AUTOMATING FORWARD AND REVERSE SUPPLY CHAINS IN THE CONTEXT OF INDUSTRY 4.0

Authors

  • Florina Livia COVACI Faculty of Economics and Business Administration, Babeș-Bolyai University, Romania, Teodor Mihali str 58-60, Email: florina.covaci@ubbcluj.ro https://orcid.org/0000-0003-1184-5992

DOI:

https://doi.org/10.2478/subboec-2019-0002

Keywords:

Forward Supply Chain, Reverse Supply Chain, Closed-Loop, Automated Supply Chain Formation, Belief Propagation

Abstract

The 4ᵗʰ industrial revolution brings in a transformation of the traditional supply chain towards a digital supply chain. The machines will be able to use algorithms that will enable them to automate the supply chain formation process and to quickly react to disruptions. The current approach proposes a mechanism based on a message passing inference scheme in order to address the automated supply chain formation problem in a closed-loop supply chain by integrating forward and reverse supply chains. Forward supply chain imply a series of activities required to produce new products from virgin materials and distribute them to consumers while reverse supply chains require collecting used products from consumers and reprocessing them to either recover their leftover market values or dispose of them. It has become common for companies involved in a forward supply chain to also carry out collection and reprocessing of used products. Strict environmental regulations and diminishing raw material resources have intensified the importance of reverse supply chains at an increasing rate. The proposed mechanism is evaluated using two type of supply chain configurations from textile and automobile industry, demonstrating that automated integration of reverse supply chains along with forward supply chains, lead to benefits for the participants in the supply chain.

JEL classification: C61

References

Aitken, J., Harrison, A. (2013). Supply governance structures for reverse logistics systems. International Journal of Operations & Production Management, 33(6), 745-764.

Bishop, C. (2006). Pattern recognition and machine learning. New York: Springer.

Brettel, M., Friederichsen, N., Keller, M., Rosenberg, M., 2014. How Virtualization, Decentralization and Network Building Change the Man-Ufacturing Landscape: An Industry 4.0 Perspective. International Journal of Mechanical, Industrial Science and Engineering, Volume 8, 37-44.

Cerquides, J., Endriss, U., Giovannucci, . A., Rodriguez-Aguilar, J. A. (2007). Bidding languages and winner determination for mixed multi-unit combinatorial auctions. s.l., IJCAI, Morgan Kaufmann Publishers Inc..

Chan, F., Chan, H., Jain, V. (2012). A framework of reverse logistics for the automobile industry. International Journal of Production Research, 50(5), 1318-1331.

Collins, J., Ketter, W., Gini, M., Mobasher, B. (2002). A multi-agent negotiation testbed for contracting tasks with temporal and precedence constraints. International Journal of Electronic Commerce, Vol. 7.

Cooper, M., Lambert, D., Pagh, J. D. (1997). Supply chain management: more than a new name for logistics. The International Journal of Logistics Management, 8(1), 1-9.

Demirel, E., Demirel, N., Gökçen , H. (2014). A mixed integer linear programming model to optimize reverse logistics activities of end-of-life vehicles in Turkey. Journal of Cleaner Production.

Fleischmann, M. (2001). Quantitative Models for Reverse Logistics. In: Lecture Notes in Economics and Mathematical Systems., p. 501.

Geissbauer, R., Vedsø, J., Schrauf, S. (2016). A Strategist’s Guide to Industry 4.0: Global businesses are about to integrate their operations into a seamless digital whole, and thereby change the world.. Strategy and Business.

Giovannucci, A., Cerquides, J., Rodríguez-Aguilar, J. (2010). Composing supply chains through multiunit combinatorial reverse auctions with transformability relationships among goods. IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans, 40(4), 767-778.

Giovannucci, A., Vinyals, M., Rodriguez-Aguilar, J., Cerquides, J. (2008). Computationally-efficient winner determination for mixed multi-unit combinatorial auctions. s.l., Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems, Vol. 2, 1071–1078.

Govindan, K., Soleimani, H., Kannan, D. (2015). Reverse logistics and closed-loop supply chain: A comprehensive review to explore the future, European Journal of Operational Research, Volume 240, Issue 3, 603-626

Grabara, J., Man, M., Kolcun, M. (2014). The benefits of reverse logistics. International Letters of Social and Humanistic Sciences, Vol. 15, 138-147.

