Skip to main content

Advertisement

Log in

A system dynamics model for the impact of capacity limits on the Bullwhip effect (BWE) in a closed-loop system with remanufacturing

  • Research
  • Published:
Journal of Remanufacturing Aims and scope Submit manuscript

Abstract

Every organisation has an upper limit to the number of orders or products that it can manufacture or remanufacture per unit time. In reverse logistics operations, capacity limits can lead to inefficiencies in the remanufacturing process. In this paper, comparisons were made of the Bullwhip effect (BWE) in closed-loop systems that have collection and remanufacturing capacity limits and those that do not. Collection and remanufacturing capacity limits were introduced for a system where a company had to collect ‘enough’ products before remanufacturing can begin. This introduced collection backlogs, remanufacturing backlogs and remanufacturing downtimes to the closed loop supply chain (CLSC). By adopting a systems dynamics approach, the research performed ‘what-if’ analyses of the closed-loop system under different levels of the factors under investigation. Two case studies were investigated: one remanufacturing electric vehicle batteries (low demand, slow moving item) and the other remanufacturing kitchen appliances (high demand, fast moving item). Firstly, introducing collection and remanufacturing capacity limits in the reverse chain increased the BWE to a level higher than the reverse chain without any capacity limits, but not to the level of the forward chain without any product returns. Secondly, introducing collection and remanufacturing capacity limits for a closed-loop system where a company had to collect ‘enough’ products before remanufacturing begins had different impacts depending on the product demand size and speed. The presence of external returns by other parties not regulated by an organisation had an impact of lowering the BWE in the closed-loop system and it also impacted how the other factors under investigation affected the Bullwhip effect. These findings were used to provide managerial insights for organisations venturing into reverse logistics.

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

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

References

  1. Adenso-Díaz B, Moreno P, Gutiérrez E, Lozano S (2012) An analysis of the main factors affecting bullwhip in reverse supply chains. Int J Prod Econ 135(2):917–928. https://doi.org/10.1016/j.ijpe.2011.11.007

    Article  Google Scholar 

  2. Aksoy HK, Gupta SM (2001) Capacity and buffer trade-offs in a remanufacturing system. In: Proceedings of the SPIE International Conference on Environmentally Conscious Manufacturing II, pp 167–174. https://doi.org/10.1117/12.455276

  3. Asif FMA, Bianchi C, Rashid A, Nicolescu CM (2012) Performance analysis of the closed loop supply chain. J Remanufacturing 2(4):1–21

    Google Scholar 

  4. Cannella S, Bruccoleri M, Framinan JM (2016) Closed-loop supply chains : what reverse logistics factors in fl uence performance ? Int J Prod Econ 175:35–49. https://doi.org/10.1016/j.ijpe.2016.01.012

    Article  Google Scholar 

  5. Cannella S, Ciancimino E, Marquez AC (2008) Capacity constrained supply chains : a simulation study. Int J Simul Process Model 4(2):139. https://doi.org/10.1504/IJSPM.2008.022075

    Article  Google Scholar 

  6. Cannella S, Dominguez R, Ponte B, Framinan JM (2018) Capacity restrictions and supply chain performance: Modelling and analysing load-dependant lead times. Int J Prod Econ 204:264–277

    Article  Google Scholar 

  7. Chen L, Lee HL (2012) Bullwhip effect measurement and its implications bullwhip effect measurement and its implications. Oper Res 60(4):771–784

    Article  MathSciNet  Google Scholar 

  8. Chittamvanich S (2007) Adjusting remanufacturing capacity using sales and return information. In: Retrospective theses and dissertations 15508. UNIVERSITY, IOWA STATE

  9. Chopra S, Meindl P (2007) In: Pfaltzraff M (ed) SUPPLY CHAIN MANAGEMENT: Strategy, Planning and Operation, 3rd edn. Pearson prentice hall

  10. Das D, Dutta P (2013) A system dynamics framework for integrated reverse supply chain with three way recovery and product exchange policy. Comput Ind Eng 66(4):720–733. https://doi.org/10.1016/j.cie.2013.09.016

    Article  Google Scholar 

  11. de Souza R, Zice S, Chaoyang L (2000) Supply chain dynamics and optimization. Integr Manuf Syst 11(5):348–364

