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International Journal of Production Research

Modeling and Decision Support to Integrate Industry 4.0 Technologies and the Circular Economy

IJPR seeks submissions for a special issue on Modeling and Decision Support to integrate Industry 4.0 Technologies and the Circular Economy. The circular economy (CE) has become an important social innovation that has received recent and significant global attention due to issues related to reaching our planetary resource boundaries. It is a system of production and consumption that ensures products, components, materials, and energy remain in circulation—adding, recreating, and maintaining their value over an extended period of time (Govindan and Hasanagic, 2018). CE adoption requires systemic changes cross-functional decision-making including product development, sourcing, production, and logistics (Batista et al., 2018). CE adoption includes multiple antecedents, barriers, practices, and outcomes (Govindan and Hasanagic, 2018; Bhatia et al., 2020).  


Industry 4.0 technologies such as the Internet of Things (IoT), cyber-physical systems (CPS), additive manufacturing (AM), and blockchains can facilitate CE transition (de Sousa Jabbour et al., 2018). The integration of Industry 4.0 technologies increases decision complexity especially with respect to CE product and process design, refurbishment, remanufacturing, and logistics processes. As an example IoT, virtual and augmented reality application in remanufacturing require strategic decision models to optimize product remanufacturing (Moosmayer et al., 2020). 


Opportunities exist for exploring the linkages between product life cycle, optimal remanufacturing process design (Kerin and Pham, 2019), and proactive application of CE innovations. Big data analytics can also help firms uncover hidden patterns and unlock the potential of circularity (Cheng et al., 2021). Such analytics may help in extracting meaningful insights regarding environmental impact and consumption aspects of a product’s entire life cycle. These analytics can be leveraged to support decision-making during multiple product lifecycle stages. 


Small and medium enterprises engaged in CE face concerns related to understanding Industry 4.0 technology returns and issues with behavioural decision-making from individual and organizational learning  (Moosmayer et al., 2020).  


AM can support remanufacturing of products or components (Lahrour and Brissaud, 2018) in connection with CE business models focused on recycled materials (Mattos Nascimento et al., 2018).  AM can also be exploited to improve overall CE performance (Angioletti et al., 2017) through improved product development and design (Müller et al., 2018). However, few normative and formal modeling attempts have been introduced to accommodate recycled materials for strategic product development decisions using AM.  


Blockchain technology can also contribute to CE by reducing transaction costs and reducing the carbon footprint (Choi et al., 2020; Kouhizadeh et al., 2020). As an example, various blockchain service providers have developed solutions to ensure traceability of plastic recycled from the ocean and urban waste, which can be used to produce packaging for multiple industries. There is a need to quantify blockchain technology risks and benefits for tracing and tracking of end-of-life products for recycling (Subramanian et al, 2020) and to design suitable contracts between supply chain partners to adopt blockchains for incentivization, risk mitigation, traceability and CE.


While there is potential to utilise Industry 4.0 technologies to achieve CE related targets with a significant research agenda identified (de Sousa Jabbour et al. 2018)—there is limited research on the development of formal decision-making models and tools for integrating digital technologies for optimal CE-related decisions such as product and process design, refurbishment and remanufacturing, logistics planning. Some initial efforts do exist (e.g. Bhatia et al., 2020; Dev et al. 2020; Kamble et al. 2021; Lin, 2018), but there is ample need and opportunity to further establish additional support tools that can more effectively link these two critical and emergent social, technological, and organizational innovations—innovations that require multi-stakeholder, multi-organization, and multi-functional considerations. 

Hence, the objective of this special issue is to encourage investigation that will advance the body of knowledge on strategic, tactical and operational decision making supporting integration of Industry 4.0 technologies with CE. We do not have a bias or preference for which modelling technique to be used and encourage optimization, simulation, game theory, machine learning, decision analysis, and set theory techniques. Manuscripts which utilize modeling techniques incorporating real data from case companies and validation are most welcome.  

The main topics of interest include, but are not limited to:

  • Models to analyse the risk and benefits of Industry 4.0 technologies in the CE context.

  • Behavioral decision-making frameworks including strategic models for firms and individuals dealing with applications of Industry 4.0 technologies for CE.

  • Strategic models to capture trade-offs between digital CE and CE.

  • Big data analytics for eco-design to support circular economy transitions.

  • Development of decision support systems to facilitate and justify the adoption of AM and blockchain for a circular economy.

  • Coordination models between SMEs and their supply chain partners engaged in digital CE.

  • Modeling for the use of recycled material for AM

  • Big data analytics for optimal refurbishment and remanufacturing of spare parts using IoT captured real-time data.

  • Big data analytics for real-time transportation and routing decisions to facilitate reverse logistics and CE.

  • Deep learning methods for digital circular supply chains.

  • Leasing, servitization, and contract design for digital CE.

  • Collaboration models for digital circular economy

  • Multi-stakeholder integrative and decision support tools for the digitalized CE environment.

  • The linkage of descriptive, predictive, and prescriptive analytical approaches to support this decision environment.

  • How can supply chain selection and development be managed through decision support in the digitalized CE context?

  • How can Sustainable Development Goals (SDGs) be met and optimized through digitalized CE?

Submission Requirements: 
Manuscripts should be submitted via the submission site for International Journal of Production Research: https://mc.manuscriptcentral.com/tprs

On Step 5 of the submission form, please select “MDS I4.0 in CE” from the special issue dropdown menu.

Manuscripts should not have been previously published nor be currently under consideration for publication elsewhere.

