IDEAS home Printed from https://ideas.repec.org/a/eee/ejores/v271y2018i2p474-489.html
   My bibliography  Save this article

Markov chain modeling and forecasting of product returns in remanufacturing based on stock mean-age

Author

Listed:
  • Tsiliyannis, Christos Aristeides

Abstract

A method is presented for real-time forecasting of product returns in remanufacturing. It determines the quantity of imminent returns and quality features such as age distribution and number of past cycles. Required data in real-time include the mean age of stock, a scaled quantity (population average) reliably monitored, even from small-size or decentralized stock samples, the maximum and minimum age in return samples and past volumes of net demand or sales. The characteristic parameters of the return distribution (center axis and spread) are updated in real-time. The method sequentially determines the retention probability in each time period, a key random variable that unties the dynamic closed-loop-supply chain knot. The retention probability sequence is used in explicit expressions for the product return flow and age distribution (a quality index), based on Markov representation of stock and flows. The model allows for arbitrarily random early loss and non-stationarities, uncertain demand and varying utilization of reusable returns. Markov-chain Monte-Carlo simulation enables assessment of the efficacy of the forecasting method. Exploiting reliable, current information, the method may provide improved estimates of product returns compared to linear models that relate returns to past levels of sales and/or returns, and utilize conventional regression, recursive least squares, or adaptive identification methods. Forecasting efficiency is higher as measured by mean or integral absolute error, and particularly so, regarding peaks and lows of the return flow. The results may be useful for enhanced acquisition of returns with reduced stock inventories and efficient planning of remanufacturing operations.

Suggested Citation

  • Tsiliyannis, Christos Aristeides, 2018. "Markov chain modeling and forecasting of product returns in remanufacturing based on stock mean-age," European Journal of Operational Research, Elsevier, vol. 271(2), pages 474-489.
  • Handle: RePEc:eee:ejores:v:271:y:2018:i:2:p:474-489
    DOI: 10.1016/j.ejor.2018.05.026
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0377221718304260
    Download Restriction: Full text for ScienceDirect subscribers only