    Article  Google Scholar 

  12. Ding X, Gan X (2009) System dynamics model to analysis oscillation and amplification in the closed-loop supply chain. In: International Conference on Management of e-Commerce and e-Government, pp 343–346. https://doi.org/10.1109/ICMeCG.2009.70

  13. Disney SM, Lambrecht MR (2008) On replenishment rules , forecasting and the bullwhip effect in supply chains. Foundations and Trends in Technology, Information and Operations Management 2(1):1–80

    Google Scholar 

  14. Dominguez R, Ponte B, Cannella S, Framinan JM (2019) On the dynamics of closed-loop supply chains with capacity constraints. Comput Ind Eng 128(December 2018):91–103. https://doi.org/10.1016/j.cie.2018.12.003

    Article  Google Scholar 

  15. Editor, M. B. (2014). The power of multivariate ANOVA (MANOVA) Available https://blog.minitab.com/blog/adventures-in-statistics-2/the-power-of-multivariate-anova-manova [13 March 2019]

  16. El-Beheiry, M., Wong, C. Y., & El-Kharbotly, A. (2004). Empirical quantification of bullwhip effect ( with application to a toy supply chain ). In Thirteenth International Working Seminar on Production Economics

  17. Evans G, Naim MM (1994) The dynamics of capacity constrained supply chains. In: International Systems Dynamics Conference, pp 28–33

  18. Feng L, Zhang J, Tang W (2013) Optimal control of production and remanufacturing for a recovery system with perishable items. Int J Prod Res 51(13):3977–3994. https://doi.org/10.1080/00207543.2012.762133

    Article  Google Scholar 

  19. Forrester JW, Senge PM (1980) Tests for building confidence in systems dynamics models. In: TIMS Studies in the Management Sciences, 14th edn. North Holland Publishing Company, pp 209–228

  20. Fransoo JC, Wouters MJF (2000) Measuring the bullwhip effect in the supply chain. Supply Chain Manag 5(2):78–89. https://doi.org/10.1108/13598540010319993

    Article  Google Scholar 

  21. Georgiadis P, Athanasiou E (2013) Flexible long-term capacity planning in closed-loop supply chains with remanufacturing. Eur J Oper Res 225(1):44–58. https://doi.org/10.1016/j.ejor.2012.09.021

    Article  Google Scholar 

  22. Gong X, Chao X (2013) Technical note—optimal control policy for capacitated inventory systems with remanufacturing. Oper Res 61(3):603–611. https://doi.org/10.1287/opre.2013.1168

    Article  MathSciNet  MATH  Google Scholar 

  23. He S, Yuan X, Zhang X (2016) The Government’s environment policy index impact on recycler behavior in electronic products closed-loop supply chain. Discret Dyn Nat Soc 2016:1–8. https://doi.org/10.1155/2016/7646248

    Article  Google Scholar 

  24. Heydari J, Govindan K, Jafari A (2017) Reverse and closed loop supply chain coordination by considering government role. Transp Res Part D: Transp Environ 52:379–398. https://doi.org/10.1016/j.trd.2017.03.008

    Article  Google Scholar 

  25. Heydari J, Govindan K, Sadeghi R (2018) Reverse supply chain coordination under stochastic remanufacturing capacity. Int J Prod Econ 202(March):1–11. https://doi.org/10.1016/j.ijpe.2018.04.024

    Article  Google Scholar 

  26. Hillston J (2003) Performance modelling-lecture 16: model validation and verification. School of Informatics, Univ. of Edinburgh, Scotland

  27. Hintze JL (2007) D-optimal designs. In: NCSS User’s Guide II Descriptive Statistics, Means, Quality Control, and Design of Experiments. NCSS, p 267-1–23

  28. Holweg, M., & Disney, S. M. (2005). The evolving frontiers of the bullwhip effect. In EUROMA Annual Conference. Budapest

  29. Hosoda T, Altekin FT, Sahin G, Disney SM, Gavirneni S (2015) The impact of information sharing, random yield correlation and lead times in closed loop suply chains. Eur J Oper Res 246(3):827–836. https://doi.org/10.1016/j.ejor.2015.05.036

    Article  MATH  Google Scholar 

  30. Hussain M, Drake PR (2011) Analysis of the bullwhip effect with order batching in multi-echelon supply chains. Int J Phys Distrib Logistics Manage 41(8):797–814. https://doi.org/10.1108/09600031111166438

