Simulation-Based Analysis of (Reverse) Supply Chains in Circular Product-Service-Systems

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Grosse Erdmann, Julian ; Koller, Jan ; Amir, Saman ; Mihelič, Aleš ; Döpper, Frank:
Simulation-Based Analysis of (Reverse) Supply Chains in Circular Product-Service-Systems.
In: Kohl, Holger ; Seliger, Günther ; Dietrich, Franz (Hrsg.): Manufacturing Driving Circular Economy : Proceedings. - Cham : Springer , 2023 . - S. 111-118 . - (Lecture Notes in Mechanical Engineering )
ISBN 978-3-031-28838-8
DOI: https://doi.org/10.1007/978-3-031-28839-5_13

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Abstract

With an expected growth of global waste to 3.40 billion tonnes by 2050 and a circularity today of only 8.6% of the world, the earth’s sustainable resources are being exploited beyond their regeneration capacity. Hence, it is necessary to step away from a take – make – dispose principal and transform from a linear towards a circular economy to close product cycles to optimize resource consumption and reduce waste. Product-Service-Systems (PSSs), based on multiple product life cycles combined with remanufacturing, offer a solution to close product cycles. In such PSS, the responsibility for returning, remanufacturing, and repairing used products remains with the Original Equipment Manufacturer (OEM) and increases its need in (reverse) supply chain activities. Essential factors for (reverse) supply chains are, e.g., determining the distribution network, the location of recovery facilities, the geographical dispersion of the customers, and the information flows between the different stakeholders. In this context, this work proposes a multi-method simulation model to support practitioners in determining the optimal infrastructure for storing, remanufacturing, and repairing the used products regarding economic and ecological target criteria. The applicability of the proposed approach is illustrated through a case study of a white goods manufacturing company. This case study highlights the importance of determining the optimal infrastructure in a (reverse) supply chain in PSS business models.