The optimized cash flow may affect any smart production system to control a material requirement planning and to reduce the carbon footprint within the environment. An automation policy is utilized within a smart production system under a forward and a backward logistics system. Such logistic network generally consists of a well-structured transportation system, which may increase the carbon footprint. This study deals how the carbon footprint can be controlled by a smart production system and it obtains the net present value of products for the four sub-systems associated with the logistics system. To investigate this, four sub-systems as manufacturing, distribution, consumption, and remanufacturing are implemented. A solution methodology is designed with an integral transformation through the frequency domain. This smart logistics system can be used by an associated matrix through an input-output analysis based on the distribution center. An illustrative numerical experiment is conducted and the study reveals that the discounted sale in disposal subsection at the end of the logistic cycle gives high positive impact, where the efficiency is increased due to discarding defective products by the automation policy. Graphical studies on the effect of transportation time for the total net present value and the net present value for disposal items are compared. It is found that two-stage inspection process reveals less amount of defective items and less pollution. As the closed-loop supply chain management is considered and due to transportation, huge amount of carbon emissions are passing through the environment, this study gives the reduced amount of carbon and more perfect products by an optimum cash-flow within a smart production system under advanced logistics management.
Bibliographical noteFunding Information:
This research is supported by National Research Foundation of Korea (NRF) funded by the Ministry of Education, Science and Technology (Project Number 2017R1D1A1B03033846 ). The fourth author was supported by the Tecnológico de Monterrey Research Group in Optimization and Data Science 0822B01006 .
This research is supported by National Research Foundation of Korea (NRF) funded by the Ministry of Education, Science and Technology (Project Number 2017R1D1A1B03033846). The fourth author was supported by the Tecnológico de Monterrey Research Group in Optimization and Data Science 0822B01006.
© 2019 Elsevier B.V.
All Science Journal Classification (ASJC) codes
- Business, Management and Accounting(all)
- Economics and Econometrics
- Management Science and Operations Research
- Industrial and Manufacturing Engineering