    Article  Google Scholar 

  31. Lee HL, Padmanabhan V, Whang S (1997) Information distortion in a supply chain: the bullwhip effect. Manag Sci 43(4):546–558. https://doi.org/10.1287/mnsc.43.4.546

    Article  MATH  Google Scholar 

  32. Lieckens K (2009) Reverse logistics network design: the impact of Lead times and Stochasticity. Unpublished thesis, University of Antwerp, Belgium

  33. Ma L, Chai Y, Zhang Y, Zheng L (2014) Modelling and analysis of the bullwhip effect in remanufacturing. Appl Mech Mater 541–542:1556–1561

    Article  Google Scholar 

  34. Nepal B, Murat A, Chinnam RB (2012) The bullwhip effect in capacitated supply chains with consideration for product life-cycle aspects. Int J Prod Econ 136:318–331

    Article  Google Scholar 

  35. Poles R, Cheong F (2011) An investigation on capacity planning and Lead times for remanufacturing systems using system dynamics BT. In: 44th Hawaii International Conference on System Sciences (HICSS 2011), pp 1–10. https://doi.org/10.1109/HICSS.2011.60

  36. Poles R (2013) System dynamics modelling of a production and inventory system for remanufacturing to evaluate system improvement strategies. Int J Prod Econ 144(1):189–199. https://doi.org/10.1016/j.ijpe.2013.02.003

    Article  Google Scholar 

  37. Ponte B, Wang X, de la Fuente D, Disney SM (2017) Exploring nonlinear supply chains : the dynamics of capacity constraints. Int J Prod Res 55(14):4053–4067. https://doi.org/10.1080/00207543.2016.1245884

    Article  Google Scholar 

  38. Prahinski C, Kocabasoglu C (2006) Empirical research opportunities in reverse supply chains. Int J Manag Sci 34:519–532. https://doi.org/10.1016/j.omega.2005.01.003

    Article  Google Scholar 

  39. Souza GC (2013) Closed-Loop Supply Chains : A Critical Review , and Future Research*. Decis Sci 44(1):7–38

    Article  Google Scholar 

  40. Spiegler VLM, Naim MM (2014) The impact of freight transport capacity limitations on supply chain dynamics. Int J Log Res Appl 17(1):64–88. https://doi.org/10.1080/13675567.2013.838012

    Article  Google Scholar 

  41. Sterman JD (2000) Business Dynamics: Systems Thinking Modelling for a complex World (Internatio). McGraw- Hill Irwin

  42. Sy C (2017) A policy development model for reducing bullwhips in hybrid production-distribution systems. Int J Prod Econ 190:67–79. https://doi.org/10.1016/j.ijpe.2016.09.005

    Article  Google Scholar 

  43. Tang O, Naim MM (2004) The impact of information transparency on the dynamic behaviour of a hybrid manufacturing/remanufacturing system. Int J Prod Res 42(19):4135–4152. https://doi.org/10.1080/00207540410001716499

    Article  MATH  Google Scholar 

  44. Tombido L, Baihaqi I (2020) The impact of a substitution policy on the bullwhip effect in a closed loop supply chain with remanufacturing. J Remanuf 10(3):177–205. https://doi.org/10.1007/s13243-020-00084-w

    Article  Google Scholar 

  45. Tombido L, Louw L, van Eeden J (2020) The bullwhip effect in closed-loop supply chains: a comparison of series and divergent networks. J Remanuf 10(3):207–238. https://doi.org/10.1007/s13243-020-00085-9

    Article  Google Scholar 

  46. Turrisi M, Bruccoleri M, Cannella S (2013) Impact of reverse logistics on supply chain performance. Int J Phys Distrib Logist Manag 43(7):564–585. https://doi.org/10.1108/IJPDLM-04-2012-0132

    Article  Google Scholar 

  47. Van der Laan E, Salomon M, Dekker R, Van Wassenhove L (1999) Inventory control in hybrid systems with remanufacturing. Manag Sci 45(5)733–747

  48. Vlachos D, Georgiadis P, Iakovou E (2007) A system dynamics model for dynamic capacity planning of remanufacturing in closed-loop supply chains, 34, 367–394. https://doi.org/10.1016/j.cor.2005.03.005

  49. Wan Z, Li C-BC (2012) Bullwhip effect in closed-loop supply chain based on system dynamics. Comput Integr Manuf Syst 18(5):1093–1098

