability to deal quickly and cost-effectively with the market disruptions. For this reason, companies should have an agile and flexible supply chains to solve problems related to the market fluctuation. In fact, supply chain reconfigurability is the ability to modify their capacity and functionality at the lowest cost. The objective of this article is to assess reconfigurability based on its characteristics (modularity, scalability, integrability, convertibility, diagnosability and customization) using Fuzzy logic. For this purpose, a quantitative evaluation of reconfigurability is proposed. In order to validate our assessment model, a case study is applied to evaluate the degree of reconfigurability after supply chain reconfiguration.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  1. Estampe, D.: Performance de la Supply Chain et modèles d’évaluation. ISTE Group (2015)

  2. Gunasekaran, A., Patel, C., Tirtiroglu, E.: Performance measures and metrics in a supply chain environment. Int. J. Oper. Prod. Manag. 21, 71–87 (2001). https://doi.org/10.1108/01443570110358468

    Article  Google Scholar 

  3. Gunasekaran, A., Patel, C., McGaughey, R.E.: A framework for supply chain performance measurement. Int. J. Prod. Econ. 87, 333–347 (2004). https://doi.org/10.1016/j.ijpe.2003.08.003

    Article  Google Scholar 

  4. Beamon, B.M.: Measuring supply chain performance. Int. J. Oper. Prod. Manag. 19, 275–292 (1999). https://doi.org/10.1108/01443579910249714

    Article  Google Scholar 

  5. Beamon, B.M.: Supply chain design and analysis: models and methods. Int. J. Prod. Econ. 55, 281–294 (1998). https://doi.org/10.1016/S0925-5273(98)00079-6

    Article  Google Scholar 

  6. Chan, F.T.S.: Performance measurement in a supply chain. Int. J. Adv. Manuf. Technol. 21, 534–548 (2003). https://doi.org/10.1007/s001700300063

    Article  Google Scholar 

  7. Chandra, C., Grabis, J.: Supply Chain Configuration: Concepts, Solutions, and Applications. Springer, New York (2016)

    Book  Google Scholar 

  8. Dolgui, A., Ivanov, D., Sokolov, B.: Reconfigurable supply chain: the X-network. Int. J. Prod. Res. 58, 4138–4163 (2020). https://doi.org/10.1080/00207543.2020.1774679

    Article  Google Scholar 

  9. Dolgui, A., Ivanov, D., Sokolov, B.: Ripple effect in the supply chain: an analysis and recent literature. Int. J. Prod. Res. 56, 414–430 (2018). https://doi.org/10.1080/00207543.2017.1387680

    Article  Google Scholar 

  10. Ivanov, D.: Viable supply chain model: integrating agility, resilience and sustainability perspectives—lessons from and thinking beyond the COVID-19 pandemic. Ann. Oper. Res. (2020). https://doi.org/10.1007/s10479-020-03640-6

    Article  Google Scholar 

  11. Olivares-Aguila, J., ElMaraghy, W.: System dynamics modelling for supply chain disruptions. Int. J. Prod. Res. (2020). https://doi.org/10.1080/00207543.2020.1725171

    Article  Google Scholar 

  12. Zidi, S., Hamani, N., Kermad, L.: Reconfigurable supply chain performance: a bibliometric analysis. In: PRO-VE 2021. IFIP Advances in Information and Communication Technology, Saint Etienne, France (2021). https://doi.org/10.1007/978-3-030-85969-5_14

  13. Chuu, S.-J.: Evaluating the flexibility in a manufacturing system using fuzzy multi-attribute group decision-making with multi-granularity linguistic information. Int. J. Adv. Manuf. Technol. 32, 409–421 (2007). https://doi.org/10.1007/s00170-005-0342-0

    Article  Google Scholar 

  14. Guan, J.: Measurement of manufacturing system flexibility with fuzzy set theory. In: 2008 International Conference on Management Science and Engineering 15th Annual Conference Proceedings, pp. 713–718. IEEE, Long Beach, CA (2008)

  15. Francalanza, E., Borg, J.C., Constantinescu, C.: A fuzzy logic based approach to explore manufacturing system changeability level decisions. Procedia CIRP. 41, 3–8 (2016). https://doi.org/10.1016/j.procir.2015.12.011

    Article  Google Scholar 

  16. Lin, C.-T., Chiu, H., Tseng, Y.-H.: Agility evaluation using fuzzy logic. Int. J. Prod. Econ. 101, 353–368 (2006). https://doi.org/10.1016/j.ijpe.2005.01.011

    Article  Google Scholar 

  17. Ma, B., Xia, L.X.X., Lim, R.: Modeling supply chain’s reconfigurability using fuzzy logic. In: 2007 IEEE Conference on Emerging Technologies and Factory Automation (EFTA 2007), pp. 234–241. IEEE, Patras (2007)

