Skip to main content

Advertisement

SpringerLink
Log in

A closed-loop supply chain model with learning effect, random return and imperfect inspection under price- and quality-dependent demand

  • Theoretical Article
  • Published:
OPSEARCH Aims and scope Submit manuscript

Abstract

This paper addresses a closed-loop supply chain consisting of a manufacturer and a retailer. While producing a single item, the manufacturer executes a perfect production process influenced by learning effect. Production process is supported by raw materials as well as used materials. Collected used items follow an inspection process which is subject to learning and incurs Type I and Type II errors. Return of used items is random. The demand of the end-product is in a linear relationship with retail price and product quality. The proposed model is developed and optimal results are analysed with a numerical example. Sensitivity analysis is carried out to investigate the effects of various parameters on optimal decisions. It is observed from the numerical study that higher learning in production and inspection results in achieving a higher system profit, and the price sensitivity factor in demand has a significant impact on the retail price. Some important managerial insights of the proposed model are also discussed.

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

Access this article

Log in via an institution

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

Similar content being viewed by others

References

  1. Afshari, H., Jaber, M.Y., Searcy, C.: Investigating the effects of learning and forgetting on the feasibility of adopting additive manufacturing in supply chains. Comput. Ind. Eng. 128, 576–590 (2019)

    Article  Google Scholar 

  2. Alshamsi, A., Diabat, A.: A genetic algorithm for reverse logistics network design: a case study from the GCC. J. Clean. Prod. 151, 652–669 (2017)

    Article  Google Scholar 

  3. Baiman, S., Fischer, P.E., Rajan, M.V.: Information, contracting, and quality costs. Manag. Sci. 46(6), 776–789 (2000)

    Article  Google Scholar 

  4. Carlson, J.G., Rowe, A.J.: How much does forgetting cost. Ind. Eng. 8, 40–47 (1976)

    Google Scholar 

  5. Das, D., Dutta, P.: Design and analysis of a closed-loop supply chain in presence of promotional offer. Int. J. Prod. Res. 53, 141–165 (2015)

    Article  Google Scholar 

  6. Das, D., Dutta, P.: Performance analysis of a closed-loop supply chain with incentive-dependent demand and return. Int. J. Adv. Manuf. Technol. 86, 621–639 (2016)

    Article  Google Scholar 

  7. De Giovanni, P.: Quality improvement vs. advertising support: which strategy works better for a manufacturer? Eur. J. Oper. Res. 208(2), 119–130 (2011)

  8. Dey, O., Giri, B.C.: A new approach to deal with learning in inspection in an integrated vendor-buyer model with imperfect production process. Comput. Ind. Eng. 131, 515–523 (2019)

    Article  Google Scholar 

  9. Duffuaa, S.O., El-Ga’aly, A.: Impact of inspection errors on the formulation of a multi-objective optimization process targeting model under inspection sampling plan. Comput. Ind. Eng. 80, 254–260 (2015)

  10. Dutta, P., Talaulikar, S., Xavier, V., Kapoor, S.: Fostering reverse logistics in India by prominent barrier identification and strategy implementation to promote circular economy. J. Clean. Prod. 294, 126241 (2021)

  11. Giri, B.C., Dey, S.K.: Game theoretic analysis of a closed-loop supply chain with backup supplier under dual channel recycling. Comput. Ind. Eng. 129, 179–191 (2019)

    Article  Google Scholar 

  12. Giri, B.C., Glock, C.H.: A closed-loop supply chain with stochastic product returns and worker experience under learning and forgetting. Int. J. Prod. Res. 55, 6760–6778 (2017)

    Article  Google Scholar 

  13. Giri, B.C., Sharma, S.: Optimizing a closed-loop supply chain with manufacturing defects and quality dependent return rate. J. Manuf. Syst. 35, 92–111 (2015)

    Article  Google Scholar 

  14. Glock, C.H., Jaber, M.Y.: A multi-stage production-inventory model with learning and forgetting effects, rework and scrap. Comput. Ind. Eng. 64, 708–720 (2013)