    Google Scholar 

  50. Wang Q, Li J, Yan H, Zhu SX (2016) Optimal remanufacturing strategies in name-your-own-price auctions with limited capacity. Int J Prod Econ 181:113–129. https://doi.org/10.1016/j.ijpe.2016.01.008

    Article  Google Scholar 

  51. Wang X, Disney SM (2015) The bullwhip effect: Progress, trends and directions. Eur J Oper Res 250:691–701. https://doi.org/10.1016/j.ejor.2015.07.022

    Article  MathSciNet  MATH  Google Scholar 

  52. Wei S, Tang O, Sundin E (2015) Core (product) acquisition management for remanufacturing: a review. J Remanuf 5(1):4. https://doi.org/10.1186/s13243-015-0014-7

    Article  Google Scholar 

  53. Yuan X, Zhang X (2015) Recycler reaction for the government behavior in closed-loop supply chain distribution network : based on the system dynamics. Discret Dyn Nat Soc 2015:11

    Google Scholar 

  54. Zanoni S, Ferretti I, Tang O (2006) Cost performance and bullwhip effect in a hybrid manufacturing and remanufacturing system with different control policies. Int J Prod Res 44(18–19):3847–3862. https://doi.org/10.1080/00207540600857375

    Article  MATH  Google Scholar 

  55. Zhang X, Yuan X (2016) The system dynamics model in electronic products closed-loop supply chain distribution network with three-way recovery and the old-for-new policy. Discret Dyn Nat Soc 2016:10

    Google Scholar 

  56. Zhou L, Disney SM (2006) Bullwhip and inventory variance in a closed loop supply chain. OR Spectr 28(1):127–149. https://doi.org/10.1007/s00291-005-0009-0

    Article  MATH  Google Scholar 

  57. Zhou L, Disney SM, Lalwani CS, Wu H (2004) Reverse logistics: a study of bullwhip in continuous time. In: Proceedings of the 5th World Congress on Intelligent Control and Automation (WCICA), vol 4, pp 3539–3542. https://doi.org/10.1109/WCICA.2004.1343205

  58. Zhou L, Naim MM, Disney SM (2017) The impact of product returns and remanufacturing uncertainties on the dynamic performance of a multi-echelon closed-loop supply chain. Int J Prod Econ 183:487–502. https://doi.org/10.1016/j.ijpe.2016.07.021

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Linda Tombido.

Additional information

Publisher’s note

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

Appendix

Appendix

Stock and flow diagrams and model equations

Fig. 10
figure 10

Stock and flow diagram for the serial forward chain in STELLA

Fig. 11
figure 11

Stock and flow diagram for the reverse chain without some components of the forward chain in STELLA

Model equations

STOCKS

  1. 1.

    Collected products

    $$ Collected\ products(t)= collected\ products\left(t- dt\right)+\left( total\ collection\ rate- acce\ rate\ for\ reuse- reje\ rate\ for\ reuse\right)\times dt $$
$$ Initial\ collected\ products=0 $$

Inflows

$$ total\ collection\ rate=\mathit{\operatorname{MIN}}\left( compan{y}^{\prime } scollection\ capacity, available\ used\ products+ products\ not\ collected\right) $$

Outflows

$$ acce\ rate\ for\ prod\ reuse= collected\ products\times \frac{reuse\%}{ reuse\ inspe\ time} $$
$$ reje\ rate\ for\ prod\ reuse=\left(1- reuse\%\right)\times \frac{collected\ products}{reuse\ insp\ time} $$
  1. 2.

    Components inventory

    $$ Components\ inventory\ (t)= components\ inventory\ \left(t- dt\right)+\left( comp\ prdn\ rate+ comp\ reman\ rate+ comp\ acce\ rate\ for\ dir\ reuse- comp\ used\ for\ prdn\right)\times dt $$
$$ Initial\ components\ inventory=0 $$

Inflows

$$ comp\ prdn\ rate=\mathit{\operatorname{MAX}}\left(\mathit{\operatorname{MIN}}\left(\mathit{\operatorname{MIN}}\left(\frac{raw\ mat\ inventory}{comp\ prod\ time},\left( expe\ distributor\ orders\times comp\ per\ product- expe\ reusable\ compo+\frac{CI\ discrep}{CI\ adj\ time}\right), comp\ prod\ capacity\right),0\right)\right) $$
$$ comp\ reman\ rate=\mathit{\operatorname{MIN}}\left( comp\ reman\ capacity,\frac{\left(1- disposal\%\right)\times comp\ reje\ for\ dir\ reuse}{reproce\ time}\right) $$
$$ comp o\ acceptance\ rate\ for\ dir\ reuse=\frac{dir\ reuse\%\times inv\ of\ comp\ from\ reje\ prdcts}{insp\ and\ disasse\ time} $$