Guide, V., Van Wassenhove, L. (2009). The evolution of closed-loop supply chain research. Operations Research, 57(1), 10-18.

Hahn, T. (2014). Future of Manufacturing - View on enabling technologies, s.l.: Siemens Corporate Technology.

Khan, S. A. R., Qianli, D., SongBo, W., Zaman, K., 2017. Environmental logistics performance indicators affecting per capita income and sectoral growth: evidence from a panel of selected global ranked logistics countries. Environmental Science and Pollution Research, 24(2), 1518-1531.

Khan, S. A. R., Zaman, K., Zhang, Y. (2016). The relationship between energy-resource depletion, climate change, health resources and the environmental Kuznets curve: Evidence from the panel of selected developed countries. Renewable and Sustainable Energy Review, Vol. 62, 468–477.

Kong, Y., Zhang M., Ye, D. (2017) A Belief Propagation-based Method for Task Allocation in Open and Dynamic Cloud Environments, Knowledge-Based Systems, vol. 115, 123-132.

Melo, M., Nickel, S., Saldanha-da-Gama, F. (2009). Facility location and supply chain management – A review. European Journal of Operational Research, 196(2), 401-412.

Mikhaylov, B., Cerquides, J., Rodriguez-Aguilar, J. (2011). Solving sequential mixed auctions with integer programming. Advances in Artificial Intelligence, Springer, 42-53.

Mooij, J. M. (2010). libDAI: A free and open source c++ library for discrete approximate inference in graphical models. Journal of Machine Learning Research, Volume 11, 2169–2173.

Parker, J., Farinelli A., Gini, M. (2017). Max-Sum for Allocation of Changing Cost Tasks, Intelligent Autonomous System. Advances in Intelligent Systems and Computing, vol. 531, 629-642.

Patroklos, P., Besoiu, M., 2010. Environmental and economical sustainability of WEEE closed-loop supply chains with recycling: a system dynamics analysis. The International Journal of Advanced Manufacturing Technology, 47(5-8), 475–493.

Penya-Alba, T., Vinyals, M., Cerquides, J., Rodriguez-Aquilar, J. (2012). A scalable Message-Passing Algorithm for Supply Chain Formation. s.l., 26th Conference on Artificial Intelligence.

Quariguasi Frota Neto, J., Walther, G., Bloemhof, J., van Nunen, J.A.E.E., Spengler, T. (2010). From closed-loop to sustainable supply chains:The WEEE case. International Journal of Production Research, 48(15), 4463-4481.

Ravi, V., Shankar, R. (2012). Evaluating alternatives in reverse logistics for automobile organisations. International, 12(1), 32-51.

Rubio, S., Parra, B. (2014). Reverse Logistics: Overview and Challenges for Supply Chain Management. International Journal of Engineering Business Management, vol. 6.

Snyder, L. V. (2006). Facility location under uncertainty: A review. IIE Transactions, 38(7), 537-554.

Tibben-Lembke, R., Rogers, D. (2002). Differences between forward and reverse logistics in a retail environment. Supply Chain Management: An International Journal, 7(5), 271-282.

Walsh , W. E., Wellman, M. P., Ygge, F. (2000). Combinatorial auctions for supply chain formation, Proceedings of the 2nd ACM conference on Electronic commerce.

Walsh, W., Wellman, M. P. (2003). Decentralized supply chain formation: A market protocol and competitive equilibrium analysis. Journal of Artificail Intelligence Research, Volume 19, 513-567.

Winsper, M., Chli, M. (2013). Decentralized supply chain formation using max-sum loopy belief propagation. Computational Intelligence, 29(2), 281-309.

Winsper, M., Chli, M. (2010). Decentralised supply chain formation: A belief propagation-based approach. Agent-Mediated Electronic Commerce.

Winsper, M., Chli, M. (2012). Using the max-sum algorithm for supply chain formation in dynamic multi-unit environments. s.l.: Proceedings of the 11th International Conference on Autonomous Agents and Multiagent Systems.

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Published

2019-04-30

How to Cite

COVACI, F. L. (2019). AUTOMATING FORWARD AND REVERSE SUPPLY CHAINS IN THE CONTEXT OF INDUSTRY 4.0. Studia Universitatis Babeș-Bolyai Oeconomica, 64(1), 19–32. https://doi.org/10.2478/subboec-2019-0002

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Articles