A holistic framework for evaluation and selection of remanufacturing operations: an approach

  • John Mbogo Kafuku
  • Muhamad Zameri Mat Saman
  • Sha’ri Mohd Yusof
  • Salwa Mahmood
ORIGINAL ARTICLE
First Online:
Received:
Accepted:

Abstract

Lack of efficient evaluation methods to help decision makers in making informed decision regarding in-house technology investments, outsourcing technology, and manual activities in the remanufacturing industry has raised serious concerns in the selection process of technologies based on company size and resources. This has resulted in the inability of decision makers to broadly assess the scope of alternative technologies against remanufacturing processes. Thus, it has resulted in the choice of a common solution based on inconsistencies and uncertainties. This paper proposes a holistic framework that can assist decision makers in evaluating the remanufacturing operations by offering better understanding of the performance and potential capabilities in the selection of appropriate technology. The salient feature of the proposed framework is its utilization of the fuzzy logic method as guiding tool to analyze criteria for technology selection in order to make informed decisions on whether to invest or outsource a technology or use labor-intensive operations to the specific process. The paper also highlights importance of the technology assessment framework in aligning and consolidating the dedicated thinking for the life cycle of the particular technology. The major contribution of the framework is its integration of obsolete and disposal phases with the acquisition and adoption phases. Therefore, during technology assessment, decision makers are guided by a step-by-step iterative process on technology requirement verification.

