Skip to main content
SpringerLink
Log in

Measuring the Relative Importance of Reconfigurable Manufacturing System (RMS) Using Best–Worst Method (BWM)

  • Conference paper
  • First Online:
Advances in Electromechanical Technologies

Part of the book series: Lecture Notes in Mechanical Engineering ((LNME))

Abstract

Manufacturing companies have since been continuously facing volatile market conditions, primarily due to rapidly changing customers demand and quick introduction of new products. Therefore, to stay competitive in globally ever-changing manufacturing environment, manufacturing sectors need to adopt and implement those manufacturing systems which are capable to keep pace with the changes occurring and this is why a new concept, namely “reconfigurable manufacturing system (RMS)”, has come into being. The manufacturing organizations have to meet the requirements of RMS by developing a suitable system which keeps their entity globally viable. The main objective of this paper is to identify and analyse the factors which directly or indirectly influence reconfigurable manufacturing system. In this present work, local and global weights have been calculated to measure the global ranking of all the possible RMS factors (e.g. modularity, scalability, integrability, flexibility, convertibility and diagnosability) based on which the best and worst alternatives are selected right at the design stage of RMS. All these factors have been mapped together through multi-criteria decision-making approach known as best–worst method (BWM). Finally, sensitivity analysis has been done to validate the proposed results. The proposed multi-decision approach is quite versatile from the point of view that it provides an opportunity to integrate all possible factors and sub-factors which could impact manufacturing processes.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 259.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 329.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 329.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Azadeh A, Allahverdiloo M, Shirkouhi SN (2011) A computer simulation model for analyzing performance of inventory policy in multi-product mode in two-echelon supply chain. Int J Logistics Syst Manage 8(1):66–85

    Article  Google Scholar 

  2. Ahmad S, Schroeder RG, Mallick DN (2010) The relationship among modularity, functional coordination, and mass customization: implications for competitiveness. Eur J Innov Manage 13(1):46–61

    Article  Google Scholar 

  3. Ali A, Erwin P, Michael P, Frank W (2018) Scheduling in manufacturing systems: new trends and perspectives. Int J Prod Res 56(19):6333–6335

    Article  Google Scholar 

  4. Asl FM, Ulsoy AG (2003) Stochastic optimal capacity management in reconfigurable manufacturing systems. CIRP Ann Manuf Technol 52(1):371–374

    Article  Google Scholar 

  5. Ashraf M, Hasan F (2018) Configuration selection for a reconfigurable manufacturing flow line involving part production with operation constraints. Int J Adv Manuf Technol 98(5–8):2137–2156

    Article  Google Scholar 

  6. Benderbal HH, Dahane M, Benyoucef L (2018) Modularity assessment in reconfigurable manufacturing system (RMS) design: an archived multi-objective simulated annealing-based approach. Int J Adv Manuf Technol 94(1–4):729–749

    Article  Google Scholar 

  7. Bortolini M, Galizia FG, Mora C (2018) Reconfigurable manufacturing systems: literature review and research trend. J Manuf Syst 49:93–106

    Article  Google Scholar 

  8. Cheng CH, Chen Y (2012) Autonomous intelligent manufacturing systems and its applications. J Adv Eng 31(1):409–412

    Google Scholar 

  9. Dubey R, Gunasekaran A, Helo P, Papadopoulos T, Childe SJ, Sahay BS (2017) Explaining the impact of reconfigurable manufacturing systems on environmental performance: the role of top management and organizational culture. J Clean Prod 141:56–66

    Article  Google Scholar 

  10. Deif AM, ElMaraghy HA (2007) Assessing capacity scalability policies in RMS using system dynamics. Int J Flex Manuf Syst 19(3):128–150

    Article  MATH  Google Scholar 

  11. Dolgui A, Guschinsky N, Levin G (2009) Graph approach for optimal design of transfer machine with rotary table. Int J Prod Res 47(2):321–341

    Article  Google Scholar 

  12. Gyulai D, Monostori L (2017) Capacity management of modular assembly systems. J Manuf Syst 43(Part 1):88–99

    Article  Google Scholar 

  13. Gupta S, Gupta P (2018) Setting-up material handling network in manufacturing systems using graph theory. J Adv Manage Res 15(1):58–67

    Article  Google Scholar 

  14. Gupta P (2018) Modularity enablers: a tool for Industry 4.0. Life Cycle Reliab Saf Eng 1–7

    Google Scholar 

  15. Gupta YP, Goyal S (1989) Flexibility of manufacturing systems: concepts and measurements. Eur J Oper Res 43(2):119–135

    Article  MathSciNet  Google Scholar 

  16. Holtta K, Suh ES, De Weck OL (2005) Trade-off between modularity and performance for engineered systems and products. In: Proceedings of the 15th international conference on engineering design, Melbourne, Australia, 15–18 Aug 2005

