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Rodríguez Aguilar, Román
Structural Dynamics and disruption events in Supply Chains using Fat Tail Distributions
The supply chain event management application: a case study
2019
,
Rodríguez Aguilar, Román
,
Marmolejo Saucedo, José Antonio
The analysis of structural dynamics in a supply chain requires robust methods for the modeling of disruption events that can be faced. Statistical modeling, the machine learning application and access to large amounts of data require much more realistic models to manage risk in the supply chain. This study proposes a statistical methodology for the modeling of disruption events in the supply chain with heavy tailed distributions, which will allow the construction of models more closely linked to reality for risk management in the supply chain. © 2019, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.
2019
,
Palma, Fanny
,
Saucedo, Jania A.
,
Marmolejo Saucedo, José Antonio
,
Rodríguez Aguilar, Román
A deviation from planned processes reveals the consequences that a disruption can cause in any area, even more a disruption with Ripple Effect (RE), which implied for the global Supply Chain (SC) when depends essentially from the stakeholders in all levels and chains. The learnt lessons from these tragical events show that many companies could not assess the impact and its side effects, therefore they cannot respond adequately, prolonging the crisis and expanding the disruption. A disruption evaluation model would allow to know from the beginning the potential impact to check carefully into the critical events to provide the necessary resources to control it. Hence, it is relevant that SC managers know and employ the right tools to focus on the control and the solution; as well to evaluate the impact and the disruption criticality level to mitigate and control the impact with total certainty that the appropriate actions are being taken according to the problematic. © 2019, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.