| Title | Environmental and economic sustainability through innovative green products by remanufacturing |
|---|---|
| ID_Doc | 30832 |
| Authors | Sarkar, B; Ullah, M; Sarkar, M |
| Title | Environmental and economic sustainability through innovative green products by remanufacturing |
| Year | 2022 |
| Published | |
| Abstract | The rapid advancements in technology demand the massive development of innovative green products to save global warming issues. Older non-green products are quickly outdated with the expansion of technology. Consequently, their market demand declines sharply. It leads to shorter life cycles, causing a remarkably uncertain environment and high waste generation. This problem is more in innovative green products compared to primary non-green products, primarily characterized by swift technological advancement and low acceptance rate among consumers. This study's focal point is to simultaneously consider the environmental risks associated with innovative green product manufacturing, while both the return rate and the demand are random with insufficient information. Three models are developed based on different production strategies for two different types of innovative green products. Moreover, a distribution-free approach is used rather than assuming any specific distribution for the random variables (demand and return rate). Models dealing with manufacturing strategies of innovative green products are rare in the literature; furthermore, a novel concept of green innovation by remanufacturing is used to improve waste management. The findings show that highly green innovative products perform better compared to less innovative products when uncertainties in demand and supply are high. Furthermore, new green products should be introduced only when the expected profit of new products is more than the loss of the existing products. The new policy innovation by remanufacturing is cost-effective compared to the traditional innovation policy. All three models are solved with numerical examples. Sensitivity analysis is performed to illustrate the robustness of models. |
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