Preprint Article Version 1 This version is not peer-reviewed

A Systems Dynamics Enabled Real-Time Efficiency for Fuel Cell Data-Driven Remanufacturing

Version 1 : Received: 31 August 2018 / Approved: 31 August 2018 / Online: 31 August 2018 (05:31:03 CEST)

A peer-reviewed article of this Preprint also exists.

Okorie, O.; Salonitis, K.; Charnley, F.; Turner, C. A Systems Dynamics Enabled Real-Time Efficiency for Fuel Cell Data-Driven Remanufacturing. J. Manuf. Mater. Process. 2018, 2, 77. Okorie, O.; Salonitis, K.; Charnley, F.; Turner, C. A Systems Dynamics Enabled Real-Time Efficiency for Fuel Cell Data-Driven Remanufacturing. J. Manuf. Mater. Process. 2018, 2, 77.

Journal reference: J. Manuf. Mater. Process. 2018, 2, 77
DOI: 10.3390/jmmp2040077

Abstract

Remanufacturing is a viable option to extend the useful life of an end-of-use product or its parts, ensuring sustainable competitive advantages under the current global economic climate. Challenges typical to remanufacturing still persist, despite its many benefits. According to the European Remanufacturing Network a key challenge is lack of accurate, timely and consistent product knowledge as highlighted in a 2015 survey of 188 European remanufacturers. With more data being produced by electric and hybrid vehicles, this adds to the information complexity challenge already experienced in remanufacturing. Therefore, it is difficult to implement real-time and accurate remanufacturing for the shop floor; there are no papers that focus on this within an electric and hybrid vehicle environment. To address this problem this paper attempts to (1) identify the required parameters/ variables needed for fuel cell remanufacturing by means of interviews (2) rank the variables by Pareto analysis (3) develop a casual loop diagram for the identified parameters/ variables to visualise its impact on remanufacturing (4) model a simple stock and flow diagram to simulate and understand data and information-driven schemes in remanufacturing.

Subject Areas

circular economy; remanufacturing; fuel cells; data-driven; systems dynamics

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