14 research outputs found
Emergent technologies for inter-enterprises collaboration and business evaluation
International audienceConventional manufacturing systems are designed for intra-enterprise process management, and they hardly handle processes with tasks using extra-enterprise boundaries data. Besides, inter-enterprise collaboration and new IT enablers for industry 4.0 are becoming a highly topical issue to study, due to : (a) The emergence of new technologies mainly Internet of Things, big data processing and Cyber-Physical systems (b) The new customers' needs that face the SMEs. Many constraints and issues have to be taken into account before establishing Inter-enterprises collaboration, namely: The product information, the business processes and the heterogeneous data. Moreover, the exponential growth of data coming from all the enterprises causes several challenges regarding their exploitation. In this context, this study is interested in Big Data capabilities to help Small and Medium Enterprises to find out more lurking opportunities. We have focus on the combination between emergent IT technologies, mainly Big Data, and inter-interprises collaboration in order to provide an added value. The result of this study is a new approach, that could be adapted by SMEs, for new project evaluation within a network of enterprises
Business Decision Making by Big Data Analytics
Information is the key component towards success when it comes to controlling the decision-makers performance with the quality of a decision. In the modern era, an absolute amount of data is available to organizations for analysis usage. Data is the most important component of the business in the 21st century and a significant number of devices are already equipped with the internet. Based on this the solutions should be studied in order to control and capture the knowledge value pair out of the datasets. Following this, the decision-makers should have access to insightful and valuable data based on the dynamic high volume & velocity using big data analytics. Our research focuses on how to integrate big data analytics into the decision-making process. The B-DAD (big data analytics and decision) framework was created to map the big data tools, its architecture, and analytics for the several decision-making steps by the adoption of methodology based on design science. The ideal goal and offerings of the framework are adopting big data analytics in order to intensify & aid decision making for the organization using an integration of big data analytics into the corresponding decision-making process. Thus, the experiment was carried out in the retail domain to test the framework. As an end result, the results showcased the value-added if big data analytics is integrated with corresponding decision-making activity
Barriers of embedding big data solutions in smart factories: insights from SAP consultants
Purpose: Big data is a key component to realize the vision of smart factories, but the implementation and usage of big data analytical tools in the smart factory context can be fraught with challenges and difficulties. The study reported in this paper aimed to identify potential barriers that hinder organisations from applying big data solutions in their smart factory initiatives, as well as to explore causal relationships between these barriers.
Design/Methodology: The study followed an inductive and exploratory nature. Ten in-depth semi-structured interviews were conducted with a group of highly experienced SAP Consultants and Projects Managers. The qualitative data collected was then systematically analysed by using a thematic analysis approach.
Findings: A comprehensive set of barriers affecting the implementation of big data solutions in smart factories had been identified and divided into individual, organisational and technological categories. An empirical framework was also developed to highlight the emerged inter-relationships between these barriers.
Originality /value: This study built on and extended existing knowledge and theories on smart factory, big data and information systems research. Its findings can also raise awareness of business managers regarding the complexity and difficulties for embedding big data tools in smart factories, and so assist them in strategic planning and decision-making
Processos e ferramentas de análise de Big Data : a análise de sentimento no twitter
