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Forecasting product returns for recycling in Indian electronics industry


  • Saurabh Agrawal
  • Rajesh K. Singh
  • Qasim Murtaza


Purpose - – The purpose of this paper is to develop a model for forecasting product returns to the company for recycling in terms of quantity and time. Design/methodology/approach - – Graphical Evaluation and Review Technique (GERT) has been applied for developing the forecasting model for product returns. A case of Indian mobile manufacturing company is discussed for the validation of this model. Survey conducted by the company and findings from previous research were used for data collection on probabilities and product life cycle. Findings - – Product returns for their recycling are stochastic, random and uncertain. Therefore, to address the uncertainty, randomness and stochastic nature of product returns, GERT is very useful tool for forecasting product returns. Practical implications - – GERT provides the visual picture of the reverse supply chain system and helps in determining the expected time of product returns in a much easier way but it requires probabilities of different flows and product life cycle. Both factors vary over a period, so require data update time to time before implementation. Originality/value - – This model is developed by considering all possible flows of sold products from customer to their reuse, store or recycle or landfill. First time this type of real life flows have been considered and GERT has been applied for forecasting product returns. This model can be utilized by managers for better forecasting that will help them for effective reverse supply chain design.

Suggested Citation

  • Saurabh Agrawal & Rajesh K. Singh & Qasim Murtaza, 2014. "Forecasting product returns for recycling in Indian electronics industry," Journal of Advances in Management Research, Emerald Group Publishing, vol. 11(1), pages 102-114, April.
  • Handle: RePEc:eme:jamrpp:v:11:y:2014:i:1:p:102-114

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    Cited by:

    1. 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.
    2. Ayşe Nur Adıgüzel Tüylü Author-Name:Ergün Eroğlu, 2019. "Using Machine Learning Algorithms For Forecasting Rate of Return Product In Reverse Logistics Process," Alphanumeric Journal, Bahadir Fatih Yildirim, vol. 7(1), pages 143-156, June.
    3. Ene, Seval & Öztürk, Nursel, 2017. "Grey modelling based forecasting system for return flow of end-of-life vehicles," Technological Forecasting and Social Change, Elsevier, vol. 115(C), pages 155-166.


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