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Forecasting return of used products for remanufacturing using Graphical Evaluation and Review Technique (GERT)

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  • Zhou, Li
  • Xie, Jiaping
  • Gu, Xiaoyu
  • Lin, Yong
  • Ieromonachou, Petros
  • Zhang, Xiaole

Abstract

This research develops a forecasting model that can predict the quantity, time and probability of product return, recyclable parts/components/materials and disposal. It adopts the Graphical Evaluation and Review Technique (GERT) by translating the remanufacturing operational process into a stochastic network. This stochastic network possesses two characteristics: activities having a probability of occurrence associated with them; and time to perform an activity. Together with the GERT method, Mason's rule is applied to calculate the equivalence transfer function of the system, therefore predicting the desired outcomes. A generic eight-step process on how to implement this method in any structure of return products and remanufacturing network is provided. A numerical example is presented to demonstrate the result of using GERT on forecasting printer remanufacturing outcomes. The main contribution of this research is: Instead of giving one result such as either return quantity, or time, or probability, our research can forecast three of these outcomes simultaneously, and the algorithm is generalised to be applicable to any product structure and remanufacturing network.

Suggested Citation

  • Zhou, Li & Xie, Jiaping & Gu, Xiaoyu & Lin, Yong & Ieromonachou, Petros & Zhang, Xiaole, 2016. "Forecasting return of used products for remanufacturing using Graphical Evaluation and Review Technique (GERT)," International Journal of Production Economics, Elsevier, vol. 181(PB), pages 315-324.
    • Handle: RePEc:eee:proeco:v:181:y:2016:i:pb:p:315-324
    DOI: 10.1016/j.ijpe.2016.04.016
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    Cited by:

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    3. Van Nguyen, Truong & Zhou, Li & Chong, Alain Yee Loong & Li, Boying & Pu, Xiaodie, 2020. "Predicting customer demand for remanufactured products: A data-mining approach," European Journal of Operational Research, Elsevier, vol. 281(3), pages 543-558.
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    5. Zeng, Jing & Liu, Sifeng, 2023. "Forecasting the sustainable classified recycling of used lithium batteries by gray Graphical Evaluation and Review Technique," Renewable Energy, Elsevier, vol. 202(C), pages 602-612.
    6. Liu, Zhenyuan & Han, Shuihua & Li, Chao & Gupta, Shivam & Sivarajah, Uthayasankar, 2022. "Leveraging customer engagement to improve the operational efficiency of social commerce start-ups," Journal of Business Research, Elsevier, vol. 140(C), pages 572-582.
    7. Wang, Haiyan & Zhan, Sha-lei & Ng, Chi To & Cheng, T.C.E., 2020. "Coordinating quality, time, and carbon emissions in perishable food production: A new technology integrating GERT and the Bayesian approach," International Journal of Production Economics, Elsevier, vol. 225(C).
    8. Wenjie Dong & Sifeng Liu & Zhigeng Fang & Yingsai Cao & Ye Ding, 2019. "A model based on hidden graphic evaluation and review technique network to evaluate reliability and lifetime of multi-state systems," Journal of Risk and Reliability, , vol. 233(3), pages 369-378, June.

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