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About

About

Américo Azevedo - [PhD], he is head of CESE Centre for Enterprise Systems Engineering and Cientific Director of FABTEC Laboratory of Processes and Technologies for Production Advanced Systems

He is an Associate Professor with Aggregation in the Department of Industrial Engineering and Management at Faculty of Engineering of University of Porto (FEUP). He has gained large experience in the academic, industrial and consultancy environments.

He teaches in the academic programmes of FEUP and PBS (Porto Business School) and in specific programmes such as EDAM (Engineering Design and Advanced Manufacturing) of the MIT-Portugal Program.

His research and teaching focuses on operations management, business processes management and enterprise collaborative networks. He has been active in supervising PhD and M.Sc research thesis on this research areas.

He has been author of many articles in international journals and technical publications and also active in preparing and participating in R&D projects involving industrial companies. He has been reviewer and evaluator of several international R&D Industrial projects and member of several scientific programmes committees.

Responsible for leading more than 45 company based national and international R&D and consulting projects in the domain of enterprise networks and industrial and operations management. He has been responsible in several consulting assignments with industrial companies, with special emphasis in operations and industrial management as well as in designing and developing new facilities, process optimization and development and implementation of decision support and planning tools for order management. Experience in several sectors/industries: machinery, semiconductors, ceramics, furniture, packaging, shoes and cork processing.

 

Interest
Topics
Details

Details

019
Publications

2023

Scalable Digital Twins for industry 4.0 digital services: a dataspaces approach

Authors
Moreno, T; Almeida, A; Toscano, C; Ferreira, F; Azevedo, A;

Publication
PRODUCTION AND MANUFACTURING RESEARCH-AN OPEN ACCESS JOURNAL

Abstract

2023

A Digital Twin Platform-Based Approach to Product Lifecycle Management: Towards a Transformer 4.0

Authors
Silva H.; Moreno T.; Almeida A.; Soares A.L.; Azevedo A.;

Publication
Lecture Notes in Mechanical Engineering

Abstract

2022

Self-adapting WIP parameter setting using deep reinforcement learning

Authors
Silva, MTDE; Azevedo, A;

Publication
COMPUTERS & OPERATIONS RESEARCH

Abstract
This study investigates the potential of dynamically adjusting WIP cap levels to maximize the throughput (TH) performance and minimize work in process (WIP), according to real-time system state arising from process variability associated with low volume and high-variety production systems. Using an innovative approach based on state-of-the-art deep reinforcement learning (proximal policy optimization algorithm), we attain WIP reductions of up to 50% and 30%, with practically no losses in throughput, against pure-push systems and the statistical throughput control method (STC), respectively. An exploratory study based on simulation experiments was performed to provide support to our research. The reinforcement learning agent's performance was shown to be robust to variability changes within the production systems.

2022

Unveiling undergraduate production engineering students’ comprehension of process flow measures

Authors
Torres, N; Jr; de Azevedo, AL; Simões, AC; Ladeira, MB; de Sousa, PR; de Freitas, LS;

Publication
Production

Abstract
Paper aims: This study analyzes the comprehension of production engineering students about the influence of some key variables on the process performance measures in a service process, Originality: This paper points out the need for educators to re-evaluate their approaches to teaching the Operations Management (OM) principles related to process flow measures, Research method: This study used scenario-based role-playing experiments with 2×2×2 between-subject factorial design with three independent variables (variability of activities, capacity utilization, and resource pooling) and four dependent variables related to key internal process performance measures (Flow Time, Overall Quality of service, Quality of service employees, and Queue Size), The sample was composed of 178 undergraduate production engineering students from a large university in Brazil from various institution units, Main findings: These results show that students perceived the use of resource pooling as an impactful practice, However, the students did not correctly identify the effects of increasing resource utilization and the variability on flow time and queue size when activities are pooled, Implications for theory and practice: The teaching of basic concepts of OM requires the support of computational tools, Undergraduate courses that contemplate subjects in the field of OM should work more intensely on simulation-based learning. © This is an Open Access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

2021

Grasp the Challenge of Digital Transition in SMEs—A Training Course Geared towards Decision-Makers

Authors
Azevedo, A; Almeida, AH;

Publication
EDUCATION SCIENCES

Abstract
Small and medium-sized enterprises (SMEs) in Europe risk their competitiveness if they fail to embrace digitalization. Indeed, SMEs are aware of the need to digitalize—more than one in two SMEs are concerned that they may lose competitiveness if they do not adopt new digital technologies. However, a key obstacle is related with decision-makers’ lack of awareness concerning digital technologies potential and implications. Some decision-makers renounce digital transition simply because they do not understand how it can be incorporated into the business. Take into account this common reality, especially among SMEs, this research project intends to identify the skills and subjects that need to be addressed and suggests the educational methodology and implementation strategy capable of maximizing its success. Therefore, and supported by a focused group research methodology, an innovative training program, oriented to decision-makers, was designed and implemented. The program was conceived based on a self-directed learning methodology, combining both asynchronous lecture/expositive and active training methodologies, strongly based on state-of-the-art knowledge and supported by reference cases and real applications. It is intended that the trainees/participants become familiar with a comprehensive set of concepts, principles, methodologies, and tools, capable of significantly enhancing decision-making capability at both strategic and tactical level. The proposed programme with a multidisciplinary scope explores different thematic chapters (self-contained) as well as cross-cutting thematic disciplines, oriented to the Industry 4.0 and digital transformation paradigm. Topics related with Digital Maturity Assessment, Smart Factories and Flexible Production Systems, Big Data, and Artificial Intelligence for Smarter Decision-Making in Industry and Smart Materials and Products, as well as new production processes for new business models. Each thematic chapter in turn is structured around a variable set of elementary modules and includes examples and case studies to illustrate the selected topics. A teaching-learning methodology centered on an online platform is proposed, having as a central element, a collection of videos complemented by a set of handouts that organize the set of key messages and take-ways associated with each module. In this paper, we present the design and practice of this training course specifically oriented to decision-makers in SME.

Supervised
thesis

2022

A Value-Oriented Framework for Return Evaluation of Digital Business Transformation in SMEs

Author
Alexander Dutra Tostes

Institution
UP-FEUP

2022

Deep Reinforcement Learning for Production Flow Control

Author
Manuel Tomé de Andrade e Silva

Institution
UP-FEUP

2022

Digital Twin for Manufacturing Equipment in Industry 4.0

Author
Tomás Miguel Antero Moreno

Institution
UP-FEUP

2021

Analysis and Assessment of Sellers' Operational Performance in an E-Marketplace

Author
André Filipe da Silva Ramos

Institution
UP-FEUP

2021

Strengthening a public Hospital’s internal Logistics system through Lean and optimization techniques

Author
Leonor Cid Meneses

Institution
UP-FEUP