    Google Scholar 

  17. Koren Y, Jovane F, Heisel U, Moriwaki T, Pritschow G, Ulsoy G, Van Brussel H (1999) Reconfigurable manufacturing systems. A Keynote Pap CIRP Ann 48(2):527–540

    Article  Google Scholar 

  18. Kristianto Y, Gunasekaran A, Jiao J (2014) Logical reconfiguration of reconfigurable manufacturing systems with stream of variations modelling: a stochastic two-stage programming and shortest path model. Int J Prod Res 52(5):1401–1418

    Article  Google Scholar 

  19. Koren Y, Gu X, Guo W (2017a) Reconfigurable manufacturing systems: principles, design, and future trends. Front Mech Eng, 1–16. https://doi.org/10.1007/s11465-018-0483-0

    Google Scholar 

  20. Koren Y, Wang W, Gu X (2017) Value creation through design for scalability of reconfigurable manufacturing systems. Int J Prod Res 55(5):1227–1242

    Article  Google Scholar 

  21. Lameche K, Najid NM, Castagna P, Kouiss K (2017) Modularity in the design of reconfigurable manufacturing systems. IFAC-Papers On Line 50(1):3511–3516

    Article  Google Scholar 

  22. Mehrabi MG, Ulsoy AG (eds) (1997) State-of the-art in technologies related to reconfigurable manufacturing systems, Report #2, vol II, Engineering Research Center for Reconfigurable machining systems (ERC/RMS), The University of Michigan, Ann Arbor, USA (1997)

    Google Scholar 

  23. Mehrabi MG, Ulsoy AG, Koren Y (2000) Reconfigurable manufacturing systems and their enabling technologies. Int J Manuf Technol Manage 1(1):113–130

    Article  Google Scholar 

  24. Mangla SK, Kumar P, Barua MK (2015) Risk analysis in green supply chain using fuzzy AHP approach: a case study. Resour Conserv Recycl 104:375–390

    Article  Google Scholar 

  25. Malhotra V (2014) Modelling the barriers affecting design and implementation of reconfigurable manufacturing system. Int J Logistics Syst Manage 17(2):200–217

    Article  Google Scholar 

  26. Malhotra V (2014) Analysis of factors affecting the reconfigurable manufacturing system using an interpretive structural modelling technique. Int J Ind Syst Eng 16(3):396–413

    Google Scholar 

  27. Malhotra V, Raj T, Arora A (2012) Evaluation of barriers affecting reconfigurable manufacturing systems with graph theory and matrix approach. Mater Manuf Processes 27(1):88–94

    Article  Google Scholar 

  28. Malhotra V, Raj T (2012) Quantifying the factors affecting the reconfigurable manufacturing system. Int J Serv Oper Manage 13(2):226–246

    Google Scholar 

  29. Mesa J, Maury H, Arrieta R, Bula A, Riba C (2015) Characterization of modular architecture principles towards reconfiguration: a first approach in its selection process. Int J Adv Manuf Technol 80(1–4):221–232

    Article  Google Scholar 

  30. Maganha I, Silva C, Ferreira LMD (2018) Understanding reconfigurability of manufacturing systems: an empirical analysis. J Manuf Syst 48:120–130

    Article  Google Scholar 

  31. Maier-Speredelozzi V, Koren Y, Hu SJ (2003) Convertibility measures for manufacturing systems. CIRP Ann Manuf Technol 52(1):367–370

    Article  Google Scholar 

  32. Prakash C, Barua MK (2015) Integration of AHP-TOPSIS method for prioritizing the solutions of reverse logistics adoption to overcome its barriers under fuzzy environment. J Manuf Syst 37:599–615

    Article  Google Scholar 

  33. Rezaei J (2015) Best-worst multi-criteria decision-making method. Omega 53, 49–57

    Google Scholar 

  34. Rezaei J, Nispeling T, Sarkis J, Tavasszy LA (2016) Supplier selection life cycle approach integrating traditional and environmental criteria using the best worst method. J Clean Prod 135:577–588

    Article  Google Scholar 

  35. Rezaei J, Wang J, Tavasszy L (2015) Linking supplier development to supplier segmentation using best worst method. Expert Syst Appl 42(23):9152–9164

    Article  Google Scholar 

  36. Rezaei J, van Roekel WS, Tavasszy L (2018) Measuring the relative importance of the logistics performance index indicators using Best Worst Method. Transp Policy 68:158–169

    Article  Google Scholar 

  37. Rezaei J (2016) Best-worst multi-criteria decision-making method: some properties and a linear model. Omega 64:126–130

