System dynamics research of remanufacturing closed-loop supply chain dominated by the third party

Miao, Shidi; Wang, Tengfei; Chen, Deyun

doi:10.1177/0734242x16684384

Cited by 23 publications

(10 citation statements)

References 6 publications

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“…Sudarto, Takahashi, and Morikawa (2017) proposed an SD model to optimize capacity‐planning decision for efficient flexible control while identifying product lifecycle uncertainty in a CLSC with RL processes and social responsibility objectives. Miao, Wang, and Chen (2017) carried out research on third‐party enterprise‐dominating remanufacturing CLSC through an SD methodology. Ghisolfi, Chaves, Siman, and Xavier (2017) proposed a model on social inclusion of informal waste pickers into the environmental policy in CLSC in Brazil using SD methodology.…”

Section: Theoretical Backgroundmentioning

confidence: 99%

Performance evaluation of reverse logistics in food supply chains in a circular economy using system dynamics

et al. 2020

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Supply chains are composed of multiple stakeholders who have complex interrelationships. In addition, the forward and reverse flow of materials, information, human resources, and finance occurs among different stakeholders in closing the loop of supply chains. Reverse logistics (RL) activities are gaining importance in terms of size and quantity due to both economic and environmental concerns. These flows in RL in supply chains are both dynamic and complex in nature. Further, the environmental impact of RL activities has barely been considered in holistic way in available literature. In this study, a system dynamics model has been developed to analyze and comprehend the green performance of RL activities by predicting the environmental impact of RL activities. The proposed model has been validated by a case study in the context of a food supply chain. In the company where the case study is carried out, the environmental effects of RL activities have been analyzed. These activities in a food supply chain in terms of CO2 (carbon dioxide), NOx (nitrogen oxide), SO2 (sulfur dioxide), and PM (particulate matter) emissions have been predicted through a system dynamics model for the years 2020 to 2024. The proposed methodology is applied in a food supply context, a major player in retail business, especially in emerging economies. According to our findings, the RL activities in a food supply chain can significantly contribute to green performance management by minimizing food waste and loss; hence, the environmental impacts of such activities should be closely examined from a managerial perspective.

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Section: Theoretical Backgroundmentioning

confidence: 99%

Performance evaluation of reverse logistics in food supply chains in a circular economy using system dynamics

et al. 2020

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“…Following this, a number of papers have studied simulation modelling as it relates to remanufacturing. Using system dynamics modelling investigated remanufacturing closed-loop supply chain dominated by third party [40]. Their studies show that a remanufacturing model dominated by the third party is more effective than the traditional remanufacturing cycle.…”

Section: Simulation Modelling In Remanufacturingmentioning

confidence: 99%

“…Reconditioning time per component: Uniform distribution (30,40) minutes. We estimate it with minimum = 30, maximum = 40.…”

Section: Simulation Model Assumptionsmentioning

confidence: 99%

Towards a simulation-based understanding of smart remanufacturing operations: a comparative analysis

et al. 2020

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While the majority of literature on remanufacturing operations examines an end-oflife (EOL) strategy which is both manual and mechanised, authors generally agree that digitalisation of remanufacturing is expected to increase in the next decade. Subsequently, a new research area described as digitally-enabled remanufacturing, remanufacturing 4.0 or smart remanufacturing is emerging. This is an automated, data-driven system of remanufacturing by means of Industry 4.0 (I4.0) paradigms. Insights into smart remanufacturing can be provided through simulation modelling of the remanufacturing process. While the use of simulation modelling in order to predict responses and behaviour is prevalent in remanufacturing, the use of these tools in smart remanufacturing is still limited in literature. The goal of this research is to present, as a first of its kind, a comparative understanding of simulation modelling in remanufacturing in order to suggest the ideal modelling tool for smart remanufacturing. The proposed comparison includes system dynamics, discrete event simulation and agent based modelling techniques. We apply these modelling techniques on a smart remanufacturing space of a sensor-enabled product and use assumptions derived from industry experts. We then proceed to model the remanufacturing operation from sorting and inspection of cores to final inspection of the remanufactured product. Through our analysis of the assumptions utilised and simulation modelling results we conclude that, while individual modelling techniques present important strategic and operational insights, their individual use may not be sufficient to offer comprehensive knowledge to remanufacturers due to the challenge of data complexity that smart remanufacturing offers.

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“…Kim et al [9] established a supply planning model for the remanufacturing system in reverse logistics environment and verified the validity of the model and algorithm by numerical analysis. Miao et al [10] constructed a recycling mode dominated by the third party of reverse logistics, and carried out empirical analysis by the example of Midea Corp. Georgiadis et al [11] studied reverse channel capacity planning of closed-loop supply chains by developing a system dynamics (SD) model for two sequential types of products under two different scenarios regarding the consumer preferences over the products. Bal et al [12] considered the triple-bottom-line approach and multi-objective programming to achieve the maximization of economic, social and environmental goals at the same time, proposing a multi-equipment, multi-product and multi-cycle reverse logistics recycling model.…”

Section: Literature Reviewmentioning

confidence: 99%

Pricing Decision for Reverse Logistics System under Cross-Competitive Take-Back Mode Based on Game Theory

Dai

Mao

et al. 2019

Sustainability

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Considering the reverse logistics system composed of two manufacturers and two recyclers under the cross-competitive take-back mode, which is influenced by multiple factors (industry competition, economies of scale, government subsidies, remanufacturing rate, etc.), a model for remanufacturing reverse logistics system based on Stackelberg game is established. Then, the Nash equilibrium solution of decision variables is solved to obtain the best profit of all participants under the cross-competitive take-back mode. Furthermore, the parameter constraint analysis is carried out, and the monopolistic take-back mode is introduced for comparative analysis. Then, the sensitivity analysis of the model is carried out. At last, a case analysis is carried out based on the current situation of waste electrical and electronic equipment (WEEE) recycling in China. The results show that the cross-competitive take-back mode is more advantageous to all participants in the reverse logistics system than the monopolistic take-back mode. Recyclers should actively sign contracts with multiple manufacturers to recycle waste products, making full use of the advantages of cross-competitive take-back mode to maximize the profits of all participants, so as to encourage them to recycle waste products and achieve sustainable development.

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System dynamics research of remanufacturing closed-loop supply chain dominated by the third party

Cited by 23 publications

References 6 publications

Performance evaluation of reverse logistics in food supply chains in a circular economy using system dynamics

Performance evaluation of reverse logistics in food supply chains in a circular economy using system dynamics

Towards a simulation-based understanding of smart remanufacturing operations: a comparative analysis

Pricing Decision for Reverse Logistics System under Cross-Competitive Take-Back Mode Based on Game Theory

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