Outflows

$$ comp\ used\ for\ prdn=\mathit{\operatorname{MAX}}\left(\mathit{\operatorname{MIN}}\left(\mathit{\operatorname{MIN}}\left(\frac{compo\ inventory}{prdct\ prdn\ time}, prdn\ capacity\times comp\ per\ prdt\right),\left( expe\ dist\ orders- expe\ reman\ prdcts+\frac{SI\ discrep}{SI\ adj\ time}\right)\times comp\ per\ prdt\right),0\right) $$
  1. 3.

    Components rejected for direct reuse

    $$ comp\ reje\ for\ dir\ reuse\ (t)= comp\ reje\ for\ dir\ reuse\ \left(t- dt\right)+\left( comp\ repla\ rate+ comp\ reje\ rate\ for\ dir\ reuse- recycle\ rate- coomp\ reman\ rate- disposal\ rate\right)\times dt $$
$$ initial\ comp\ reje\ for\ dir\ reuse=0 $$

Inflows

$$ comp\ repla\ rate= reman\ rate\times comp\ per\ prdct\times comp\ repla\% comp\ reje\ rate\ for\ dir\ reuse=\frac{\left(1- dir\ reuse\%\right)\times inv\ of\ comp\ from\ reje\ prdcts}{insp\ and\ disasse\ time} $$
$$ comp\ reje\ rate\ for\ dir\ reuse=\frac{\left(1- dir\ reuse\%\right)\times inv\ of\ comp\ from\ reje\ prdcts}{insp\ and\ disasse\ time} $$

Outflows

$$ recycling\ rate=\frac{comp\ reje\ for\ dir\ reuse}{reproce\ time} $$
$$ comp\ reman\ rate=\frac{\left(1- disposal\%\right)\times comp\ reje\ for\ dir\ reuse}{reproce\ time} $$
$$ disposal\ rate= comp\ reje\ for\ dire\ reuse\times disposal\% $$
  1. 4.

    Controllable disposal

    $$ controllable\ disposal\ (t)= controllable\ disposal\ \left(t- dt\right)+\left( disposal\ rate\right)\times dt $$
$$ initial\ controllable\ disposal=0 $$

Inflows

$$ disp osal\ rate= comp\ reje\ for\ dir\ reuse\times disp\% $$
  1. 5.

    Distributor orders backlog

    $$ distributor\ orders\ backlog\ (t)= distributor\ orders\ backlog\ \left(t- dt\right)+\left( distributor\ orders- distributor\kern0.5em backlog\ red\ rate\right)\times dt $$
$$ initial\ distributor\ orders\ backlog=0 $$

Inflows

$$ distributors\ orders= expe\ wholesale\ orders+\frac{DI\ discrepancy}{DI\ adj\ time} $$

Outflows

$$ distributors\ backlog\ red\ rate= shipments\ to\ distributor $$
  1. 6.

    Distributor inventory

    $$ distributor\ inventory\ (t)= distributor\ inventory\ \left(t- dt\right)+\Big( shipments\ to\ distributor- shipments\ to\ wholesaler\times dt $$
$$ initial\ distributor\ inventory=0 $$

Inflows

$$ {\displaystyle \begin{array}{l} shipments\ to\ distributor=\\ {}\frac{\left( IF\left( serviceable\ inventory- distributors\ orders\ backlog\ge 0\right) THEN\left( distributors\ orders\ backlog\right) ELSE\left( serviceable\ inventory\right)\right)}{distr\ shipment\ time}\end{array}} $$

Outflows

$$ shipment s\ to\ wholesaler=\frac{\left( IF\left( distributor\ inventory- wholesale\ orders\ backlog\ge 0\right) THEN\left( wholesale\ orders\ backlog\right) ELSE\left( distributor\ inventory\right)\right)}{wholesale\ shipment\ time} $$
  1. 7.