    Article  Google Scholar 

  38. Renna P (2017) Decision-making method of reconfigurable manufacturing systems’ reconfiguration by a Gale-Shapley model. J Manuf Syst 45:149–158

    Article  Google Scholar 

  39. Singh A, Gupta S, Asjad M, Gupta P (2017) Reconfigurable manufacturing systems: journey and the road ahead. Int J Syst Assur Eng Manage 8(Suppl. 2):1849–1857

    Article  Google Scholar 

  40. Singh A, Asjad M, Gupta P, Quamar J (2019a) An approach to develop Shaper cum Slotter mechanism: a reconfigurable machine tool. South Asian J Bus Manage Case, 1–12. https://doi.org/10.1177/2277977919833765

  41. Singh A, Gupta P, Asjad M (2019b) Reconfigurable manufacturing system (Rms): accelerate towards Industries 4.0 (18 Mar 2019). In: Proceedings of international conference on sustainable computing in science, technology and management (SUSCOM-2019), 26–28 Feb 2019. Amity University Rajasthan, Jaipur, India. Available at SSRN: https://ssrn.com/abstract=3354485

  42. Singh A, Asjad M, Gupta P (2019) Reconfigurable machine tool: a perspective. Life Cycle Reliab Saf Eng 8(4):365–376

    Article  Google Scholar 

  43. Setchi RM, Lagos N (2004) Reconfigurability and reconfigurable manufacturing systems: state-of-the-art review. In: IEEE international conference on industrial informatics, pp 529–535

    Google Scholar 

  44. Son SY, Olsen TL, Yip-Hoi D (2001) An approach to scalability and line balancing for reconfigurable manufacturing systems. Integr Manuf Syst 12(7):500–511

    Article  Google Scholar 

  45. Tiwari MK, Gumasta K, Gupta SK, Benyoucef L (2011) Developing a reconfigurability index using multi-attribute utility theory. Int J Prod Res 49(6):1669–1683

    Article  Google Scholar 

  46. Tu Q, Vonderembse MA, Ragu Nathan TS, RaguNathan B (2004) Measuring modularity based manufacturing practices and their impact on mass customization capability: a customer driven perspective. Decis Sci 35(2):147–168

    Article  Google Scholar 

  47. Van de Kaa G, Fens T, Rezaei J (2018) Residential grid storage technology battles: a multi-criteria analysis using BWM. Technol Anal Strat Manage, 1–13. https://doi.org/10.1080/09537325.2018.1484441

  48. Zhong H, Zheng W (2012) Reconfigurable machine tools design methodology. Master thesis. Department of Production Engineering and Management, Royal Institute of Technology, Stockholm, Sweden, p 56

    Google Scholar 

  49. Yin Y, Stecke KE, Swink M, Kaku I (2017) Lessons from seru production on manufacturing competitively in a high cost environment. J Oper Manage 49:67–76

    Article  Google Scholar 

Download references

Compliance with Ethical Standards

We are the authors of the chapter. Thereby, fulfil all the ethics and compliance for them. The authors declare that they have no conflict of interest.

Author information

Authors and Affiliations

  1. Department of Mechanical Engineering, Faculty of Engineering and Technology, Jamia Millia Islamia, New Delhi, 110025, India

    Ashutosh Singh, Mohammad Asjad, Zahid Akhtar Khan & Arshad Noor Siddiquee

  2. Inter-University Accelerator Centre, New Delhi, 110025, India

    Piyush Gupta

Authors
  1. Ashutosh Singh
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mohammad Asjad
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Piyush Gupta
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Zahid Akhtar Khan
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Arshad Noor Siddiquee
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ashutosh Singh .

Editor information

Editors and Affiliations

  1. Department of Mechanical and Automation Engineering, HMR Institute of Technology and Management, New Delhi, Delhi, India

    V. C. Pandey

  2. Department of Mechanical Engineering, Indian Institute of Technology Delhi, New Delhi, Delhi, India

    P. M. Pandey

  3. Department of Mechanical Engineering, Delhi Technical University, New Delhi, Delhi, India

    S. K. Garg

Rights and permissions

Reprints and permissions

Copyright information

© 2021 Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Singh, A., Asjad, M., Gupta, P., Khan, Z.A., Siddiquee, A.N. (2021). Measuring the Relative Importance of Reconfigurable Manufacturing System (RMS) Using Best–Worst Method (BWM). In: Pandey, V.C., Pandey, P.M., Garg, S.K. (eds) Advances in Electromechanical Technologies. Lecture Notes in Mechanical Engineering. Springer, Singapore. https://doi.org/10.1007/978-981-15-5463-6_24

Download citation

  • DOI: https://doi.org/10.1007/978-981-15-5463-6_24

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-15-5462-9

  • Online ISBN: 978-981-15-5463-6

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation