K-means clustering combined with principal component analysis for material profiling in automotive supply chains

At a time where available data is rapidly increasing in both volume and variety, descrip- tive Data Mining (DM) can be an important tool to support meaningful decision-making processes in dynamic Supply Chain (SC) contexts. Up until now, however, scarce attention has been given to the application of DM techniques in the field of inventory management. Here, we take advantage of descriptive DM to detect and grasp important patterns among several features that coexist in a real-world automotive electronics SC. Concretely, Principal Component Analysis (PCA) is employed to analyze and understand the interrelations between ten quantitative and dependent variables in a multi-item/multi-supplier environment. Afterwards, the principal component scores are character- ized via a K-means clustering, allowing us to classify the samples into four clusters and to derive di↵erent profiles for the multiple inventory items. This work provides evidence that descriptive DM contributes to find interesting feature-patterns, resulting in the identification of important risk profiles that may e↵ectively leverage inventory management for superior performance.This work has been supported by FCT - Fundacao para a Ciencia e Tecnologia within the R&D Units Project Scope: UIDB/00319/2020. The authors want to extend grateful thanks to the editors and reviewers, whose comments have greatly improved the quality of the paper

A multivariate approach for multi-step demand forecasting in assembly industries: Empirical evidence from an automotive supply chain

PreprintDemand forecasting works as a basis for operating, business and production planning decisions in many supply chain contexts. Yet, how to accurately predict the manufacturer's demand for components in the presence of end-customer demand uncertainty remains poorly understood. Assigning the proper order quantities of components to suppliers thus becomes a nontrivial task, with a significant impact on planning, capacity and inventory-related costs. This paper introduces a multivariate approach to predict manufacturer's demand for components throughout multiple forecast horizons using different leading indicators of demand shifts. We compare the autoregressive integrated moving average model with exogenous inputs (ARIMAX) with Machine Learning (ML) models. Using a real case study, we empirically evaluate the forecasting and supply chain performance of the multivariate regression models over the component's life-cycle. The experiments show that the proposed multivariate approach provides superior forecasting and inventory performance compared with traditional univariate benchmarks. Moreover, it reveals applicable throughout the component's life-cycle, not just to a single stage. Particularly, we found that demand signals at the beginning of the life-cycle are predicted better by the ARIMAX model, but it is outperformed by ML-based models in later life-cycle stages.INCT-EN - Instituto Nacional de Ciência e Tecnologia para Excitotoxicidade e Neuroproteção(UIDB/00319/2020

Deep dense and convolutional autoencoders for machine acoustic anomaly detection

Recently, there have been advances in using unsupervised learning methods for Acoustic Anomaly Detection (AAD). In this paper, we propose an improved version of two deep AutoEncoders (AE) for unsupervised AAD for six types of working machines, namely Dense and Convolutional AEs. A large set of computational experiments was held, showing that the two proposed deep autoencoders, when combined with a mel-spectrogram sound preprocessing, are quite competitive and outperform a recently proposed AE baseline. Overall, a high-quality class discrimination level was achieved, ranging from 72% to 92%.European Structural and Investment Funds in the FEDER component, through the Operational Competitiveness and Internationalization Programme (COMPETE 2020) - Project n ∘ 039334; Funding Reference: POCI-01-0247-FEDER-039334

Quantification of the hipoeutectic withe cast iron microstructure from images using mathematical morphology and an artificial neural network

This work presents a comparative analysis between two automatic methods to segment and quantify the microstructure of white cast iron from images. The comparative methods are the SVRNA (Microstructure Segmentation by Computational Vision using Artificial Neural Networks), developed during this work, and the Image Pro-Plus, a common tool used for material microstructure analysis.In our SVRNA system, mathematical morphology algorithms are used to segment the microstructure elements of the white cast iron, which are then identified and quantified by an artificial neural network. The development of a new computational system was necessary because the usual commercial software, like the Image Pro-Plus, does not segment correctly the microstructure elements of this cast iron, which are: cementite, pearlite and ledeburite.To validate our SVRNA system, 30 samples of white cast iron were analyzed. The results obtained are very similar to the ones accomplished by visual examination. In fact, the microstructure elements of the material in analysis were correctly segmented and quantified by our SVRNA system, what did not happened when we used the Image Pro-Plus system. Therefore, the proposed system, based on mathematical morphology operators and an artificial neural network, offers to researchers, engineers, specialists and others of the Material Sciences field, a valuable and adequate tool for automatic and efficient microstructural analysis from images

Uma abordagem computacional para segmentação das microestruturas do ferro fundido branco hipoeutético baseado em morfologia matemática

Este trabalho apresenta uma abordagem computacional para classicação automática das microestruturas de um ferro fundido branco hipoeutético usando morfologia matemática binária. Tal abordagem assume especial importância porque os softwares comerciais não segmentam corretamente essas microestruturas, que são: cementita, perlita e ledeburita. Para validar o algoritmo automático de segmentação proposto neste trabalho, são analisadas 30 amostras de ferro fundido branco hipoeutético, sendo binarizadas através de um limiar automático obtido usando o menor número de pixel em um histograma. Os resultados obtidos são semelhantes aos da examinação visual humana, segmentando ecientemente a cementita, perlita e ledeburita separadamente, diferentemente dos sistemas comerciais, que classicam a perlita e a ledeburita com sendo uma única microestrutura. Portanto, a abordagem computacional proposto neste trabalho, baseada nas técnicas da morfologia matemática com operações binárias, oferece aos estudantes, engenheiros, especialistas e outros da área das Ciências dos Materiais mais uma opção para uma análise microestrutural

A framework for increasing the value of predictive data-driven models by enriching problem domain characterization with novel features