    Inventory of components from rejected products

    $$ inv\ of\ comp\ from\ reje\ prdcts\ (t)= inv\_ of\_ comp\_ from\_ reje\_ prdcts\left(t- dt\right)+\left( comp\_ from\_ reje\_ prodcts- comp\_ reje\_ rate\_ for\_ dir\_ reuse- comp o\_ acce\_ rate\_ for\_ dir ect\_ reuse\right)\times dt $$
$$ initial\ inventory\ of\ comp\ from\ rejected\ products=0 $$

Inflows

$$ comp\ from\ reje\ products= reje\_\_ rate\_ for\_ reman\times comp onents\_ per\_ product $$

Outflows

$$ comp\ reje\ rate\ for\ dir\ reuse=\frac{\left(1- direct\_ reuse\_\%\right)\times inv\_ of\_ comp\_ from\_ reje\_ prdcts}{inspe\ and\ dissasse\ time} $$
$$ compo\ acce\ for\ dire\ reuse=\frac{\left( direct\_ reuse\_\%\times inv\_ of\_ comp\_ from\_ reje\_ prdcts\right)}{insp\ and\ dissasse\ time} $$
  1. 8.

    Products for remanufacturing

    $$ products\ for\ remn\ (t)= products\ for\ reman\ \left(t- dt\right)+\left( acce\ rate\ for\ reman- reman\ rate- produsctscotremanufactured\right)\times dt $$
$$ initial\ products\ for\ reman=0 $$

Inflows

$$ acce\ rate\ for\ reman=\frac{reje\ prod\ for\ reuse\times reman\%}{initial\ inspe\ time} $$

Outflows

$$ reman\ rate= IF\left(\frac{products\ for\ reman+ reman\ backlogs}{reprocess\ time}\ge reman\ capacity\right) THEN\left( reman\ capacity\right) ELSE(0) $$
$$ products\ not\ reman ufactured=\mathit{\operatorname{MAX}}\left(\left(\frac{products\ for\ reman}{reprocess\ time}\right)- reman\ capacity,0\right) $$
  1. 9.

    Products for cannibalisation

    $$ prod\ for\ cannibalisation\ (t)= prod\ for\ cannibalisation\ \left(t- dt\right)+\left( reje\ rate\ for\ reman\right)\times dt $$
$$ initial\ products\ for\ cannibalisation=0 $$

Inflows

$$ reje\ rate\ for\ reman=\frac{\left(1- reman\%\right)\times reje\ prod\ for\ reuse}{initial\ inspe\ time} $$
  1. 10.

    Raw material inventory

    $$ raw\ mat\ inventory\ (t)= raw\ mat\ inventory\ \left(t- dt\right)+\left( recycling\ rate- comp\ prod\ rate\right)\times dt $$
$$ initial\ raw\ mat\ inventory= infinity $$
$$ recycling\ rate=\frac{comp\ reje\ for\ dir\ reuse}{reproce\ time} $$

Outflows

$$ comp\ prdn\ rate=\mathit{\operatorname{MAX}}\left(\mathit{\operatorname{MIN}}\left(\mathit{\operatorname{MIN}}\left(\frac{raw\ mat\ inventory}{comp\ prod\ time},\left(\left( expe\ distri\ orders\times comp\ per\ prdct\right)- expe\ reusable\ compo+\frac{CI\ discrepe}{CI\ adj\ time}\right)\right), comp\ prod\ capacity\right),0\right) $$
  1. 11.

    Rejected products for reuse

    $$ reje\ prod\ for\ reuse\ (t)= reje\ prod\ for\ reuse\ \left(t- dt\right)+\left( reje\ rate\ for\ prod\ reuse- acce\ rate\ for\ reman- reje\ rate\ for\ reman\right)\times dt $$
$$ initial\ reje\ prod\ for\ reuse=0 $$

Inflows

$$ reje\ rate\ for\ prod\ reuse=\frac{\left(1- reuse\%\right)\times collected\ products}{reuse\ insp\ time} $$

Outflows

$$ acce\ rate\ for\ reman=\frac{reje\ prod\ for\ reuse\times reman\%}{initial\ inspe\ time} $$
$$ reje\ rate\ for\ reman=\frac{\left(1- reman\%\right)\times reje\ prod\ for\ reuse}{initial\ inspe\ time} $$
  1. 12.