Mestrado em Gestão de Sistemas de InformaçãoCom o aumento exponencial na produção de dados a nível mundial, torna-se crucial encontrar processos e ferramentas que permitam analisar este grande volume de dados (comumente denominado de Big Data), principalmente os não estruturados como é o caso dos dados produzidos em formato de texto. As empresas, hoje, tentam extrair valor destes dados, muitos deles gerados por clientes ou potenciais clientes, que lhes podem conferir vantagem competitiva. A dificuldade subsiste na forma como se analisa dados não estruturados, nomeadamente, os dados produzidos através das redes digitais, que são uma das grandes fontes de informação das organizações. Neste trabalho será enquadrada a problemática da estruturação e análise de Big Data, são apresentadas as diferentes abordagens para a resolução deste problema e testada uma das abordagens num bloco de dados selecionado. Optou-se pela abordagem de análise de sentimento, através de técnica de text mining, utilizando a linguagem R e texto partilhado na rede Twitter, relativo a quatro gigantes tecnológicas: Amazon, Apple, Google e Microsoft. Conclui-se, após o desenvolvimento e experimento do protótipo realizado neste projeto, que é possível efetuar análise de sentimento de tweets utilizando a ferramenta R, permitindo extrair informação de valor a partir de grandes blocos de dados.Due to the exponential increase of global data, it becomes crucial to find processes and tools that make it possible to analyse this large volume (usually known as Big Data) of unstructured data, especially, the text format data. Nowadays, companies are trying to extract value from these data, mostly generated by customers or potential customers, which can assure a competitive leverage. The main difficulty is how to analyse unstructured data, in particular, data generated through digital networks, which are one of the biggest sources of information for organizations. During this project, the problem of Big Data structuring and analysis will be framed, will be presented the different approaches to solve this issue and one of the approaches will be tested in a selected data block. It was selected the sentiment analysis approach, using text mining technique, R language and text shared in Twitter, related to four technology giants: Amazon, Apple, Google and Microsoft. In conclusion, after the development and experimentation of the prototype carried out in this project, that it is possible to perform tweets sentiment analysis using the tool R, allowing to extract valuable information from large blocks of data.info:eu-repo/semantics/publishedVersio
INDUSTRY 4.0 ADOPTION FRAMEWORK IN MSMES USING A HYBRID FUZZY AHP-TOPSIS APPROACH
This work presents an outline for adopting industry 4.0 enabling technologies, and appropriate strategies are prioritized to implement them. A hybrid fuzzy Analytic Hierarchy Process (AHP) and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) approaches are applied to achieve the objectives. The enabling technologies and strategies were identified based on the literature review and expert’s opinions, a total of 26 enabling technologies and eight strategies were identified. Later fuzzy AHP technique is used to rank the enablers and TOPSIS is applied to order the implementation strategies. From 26 enablers, a total of ten enabling technologies were found to be the most effective. Artificial intelligence (AI), top management commitment and support, virtual reality, and enterprise resource planning (ERP) systems were the top-ranked enablers in the list, whereas edge computing was the least effective enabler. Among the strategies, lean manufacturing, green supply chain and logistics, and integrated and smart manufacturing systems were the top priorities in implementing industry 4.0, while recruiting and managing talents was the least important strategy in the study. The findings from this framework will provide a deep insight to the managers and practitioners of MSMEs to adopt the industry 4.0 technologies in their organizations
A Big Data perspective on Cyber-Physical Systems for Industry 4.0: modernizing and scaling complex event processing
Doctoral program in Advanced Engineering Systems for IndustryNowadays, the whole industry makes efforts to find the most productive ways of working and it already
understood that using the data that is being produced inside and outside the factories is a way to improve
the business performance. A set of modern technologies combined with sensor-based communication
create the possibility to act according to our needs, precisely at the moment when the data is being
produced and processed. Considering the diversity of processes existing in a factory, all of them producing
data, Complex Event Processing (CEP) with the capabilities to process that amount of data is needed in
the daily work of a factory, to process different types of events and find patterns between them. Although
the integration of the Big Data and Complex Event Processing topics is already present in the literature,
open challenges in this area were identified, hence the reason for the contribution presented in this thesis.