The need to leverage knowledge through data mining has driven enterprises in a demand for more data. However, there is a gap between the availability of data and the application of extracted knowledge for improving decision support. In fact, more data do not necessarily imply better predictive data-driven marketing models, since it is often the case that the problem domain requires a deeper characterization. Aiming at such characterization, we propose a framework drawn on three feature selection strategies, where the goal is to unveil novel features that can effectively increase the value of data by providing a richer characterization of the problem domain. Such strategies involve encompassing context (e.g., social and economic variables), evaluating past history, and disaggregate the main problem into smaller but interesting subproblems. The framework is evaluated through an empirical analysis for a real bank telemarketing application, with the results proving the benefits of such approach, as the area under the receiver operating characteristic curve increased with each stage, improving previous model in terms of predictive performance.The work of P. Cortez was supported by FCT within the Project Scope UID/CEC/00319/2013. The authors would like to thank the anonymous reviewers for their helpful comments.info:eu-repo/semantics/publishedVersio

Advancing logistics 4.0 with the implementation of a big data warehouse: a demonstration case for the automotive industry

The constant advancements in Information Technology have been the main driver of the Big Data concept’s success. With it, new concepts such as Industry 4.0 and Logistics 4.0 are arising. Due to the increase in data volume, velocity, and variety, organizations are now looking to their data analytics infrastructures and searching for approaches to improve their decision-making capabilities, in order to enhance their results using new approaches such as Big Data and Machine Learning. The implementation of a Big Data Warehouse can be the first step to improve the organizations’ data analysis infrastructure and start retrieving value from the usage of Big Data technologies. Moving to Big Data technologies can provide several opportunities for organizations, such as the capability of analyzing an enormous quantity of data from different data sources in an efficient way. However, at the same time, different challenges can arise, including data quality, data management, and lack of knowledge within the organization, among others. In this work, we propose an approach that can be adopted in the logistics department of any organization in order to promote the Logistics 4.0 movement, while highlighting the main challenges and opportunities associated with the development and implementation of a Big Data Warehouse in a real demonstration case at a multinational automotive organization.This work was supported by FCT–Fundação para a Ciência e Tecnologia—within the R&D Units Project Scope: UIDB/00319/2020 and doctoral scholarship grants: PD/BDE/142895/2018 and PD/BDE/142900/2018

Postharvest characterization of oranges 'Baianinha' stored under refrigeration and ambient conditions

The postharvest quality of fresh oranges (Citrus sinensis (L.) Osbeck, 'Baianinha') was evaluated in terms of chemical and physical properties, in May of 1998. An experiment was carried out in a completely randomized design, with two replications, submitting the fruits to two treatments: storage at ambient conditions (21 ± 0.4°C and RH = 60 ± 1.0%, and in cold room (1 ± 0,5°C and RH = 85 ± 2,5%). After 15 days of storage, a highly significant difference was observed (P=0,01) in the levels of ascorbic acid (decrease of 20% and 8%, at ambient condition and refrigerated fruits, respectively). Titratable acidity decrease approximately 14%, for fruits at ambient condition, and 8% for the refrigerated fruits. It was observed an increase of 30% (ambient condition) and 18% (refrigerated fruits), in the brix/titratable acidity ratio. The results demonstrated that there is a tendency of quality loss for fruits maintained at ambient condition. Therefore, the use of refrigeration is necessary for the maintenance of post harvest quality of fresh orange.A qualidade pós colheita de laranjas in natura (Citrus sinensis (L.) Osbeck, 'Baianinha'), foi avaliada em termos de suas propriedades químicas e físicas, em maio de 1998. Foi aplicado um delineamento inteiramente casualizado, com duas repetições, submetendo os frutos a dois tratamentos: armazenamento a temperatura ambiente (21 ± 0,4°C e UR= 60 ± 1,0%), e em uma câmara frigorífica (1 ± 0,5°C e UR = 85 ± 2,5%). Após 15 dias de armazenamento, foi observada uma diferença altamente significativa (P=0,01), nos níveis de ácido ascórbico (diminuição de 20% e 8%, nas laranjas expostas a temperatura ambiente e nos frutos refrigerados, respectivamente). A acidez titulável teve uma diminuição de aproximadamente 14%, para os frutos mantidos à temperatura ambiente, e de 8% para os frutos refrigerados. Comprovou-se um aumento de 30% (frutos em condições ambientais) e de 18% (frutos refrigerados), na relação sólidos solúveis e acidez titulável. Os resultados apontam uma tendência à perda da qualidade nos frutos mantidos em condições ambientais, o que reafirma a necessidade do uso de refrigeração para a conservação pós-colheita de laranja in natura.464

Ferramenta de análise não destrutiva para obtenção de parâmetros microestruturais baseada em Visão Computacional

Este trabalho apresenta novos parâmetros de medida calculados por um Sistema de Visão Computacional desenvolvido para a Classificação de Microestruturas em Materiais Metálicos. Este sistema é uma ferramenta de análise de imagens adequada para a área de Ciência dos Materiais, permitindo realizar automaticamente a segmentação e quantificação de microestruturas em materiais metálicos. Como evolução deste sistema, este trabalho apresenta novos parâmetros de medida que possibilitam uma análise mais detalhada das microestruturas através de medidas de comprimento, área e perímetro. Para obter estas medidas, utiliza-se o algoritmo de crescimento de regiões e o filtro de Roberts. Após a calibração correta do microscópico óptico usado obtêm-se as fotomicrografias necessárias para a aplicação do sistema desenvolvido. Para validar os resultados obtidos é realizada uma comparação com a análise de microscopia convencional. Portanto, o sistema apresentado é capaz, para além de realizar segmentação e quantificação de microestruturas, de obter parâmetros importantes para uma análise mais detalhada das propriedades mecânica dos materiais baseados em ensaios não destrutivos