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Data and Decision Sciences in Action pp 243–257Cite as

A Recovery Model for Sudden Supply Delay with Demand Uncertainty and Safety Stock

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Part of the book series: Lecture Notes in Management and Industrial Engineering ((LNMIE))

Abstract

In this paper, a recovery model is developed for managing sudden supply delays that affect retailers’ Economic Order Quantity (EOQ) model. For this, a mathematical model is developed that considers demand uncertainty and safety stock, and generates a recovery plan for a finite future period immediately after a sudden supply delay. Solving recovery problems involve high commercial software costs, and their solutions are complex. Therefore, an efficient heuristic solution is developed that generates the recovery plan after a sudden supply delay. An experiment is conducted to test the proposed approach. To assess the quality and consistency of solutions, the performance of the proposed heuristic is compared with the performance of the Generalized Reduced Gradient (GRG) method, which is widely applied in constrained mathematical programming. Several numerical examples are presented and a sensitivity analysis is performed to demonstrate the effects of various parameters on the performance of the heuristic method. The results show that safety stock plays an important role in recovery from sudden supply delays, and there is a trade-off between backorder and lost sales costs in the recovery plan.

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References

  1. Agnetis, A., et al.: Set-up coordination between two stages of a supply. Ann. Oper. Res. 107, 15–32 (2001)

    Article  MathSciNet  MATH  Google Scholar 

  2. Alcantara, P., Riglietti, G.: Supply chain resilience report, pp. 1–36. The Business Continuity Institute (2015)

    Google Scholar 

  3. Bag, S., et al.: A production inventory model with fuzzy random demand and with flexibility and reliability considerations. Comput. Ind. Eng. 56(1), 411–416 (2009)

    Article  MathSciNet  Google Scholar 

  4. Chen, S.H., Hsieh, C.H.: Graded mean integration representation of generalized fuzzy number. J. Chin. Fuzzy Syst. 5(2), 1–7 (1999)

    Google Scholar 

  5. Fahimnia, B., et al.: Quantitative models for managing supply chain risks: a review. Eur. J. Oper. Res. 247(1), 1–15 (2015)

    Article  MATH  Google Scholar 

  6. Gabriele, G.A., Ragsdell, K.M.: The generalized reduced gradient method: a reliable tool for optimal design. J. Eng. Ind. 99(2), 394–400 (1977)

    Article  Google Scholar 

  7. Hishamuddin, H., et al.: A disruption recovery model for a single stage production-inventory system. Eur. J. Oper. Res. 222(3), 464–473 (2012)

    Article  MathSciNet  MATH  Google Scholar 

  8. Hishamuddin, H., et al.: A recovery mechanism for a two echelon supply chain system under supply disruption. Econ. Model. 38, 555–563 (2014)

    Article  Google Scholar 

  9. Hishamuddin, H., et al.: A recovery model for a two-echelon serial supply chain with consideration of transportation disruption. Comput. Ind. Eng. 64(2), 552–561 (2013)

    Article  Google Scholar 

  10. Kogan, K., Tapiero, C.S.: Coordination of co-investments in supply chain infrastructure. J. Intell. Manuf. 23(6), 2471–2475 (2012)

    Article  Google Scholar 

  11. Liao, C.J., et al.: An ant colony optimization algorithm for setup coordination in a two-stage production system. Appl. Soft Comput. 11(8), 4521–4529 (2011)

    Article  MathSciNet  Google Scholar 

  12. Pal, B., et al.: A multi-echelon production–inventory system with supply disruption. J. Manuf. Syst. 33(2), 262–276 (2014)

    Article  Google Scholar 

  13. Panda, D., Maiti, M.: Multi-item inventory models with price dependent demand under flexibility and reliability consideration and imprecise space constraint: a geometric programming approach. Math. Comput. Model. 49(9–10), 1733–1749 (2009)

    Article  MathSciNet  MATH  Google Scholar 

  14. Paul, S.K., et al.: A disruption recovery model in a production-inventory system with demand uncertainty and process reliability. Lect. Notes Comput. Sci. 8104, 511–522 (2013)

    Article  Google Scholar 

  15. Paul, S.K., et al.: A disruption recovery plan in a three-stage production-inventory system. Comput. Oper. Res. 57, 60–72 (2015)

    Article  MathSciNet  MATH  Google Scholar 

  16. Paul, S.K., et al.: A quantitative model for disruption mitigation in a supply chain. Eur. J. Oper. Res. 257, 881–895 (2017)