Keywords

Holistic framework Assessing remanufacturing technology Technology evaluation Technology selection method 
This is a preview of subscription content, log in to check access.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Chen M, Abrishami P (2014) A mathematical model for production planning in hybrid manufacturing-remanufacturing systems. Int J Adv Manuf Technol 71(5-8):1187–1196CrossRefGoogle Scholar
  2. 2.
    Ke Q, Zhang H-C, Liu G, Li B (2011) Remanufacturing engineering literature overview and future research needs, in Glocalized Solutions for Sustainability in Manufacturing: Proceedings of the 18th CIRP International Conference on Life Cycle Engineering, Technische Universität Braunschweig, Braunschweig, Germany, May 2nd - 4th, 2011, J. Hesselbach and C. Herrmann, Editors. Springer Berlin Heidelberg: Berlin, Heidelberg. p. 437–442Google Scholar
  3. 3.
    Liu G, Liu T, Ke Q, Song S, Zhou D (2013) Time interval decision-making methods for active remanufacturing product based on game theory and neural network. Jixie Gongcheng Xuebao (Chin J Mech Eng) 49(7):29–35CrossRefGoogle Scholar
  4. 4.
    Matsumoto M, Komatsu S (2015) Demand forecasting for production planning in remanufacturing. Int J Adv Manuf Technol 1–15Google Scholar
  5. 5.
    Hatcher G, Ijomah W, Windmill J (2011) Design for remanufacture: a literature review and future research needs. J Clean Prod 19(17):2004–2014CrossRefGoogle Scholar
  6. 6.
    Farel RY, Ghaffari B, Asma Leroy Y (2013) A cost and benefit analysis of future end-of-life vehicle glazing recycling in France: a systematic approach. Resour Conserv Recycl 74:54–65CrossRefGoogle Scholar
  7. 7.
    Alvarado A (2013) Problems in the implementation process of advanced manufacturing technologies. Int J Adv Manuf Technol 64(1-4):123–131CrossRefGoogle Scholar
  8. 8.
    Wolf MI, Gutowski TG (2013) EOL product remanufacturing and reuse economics: consumer side and producer side. Proc Int Symp Sustain Syst Technol v1Google Scholar
  9. 9.
    Gallo M, Romano E, Santillo LC (2012) A perspective on remanufacturing business: issues and opportunities, International Trade from Economic and Policy Perspective, Prof. Vito Bobek (Ed.), InTech, doi:  10.5772/48103. Available from: http://www.intechopen.com/books/international-trade-from-economic-and-policy-perspective/a-perspective-on-remanufacturing-business-issues-and-opportunities
  10. 10.
    Ferrer G, Ayres RU (2000) The impact of remanufacturing in the economy. Ecol Econ 32(3):413–429CrossRefGoogle Scholar
  11. 11.
    Macedo PB, Alem D, Santos M, Junior ML, Moreno A (2015) Hybrid manufacturing and remanufacturing lot-sizing problem with stochastic demand, return, and setup costs. Int J Adv Manuf Technol 82(5):1241–1257Google Scholar
  12. 12.
    Jiang Z, Fan Z, Sutherland JW, Zhang H, Zhang X (2014) Development of an optimal method for remanufacturing process plan selection. Int J Adv Manuf Technol 72(9-12):1551–1558CrossRefGoogle Scholar
  13. 13.
    Kapetanopoulou P, Tagaras G (2011) Drivers and obstacles of product recovery activities in the Greek industry. Int J Oper Prod Manag 31(2):148–166CrossRefGoogle Scholar
  14. 14.
    Sarkis J, Helms MM, Hervani AA (2010) Reverse logistics and social sustainability. Corp Soc Responsib Environ Manag 17(6):337–354CrossRefGoogle Scholar
  15. 15.
    Zhang T, Chu J, Wang X, Liu X, Cui P (2011) Development pattern and enhancing system of automotive components remanufacturing industry in China. Resour Conserv Recycl 55(6):613–622CrossRefGoogle Scholar
  16. 16.
    Hong X, Wang Z, Wang D, Zhang H (2013) Decision models of closed-loop supply chain with remanufacturing under hybrid dual-channel collection. Int J Adv Manuf Technol 68(5-8):1851–1865CrossRefGoogle Scholar
  17. 17.
    Matsumoto M, Komatsu S (2015) Demand forecasting for production planning in remanufacturing. Int J Adv Manuf Technol 79(1-4):161–175CrossRefGoogle Scholar
  18. 18.
    Torkkeli M, Tuominen M (2002) The contribution of technology selection to core competencies. Int J Prod Econ 77(3):271–284CrossRefGoogle Scholar
  19. 19.
    Jiang Z, Zhang H, Sutherland JW (2011) Development of multi-criteria decision making model for remanufacturing technology portfolio selection. J Clean Prod 19(17):1939–1945CrossRefGoogle Scholar
  20. 20.
    Evans LL, Niels Summers M (2013) A fuzzy-decision-tree approach for manufacturing technology selection exploiting experience-based information. Expert Syst Appl 40(16):6412–6426CrossRefGoogle Scholar
