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Special issue: Data-driven decision making in supply chains

Abstract : Since regionalization of global economy is becoming a tendency, supply chains must currently face the competitive challenge of integrating global value-added networks with more local content. Actually, industrial policy is currently being used to provoque friction to global flows and “indigenize” production (Kearney, 2018), which represents a great challenge when it comes to improving supply chain fluidity (Cedillo-Campos, Lizarraga-Lizarraga, & Martner-Peyrelongue, 2017). Furthermore, with the growth of international trade, port operations have steadily grown in importance worldwide. However, expansion and sustainable growth demand modern, higher-capacity infrastructure, and environmental awareness (Schulte, González-Ramírez, Ascencio, & Voß, 2016). For this reason, several contributions in the literature are focused on proposals that can foster more sustainable operations based on collaborative schemes (Gonzalez-Feliu et al., 2014, Schulte et al., 2017). On the other hand, and considering the increasing number of elements related to global uncertainty such as political risk, but also the feasibility to produce some amounts in a cost-effective way, as well as the fact that customers now tend to prefer local products, industries, and cultures, we are now facing a new stage in which companies require more and better data to make supply chain decisions. There is a need for analytical methods that would be able to process great amount of data and generate support systems for decision making in different organizations (Maldonado, González-Ramírez, Quijada, & Ramírez-Nafarrate, 2019).
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https://hal-emse.ccsd.cnrs.fr/emse-02394871
Contributor : Florent Breuil <>
Submitted on : Thursday, December 5, 2019 - 9:40:15 AM
Last modification on : Wednesday, August 5, 2020 - 3:45:54 AM

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Miguel Gaston Cedillo-Campos, Rosa González-Ramírez, Christopher Mejía-Argueta, Jesus Gonzalez-Feliu. Special issue: Data-driven decision making in supply chains. Computers and Industrial Engineering, Elsevier, 2019, pp.106022. ⟨10.1016/j.cie.2019.106022⟩. ⟨emse-02394871⟩

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