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Grey modelling based forecasting system for return flow of end-of-life vehicles

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  • Ene, Seval
  • Öztürk, Nursel

Abstract

Due to legislation and economic reasons, firms in most industries are forced to be responsible and manage their products at the end of their lives. Management of product returns is critical for the stability and profitability of a reverse supply chain. Forecasting the return amounts and timing is beneficial. The purpose of this paper is to develop a forecasting system for discarded end-of-life vehicles and to predict the number of end-of-life vehicles that will be generated in the future. To create the forecasting system, grey system theory, which uses a small amount of the most recent data, is employed. The accuracy of the grey model is improved with parameter optimization, Fourier series and Markov chain correction. The proposed models are applied to the case of Turkey and data sets of twelve regions in Turkey are considered. The obtained results show that the proposed forecasting system can successfully govern the phenomena of the data sets, and high accuracy can be provided for each region in Turkey. The proposed forecasting system can be used as a strategic tool in similar forecasting problems, and supportive guidance can be achieved.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:tefoso:v:115:y:2017:i:c:p:155-166
    DOI: 10.1016/j.techfore.2016.09.030
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    1. Hamzacebi, Coskun & Es, Huseyin Avni, 2014. "Forecasting the annual electricity consumption of Turkey using an optimized grey model," Energy, Elsevier, vol. 70(C), pages 165-171.
    2. de Brito, M.P. & Dekker, R., 2002. "Reverse logistics - a framework," Econometric Institute Research Papers EI 2002-38, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    3. Kumar, Ujjwal & Jain, V.K., 2010. "Time series models (Grey-Markov, Grey Model with rolling mechanism and singular spectrum analysis) to forecast energy consumption in India," Energy, Elsevier, vol. 35(4), pages 1709-1716.
    4. Prahinski, Carol & Kocabasoglu, Canan, 2006. "Empirical research opportunities in reverse supply chains," Omega, Elsevier, vol. 34(6), pages 519-532, December.
    5. 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.
    6. Wang, Xingwei & Cai, Yanpeng & Chen, Jiajun & Dai, Chao, 2013. "A grey-forecasting interval-parameter mixed-integer programming approach for integrated electric-environmental management–A case study of Beijing," Energy, Elsevier, vol. 63(C), pages 334-344.
    7. 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 Limited, vol. 11(1), pages 102-114, April.
    8. Zhou, P. & Ang, B.W. & Poh, K.L., 2006. "A trigonometric grey prediction approach to forecasting electricity demand," Energy, Elsevier, vol. 31(14), pages 2839-2847.
    9. 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.
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    6. Yi-Chung Hu, 2021. "Developing grey prediction with Fourier series using genetic algorithms for tourism demand forecasting," Quality & Quantity: International Journal of Methodology, Springer, vol. 55(1), pages 315-331, February.
    7. Xie, Xuemei & Liu, Xiaojie & Blanco, Cristina, 2023. "Evaluating and forecasting the niche fitness of regional innovation ecosystems: A comparative evaluation of different optimized grey models," Technological Forecasting and Social Change, Elsevier, vol. 191(C).
    8. Ye, Li & Dang, Yaoguo & Fang, Liping & Wang, Junjie, 2023. "A nonlinear interactive grey multivariable model based on dynamic compensation for forecasting the economy-energy-environment system," Applied Energy, Elsevier, vol. 331(C).
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    10. Tripathy, Satchidananda & Kumar, Akhilesh & Mahanty, Biswajit, 2023. "Short-lived product returns forecasting when customers are unwilling to return the product: A grey-graphical evaluation and review technique," Technological Forecasting and Social Change, Elsevier, vol. 195(C).
    11. Meiling He & Tianhe Lin & Xiaohui Wu & Jianqiang Luo & Yongtao Peng, 2020. "A Systematic Literature Review of Reverse Logistics of End-of-Life Vehicles: Bibliometric Analysis and Research Trend," Energies, MDPI, vol. 13(21), pages 1-22, October.
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    13. Fatin Amrina A. Rashid & Hawa Hishamuddin & Nizaroyani Saibani & Mohd Radzi Abu Mansor & Zambri Harun, 2022. "A Review of Supply Chain Uncertainty Management in the End-of-Life Vehicle Industry," Sustainability, MDPI, vol. 14(19), pages 1-28, October.
    14. Chen, Yan & Lifeng, Wu & Lianyi, Liu & Kai, Zhang, 2020. "Fractional Hausdorff grey model and its properties," Chaos, Solitons & Fractals, Elsevier, vol. 138(C).
    15. Yuan, Yanhong & Yang, Jinyao & Li, Yang & Li, Wei, 2020. "Necessary conditions for coordination of dual-channel closed-loop supply chain," Technological Forecasting and Social Change, Elsevier, vol. 151(C).
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