    Article  Google Scholar 

  15. Govindan, K., Soleimani, H., Kannan, D.: Reverse logistics and closed-loop supply chain: a comprehensive review to explore the future. Eur. J. Oper. Res. 240, 603–626 (2015)

    Article  Google Scholar 

  16. Hong, X., Wang, Z., Wang, D., Zhang, H.: Decision models of closed-loop supply chain with remanufacturing under hybrid dual-channel collection. Int. J. Adv. Manuf. Technol. 68(5–8), 1851–1865 (2013)

    Article  Google Scholar 

  17. Huang, Y., Wang, Z.: Information sharing in a closed-loop supply chain with learning effect and technology licensing. J. Clean. Prod. (2020). https://doi.org/10.1016/j.jclepro.2020.122544

    Article  Google Scholar 

  18. Inderfurth, K.: Optimal policies in hybrid manufacturing/remanufacturing systems with product substitution. Int. J. Prod. Econ. 90(3), 325–343 (2004)

    Article  Google Scholar 

  19. Inderfurth, K., De Kok, A., Flapper, S.: Product recovery in stochastic remanufacturing systems with multiple reuse options. Eur. J. Oper. Res. 133(1), 130–152 (2001)

    Article  Google Scholar 

  20. Jaber, M.Y., Bonney, M.: Production breaks and the learning curve: the forgetting phenomenon. Appl. Math. Model. 20, 162–169 (1996)

    Article  Google Scholar 

  21. Jaber, M.Y., El Saadany, A.M.: An economic production and remanufacturing model with learning effects. Int. J. Prod. Econ. 131, 115–127 (2011)

    Article  Google Scholar 

  22. Jacobson, H.J.: A study of inspector accuracy. Ind. Quality Control 9(2), 16–25 (1952)

    Google Scholar 

  23. Jauhari, W.A., Sofiana, A., Kurdhi, N.A., Laksono, P.W.: An integrated inventory model for supplier–manufacturer–retailer system with imperfect quality and inspection errors. Int. J. Logist. Syst. Manag. 24(3), 383–407 (2016)

    Google Scholar 

  24. Jena, S.K., Sarmah, S.P., Sarin, S.C.: Joint-advertising for collection of returned products in a closed-loop supply chain under uncertain environment. Comput. Ind. Eng. 113, 305–322 (2017)

    Article  Google Scholar 

  25. Juran, J. M., Gryna, F. M., Bingham, R. S.: Quality control handbook. No. 658.562 Q-1q. McGraw Hill. (1974)

  26. Kar, M.B., Bera, S., Das, D., Kar, S.: A production-inventory model with permissible delay incorporating learning effect in random planning horizon using genetic algorithm. J. Ind. Eng. Int. 11(4), 555–574 (2015)

    Article  Google Scholar 

  27. Karimi-Mamaghan, M., Mohammadi, M., Jula, P., Pirayesh, A., Ahmadi, H.: A learning-based metaheuristic for a multi-objective agile inspection planning model under uncertainty. Eur. J. Oper. Res. 285(2), 513–537 (2020)

    Article  Google Scholar 

  28. Khan, M., Jaber, M.Y., Wahab, M.I.M.: Economic order quantity model for items with imperfect quality with learning in inspection. Int. J. Prod. Econ. 124(1), 87–96 (2010)

    Article  Google Scholar 

  29. Khan, M., Jaber, M.Y., Guiffrida, A.L., Zolfaghari, S.: A review of the extensions of a modified EOQ model for imperfect quality items. Int. J. Prod. Econ. 132(1), 1–12 (2011)

    Article  Google Scholar 

  30. Khan, M., Jaber, M.Y., Ahmad, A.R.: An integrated supply chain model with errors in quality inspection and learning in production. Omega. 42, 16–24 (2014)

    Article  Google Scholar 

  31. Lolli, F., Messori, M., Gamberini, R., Rimini, B., Balugani. E.: Modelling production cost with the effects of learning and forgetting. IFAC-PapersOnLine. 49(12), 503–508 (2016)

