Determining the optimal quantity and quality levels of used product returns for remanufacturing under multi-period and uncertain quality of returns

  • R. Aydin
  • C. K. Kwong
  • M. W. Geda
  • G. E. Okudan Kremer
ORIGINAL ARTICLE
  • 44 Downloads

Abstract

Managing product returns has been considered essential in the production planning and control of remanufacturing, as well as in the inventory control management of product returns. Uncertainties in the quantity and quality of returned products collected for remanufacturing could largely affect the profit generated from remanufactured products. However, determining the optimal quantity and quality levels of used products to be collected for remanufacturing in multiple periods under uncertain quality of the returns has not been properly addressed in previous studies. On the other hand, the effects of new product sales and demand for remanufactured products on used product returns, as well as the effect of quality of returns on the take-back and remanufacturing costs, have not been properly considered in previous studies. In this paper, a novel methodology is proposed to determine the optimal product returns for remanufacturing with consideration of the uncertainty in the quantity and quality of returns and study the effects of new product sales and demand for remanufactured products on used product returns, as well as the effect of quality of returns on the remanufacturing cost. A case study of determining the optimal returns of tablet PCs is conducted to illustrate the proposed methodology.

Keywords

Remanufacturing Uncertainty Product returns Multi-period models Dynamic demand models 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Notes

Funding information

The work described in this paper was supported by a grant from The Hong Kong Polytechnic University (Project No. G-YBDZ and A/C No. RTQ9).

