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Detalhes

Detalhes

002
Publicações

2022

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

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

Publicação
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

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

Publicação
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.

2022

Reducing fresh fish waste while ensuring availability: Demand forecast using censored data and machine learning

Autores
Miguéis, VL; Pereira, A; Pereira, J; Figueira, G;

Publicação
Journal of Cleaner Production

Abstract

2021

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

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

Publicação
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.

2021

Applying data mining techniques and analytic hierarchy process to the food industry: Estimating customer lifetime value

Autores
Carneiro, F; Miguéis, V;

Publicação
Proceedings of the International Conference on Industrial Engineering and Operations Management

Abstract
Customer segmentation is increasingly needed in a context where customer interests are vital for companies to survive. This study proposes the use of the weighted RFM (Recency, Frequency, Monetary) supported by data mining techniques and the Analytic Hierarchy Process (AHP), to classify the customers according to their lifetime value (CLV). The customer segments obtained can be used to boost marketing strategies, as these segments enable to differentiate the customers. Each segment of customers is described by a set of rules based on the customers’ purchasing patterns. The methodology developed is validated by using a real case study, i.e. a food industry company, whose core business is the production of biscuits. © IEOM Society International.

Teses
supervisionadas

2021

Service Design-for-X: a framework to evaluate sustainability performance of services.

Autor
Marcelo Macedo Sousa

Instituição
UP-FEUP

2021

Structural Health Monitoring: A machine learning approach

Autor
Manuel Lobo Fernandes de Castro Mota

Instituição
UP-FEUP

2021

Avaliação de Cibersegurança em Infraestruturas Críticas

Autor
Joana Isabel Ferreira Miranda

Instituição
UTAD

2021

Design, planning and evaluation of two-tier distribution systems in the context of City Logistics

Autor
Bruno Miguel Craveiro de Oliveira

Instituição
UP-FEUP

2021

Retail Product Matching

Autor
Francisco Teixeira Ferreira

Instituição
UP-FEUP