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002
Publications

2022

Understanding Overlap in Automatic Root Cause Analysis in Manufacturing Using Causal Inference

Authors
Oliveira, EE; Migueis, VL; Borges, JL;

Publication
IEEE ACCESS

Abstract
Overlap has been identified in previous works as a significant obstacle to automated diagnosis using data mining algorithms, since it makes it impossible to discern how each machine influences product quality. Several solutions that handle overlap have been proposed, but the final result is a list of potential overlapped root causes. The goal of this paper is to develop a solution resilient to overlap that can determine the true root cause from a list of possible root causes, when possible, and determine the conditions in which it is possible to identify the root causes. This allows for a better understanding of overlap, and enables the development of a fully automatic root cause analysis for manufacturing. To do so, we propose an automatic root cause analysis approach that uses causal inference and do calculus to determine the true root cause. The proposed approach was validated on simulated and real case-study data, and allowed for an estimation of the effect of a product passing through a certain machine while disregarding the effect of overlap, in certain conditions. The results were on par with the state-of-the-art solutions capable of handling overlap. The contributions of this paper are a graphical definition of overlap, the identification of the conditions in which is possible to overcome the effect of overlap, and a solution that can present a single true root cause when such conditions are met.

2022

Automatic root cause analysis in manufacturing: an overview & conceptualization

Authors
e Oliveira E.; Miguéis V.L.; Borges J.L.;

Publication
Journal of Intelligent Manufacturing

Abstract
Root cause analysis (RCA) is the process through which we find the true cause of a problem. It is a crucial process in manufacturing, as only after finding the root cause and addressing it, it is possible to improve the manufacturing operation. However, this is a very time-consuming process, especially if the amount of data about the manufacturing operation is considerable. With the increase in automation and the advent of Industry 4.0, sensorization of manufacturing environments has expanded, increasing with it the data available. The conjuncture described gives rise to the challenge and the opportunity of automatizing root cause analysis (at least partially), making this process more efficient, using tools from data mining and machine learning to help the analyst find the root cause of a problem. This paper presents an overview of the literature that has been published in the last 17 years on developing automatic root cause analysis (ARCA) solutions in manufacturing. The literature on the topic is disperse and it is currently lacking a connecting thread. As such, this study analyzes how previous studies developed the different elements of an ARCA solution for manufacturing: the types of data used, the methodologies, and the evaluation measures of the methods proposed. The proposed conceptualization establishes the base on which future studies on ARCA can develop results from this analysis, identifying gaps in the literature and future research opportunities.

2021

On the influence of overlap in automatic root cause analysis in manufacturing

Authors
Oliveira, EE; Migueis, VL; Borges, JL;

Publication
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH

Abstract
To improve manufacturing processes, it is essential to find the root causes of occurring problems, in order to solve them permanently. Automatic Root Cause Analysis (ARCA) solutions aid analysts in finding such root causes, by using automatic data analysis to improve the digital decision. When trying to locate the root cause of a problem in a manufacturing process, a phenomenon can occur that disrupts the application of ARCA solutions. Overlap, as we denominated, is a phenomenon where local synchronicities in the manufacturing process lead to data where it is impossible to discern the influence of each location in the quality of products, which impedes automated diagnosis, especially when using classifiers. This paper identifies and defines overlap, and proposes a two-phase ARCA solution that uses factor-ranking algorithms, instead of classifiers. The proposed solution is evaluated in simulated and real case-study data. Results proved the presence of overlap in the datasets, and its negative impact on classifiers. The proposed solution has a positive performance detecting root causes even in the presence of overlap.

2020

Ten Years Exploring Service Science: Looking Back to Move Forward

Authors
Teixeira, JG; Migueis, V; Ferreira, MC; Novoa, H; Cunha, JFE;

Publication
Lecture Notes in Business Information Processing

Abstract
In celebration of the 10th anniversary of the International Conference on Exploring Service Science (IESS), this paper takes a historical look at the papers that have been published in the IESS proceedings. The analysis is focused on the development and evolution of the IESS community and of the main research topics covered by the published papers over time. The IESS community is portrayed in terms of authors, their affiliations and co-authoring network, while the topics are analyzed according to the papers’ keywords. Moreover, this paper analyzes the impact of the papers published in this decade, in terms of citations. These results are then discussed in light of the observed trends and of the evolution of the service science field, to guide the future development of the IESS conference and of research on service science. © Springer Nature Switzerland AG 2020.

2020

Prediction of academic dropout in a higher education institution using data mining [Previsão do abandono académico numa instituição de ensino superior com recurso a data mining]

Authors
Martins, MPG; Migueis, VL; Fonseca, DSB; Gouveia, PDF;

Publication
RISTI - Revista Iberica de Sistemas e Tecnologias de Informacao

Abstract
This study proposes two predictive models of classification that allow to identify, at the end of the 1st and 2nd semesters, the undergraduate students of a higher education institution more prone to academic dropout. The proposed methodology, which combines 3 popular data mining algorithms, such as random forest, support vector machines and artificial neural networks, in addition to contributing to predictive performance, allows to identify the main factors behind academic dropout. The empirical results show that it is possible to reduce to about 1/4 the 4 tens potential predictors of dropout, and show that there are essentially two predictors, concerning student’s curriculum context, that explain this propensity. This knowledge is useful for decision-makers to adopt the most appropriate strategic measures and decisions in order to reduce student dropout rates.

Supervised
thesis

2021

Churn Prediction in SaaS CAE Industry

Author
João Paulo Sousa Morais

Institution
UP-FEUP

2021

Use of data mining techniques to support taxi vehicles operations

Author
Ana Luísa Correia Dias Loureiro

Institution
UP-FEUP

2021

Retail Product Matching

Author
Francisco Teixeira Ferreira

Institution
UP-FEUP

2021

Using machine learning and optimization models to support customer lifecycle management in financial services

Author
Hermano Emanuel Rodrigues Maia

Institution
UP-FEUP

2021

Tecnologias Digitais na Educação Básica - A utilização de TIC para fins educativos e o seu impacto na performance dos estudantes

Author
João Pedro Soares Coelho da Silva

Institution
UP-FEP