References

  1. 1.
    Guide VDR, Wassenhove LNV (2001) Managing product returns for remanufacturing. Prod Oper Manag 10(2):142–155CrossRefGoogle Scholar
  2. 2.
    Chang D, Lee CKM, Chen CH (2014) Review of life cycle assessment towards sustainable product development. J Clean Prod 83:48–60CrossRefGoogle Scholar
  3. 3.
    Hatcher GD, Ijomah WL, Windmill JFC (2013) Integrating design for remanufacture into the design process: the operational factors. J Clean Prod 39:200–208CrossRefGoogle Scholar
  4. 4.
    De Giovanni P, Zaccour G (2014) A two-period game of a closed-loop supply chain. Eur J Oper Res 232:22–40MathSciNetCrossRefMATHGoogle Scholar
  5. 5.
    Chuang CH, Wang CX, Zhao Y (2014) Closed-loop supply chain models for a high-tech product under alternative reverse channel and collection cost structures. Int J Prod Econ 156:108–123CrossRefGoogle Scholar
  6. 6.
    Akcali E, Cetinkaya S (2011) Quantitative models for inventory and production planning in closed-loop supply chains. Int J Prod Res 49(8):2373–2407CrossRefGoogle Scholar
  7. 7.
    Srivastava SK, Srivastava RK (2006) Managing product returns for reverse logistics. Int J Phys Distr Log 36(7):524–546CrossRefGoogle Scholar
  8. 8.
    Shaharudin MR, Govindan K, Zailani S, Tan KC (2015) Managing product returns to achieve supply chain sustainability: an exploratory study and research propositions. J Clean Prod 101:1–15CrossRefGoogle Scholar
  9. 9.
    Aras N, Boyaci T, Verter V (2004) The effect of categorizing returned products in remanufacturing. IIE Trans 36(4):319–331CrossRefGoogle Scholar
  10. 10.
    Wang J, Zhao J, Wang X (2011) Optimum policy in hybrid manufacturing/remanufacturing system. Comput Ind Eng 60(3):411–419CrossRefGoogle Scholar
  11. 11.
    Souza GC (2013) Closed-loop supply chains: a critical review, and future research. Decis Sci 44(1):7–38CrossRefGoogle Scholar
  12. 12.
    Denizel M, Ferguson M, Souza GGC (2010) Multiperiod remanufacturing planning with uncertain quality of inputs. IEEE Trans Eng Manag 57(3):394–404CrossRefGoogle Scholar
  13. 13.
    Guide VDR (2000) Production planning and control for remanufacturing: industry practice and research needs. J Oper Manag 18(4):467–483CrossRefGoogle Scholar
  14. 14.
    Goh TN, Varaprasad N (1986) A statistical methodology for the analysis of the life-cycle of reusable containers. IIE Trans 18(1):42–47CrossRefGoogle Scholar
  15. 15.
    Kelle P, Silver EA (1989) Purchasing policy of new containers considering the random returns of previously issued containers. IIE Trans 21(4):349–354CrossRefGoogle Scholar
  16. 16.
    Toktay LB, Wein LM, Zenios SA (2000) Inventory management of remanufacturable products. Manag Sci 46(11):1412–1426CrossRefMATHGoogle Scholar
  17. 17.
    Clottey T, Benton WC, Srivastava R (2012) Forecasting product returns for remanufacturing operations. Decis Sci 43(4):589–614CrossRefGoogle Scholar
  18. 18.
    Krapp M, Nebel J, Sahamie R (2013) Forecasting product returns in closed-loop supply chains. Int J Phys Distr Log Manage 43(8):614–637CrossRefMATHGoogle Scholar
  19. 19.
    Krapp M, Nebel J, Sahamie R (2013) Using forecasts and managerial accounting information to closed-loop supply chain management. OR Spectr 35(4):975–1004MathSciNetCrossRefMATHGoogle Scholar
  20. 20.
    Aydin R, Kwong CK, Ji P (2015) Coordination of the closed-loop supply chain for product line design with consideration of remanufactured products. J Clean Prod 114:286–298CrossRefGoogle Scholar
  21. 21.
    Kwak M, Kim HM (2015) Design for life-cycle profit with simultaneous consideration of initial manufacturing and end-of-life remanufacturing. Eng Optimiz 47(1):18–35CrossRefGoogle Scholar
  22. 22.
    Demirel NO, Gokcen H (2008) A mixed integer programming model for remanufacturing in reverse logistics environment. Int J Adv Manuf Technol 39(11–12):1197–1206CrossRefGoogle Scholar
  23. 23.
    Sun X, Li Y, Govindan K, Zhou Y (2013) Integrating dynamic acquisition pricing and remanufacturing decisions under random price-sensitive returns. Int J Adv Manuf Technol 68:933–947CrossRefGoogle Scholar
  24. 24.
    Ferguson M, Guide VD, Koca E, Souza C (2009) The value of quality grading in remanufacturing. Prod Oper Manage 18(3):300–314CrossRefGoogle Scholar
  25. 25.
    Zikopolous C, Tagaras G (2007) Impact of uncertainty in the quality of returns on the profitability of a single-period refurbishing operation. Eur J Oper Res 182:205–225MathSciNetCrossRefMATHGoogle Scholar
  26. 26.
    Zeballos LJ, Gomes MI, Barbosa-Povoa AP, Novais AQ (2012) Addressing the uncertain quality and quantity of returns in closed-loop supply chains. Comput Chem Eng 47:237–247CrossRefGoogle Scholar
  27. 27.
    Galbreth MR, Blackburn JD (2010) Optimal acquisition quantities in remanufacturing with condition uncertainty. Prod Oper Manage 19(1):61–69CrossRefGoogle Scholar
  28. 28.
    Guide VDR, Teunter RH, Wassenhove LNV (2003) Matching demand and supply to maximize profits from remanufacturing. Manuf Ser Oper Manage 5(4):303–316CrossRefGoogle Scholar
  29. 29.
    Teunter RH, Flapper SD (2011) Optimal core acquisition and remanufacturing policies under uncertain core quality fractions. Eur J Oper Res 210(2):241–248CrossRefMATHGoogle Scholar
  30. 30.
    Liang X, Jin X, Ni J (2014) Forecasting product returns for remanufacturing systems. J Remanuf 4:8CrossRefGoogle Scholar
  31. 31.
    Niknejad A, Petrovic D (2014) Optimisation of integrated reverse logistics network with different product recovery routes. Eur J Oper Res 238:143–154MathSciNetCrossRefMATHGoogle Scholar
  32. 32.
    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
  33. 33.
    Macedo RB, 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(5–8):1241–1257Google Scholar
  34. 34.
    Mukhopadhyay SK, Ma H (2009) Joint procurement and production decisions in remanufacturing under quality and demand uncertainty. Int J Prod Econ 120(1):5–17CrossRefGoogle Scholar
  35. 35.
    El Saadany AMA, Jaber MY (2010) A production/remanufacturing inventory model with price and quality dependant return rate. Comput Ind Eng 58(3):352–362CrossRefGoogle Scholar
  36. 36.
    Aydin R, Kwong CK, Ji P (2015) A novel methodology for simultaneous consideration of remanufactured and new products in product line design. Int J Prod Econ 169:127–140CrossRefGoogle Scholar
  37. 37.
    Chen W, Hoyle C, Wassenaar HJ (2013) Decision-based design: integrating consumer preferences into engineering design. Springer-Verlag, LondonCrossRefMATHGoogle Scholar
  38. 38.
    Train KE (2009) Discrete choice methods with simulation. Cambridge University Press, CambridgeCrossRefMATHGoogle Scholar
  39. 39.
    Eggers F, Eggers F (2011) Where have all the flowers gone? Forecasting green trends in the automobile industry with a choice-based conjoint adoption model. Technol Forecast Soc 78:51–62CrossRefGoogle Scholar
  40. 40.
    Bass FM (1969) A new product growth for model consumer durables. Manag Sci 15(5):215–227CrossRefMATHGoogle Scholar
  41. 41.
    Hauser WM, Lund RT (2003) The remanufacturing industry: anatomy of a giant. Boston University, BostonGoogle Scholar

Copyright information

© Springer-Verlag London Ltd. 2017

Authors and Affiliations

  • R. Aydin
    • 1
  • C. K. Kwong
    • 1
  • M. W. Geda
    • 1
  • G. E. Okudan Kremer
    • 1
  1. 1.Industrial and Manufacturing Systems EngineeringIowa State UniversityAmesUSA

Personalised recommendations