  18. Zidi, S., Hamani, N., Kermad, L.: New metrics for measuring supply chain reconfigurability. J. Intell. Manuf. (2021). https://doi.org/10.1007/s10845-021-01798-9

    Article  Google Scholar 

  19. Napoleone, A., Pozzetti, A., Macchi, M.: A framework to manage reconfigurability in manufacturing. Int. J. Prod. Res. 56, 3815–3837 (2018). https://doi.org/10.1080/00207543.2018.1437286

    Article  Google Scholar 

  20. Wiendahl, H.P., Heger, C.L.: Justifying changeability: a methodical approach to achieving cost effectiveness. J. Manuf. Sci. Prod. 6, 33–40 (2004). https://doi.org/10.1515/IJMSP.2004.6.1-2.33

    Article  Google Scholar 

  21. Biswas, P.: Modeling reconfigurability in supply chains using total interpretive structural modeling. J. Adv. Manag. Res. 14, 194–221 (2017). https://doi.org/10.1108/JAMR-09-2016-0071

    Article  Google Scholar 

  22. Biswas, P., Kumar, S., Jain, V., Chandra, C.: Measuring supply chain reconfigurability using integrated and deterministic assessment models. J. Manuf. Syst. 52, 172–183 (2019). https://doi.org/10.1016/j.jmsy.2019.05.008

    Article  Google Scholar 

  23. Farid, A.M.: Measures of reconfigurability and its key characteristics in intelligent manufacturing systems. J. Intell. Manuf. 28, 353–369 (2014). https://doi.org/10.1007/s10845-014-0983-7

    Article  Google Scholar 

  24. Gumasta, K., Kumar Gupta, S., Benyoucef, L., Tiwari, M.K.: Developing a reconfigurability index using multi-attribute utility theory. Int. J. Prod. Res. 49, 1669–1683 (2011). https://doi.org/10.1080/00207540903555536

    Article  Google Scholar 

  25. Maganha, I., Silva, C., Ferreira, L.M.D.F.: An analysis of reconfigurability in different business production strategies. IFAC-Pap. 52, 1028–1033 (2019). https://doi.org/10.1016/j.ifacol.2019.11.330

    Article  Google Scholar 

  26. Wang, G.X., Huang, S.H., Yan, Y., Du, J.J.: Reconfiguration schemes evaluation based on preference ranking of key characteristics of reconfigurable manufacturing systems. Int. J. Adv. Manuf. Technol. 89, 2231–2249 (2016). https://doi.org/10.1007/s00170-016-9243-7

    Article  Google Scholar 

  27. Dahane, M., Benyoucef, L.: An adapted NSGA-II algorithm for a Reconfigurable Manufacturing System (RMS) design under machines reliability constraints. In: Talbi, E.-G., Yalaoui, F., Amodeo, L. (eds.) Metaheuristics for production systems, pp. 109–130. Springer, Cham (2016)

    Chapter  Google Scholar 

  28. Delorme, X., Malyutin, S., Dolgui, A.: A multi-objective approach for design of reconfigurable transfer lines. IFAC-Pap. 49, 509–514 (2016). https://doi.org/10.1016/j.ifacol.2016.07.675

    Article  Google Scholar 

  29. Goyal, K.K., Jain, P.K., Jain, M.: Optimal configuration selection for reconfigurable manufacturing system using NSGA II and TOPSIS. Int. J. Prod. Res. 50, 4175–4191 (2012). https://doi.org/10.1080/00207543.2011.599345

    Article  Google Scholar 

  30. Goyal, K.K., Jain, P.K.: Design of reconfigurable flow lines using MOPSO and maximum deviation theory. Int. J. Adv. Manuf. Technol. (2015). https://doi.org/10.1007/s00170-015-7760-4

    Article  Google Scholar 

  31. Gupta, A., Jain, P.K., Kumar, D.: Configuration selection of reconfigurable manufacturing system based on performance. Int. J. Ind. Syst. Eng. 20, 209 (2015). https://doi.org/10.1504/IJISE.2015.069543

    Article  Google Scholar 

  32. Mittal, K.K., Jain, P.K.: An overview of performance measures in reconfigurable manufacturing system. Procedia Eng. 69, 1125–1129 (2014). https://doi.org/10.1016/j.proeng.2014.03.100

    Article  Google Scholar 

  33. Prasad, D., Jayswal, S.C.: Assessment of a reconfigurable manufacturing system. Benchmarking Int. J. BIJ-06-2018-0147 (2019). https://doi.org/10.1108/BIJ-06-2018-0147

  34. Kelepouris, T., Wong, C.Y., Farid, A.M., Parlikad, A.K., McFarlane, D.C.: Towards a reconfigurable supply network model. In: Intelligent Production Machines and Systems, pp. 481–486. Elsevier (2006)

