Nous proposons, dans le cadre cette thése de recherche, une nouvelle méthode pour l’évaluation dynamique des états du risque dans les flux opérationnels d’un système logistique que nous avons intilulé KRIs Cost based-FMEA. Pour être compétitives, les chaînes logistiques doivent être capables d’analyser et d’évaluer en temps réel les écarts critiques entre les actions issues de la gestion prévisionnelle à court terme et les actions réellement exécutées engendrant des états de risque indésirables ou inacceptables. Our initial pilot results reveal that it is possible to drive the number of change failures down significantly using our novel Risk Engine. We describe the recipe for creating the Risk Engine along with several user studies we have conducted to justify our design choices at each step. We present a novel Risk Engine, which takes into account a rich, dynamic change context to calculate and mitigate the risk of a service related change in real-time with increased accuracy and reliability. Such deficiencies of the standard practice to assess risk of a change may, thus, result in inaccurate assessment, which can lead to unmitigated risks, ultimately materializing as failures.
Further, in the questionnaire method, a fixed set of questions implies that the change context is assumed to be the same regardless of the type of change being raised, whereas in practice, no two changes are truly identical. Assessing the risk of a change, thus, relies heavily on one person’s opinion. Today, the risk of a service related change is typically assessed at change record creation time by a Change Requester either manually or through answering a fixed set of questions.
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