    Remanufacturing backlogs

    $$ remanufacturing\ backlogs\ (t)= remanufacturing\ backlogs\ \left(t- dt\right)+\left( products\ not\ remanufactured\right)\times dt $$
$$ initial\ remanufacturing\ backlogs=0 $$

Inflows

$$ products\ not\ reman ufactured=\mathit{\operatorname{MAX}}\left(\left(\left(\frac{products\ for\ reman}{reprocess\ time}\right)- reman\ capacity\right),0\right) $$
  1. 13.

    Retailer orders backlog

    $$ retaile{r}^{\prime } s order\ backlog\ (t)= retailer\ orders\ backlog\ \left(t- dt\right)+\left( retaile{r}^{\prime } s order s- retailer\ backlog\ red\ rate\right)\times dt $$
$$ initial\ retailer\ orders\ backlog=0 $$

Inflows

$$ retaile{r}^{\prime } sorders= expected\ demand+\left(\frac{RI\ discrepency}{RI\ adj\ time}\right) $$

Outflows

$$ retailer\ backlog\ redu\ rate= shipments\ to\ retailer $$
  1. 14.

    Retailer inventory

    $$ retailer\ inventory\ (t)= retailer\ inventory\ \left(t- dt\right)+\left( shipments\ to\ retailer- retail\ sale\right)\times dt $$
$$ initial\ retailer\ inventory=0 $$

Inflows

$$ shipments\ to\ retailer=\frac{\left( IF\left( wholesaler\ inventory- retailer\ orders\ backlog\ge 0\right) THEN\left( retailer\ orders\ backlog\right) ELSE\left( wholesaler\ inventory\right)\right)}{delivery\ time} $$

Outflows

$$ retail\ sale= IF\left(\left(\frac{retail er\ inventory}{retail\ delivery\ time}\right)\ge 0\right) THEN(demand) ELSE\left(\frac{retail er\ inventory}{retail er\ delivery\ time}\right) $$
  1. 15.

    Serviceable inventory

    $$ serviceable\ inventory(t)= serviceable\ inventory\ \left(t- dt\right)+\left( production\ rate+ reman\ rate+ acce\ prod\ for\ reuse- shipments\ to\ distributor\right)\times dt $$
$$ initial\ serviceable\ inventory=0 $$

Inflows

$$ production\ rate=\frac{comp\ used\ for\ prdn}{comp onents\ per\ product} $$
$$ reman\ rate= IF\left(\left(\frac{prod\ for\ reman+ reman\ backlogs}{reprocess\ time}\right)\ge reman\ capacity\right) THEN\left( reman\ capacity\right) ELSE(0) $$
$$ acce\ rate\ for\ reuse=\frac{collected\ products\times reuse\%}{reuse\ inspe\ time} $$

Outflows

$$ shipment s\ to\ distributor=\frac{\left(\left(\left( serviceable\ inventory- distributor\ orders\ backlog\right)\ge 0\right) THEN\left( distributor\ orders\ backlog\right) ELSE\left( serviceable\ inventory\right)\right)}{distributor\ shipment\ time} $$
  1. 16.

    Wholesaler inventory

    $$ wholesaler\ inventory(t)= wholesaler\ inventory\ \left(t- dt\right)+\left( shipments\ to\ wholesaler- shipments\ to\ retailer\right)\times dt $$
$$ initial\ wholesaler\ inventory=0 $$

Inflows

$$ shipment s\ to\ wholesaler=\frac{IF\left(\left( distributor\ inventory- wholesaler\ orders\ backlog\right)\ge 0\right) THEN\left( wholesaler\ orders\ backlog\right) ELSE\left( distributor\ inventory\right)}{wholesaler\ shipment\ time} $$

Outflows

$$ shipments\ to\ retailer=\frac{IF\left(\left( wholesaler\ inventory- retailer\ orders\ backlog\right)\ge 0\right) THEN\left( retailer\ orders\ backlog\right) ELSE\left( wholesaler\ inventory\right)}{delivery\ time} $$
  1. 17.