    Google Scholar 

  17. Paul, S.K., et al.: A reactive mitigation approach for managing supply disruption in a three-tier supply chain. J. Intell. Manuf. in press, 1–17 (2016)

    Google Scholar 

  18. Paul, S.K., et al.: Development of a production inventory model with uncertainty and reliability considerations. Optim. Eng. 15(3), 697–720 (2014)

    Article  MathSciNet  MATH  Google Scholar 

  19. Paul, S.K., et al.: Managing disruption in an imperfect production-inventory system. Comput. Ind. Eng. 84, 101–112 (2015)

    Article  Google Scholar 

  20. Paul, S.K., et al.: Managing real-time demand fluctuation under a supplier-retailer coordinated system. Int. J. Prod. Econ. 158, 231–243 (2014)

    Article  Google Scholar 

  21. Paul, S.K., et al.: Managing risk and disruption in production-inventory and supply chain systems: a review. J. Ind. Manag. Optim. 12(3), 1009–1029 (2016)

    Article  MathSciNet  MATH  Google Scholar 

  22. Paul, S.K., et al.: Real time disruption management for a two-stage batch production–inventory system with reliability considerations. Eur. J. Oper. Res. 237(1), 113–128 (2014)

    Article  MathSciNet  MATH  Google Scholar 

  23. Qi, L.: A continuous-review inventory model with random disruptions at the primary supplier. Eur. J. Oper. Res. 225(1), 59–74 (2013)

    Article  MathSciNet  MATH  Google Scholar 

  24. Salehi Sadghiani, N., et al.: Retail supply chain network design under operational and disruption risks. Transp. Res. Part E Logist. Transp. Rev. 75, 95–114 (2015)

    Google Scholar 

  25. Snyder, L.V., et al.: OR/MS models for supply chain disruptions: a review. IIE Trans. 48(2), 89–109 (2016)

    Article  Google Scholar 

  26. Snyder, L.V.: A tight approximation for an EOQ model with supply disruptions. Int. J. Prod. Econ. 155, 91–108 (2014)

    Article  Google Scholar 

  27. Xia, Y., et al.: Real-time disruption management in a two-stage production and inventory system. IIE Trans. 36(2), 111–125 (2004)

    Article  Google Scholar 

  28. Xu, X., Meng, Z.: Coordination between a supplier and a retailer in terms of profit concession for a two-stage supply chain. Int. J. Prod. Res. 52(7), 2122–2133 (2014)

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. School of Business IT and Logistics, RMIT University, Melbourne, Australia

    Sanjoy Kumar Paul & Shams Rahman

  2. UTS Business School, University of Technology Sydney, Sydney, Australia

    Sanjoy Kumar Paul

Authors
  1. Sanjoy Kumar Paul
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  2. Shams Rahman
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Corresponding author

Correspondence to Sanjoy Kumar Paul .

Editor information

Editors and Affiliations

  1. School of Engineering and Information Technology, University of New South Wales, Canberra, Aust Capital Terr, Australia

    Ruhul Sarker

  2. School of Engineering and Information Technology, University of New South Wales, Canberra, Aust Capital Terr, Australia

    Hussein A. Abbass

  3. Data 61, Commonwealth Scientific and Industrial Research Organisation (CSIRO), Melbourne, Victoria, Australia

    Simon Dunstall

  4. Data 61, Commonwealth Scientific and Industrial Research Organisation (CSIRO), Melbourne, Victoria, Australia

    Philip Kilby

  5. Strategic Capability Analysis Department, Defence Science and Technology Group, Canberra, Aust Capital Terr, Australia

    Richard Davis

  6. Department of Defence, War Research Centre, Canberra, Aust Capital Terr, Australia

    Leon Young

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Paul, S.K., Rahman, S. (2018). A Recovery Model for Sudden Supply Delay with Demand Uncertainty and Safety Stock. In: Sarker, R., Abbass, H., Dunstall, S., Kilby, P., Davis, R., Young, L. (eds) Data and Decision Sciences in Action. Lecture Notes in Management and Industrial Engineering. Springer, Cham. https://doi.org/10.1007/978-3-319-55914-8_18

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  • DOI: https://doi.org/10.1007/978-3-319-55914-8_18

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-55913-1

  • Online ISBN: 978-3-319-55914-8

  • eBook Packages: EngineeringEngineering (R0)

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