Global supply chains risks and COVID-19: Supply chain structure as a mitigating strategy for small and medium-sized enterprises.

After the COVID-19 pandemic, more research is needed to understand how the impacts of global events differ among alternative network structures in the presence of supply chain risks, and how relevant these potential risk mitigation strategies are for Small and Medium Enterprises(SMEs). Thus, our main motivation is to show how SMEs can configure their supply chains, and cost-effectively mitigate the risk created by major disruptions. We combined a case study with a simulation model. The results suggest the greater usefulness of certain network configuration strategies (e.g., collaboration, multi-sourcing) compared to others during catastrophic events. Our results indicate that SMEs can avoid suffering more harm than larger competitors by adopting strategies consisting of an adequate mix of proactive and reactive elements, and that an effective proactive strategy involves building flexibility by increasing the number of geographically spread supply chain partners, allowing for deeper discounts to preserve demand without hurting profits.

Innovation and efficiency in financial institutions.

This paper proposes a new methodology that combines standard production theory with Multiple-Criteria Decision Analysis (MCDA) methods to rank banks based on their capability of using investment in new technologies to reduce the other inputs' usage, for a given level of output. Banks are first ranked based on their investment in innovation (innovation rank); afterwards, we calculate the overall rank by combining two factors of production, viz. labor and assets, using the PROMETHEE II approach that belongs to the family of the outranking methods. We then use directional efficiency measures to measure the banks' efficiency by means of relation between two ranks, for a given level of the outputs. We apply the methodology to a sample of US and EU banks sourced from Orbis BankFocus. The key findings suggest there are four types of banks in our sample: (a) banks whose innovation rank is positively correlated with the overall rank; (b) banks exhibiting a negative correlation between two ranks: their overall ranks are low while still exhibiting high innovation ranks; (c) banks with high overall rank but low innovation rank and (d) banks with the worst ranks both for the innovation rank and the overall rank. The least efficient banks belong to this group.