Thereby, this doctoral thesis proposes a system architecture that integrates the CEP concept with a rulebased
approach in the Big Data context: the Intelligent Event Broker (IEB). This architecture proposes the
use of adequate Big Data technologies in its several components. At the same time, some of the gaps
identified in this area were fulfilled, complementing Event Processing with the possibility to use Machine
Learning Models that can be integrated in the rules' verification, and also proposing an innovative
monitoring system with an immersive visualization component to monitor the IEB and prevent its
uncontrolled growth, since there are always several processes inside a factory that can be integrated in
the system. The proposed architecture was validated with a demonstration case using, as an example,
the Active Lot Release Bosch's system. This demonstration case revealed that it is feasible to implement
the proposed architecture and proved the adequate functioning of the IEB system to process Bosch's
business processes data and also to monitor its components and the events flowing through those
components.Hoje em dia as indústrias esforçam-se para encontrar formas de serem mais produtivas. A utilização dos
dados que são produzidos dentro e fora das fábricas já foi identificada como uma forma de melhorar o
desempenho do negócio. Um conjunto de tecnologias atuais combinado com a comunicação baseada
em sensores cria a possibilidade de se atuar precisamente no momento em que os dados estão a ser
produzidos e processados, assegurando resposta às necessidades do negócio. Considerando a
diversidade de processos que existem e produzem dados numa fábrica, as capacidades do
Processamento de Eventos Complexos (CEP) revelam-se necessárias no quotidiano de uma fábrica,
processando diferentes tipos de eventos e encontrando padrões entre os mesmos. Apesar da integração
do conceito CEP na era de Big Data ser um tópico já presente na literatura, existem ainda desafios nesta
área que foram identificados e que dão origem às contribuições presentes nesta tese. Assim, esta tese
de doutoramento propõe uma arquitetura para um sistema que integre o conceito de CEP na era do Big
Data, seguindo uma abordagem baseada em regras: o Intelligent Event Broker (IEB). Esta arquitetura
propõe a utilização de tecnologias de Big Data que sejam adequadas aos seus diversos componentes.
As lacunas identificadas na literatura foram consideradas, complementando o processamento de eventos
com a possibilidade de utilizar modelos de Machine Learning com vista a serem integrados na verificação
das regras, propondo também um sistema de monitorização inovador composto por um componente de
visualização imersiva que permite monitorizar o IEB e prevenir o seu crescimento descontrolado, o que
pode acontecer devido à integração do conjunto significativo de processos existentes numa fábrica. A
arquitetura proposta foi validada através de um caso de demonstração que usou os dados do Active Lot
Release, um sistema da Bosch. Os resultados revelaram a viabilidade da implementação da arquitetura
e comprovaram o adequado funcionamento do sistema no que diz respeito ao processamento dos dados
dos processos de negócio da Bosch e à monitorização dos componentes do IEB e eventos que fluem
através desses.This work has been supported by FCT – Fundação para a Ciência e Tecnologia within the R&D Units
Project Scope: UIDB/00319/2020, the Doctoral scholarship PD/BDE/135101/2017 and by European
Structural and Investment Funds in the FEDER component, through the Operational Competitiveness and
Internationalization Programme (COMPETE 2020) [Project nº 039479; Funding Reference: POCI-01-
0247-FEDER-039479]
Lean and industry 4.0: a QCA analysis
This work proposes an empirical study on the diffusion of lean practices and industry's 4.0 technologies among italian manufacturing companies. The qualitative comparative analysis (QCA) identifies the combination of practices and technologies that assure a high probability to the enterprises of the sample.ope
Digitalization and Development
This book examines the diffusion of digitalization and Industry 4.0 technologies in Malaysia by focusing on the ecosystem critical for its expansion. The chapters examine the digital proliferation in major sectors of agriculture, manufacturing, e-commerce and services, as well as the intermediary organizations essential for the orderly performance of socioeconomic agents.
The book incisively reviews policy instruments critical for the effective and orderly development of the embedding organizations, and the regulatory framework needed to quicken the appropriation of socioeconomic synergies from digitalization and Industry 4.0 technologies. It highlights the importance of collaboration between government, academic and industry partners, as well as makes key recommendations on how to encourage adoption of IR4.0 technologies in the short- and long-term.
This book bridges the concepts and applications of digitalization and Industry 4.0 and will be a must-read for policy makers seeking to quicken the adoption of its technologies
Digitalization and Development
This book examines the diffusion of digitalization and Industry 4.0 technologies in Malaysia by focusing on the ecosystem critical for its expansion. The chapters examine the digital proliferation in major sectors of agriculture, manufacturing, e-commerce and services, as well as the intermediary organizations essential for the orderly performance of socioeconomic agents.
The book incisively reviews policy instruments critical for the effective and orderly development of the embedding organizations, and the regulatory framework needed to quicken the appropriation of socioeconomic synergies from digitalization and Industry 4.0 technologies. It highlights the importance of collaboration between government, academic and industry partners, as well as makes key recommendations on how to encourage adoption of IR4.0 technologies in the short- and long-term.
This book bridges the concepts and applications of digitalization and Industry 4.0 and will be a must-read for policy makers seeking to quicken the adoption of its technologies