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Huynh, Candice H. & So, Kut C. & Gurnani, Haresh, 2016. "Managing a closed-loop supply system with random returns and a cyclic delivery schedule," European Journal of Operational Research, Elsevier, vol. 255(3), pages 787-796.
    2. Roland Geyer & Luk N. Van Wassenhove & Atalay Atasu, 2007. "The Economics of Remanufacturing Under Limited Component Durability and Finite Product Life Cycles," Management Science, INFORMS, vol. 53(1), pages 88-100, January.
    3. Bonifield, Carolyn & Cole, Catherine & Schultz, Randall L., 2010. "Product returns on the Internet: A case of mixed signals?," Journal of Business Research, Elsevier, vol. 63(9-10), pages 1058-1065, September.
    4. Akshay Mutha & Saurabh Bansal & V. Daniel R. Guide, 2016. "Managing Demand Uncertainty through Core Acquisition in Remanufacturing," Production and Operations Management, Production and Operations Management Society, vol. 25(8), pages 1449-1464, August.
    5. Abdul Azeez Erumban, 2008. "Lifetimes Of Machinery And Equipment: Evidence From Dutch Manufacturing," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 54(2), pages 237-268, June.
    6. Kiesmuller, Gudrun P. & van der Laan, Erwin A., 2001. "An inventory model with dependent product demands and returns," International Journal of Production Economics, Elsevier, vol. 72(1), pages 73-87, June.
    7. Teunter, Ruud & Kaparis, Konstantinos & Tang, Ou, 2008. "Multi-product economic lot scheduling problem with separate production lines for manufacturing and remanufacturing," European Journal of Operational Research, Elsevier, vol. 191(3), pages 1241-1253, December.
    8. Teunter, Ruud & Tang, Ou & Kaparis, Konstantinos, 2009. "Heuristics for the economic lot scheduling problem with returns," International Journal of Production Economics, Elsevier, vol. 118(1), pages 323-330, March.
    9. Fleischmann, Moritz & Kuik, Roelof & Dekker, Rommert, 2002. "Controlling inventories with stochastic item returns: A basic model," European Journal of Operational Research, Elsevier, vol. 138(1), pages 63-75, April.
    10. L. Beril Toktay & Lawrence M. Wein & Stefanos A. Zenios, 2000. "Inventory Management of Remanufacturable Products," Management Science, INFORMS, vol. 46(11), pages 1412-1426, November.
    11. Kleijn, Rene & Huele, Ruben & van der Voet, Ester, 2000. "Dynamic substance flow analysis: the delaying mechanism of stocks, with the case of PVC in Sweden," Ecological Economics, Elsevier, vol. 32(2), pages 241-254, February.
    12. Toktay, B. & van der Laan, E.A. & de Brito, M.P., 2003. "Managing Product Returns: The Role of Forecasting," ERIM Report Series Research in Management ERS-2003-023-LIS, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.
    13. Elshkaki, Ayman & van der Voet, Ester & Timmermans, Veerle & Van Holderbeke, Mirja, 2005. "Dynamic stock modelling: A method for the identification and estimation of future waste streams and emissions based on past production and product stock characteristics," Energy, Elsevier, vol. 30(8), pages 1353-1363.
    14. Fleischmann, Moritz & Bloemhof-Ruwaard, Jacqueline M. & Dekker, Rommert & van der Laan, Erwin & van Nunen, Jo A. E. E. & Van Wassenhove, Luk N., 1997. "Quantitative models for reverse logistics: A review," European Journal of Operational Research, Elsevier, vol. 103(1), pages 1-17, November.
    15. Steffens, Paul R, 2001. "An Aggregate Sales Model for Consumer Durables Incorporating a Time-Varying Mean Replacement Age," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 20(1), pages 63-77, January.
    16. Govindan, Kannan & Soleimani, Hamed & Kannan, Devika, 2015. "Reverse logistics and closed-loop supply chain: A comprehensive review to explore the future," European Journal of Operational Research, Elsevier, vol. 240(3), pages 603-626.
    17. Teunter, Ruud H. & Vlachos, Dimitrios, 2002. "On the necessity of a disposal option for returned items that can be remanufactured," International Journal of Production Economics, Elsevier, vol. 75(3), pages 257-266, February.
    18. V. Daniel R. Guide , Jr. & Gilvan C. Souza & Luk N. Van Wassenhove & Joseph D. Blackburn, 2006. "Time Value of Commercial Product Returns," Management Science, INFORMS, vol. 52(8), pages 1200-1214, August.
    19. de Brito, Marisa P. & van der Laan, Erwin A., 2009. "Inventory control with product returns: The impact of imperfect information," European Journal of Operational Research, Elsevier, vol. 194(1), pages 85-101, April.
    20. Brandenburg, Marcus & Govindan, Kannan & Sarkis, Joseph & Seuring, Stefan, 2014. "Quantitative models for sustainable supply chain management: Developments and directions," European Journal of Operational Research, Elsevier, vol. 233(2), pages 299-312.
    21. Babai, Zied & Boylan, John E. & Kolassa, Stephan & Nikolopoulos, Konstantinos, 2016. "Supply chain forecasting: Theory, practice, their gap and the futureAuthor-Name: Syntetos, Aris A," European Journal of Operational Research, Elsevier, vol. 252(1), pages 1-26.
    22. de Brito, Marisa P. & Dekker, Rommert, 2003. "Modelling product returns in inventory control--exploring the validity of general assumptions," International Journal of Production Economics, Elsevier, vol. 81(1), pages 225-241, January.
    23. Ferrer, Geraldo & Swaminathan, Jayashankar M., 2010. "Managing new and differentiated remanufactured products," European Journal of Operational Research, Elsevier, vol. 203(2), pages 370-379, June.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Bruno Damásio & João Nicolau, 2020. "Time Inhomogeneous Multivariate Markov Chains: Detecting and Testing Multiple Structural Breaks Occurring at Unknown," Working Papers REM 2020/0136, ISEG - Lisbon School of Economics and Management, REM, Universidade de Lisboa.
    2. Ponte, Borja & Naim, Mohamed M. & Syntetos, Aris A., 2019. "The value of regulating returns for enhancing the dynamic behaviour of hybrid manufacturing-remanufacturing systems," European Journal of Operational Research, Elsevier, vol. 278(2), pages 629-645.
    3. Cui, Hailong & Rajagopalan, Sampath & Ward, Amy R., 2020. "Predicting product return volume using machine learning methods," European Journal of Operational Research, Elsevier, vol. 281(3), pages 612-627.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:ejores:v:271:y:2018:i:2:p:474-489. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Haili He). General contact details of provider: http://www.elsevier.com/locate/eor .

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.