Analysis of impact of uncertainty in global production networks’ parameters

As production networks grow globally, their complexity and susceptibility to risk are increasing as well. Due to internal and external factors, risks affect individual network nodes and their impact propagates through the network to affect other nodes. A Fuzzy Dynamic Inoperability Input/output Model (FDIIM) is developed to facilitate and analyse the risk and its propagation in global production networks (GPN), at the strategic level. This method applies fuzzy arithmetic to track and operate with uncertainty in GPN parameters and to estimate the confidence in the results obtained. The expert provides a judgement on relevant risk parameters’ values in the form of linguistic values, where relevant statistical data is absent. We used the measure of ambiguity to measure uncertainty in the GPN parameters. Two types of analyses are carried out: (1) to examine the sensitivity of the FDIIM to changes in input parameter values, including interdependencies between GPN nodes, resilience of the GPN, intended revenues and impact of disruptions, and (2) to examine sensitivity to uncertainty in the GPN’s input parameters. A generic GPN example and different risk scenarios are defined to illustrate these analyses. The analyses provide an insight into the importance of different GPN’s parameters in the risk analysis. Furthermore, we demonstrated how to identify GPN parameters which are important to specify with less uncertainty. Publisher Statement: NOTICE: this is the author’s version of a work that was accepted for publication in Computers & Industrial Engineering. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Computers & Industrial Engineering, [111, (2017)] DOI: 10.1016/j.cie.2017.07.011 © 2017, Elsevier. Licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International http://creativecommons.org/licenses/by-nc-nd/4.0/