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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

035
Publications

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.

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.

2021

Process Thinking in Engineering Education

Authors
Azevedo, A;

Publication
PROCEEDINGS OF THE 2021 IEEE GLOBAL ENGINEERING EDUCATION CONFERENCE (EDUCON)

Abstract

2021

Innovative Learning Scheme to Up-skilling and Re-skilling - Designing a Collaborative Training Program Between Industry and Academia Towards Digital Transformation

Authors
Simoes, AC; Ferreira, F; Almeida, A; Zimmermann, R; Castro, H; Azevedo, A;

Publication
SMART AND SUSTAINABLE COLLABORATIVE NETWORKS 4.0 (PRO-VE 2021)

Abstract
Small and medium-sized enterprises (SMEs) in Europe are conscious that their competitive position depends on their success to embrace digitalisation challenges. However, some decision-makers in companies discard digital transformation because they do not understand how it can be incorporated into their businesses. Therefore, academia, research centres, and technological clusters are responsible for building the infrastructures and providing the support and the training that will progressively change this mindset. This paper aims to report an experience on designing a training program to train the trainers under the digital transformation topic. To define strategies to understand better the companies (and professionals) needs and motivations and the requisites to deliver the training course, the focus group methodology was applied. In this paper, we present a training program methodology and structure that intend to respond to industrial requests and, in this way to accelerate the digital transformation of companies, especially SMEs. © 2021, IFIP International Federation for Information Processing.

2021

Resilience in industry 4.0 digital infrastructures and platforms

Authors
Ribeiro D.; Almeida A.; Azevedo A.; Ferreira F.;

Publication
Advances in Transdisciplinary Engineering

Abstract
We live in a world where companies are shifting to the industry 4.0 paradigm. One of the pillars of Industry 4.0 is the digitalization of physical assets and manufacturing processes, moving toward the Cyber-Physical Production Systems concept (CPPS). In these systems, every component of the production process - machines, tools, workstations, etc. - is equipped with sensors, possesses information about itself, and can interact with each other, allowing the production of smaller batches at lower prices and increase product customization through adaptative processes. Consequently, companies are evolving their information systems to have more visibility and control over their production systems. This change increases both the production system's agility and its vulnerability to communication and information related disruptions. Hence, companies that adhere to Industry 4.0 enabling technologies must adopt new methodologies and tools to become aware of the new risks that arise by the introduction of new digital platforms, their impacts in the production systems, and how they may react to remain resilient. In this paper, disruption events and adequate mitigation strategies are analysed, modelled, and simulated as part of a methodology designed to measure the impacts of disruptive events on the production system. © 2021 The authors and IOS Press.

Supervised
thesis

2021

Formalization of Deep Learning Techniques with the Why3 Proof Platform

Author
Márcio Alexandre Mota Sousa

Institution
UM

2021

Subgraph Patterns in Colored Networks

Author
Beatriz Maria Franco Pinto

Institution
UP-FCUP

2021

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

Author
André Filipe da Silva Ramos

Institution
UP-FEUP

2021

As tecnologias IoT no contexto de formação profissional em ambiente de e-Learning

Author
Carlos Augusto Dourado Soeiro

Institution
UAB

2021

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

Author
Leonor Cid Meneses

Institution
UP-FEUP