 

For Guide for Authors, please refer to the webpage.

Deadline
The submission deadline is 31/03/2022.
The Special Issue is scheduled for publication in early 2023.

Special Issue Editors

Managing Guest Editor:

  • Kannan Govindan, Chair Professor of Sustainable Operations and Supply Chain Management, University of Southern Denmark, Denmark kgov@iti.sdu.dk 
     

Guest Editors:

  • Joseph Sarkis, Professor of Operations Management, Worcester Polytechnic Institute, Business School, USA 

  • Atanu Chaudhuri, Associate Professor of Operations and Technology Management,  Durham University Business School, UK 

  • Nachiappan Subramanian, Professor of Operations and Logistics Management, University of Sussex, UK

  • Manoj Dora,  Reader of Operations Management,  Brunel Business School, Brunel University London, UK 

  • Luciano Batista, Associate Professor in Operations Management, Aston Business School, Aston University, UK 

 

References
Angioletti, Cecilia Maria, Mélanie Despeisse, and Roberto Rocca. 2017. “Product Circularity Assessment Methodology.” in IFIP Advances in Information and Communication Technology, 514:411–418.

 

Batista, L., Bourlakis, M., Liu, Y., Smart, P., Sohal, A., 2018. Supply chain operations for a circular economy. Production Planning and Control, 29, 419- 424.
 

Bhatia, M. S., Jakhar, S. K., & Dora, M. (2020). Analysis of Barriers to Closed-Loop Supply Chain: A Case of the Indian Automotive Industry. IEEE Transactions on Engineering Management. 1-15.

Choi, T. M., Taleizadeh, A. A., & Yue, X. (2020). Game theory applications in production research in the sharing and circular economy era, International Journal of Production Research, 58, 118-127.
 

de Sousa Jabbour, A.B.L., Jabbour, C.J.C., Godinho Filho, M. and Roubaud, D., 2018. Industry 4.0 and the circular economy: a proposed research agenda and original roadmap for sustainable operations. Annals of Operations Research, 270(1), pp.273-286. 
 

Edwin Cheng, T. C., Kamble, S. S., Belhadi, A., Ndubisi, N. O., Lai, K. H., & Kharat, M. G. (2021). Linkages between big data analytics, circular economy, sustainable supply chain flexibility, and sustainable performance in manufacturing firms. International Journal of Production Research, 1-15.
 

Dev, N.K., Shankar, R. and Qaiser, F.H., 2020. Industry 4.0 and circular economy: Operational excellence for sustainable reverse supply chain performance. Resources, Conservation and Recycling, 153, p.104583.
 

Govindan, K., & Hasanagic, M. (2018). A systematic review on drivers, barriers, and practices towards circular economy: a supply chain perspective. International Journal of Production Research, 56(1-2), 278-311.
 

Govindan, K., Shankar, K. M., & Kannan, D. (2020). Achieving sustainable development goals through identifying and analyzing barriers to industrial sharing economy: A framework development. International Journal of Production Economics, 227, 107575.
 

Kamble, S.S., Belhadi, A., Gunasekaran, A., Ganapathy, L. and Verma, S., 2021. A large multi-group decision-making technique for prioritizing the big data-driven circular economy practices in the automobile component manufacturing industry. Technological Forecasting and Social Change, 165, p.120567.
 

Kerin, M. and Pham, D.T., 2019. A review of emerging industry 4.0 technologies in remanufacturing. Journal of Cleaner Production, 237, p.117805.
 

Kouhizadeh, M., Zhu, Q., & Sarkis, J. (2020). Blockchain and the circular economy: potential tensions and critical reflections from practice. Production Planning & Control, 31(11-12), 950-966.
 

Lahrour, Yahya, and Daniel Brissaud. 2018. “A Technical Assessment of Product/Component Re-Manufacturability for Additive Remanufacturing.” Procedia CIRP 69: 142–147.
 

Lin, K.Y., 2018. User experience-based product design for smart production to empower industry 4.0 in the glass recycling circular economy. Computers & Industrial Engineering, 125, pp.729-738.
 

Mattos Nascimento, Daniel Luiz, Viviam Alencastro, Osvaldo Luiz Gonçalves Quelhas, Rodrigo Goyannes Gusmão Caiado, Jose Arturo Garza-Reyes, Luis Rocha Lona, and Guilherme Tortorella. 2018. “Exploring Industry 4.0 Technologies to Enable Circular Economy Practices in a Manufacturing Context: A Business Model Proposal.” Journal of Manufacturing Technology Management 29 (6): 910–936.
 

Moosmayer, D, Abdulrahman, M, Subramanian, N and Bergkvist, L 2020. “Strategic and operational remanufacturing mental models: a study on Chinese automotive  consumers buying choice”. International Journal of Operations and Production Management 40(2): 173-195.
 

Müller, Jakob R., Massimo Panarotto, Johan Malmqvist, and Ola Isaksson. 2018. “Lifecycle Design and Management of Additive Manufacturing Technologies.” Procedia Manufacturing 19: 135–142. 
 

Nandi, S., Sarkis, J., Hervani, A. A., & Helms, M. M. (2021). Redesigning supply chains using blockchain-enabled circular economy and COVID-19 experiences. Sustainable Production and Consumption, 27, 10-22.
 

Subramanian, N., A. Chaudhuri, and Y. Kayikci. 2020. Blockchain and Supply Chain Logistics: Evolutionary Case Studies. Palgrave, London, ISBN 978-3-030-47530-7.