Product Recovery Configuration Decisions for Achieving Sustainable Manufacturing

Kuik, Swee S.; Kaihara, Toshiya; Fujii, Nobutada

doi:10.1016/j.procir.2016.01.195

Cited by 13 publications

(12 citation statements)

References 31 publications

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“…Wang, Zhang, and Zuo 2016) x (J. Xu 2009) x (Zallio and Berry 2017) x T o t a l 7 4 3 3 2 2 1 Thomas, and Trentesaux 2013) x (Deschamps et al 2018) x (He, Xu, and Xu 2010) x (Karastoyanov and Karastanev 2018) x (Kuik, Kaihara, and Fujii 2016) x x x x (Panarotto, Wall, and Larsson 2017) x (Rönnlund et al 2016) x (Schäfers and Walther 2017) x (Schroeder et al 2016b) x (Schroeder et al 2016a) x (Siddiqi et al 2017) x (Simons 2017) x (Sun and Wang 2011) x (Trentesaux and Giret 2015) x x (Turner et al 2016) x (van van Schaik and Reuter 2016) x (van Schalkwyk et al 2018) x (X. V. x x (Zhao, Dang, and Zhang 2011) x Total 6 6 5 4 2 2 1…”

Section: Simulationmentioning

confidence: 99%

“…Remanufacturing 4.0 When discussing the digitalisation of remanufacturing, the greatest number of experts identified simulation and AM as the most promising I4.0 technologies (see Table 15). First, simulation can be adopted for developing dedicated decision-support tools (Kuik, Kaihara, and Fujii 2016;X. V. Wang and Wang 2018).…”

Section: Referencementioning

confidence: 99%

“…Simulation was the prevalent technique, followed by AM, BDA, and the IoT. From one perspective, simulation (in the form of blockchain) was exploited for (i) auditing cooperative CE networks (Alexandris et al 2018), (ii) assessing the impact of business model changes (Simons 2017), for example, through agent-based or multi-method approaches , and (iii) assessing redistributed manufacturing or service-based design (Panarotto, Wall, and Larsson 2017) (French, Benakis, and Marin-Reyes 2017) x (Ge and Jackson 2014) x (Karastoyanov and Karastanev 2018) x (Kuik, Kaihara, and Fujii 2016) x (Lahrour and Brissaud 2018) x (Le, Paris, and Mandil 2017a) x (Le, Paris, and Mandil 2017b) x (Leino, Pekkarinen, and Soukka 2016) x (Liu et al 2016) x (Ruggeri et al 2017) x (Shanshan Yang et al 2018) x (Siddiqi et al 2017) x (Stark et al 2014) x (Tolio et al 2017) x (X. V. x (B. Xu 2016)…”

Section: Circular Business Models and Smart Servicesmentioning

confidence: 99%

See 2 more Smart Citations

Assessing relations between Circular Economy and Industry 4.0: a systematic literature review

Rosa

Sassanelli

Urbinati

et al. 2019

International Journal of Production Research

456

280

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Industry 4.0 (I4.0) and Circular Economy (CE) are undoubtedly two of the most debated topics of the last decades. Progressively, they gained the interest of policymakers, practitioners and scholars all over the world. Even if they have been usually described as two independent research fields, there are some examples presenting overlaps between these topics, represented by hybrid categories like Circular I4.0 and Digital CE. Starting from these two perspectives, an innovative framework both highlighting the links between I4.0 and CE and unveiling future research fields has been developed. Basing on one of the two perspectives, results show as it is possible to enhance a set of different relations. Depending on a dedicated area of either CE or I4.0 it is possible to see the prevalence of some I4.0 technology than others. However, the influence of I4.0 technologies on CE is always verified.

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Section: Simulationmentioning

confidence: 99%

Section: Referencementioning

confidence: 99%

Section: Circular Business Models and Smart Servicesmentioning

confidence: 99%

See 1 more Smart Citation

Assessing relations between Circular Economy and Industry 4.0: a systematic literature review

Rosa

Sassanelli

Urbinati

et al. 2019

International Journal of Production Research

456

280

View full text Add to dashboard Cite

show abstract

“…Among the main effects of simulation on CE practices, the literature underlines (i) support on products remanufacturing-for example, in the form of decision-support tools-and (ii) improvement of efficiency in exploiting natural resources through the calculation of eco-efficiency indexes [5,37,52,53].…”

Section: Model Conceptualizationmentioning

confidence: 99%

Integrating Virtual Reality and Digital Twin in Circular Economy Practices: A Laboratory Application Case

et al. 2020

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The increasing awareness of customers toward climate change effects, the high demand instability affecting several industrial sectors, and the fast automation and digitalization of production systems are forcing companies to re-think their business strategies and models in view of both the Circular Economy (CE) and Industry 4.0 (I4.0) paradigms. Some studies have already assessed the relations between CE and I4.0, their benefits, and barriers. However, a practical demonstration of their potential impact in real contexts is still lacking. The aim of this paper is to present a laboratory application case showing how I4.0-based technologies can support CE practices by virtually testing a waste from electrical and electronic equipment (WEEE) disassembly plant configuration through a set of dedicated simulation tools. Our results highlight that service-oriented, event-driven processing and information models can support the integration of smart and digital solutions in current CE practices at the factory level.

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“…In relation to the type of production to be made, other interesting decisions that are also reflected in the literature, are directed towards the selection of components to configure recovered products. In this area, Kuik, Kaihara and Fujii (2016) showed a model that determines an optimal recovery plan for the manufacturer, deciding on the components when producing remanufactured items, from new, reused, rebuilt, or recycled. Kim, Song and Kim (2006) also focused on the decisions regarding the type of manufacturing, where several alternatives were discussed on the supply of parts (order new parts to external suppliers or use pieces of the returned products) to decide, in an optimal way, the quantity of pieces that will be processed of each type.…”

Section: Main Decisions Of Rl According To Mathematical Modelsmentioning

confidence: 99%

Identification of reverse logistics decision types from mathematical models

Pellicer

Valero

2018

JIEM

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Purpose: The increase in social awareness, politics and environmental regulation, the scarcity of raw materials and the desired “green” image, are some of the reasons that lead companies to decide for implement processes of Reverse Logistics (RL). At the time when incorporate new RL processes as key business processes, new and important decisions need to be made. Identification and knowledge of these decisions, including the information available and the implications for the company or supply chain, will be fundamental for decision-makers to achieve the best results. In the present work, the main types of RL decisions are identified.Design/methodology/approach: This paper is based on the analysis of mathematical models designed as tools to aid decision making in the field of RL. Once the types of interest work to be analyzed are defined, those studies that really deal about the object of study are searched and analyzed. The decision variables that are taken at work are identified and grouped according to the type of decision and, finally, are showed the main types of decisions used in mathematical models developed in the field of RL. Findings: The principal conclusion of the research is that the most commonly addressed decisions with mathematical models in the field of RL are those related to the network’s configuration, followed by tactical/operative decisions such as the selections of product’s treatments to realize and the policy of returns or prices, among other decisions.Originality/value: The identification of the main decisions types of the reverse logistics will allow the managers of these processes to know and understand them better, while offer an integrated vision of them, favoring the achievement of better results.

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Product Recovery Configuration Decisions for Achieving Sustainable Manufacturing

Cited by 13 publications

References 31 publications

Assessing relations between Circular Economy and Industry 4.0: a systematic literature review

Assessing relations between Circular Economy and Industry 4.0: a systematic literature review

Integrating Virtual Reality and Digital Twin in Circular Economy Practices: A Laboratory Application Case

Identification of reverse logistics decision types from mathematical models

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