In recent years, additive manufacturing (AM) has gained considerable interest because of its capacity to facilitate the fabrication of products of various shapes better than conventional manufacturing (CM). Moreover, it can respond to the growing demand for customized products and can reduce the impact on the environment by minimizing production waste. To maximize these benefits and overcome entry barriers, researchers have conducted part consolidation (PC) research to improve sustainability by reducing the number of components, or hybrid supply chain studies involving local AM service providers. This study aims to present a new supply chain that combines the advantages of both AM and CM systems to create and analyze the potential of an AM machine that can be made available for rent at a lower cost. In this study, a closed-loop supply chain with an AM hub (CLSCAM) is first designed. Thereafter, to support manufacturers in making the decision to adopt AM, two models are developed: sustainability (cost, environment, and time) evaluation model from the lifecycle perspective of CLSCAM, and a PC method is developed to maximize the three sustainability indices. Since the PC problem with complex product structure is an NP-hard problem, the genetic algorithm is employed as a solution method. Furthermore, an experimental analysis is conducted to validate the proposed model through a real-world application (testbed product). The results reveal that the AM hub and PC model can effectively improve the sustainability of the entire lifecycle. In particular, the results of improved sustainability per unit product in mass production scenarios demonstrate the practical applicability of the proposed model. These results also demonstrate that the pre-manufacturing stage has the greatest impact on the cost sustainability index in the CLSCAM. It is further confirmed that the sustainability improvement effect of PC increases with increasing AM raw material consumption efficiency, volume reduction rate of PC.
Bibliographical noteFunding Information:
This work was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science and ICT ( NRF-2019R1F1A1061349 ).
This work was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science and ICT (NRF-2019R1F1A1061349).
© 2020 Elsevier B.V.
All Science Journal Classification (ASJC) codes
- Biomedical Engineering
- Materials Science(all)
- Engineering (miscellaneous)
- Industrial and Manufacturing Engineering