  21. 21.
    Bhaskara Rao B (2007) Estimating short and long-run relationships: a guide for the applied economist. Appl Econ 39(13):1613–1625CrossRefGoogle Scholar
  22. 22.
    Amin GR, Toloo M, Sohrabi B (2006) An improved MCDM DEA model for technology selection. Int J Prod Res 44(13):2681–2686CrossRefMATHGoogle Scholar
  23. 23.
    Çimren EÇ, Bülent Budak E (2007) Development of a machine tool selection system using AHP. Int J Adv Manuf Technol 35(3-4):363–376CrossRefGoogle Scholar
  24. 24.
    Chang C-W, Wu C-R, Lin C-T, Chen H-C (2007) An application of AHP and sensitivity analysis for selecting the best slicing machine. Comput Ind Eng 52(2):296–307CrossRefGoogle Scholar
  25. 25.
    Jaganathan S, Erinjeri JJ, Ker J-i (2007) Fuzzy analytic hierarchy process based group decision support system to select and evaluate new manufacturing technologies. Int J Adv Manuf Technol 32(11-12):1253–1262CrossRefGoogle Scholar
  26. 26.
    Kreng VB, Wu C-Y, Wang IC (2011) Strategic justification of advanced manufacturing technology using an extended AHP model. Int J Adv Manuf Technol 52(9-12):1103–1113CrossRefGoogle Scholar
  27. 27.
    Arunachalam APS, Idapalapati S, Subbiah S (2015) Multi-criteria decision making techniques for compliant polishing tool selection. Int J Adv Manuf Technol 79(1-4):519–530CrossRefGoogle Scholar
  28. 28.
    Huang H, Zhang L, Liu Z, Sutherland JW (2011) Multi-criteria decision making and uncertainty analysis for materials selection in environmentally conscious design. Int J Adv Manuf Technol 52(5-8):421–432CrossRefGoogle Scholar
  29. 29.
    Hodgett RE (2015) Comparison of multi-criteria decision-making methods for equipment selection. Int J Adv Manuf Technol 1–13. doi:  10.1007/s00170-015-7993-2
  30. 30.
    Evans L (2013) Experience-based decision support methodology for manufacturing technology selection: a fuzzy-decision-tree mining approach. University of NottinghamGoogle Scholar
  31. 31.
    Machuca JA, Ortega Jiménez CH, Garrido-Vega P, de los Ríos JLPD (2011) Do technology and manufacturing strategy links enhance operational performance? Empirical research in the auto supplier sector. Int J Prod Econ 133(2):541–550CrossRefGoogle Scholar
  32. 32.
    Jin X (2012) Modeling and analysis of remanufacturing systems with stochastic return and quality variation. The University Of MichiganGoogle Scholar
  33. 33.
    Nikravesh M, Zadeh LA, Aminzadeh F (2003) Soft computing and intelligent data analysis in oil exploration. Vol. 51. ElsevierGoogle Scholar
  34. 34.
    Liu M, Liu C, Xing L, Liu Z, Li X, Lin L (2015) Assembly process control method for remanufactured parts with variable quality grades. Int J Adv Manuf Technol 1–11. doi:  10.1007/s00170-015-8026-x
  35. 35.
    Pedrycz W, Gomide F (2007) Fuzzy systems and computational intelligence, in fuzzy systems engineering. John Wiley & Sons, Inc. p. 383–418Google Scholar
  36. 36.
    Angelov P (1994) A generalized approach to fuzzy optimization. Int J Intell Syst 9(3):261–268CrossRefMATHGoogle Scholar
  37. 37.
    Yan M, Li H, Liang J (1999) The application of fuzzy control strategy in servo feed control of wire electrical discharge machining. Int J Adv Manuf Technol 15(11):780–784CrossRefGoogle Scholar
  38. 38.
    Devi K, Yadav SP (2013) A multicriteria intuitionistic fuzzy group decision making for plant location selection with ELECTRE method. Int J Adv Manuf Technol 66(9-12):1219–1229CrossRefGoogle Scholar
  39. 39.
    Subramoniam R, Huisingh D, Chinnam RB, Subramoniam S (2013) Remanufacturing decision-making framework (RDMF): research validation using the analytical hierarchical process. J Clean Prod 40:212–220CrossRefGoogle Scholar
  40. 40.
    Yu H, Ye Z, Chen S (2013) Application of arc plasma spectral information in the monitor of Al–Mg alloy pulsed GTAW penetration status based on fuzzy logic system. Int J Adv Manuf Technol 68(9-12):2713–2727CrossRefGoogle Scholar
  41. 41.
    Nasr N, Hughson C, Varel E, Bauer R (1998) State-of-the-art assessment of remanufacturing technology. Rochester Institute of Technology, National Center for Remanufacturing, Rochester, New YorkGoogle Scholar
  42. 42.
    Gamage J, Ijomah W, Windmill J (2013) 7.1 what makes cleaning a costly operation in remanufacturing? Proceedings of the 11th Global Conference on Sustainable Manufacturing - Innovative Solutions ISBN 978-3-7983-2609-5. http://dx.doi.org/10.14279/depositonce-4719
  43. 43.
