Abstract
AbstractWhile the majority of literature on remanufacturing operations examines an end-of-life (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.
Highlights
There is an acceptance by researchers, academics, manufacturers and policymakers of the urgent need to transition from a linear model, which extracts resources and manufactures them into products that are disposed after use, into a circular model [14, 44, 47]
Using System Dynamics (SD), Discrete Event Simulation (DES) and Agent Based Modelling (ABM) and drawing from previous studies as published by the authors, we conduct a simulation modelling for a remanufacturable product as processed through a remanufacturing line
The SD modelling is developed based on assumptions driven by data while the DES and ABM assumptions is largely focused on data from product as well as the certainty of product quality concept, CPQ, which focuses on the way in which value in remanufacturing is quantified based on the amount of data that is available to provide information about the returned product
Summary
There is an acceptance by researchers, academics, manufacturers and policymakers of the urgent need to transition from a linear model, which extracts resources and manufactures them into products that are disposed after use, into a circular model [14, 44, 47]. Circular economy research has motivated the emergence of a new supply chain paradigm, the closedloop supply chain [47]. These closed-loop supply chain (CLSC) systems include material recovery processes such as remanufacturing, recycling, repairing and reusing [17, 20]. Lund defines remanufacturing as “an industrial process in which worn-out products are restored to like-new condition through a series of industrial processes in a factory environment. The United Nations Environmental Programme (UNEP) International Resource Panel (IRP) on the circular economy makes this link evident, highlighting remanufacturing as one of the key circular approach needed to redefine value for a sustainable manufacturing. Remanufacturing has been described as a value retention process, VRP [57]
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