    Wholesaler orders backlog

    $$ wholesaler\ orders\ backlog\ (t)= wholesaler\ orders\ backlog\ \left(t- dt\right)+\left( wholesaler\ orders- wholesaler\ backlog\ redu\ rate\right)\times dt $$
$$ initial\ wholesaler\ orders\ backlog=0 $$

Inflows

$$ wholesaler\ orders= expected\ retailer\ orders+\left(\frac{WI\ discrepancy}{WI\ adj\ time}\right) $$

Outflows

$$ wholesaler\ backlog\ redu\ rate= shipments\ to\ wholesaler $$

AUXILLIARY VARIABLES

$$ aDI=2\times DI\ adjustment\ time $$
$$ aRI=2\times RI\ adjustment\ time $$
$$ as\ usual\ demand= LogNormal\left(4000,2000\right) $$
$$ available\ used\ products= DELAY\left( retail\ sale, residence\ time\right) $$
$$ aWI=2\times WI\ adjustment\ time $$
$$ CI\ adjustment\ time=2 $$
$$ CI\ cover\ time=1.5 $$
$$ CI\ discrepency=\mathit{\operatorname{MAX}}\left(\left( desired\ CI- components\ inventory\right),0\right) $$
$$ compan{y}^{\prime } scollection\ capacity=6000 $$
$$ components\ per\ product=15 $$
$$ component\ production\ capacity=\left(4000\times 15\right) $$
$$ component\ production\ time=\frac{1}{\left(4000\times 15\right)} $$
$$ component\ remanufacturing\ capacity=\left(6000\times 15\right) $$
$$ comp\ replace\%=20 $$
$$ delivery\ time=1 $$
$$ demand= as\ usual\ demand $$
$$ desired\ CI= expected\ distributors\ orders\times components\ per\ product\times CI\ cover\ time $$
$$ desired\ DI= expected\ wholesaler\ orders\times DI\ cover\ time $$
$$ desired\ RI= expected\ demand\times RI\ cover\ time $$
$$ desired\ SI= expected\ mdistributors\ orders\times SI\ cover\ time $$
$$ desired\ WI= expected\ retailer\ orders\times WI\ cover\ time $$
$$ direct\ reuse\%=0 $$
$$ disposal\%=0.01 $$
$$ distributor\ shipment\ time=1 $$
$$ DI\ cover\ time=1.5 $$
$$ DI\ discrepancy=\mathit{\operatorname{MAX}}\left(\left( desired\ DI- distributor\ inventory\right),0\right) $$
$$ DI\ adjustment\ time=2 $$
$$ expected\ demand= SMTH1\left( demand,1\right) $$
$$ expected\ distributor\ orders= SMTH1\left( distributor\ orders, aDI\right) $$
$$ expected\ retailer\ orders= SMTH1\left( retailer\ orders, aRI\right) $$
$$ expected\ wholesaler\ orders= SMTH1\left( wholesaler\ orders, aWI\right) $$
$$ expected\ reman\ products= SMTH1\left( reman\ rate,1\right) $$
$$ expected\ reusable\ components= SMTH1\left( compo\ acce\ for\ dir\ reuse+ comp\ reman\ rate,1\right) $$
$$ initial\ inspection\ time=1 $$
$$ inspection\ and\ dissassembly\ time=2 $$
$$ production\ capacity=4000 $$
$$ products\ not\ collected=\mathit{\operatorname{MIN}}\left(\left( available\ used\ products- collection\ capacity\right),0\right) $$
$$ product\ product ion\ time=\frac{1}{4000} $$
$$ remanufacturing\ capacity=6000 $$
$$ remanufacturing\%=0.75 $$
$$ reprocess\ time=1 $$
$$ residence\ time=16 $$
$$ reuse\%=0 $$
$$ reuse\ inspection\ time=0.001 $$
$$ RI\ cover\ time=1.5 $$
$$ RI\ discrepency=\mathit{\operatorname{MAX}}\left(\left( desired\ RI- retailer\ inventory\right),0\right) $$
$$ RIadjustment\ time=2 $$
$$ SI\ cover\ time=1.5 $$
$$ SI\ discrepancy=\mathit{\operatorname{MAX}}\left(\left( desired\ SI- serviceable\ inventory\right),0\right) $$
$$ SI\ adjustment\ time=2 $$
$$ WI\ cover\ time=1.5 $$
$$ WI\ adjustment\ time=2 $$
$$ WI\ discrepancy=\mathit{\operatorname{MAX}}\left(\left( desired\ WI- wholesaler\ inventory\right),0\right) $$
$$ wholesaler\ shipment\ time=1 $$

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Tombido, L., Louw, L., van Eeden, J. et al. A system dynamics model for the impact of capacity limits on the Bullwhip effect (BWE) in a closed-loop system with remanufacturing. Jnl Remanufactur 12, 1–45 (2022). https://doi.org/10.1007/s13243-021-00100-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13243-021-00100-7

Keywords

Navigation