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Diffusion of green products in industry 4.0: Reverse logistics issues during design of inventory and production planning system


  • Dev, Navin K.
  • Shankar, Ravi
  • Swami, Sanjeev


Under the paradigm of Industry 4.0, the present research attempts to model the reverse logistics and examine how product diffusion dynamics in the market affect the economic and environmental performances of an inventory and production planning (I&PP) system. We use the classic Bass (1969) model of diffusion of innovation to capture the returns of a single-generation of a product under the proposed architecture of systematical deployment of information-sharing strategies and I&PP policies under the notions of Industry 4.0 components. The key feature of Industry 4.0 characterized by virtualization of factory operations is captured using the simulation model. For the analysis, using the Taguchi experimental design framework, we present valuable managerial insights. Our findings suggest the relevant adoption patterns based on the combination of information-sharing and I&PP policies for the tradeoff between environmental and economic performance. An extensive sensitivity analysis shows the robustness of the model. Further, the managerial decisions on the environmental and economic performance measures reveal that in spite of the presence of Industry 4.0 technology capabilities, a close attention should be paid to operational parameters and their related costs when socially influenced green product adoption with the parameters such as size of end-user market and collection investment are governing the returns of the product to the reverse logistics system. Accordingly, the model exhibits a real-time decision support tool for the sustainable reverse logistics system in Industry 4.0 environment at large.

Suggested Citation

  • Dev, Navin K. & Shankar, Ravi & Swami, Sanjeev, 2020. "Diffusion of green products in industry 4.0: Reverse logistics issues during design of inventory and production planning system," International Journal of Production Economics, Elsevier, vol. 223(C).
  • Handle: RePEc:eee:proeco:v:223:y:2020:i:c:s0925527319303408
    DOI: 10.1016/j.ijpe.2019.107519

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    References listed on IDEAS

    1. Peres, Renana & Muller, Eitan & Mahajan, Vijay, 2010. "Innovation diffusion and new product growth models: A critical review and research directions," International Journal of Research in Marketing, Elsevier, vol. 27(2), pages 91-106.
    2. Nativi, Juan Jose & Lee, Seokcheon, 2012. "Impact of RFID information-sharing strategies on a decentralized supply chain with reverse logistics operations," International Journal of Production Economics, Elsevier, vol. 136(2), pages 366-377.
    3. Mehdi Amini & Haitao Li, 2015. "The impact of dual-market on supply chain configuration for new products," International Journal of Production Research, Taylor & Francis Journals, vol. 53(18), pages 5669-5684, September.
    4. Gary D. Eppen & R. Kipp Martin, 1988. "Determining Safety Stock in the Presence of Stochastic Lead Time and Demand," Management Science, INFORMS, vol. 34(11), pages 1380-1390, November.
    5. Navin K. Dev & Ravi Shankar & Angappa Gunasekaran & Lakshman S. Thakur, 2016. "A hybrid adaptive decision system for supply chain reconfiguration," International Journal of Production Research, Taylor & Francis Journals, vol. 54(23), pages 7100-7114, December.
    6. Frank M. Bass, 1969. "A New Product Growth for Model Consumer Durables," Management Science, INFORMS, vol. 15(5), pages 215-227, January.
    7. Sarkis, Joseph & Zhu, Qinghua & Lai, Kee-hung, 2011. "An organizational theoretic review of green supply chain management literature," International Journal of Production Economics, Elsevier, vol. 130(1), pages 1-15, March.
    8. Wang, Wenyuan & Wang, Yue & Mo, Daniel & Tseng, Mitchell M., 2017. "Managing component reuse in remanufacturing under product diffusion dynamics," International Journal of Production Economics, Elsevier, vol. 183(PB), pages 551-560.
    9. Zhong, Ray Y. & Huang, George Q. & Lan, Shulin & Dai, Q.Y. & Chen, Xu & Zhang, T., 2015. "A big data approach for logistics trajectory discovery from RFID-enabled production data," International Journal of Production Economics, Elsevier, vol. 165(C), pages 260-272.
    10. R. Canan Savaskan & Luk N. Van Wassenhove, 2006. "Reverse Channel Design: The Case of Competing Retailers," Management Science, INFORMS, vol. 52(1), pages 1-14, January.
    11. Tang, Christopher S. & Zhou, Sean, 2012. "Research advances in environmentally and socially sustainable operations," European Journal of Operational Research, Elsevier, vol. 223(3), pages 585-594.
    12. Dev, Navin K. & Shankar, Ravi & Choudhary, Alok, 2017. "Strategic design for inventory and production planning in closed-loop hybrid systems," International Journal of Production Economics, Elsevier, vol. 183(PB), pages 345-353.
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    Cited by:

    1. Dae-Ho Byun & Han-Na Yang & Dong-Seop Chung, 2020. "Evaluation of Mobile Applications Usability of Logistics in Life Startups," Sustainability, MDPI, Open Access Journal, vol. 12(21), pages 1-1, October.


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