    Chen J, Liang Y (2015) Development of fuzzy logic-based statistical process control chart pattern recognition system. Int J Adv Manuf Technol 1–16. doi:  10.1007/s00170-015-8183-y
  44. 44.
    Lakhal SY, Khan M, Islam MR (2009) An “Olympic” framework for a green decommissioning of an offshore oil platform. Ocean Coast Manag 52(2):113–123CrossRefGoogle Scholar
  45. 45.
    Yang S, Ong S, Nee A (2015) EOL strategy planning for components of returned products. Int J Adv Manuf Technol 77(5-8):991–1003CrossRefGoogle Scholar
  46. 46.
    Bocken N, Short S, Rana P, Evans S (2014) A literature and practice review to develop sustainable business model archetypes. J Clean Prod 65:42–56CrossRefGoogle Scholar
  47. 47.
    Chan F, Chan M, Lau H, Ip R (2001) Investment appraisal techniques for advanced manufacturing technology (AMT): a literature review. Integr Manuf Syst 12(1):35–47CrossRefGoogle Scholar
  48. 48.
    Du Y, Cao H, Liu F, Li C, Chen X (2012) An integrated method for evaluating the remanufacturability of used machine tool. J Clean Prod 20(1):82–91CrossRefGoogle Scholar
  49. 49.
    Robotis A, Boyaci T, Verter V (2012) Investing in reusability of products of uncertain remanufacturing cost: the role of inspection capabilities. Int J Prod Econ 140 (1)Google Scholar
  50. 50.
    Sabharwal S, Garg S (2013) Determining cost effectiveness index of remanufacturing: a graph theoretic approach. Int J Prod Econ 144(2):521–532CrossRefGoogle Scholar
  51. 51.
    Davoudpour H, Rezaee S, Ashrafi M (2012) Developing a framework for renewable technology portfolio selection: a case study at a R&D center. Renew Sustain Energy Rev 16(6):4291–4297CrossRefGoogle Scholar
  52. 52.
    Rojo FJR, Roy R, Shehab E (2010) Obsolescence management for long-life contracts: state of the art and future trends. Int J Adv Manuf Technol 49(9-12):1235–1250CrossRefGoogle Scholar
  53. 53.
    Feldman K, Sandborn P (2007) Integrating technology obsolescence considerations into product design planning. in ASME 2007 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. Am Soc Mech Eng. doi: 10.1115/DETC2007-35881 Google Scholar
  54. 54.
    Tavana M, Zareinejad M, Santos-Arteaga FJ, Kaviani MA (2016) A conceptual analytic network model for evaluating and selecting third-party reverse logistics providers. Int J Adv Manuf Technol 1–17Google Scholar
  55. 55.
    Hsu Y-L, Lee C-H, Kreng VB (2010) The application of fuzzy Delphi method and fuzzy AHP in lubricant regenerative technology selection. Expert Syst Appl 37(1):419–425CrossRefGoogle Scholar
  56. 56.
    Wang X, Chan HK (2013) An integrated fuzzy approach for evaluating remanufacturing alternatives of a product design. J Remanuf 3(1):1–19MathSciNetCrossRefGoogle Scholar
  57. 57.
    Abdulrahman MD-A, Subramanian N, Liu C, Shu C (2015) Viability of remanufacturing practice: a strategic decision making framework for Chinese auto-parts companies. J Clean Prod 105:311–323. doi: 10.1016/j.jclepro.2014.02.065 CrossRefGoogle Scholar
  58. 58.
    SO O (2014) Development of a model for optimal utilization of manpower in cement production planning process. J Emerg Trends Eng Appl Sci (JETEAS) 5(8):210–213MathSciNetGoogle Scholar
  59. 59.
    Kafuku JM, Saman MZM, Sharif S, Zakuan N (2015) Investment decision issues from remanufacturing system perspective: literature review and further research. Procedia CIRP 26:589–594CrossRefGoogle Scholar
  60. 60.
    Xia X, Govindan K, Zhu Q (2015) Analyzing internal barriers for automotive parts remanufacturers in China using grey-DEMATEL approach. J Clean Prod 87:811–825CrossRefGoogle Scholar
  61. 61.
    Steinhilper R (2001) Recent trends and benefits of remanufacturing: from closed loop businesses to synergetic networks. In Environmentally Conscious Design and Inverse Manufacturing, 2001. Proceedings EcoDesign 2001. IEEE.  10.1109/.2001.992404
  62. 62.
    Sutherland JW, Adler DP, Haapala KR, Kumar V (2008) A comparison of manufacturing and remanufacturing energy intensities with application to diesel engine production. CIRP Ann Manuf Technol 57(1):5–8CrossRefGoogle Scholar

Copyright information

© Springer-Verlag London 2016

Authors and Affiliations

  • John Mbogo Kafuku
    • 1
    • 2
  • Muhamad Zameri Mat Saman
    • 1
  • Sha’ri Mohd Yusof
    • 1
  • Salwa Mahmood
    • 1
  1. 1.Faculty of Mechanical EngineeringUniversiti Teknologi Malaysia (UTM)JohorMalaysia
  2. 2.Department of Mechanical and Industrial Engineering, College of Engineering and TechnologyUniversity of Dar es SalaamDar es SalaamTanzania

Personalised recommendations