Bruna Alexandra Elias Mota

Assistant Professor

Instituto Superior Técnico

ORCID CIÊNCIA VITAE ResearcherID Scopus GScholar

Bruna Mota is an Assistant Professor at the Engineering and Management Department at Instituto Superior Técnico – University of Lisbon. She received her Master’s degree in Biomedical Engineering from Instituto Superior Técnico and her PhD in Leaders for Technical Industries (from the MITPortugal program) also from Instituto Superior Técnico. Bruna’s research focuses mainly on modelling and solving real-world problems related with sustainable supply chain design and planning.

Publications

Design and Planning of Socially Responsible Pharmaceutical Supply Chains Considering Procurement Strategies

Computer Aided Chemical Engineering | 2024

I. Duarte

B. Mota

T. Pinto-Varela

A. Amaro

A.P. Barbosa-Povoa

Modelling availability and affordability concerns in the design and planning of pharmaceutical supply chains

Computer Aided Chemical Engineering | 2023

I. Duarte

B. Mota

T. Pinto-Varela

A. Amaro

A.P. Barbosa-Povoa

Design of a Corporate Training Academy: The Case Study of a Pharmaceutical Company

Springer Proceedings in Mathematics and Statistics | 2023

P.A. Marques

L. Ramos

B. Mota

Pharmaceutical Industry Supply Chains: Planning Vaccines’ Distribution

Computer Aided Chemical Engineering | 2022

I. Duarte

B. Mota

A.P. Barbosa-Póvoa

Pharmaceutical industry supply chains: How to sustainably improve access to vaccines?

Chemical Engineering Research and Design | 2022

I. Duarte

B. Mota

T. Pinto-Varela

A.P. Barbosa-Povoa

Research Groups

OpLog – Operations, Logistics and Supply Chain Management

OpLog branch of CEGIST conducts multidisciplinary research driven by the challenges faced in industry and services organizations regarding the management of their operations, logistics and overall supply chain.

Projects

mSSChain - Managing Sustainable Supply Chains Sustainable Supply Chains

The m-SSChain project address the research questions how to design, plan and operate Sustainable Supply Chain (SSC) under an uncertain environment. Within this scope, the project aims to develop comprehensive and innovative general models for the design, planning and operation of resilient SSC under uncertainty. These models will be validated using real industrial cases.