  32. Maiti, T., Giri, B.C.: A closed loop supply chain under retail price and product quality dependent demand. J. Manuf. Syst. 37, 624–637 (2015)

    Article  Google Scholar 

  33. Maiti, T., Giri, B.C.: Two-period pricing and decision strategies in a two-echelon supply chain under price-dependent demand. Appl. Math. Model. 42, 655–674 (2017)

    Article  Google Scholar 

  34. Mondal, C., Giri, B.C.: Pricing and used product collection strategies in a two-period closed-loop supply chain under greening level and effort dependent demand. J. Clean. Prod. 265, 121335 (2020)

  35. Nagare, M., Dutta, P.: Single-period ordering and pricing policies with markdown, multivariate demand and customer price sensitivity. Comput. Ind. Eng. 125, 451–466 (2018)

    Article  Google Scholar 

  36. Pal, S., Mahapatra, G.S.: A manufacturing-oriented supply chain model for imperfect quality with inspection errors, stochastic demand under rework and shortages. Comput. Ind. Eng. 106, 299–314 (2017)

    Article  Google Scholar 

  37. Raouf, A., Jain, J.K., Sathe, P.T.: A cost-minimization model for multicharacteristic component inspection. AIIE Trans. 15(3), 187–194 (1983)

    Google Scholar 

  38. Reyniers, D.J., Tapiero, C.S.: The delivery and control of quality in supplier–producer contracts. Manage. Sci. 41(10), 1581–1589 (1995)

    Article  Google Scholar 

  39. Savaskan, R.C., Bhattacharya, S., Van Wassenhove, L.N.: Closed-loop supply chain models with product remanufacturing. Manage. Sci. 50, 239–252 (2004)

    Article  Google Scholar 

  40. Schrady, D.A.: A deterministic inventory model for reparable items. Nav. Res. Logist. 14, 391–398 (1967)

    Article  Google Scholar 

  41. Shen, B., Chen, C.: Quality management in outsourced global fashion supply chains: an exploratory case study. Prod. Plan. Control. 31(9), 757–769 (2020)

    Article  Google Scholar 

  42. Sun, L., Wang, Y., Hua, G., Cheng, T.C.E., Dong, J.: Virgin or recycled? Optimal pricing of 3D printing platform and material suppliers in a closed-loop competitive circular supply chain. Resour. Conserv. Recycl. 162, 105035 (2020)

    Article  Google Scholar 

  43. Whitin, T.M.: Inventory control and price theory. Manag. Sci. 2(1), 61–68 (1955)

    Article  Google Scholar 

  44. Wright, T.P.: Learning curve. J. Aeronaut. Sci. 3, 122–128 (1936)

    Article  Google Scholar 

  45. Yelle, L.E.: The learning curve: historical review and comprehensive survey. Decis. Sci. 10(2), 302–328 (1979)

    Article  Google Scholar 

  46. Yoo, S.H., Kim, D., Park, M.S.: Economic production quantity model with imperfect-quality items, two-way imperfect inspection and sales return. Int. J. Prod. Econ. 121(1), 255–265 (2009)

    Article  Google Scholar 

  47. Zhang, X.M., Li, Q.W., Liu, Z., Chang, C.T.: Optimal pricing and remanufacturing mode in a closed-loop supply chain of WEEE under government fund policy. Comput. Ind. Eng. 151, 106951 (2021)

Download references

Acknowledgements

Authors are sincerely thankful to the esteemed reviewers for their comments and suggestions based on which the manuscript has been improved. Research support from Department of Science and Technology, Government of India (IF160066) is gratefully acknowledged.

Author information

Authors and Affiliations

  1. Department of Mathematics, Jadavpur University, Kolkata, India

    M. Masanta & B. C. Giri

Authors
  1. M. Masanta
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. B. C. Giri
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to M. Masanta.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Masanta, M., Giri, B.C. A closed-loop supply chain model with learning effect, random return and imperfect inspection under price- and quality-dependent demand. OPSEARCH 59, 1094–1115 (2022). https://doi.org/10.1007/s12597-021-00558-w

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12597-021-00558-w

Keywords

Search

Navigation