Affiliations: [a] Alenia Aermacchi SpA, Venegono Superiore (VA), Italy | [b] Dipartimento di Ingegneria dell'Informazione: Elettronica, Informatica, Telecomunicazioni, University of Pisa, Pisa, Italy
Correspondence:
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Corresponding author: Beatrice Lazzerini, Dipartimento di Ingegneria dell'Informazione: Elettronica, Informatica, Telecomunicazioni, University of Pisa, Largo Lucio Lazzarino 1, 56122 Pisa, Italy. E-mail: b.lazzerini@iet.unipi.it
Abstract: In this paper we present a methodology for risk identification and evaluation in production processes. Our goal is twofold: on the one hand, we aim to allow risk analysis in a context that is clearly and formally described so as to help identify risks in a structured manner, on the other hand, we want to provide better tolerance and control of the ambiguity and vagueness that are often involved in subjective risk evaluation. To meet the first goal we adopt two different standard graphic notations, namely, the Business Process Model and Notation (BPMN) and the Fuzzy Cognitive Maps (FCMs), to represent, respectively, the production process flow, and the cause-effect relationships between risks and their causes. In particular, this allows us to represent the whole set of risks related to a given production process as a graph whose nodes identify risks/causes and directed edges stand for a cause-effect relation between a risk and one of its causes. This graph helps the risk analyst better understand how risks can influence each other and, consequently, make more effective decisions. Finally, we explicitly associate each risk with the part/activity of the production process in which that risk might be generated, thus making risk management easier. To meet the second goal we resort to fuzzy systems. Particularly, we make use of two fuzzy systems: the first fuzzy system computes the risk impact as a function of time impact, cost impact and performance impact; the second fuzzy system infers the risk priority index from risk impact and risk probability. The application of the proposed methodology to an example of production process is also shown and discussed.
Keywords: Business process modeling, fuzzy cognitive maps, fuzzy systems, production processes, risk analysis