  35. Fine, C.H.: Clockspeed: Winning Industry Control in the Age of Temporary Advantage. Perseus Books, New York (1998)

    Google Scholar 

  36. Voordijk, H., Meijboom, B., de Haan, J.: Modularity in supply chains: a multiple case study in the construction industry. Int. J. Oper. Prod. Manag. 26, 600–618 (2006). https://doi.org/10.1108/01443570610666966

    Article  Google Scholar 

  37. Wolters, M.J.J.: The business of modularity and the modularity of buisiness. Selbstverl, Rotterdam (1999)

  38. Bouaissi, A., Allaoui, H., Jean-Christophe, N.: La modularité produit et chaîne logistique dans un contexte collaboratif et durable: revue de littérature et cadre conceptuel. In: Xème Conférence Internationale: Conception et Production Intégrées. Tanger, Morocco (2015)

  39. Fabbe-Costes, N.: La gestion des chaînes logistiques multi-acteurs: les dimensions organisationnelles d’une gestion lean et agile (2007)

  40. Serdarasan, S.: A review of supply chain complexity drivers. Comput. Ind. Eng. 66, 533–540 (2013). https://doi.org/10.1016/j.cie.2012.12.008

    Article  Google Scholar 

  41. Beaulieu, M.: Définir et maîtriser la complexité des réseaux de logistique à rebours. 20 (2000)

  42. Sheffi, Y., Rice, J.B., Jr.: A supply chain view of the resilient enterprise. MIT Sloan Manag. Rev. 47, 12 (2005)

    Google Scholar 

  43. Caridi, M., Crippa, L., Perego, A., Sianesi, A., Tumino, A.: Do virtuality and complexity affect supply chain visibility? Int. J. Prod. Econ. 127, 372–383 (2010). https://doi.org/10.1016/j.ijpe.2009.08.016

    Article  Google Scholar 

  44. Ball, M.O., Ma, M., Raschid, L., Zhao, Z.: Supply chain infrastructures: system integration and information sharing. ACM SIGMOD Rec. 31, 61–66 (2002). https://doi.org/10.1145/507338.507350

    Article  Google Scholar 

  45. Durowoju, O., Chan, H., Wang, X.: The impact of security and scalability of cloud service on supply chain performance. J. Electron. Commer. Res. 12, 243–256 (2011)

    Google Scholar 

  46. Zebardast, M., Malpezi, S., Taisch, M.: Mass customization in supply chain level: development of a conceptual framework to manage and assess performance. In: Prabhu, V., Taisch, M., Kiritsis, D. (eds.) Advances in Production Management Systems. Sustainable Production and Service Supply Chains, pp. 81–90. Springer, Berlin (2013)

  47. Chandra, C., Kamrani, A.: Mass Customization. Springer, Boston (2004)

    Book  Google Scholar 

  48. Zidi, S., Hamani, N., Kermad, L.: Classification of reconfigurability characteristics of supply chain. In: 8th Changeable, Agile, Reconfigurable and Virtual Production Conference (CARV). Aalborg City University, Denmark (2021).

  49. Zidi, S., Hamani, N., Kermad, L.: Modularity metric in reconfigurable supply chains. In: Advances in Production Management Systems. Artificial Intelligence for Sustainable and Resilient Production Systems. APMS 2021. Part V, IFIP AICT 634. Springer, Cham. (2021). https://doi.org/10.1007/978-3-030-85914-5_49

  50. Zadeh, L.A.: Fuzzy sets. Inf. Control. 8, 338–353 (1965). https://doi.org/10.1016/S0019-9958(65)90241-X

    Article  MATH  Google Scholar 

  51. Ashrafzadeh, M., Mokhatab Rafiei, F., Mollaverdi, N., Zare, Z.: Application of fuzzy TOPSIS method for the selection of Warehouse location: a case study. Interdiscipl. J. Contemp. Res. Bus. 3, 655–671 (2012)

    Google Scholar 

  52. Pourjavad, E., Shahin, A.: The application of Mamdani fuzzy inference system in evaluating green supply chain management performance. Int. J. Fuzzy Syst. 20, 901–912 (2018). https://doi.org/10.1007/s40815-017-0378-y

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Laboratory of QUARTZ, University of Paris 8, 140 Rue de la Nouvelle France, Montreuil, France

    Slim Zidi & Lyes Kermad

  2. Laboratory of Innovative Technologies, University of Picardie Jules Verne, 48 Rue d’Ostende, Saint-Quentin, France

    Slim Zidi & Nadia Hamani

  3. ESTIA Research, University of Bordeaux, 90 Allée Fauste d’Elhuyar, Bidart, France

    Basma Samir

Authors
  1. Slim Zidi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Nadia Hamani
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Basma Samir
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Lyes Kermad
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Slim Zidi.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zidi, S., Hamani, N., Samir, B. et al. Use of Fuzzy Logic for Reconfigurability Assessment in Supply Chain. Int. J. Fuzzy Syst. 24, 1025–1045 (2022). https://doi.org/10.1007/s40815-021-01187-7

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s40815-021-01187-7

Keywords