An Artificial Neural Network Approach to Learning from Factory Performance in a Kanban-Based System

Many Just-In-Time (JIT) manufacturing environments generate operational data reflecting both efficient and inefficient factory performance. Frequently data for inefficient performance is lost or discarded for fear of replicating poor performance. The purpose of this paper is two fold. First, historical JIT shop data is analyzed using a genetic algorithm (GA) to determine which shop factors are important determinants offactory performance. Second, subsequent to these important factors being identified by a GA, an artificial neural network (ANN) is used to learn the relationships between these factors and factory performance. The ANN can then be used to predict factory performance for future shop conditions and enhance shop performance. While ANN learning techniques have previously been applied to JIT production systems (Wray, Rakes, and Rees, 1997) (Markham, Mathieu, and Wray, 2000), these techniques have only been trained on data sets that reflect an efficient factory. Mathieu, Wray, and Markham (2002) investigated inefficient and efficient JIT factory performance but did not deploy either ANNs or a GA. In this paper an example application is presented using a GA to specify important shop factors and to predict saturated, starved or efficient factory performance based on dynamic shop floor data

Generative Adversarial Network based machine for fake data generation

This paper introduces a first approach on using Generative Adversarial Networks (GANs) for the generation of fake data, with the objective of anonymizing patients information in the health sector. This is intended to create valuable data that can be used both, in educational and research areas, while avoiding the risk of a sensitive data leakage. For this purpose, firstly a thorough research on GAN’s state of the art and available databases has been developed. The outcome of the project is a GAN system prototype adapted to generate raw data that imitates samples such as users variable status on hypothyroidism or a cardiogram report. The performance of this prototype has been checked and satisfactory results have been obtained for this first phase. Moreover, a novel research pathway has been opened so further research can be developed

Proactive model to determine information technologies supporting expansion of air cargo network

Shippers and recipients expect transportation companies to provide more than just the movement of a package between points; certain information must be available to them as well, to enable forecasts and plans within the supply chain. The transportation companies also need the information flow that undergirds a transportation grid, to support ad-hoc routing and strategic structural re-alignment of business processes. This research delineates the information needs for an expanding air cargo network, then develops a new model of the information technologies needed to support expansion into a new country. The captured information will be used by shippers, recipients, and the transportation provider to better guide business decisions. This model will provide a method for transportation companies to balance the tradeoffs between the operating efficiencies, capital expenditures, and customer expectations of their IT systems. The output of the model is a list of technologies – optimized by cost – which meet the specific needs of internal and external customers when expanding air cargo networks into a new country

1st Symposium of Applied Science for Young Researchers: proceedings

SASYR, the rst Symposium of Applied Science for Young Researchers, welcomes works from young researchers (master students) covering any aspect of all the scienti c areas of the three research centres ADiT-lab (IPVC, Instituto Polit ecnico de Viana do Castelo), 2Ai (IPCA, Instituto Polit ecnico do C avado e do Ave) and CeDRI (IPB, Instituto Polit ecnico de Bragan ca). The main objective of SASYR is to provide a friendly and relaxed environment for young researchers to present their work, to discuss recent results and to develop new ideas. In this way, it will provide an opportunity to the ADiT-lab, 2Ai and CeDRI research communities to gather synergies and indicate possible paths for future joint work. We invite you to join SASYR on 7 July and to share your research!info:eu-repo/semantics/publishedVersio

Intelligent systems in manufacturing: current developments and future prospects

Global competition and rapidly changing customer requirements are demanding increasing changes in manufacturing environments. Enterprises are required to constantly redesign their products and continuously reconfigure their manufacturing systems. Traditional approaches to manufacturing systems do not fully satisfy this new situation. Many authors have proposed that artificial intelligence will bring the flexibility and efficiency needed by manufacturing systems. This paper is a review of artificial intelligence techniques used in manufacturing systems. The paper first defines the components of a simplified intelligent manufacturing systems (IMS), the different Artificial Intelligence (AI) techniques to be considered and then shows how these AI techniques are used for the components of IMS

An investigation upon Industry 4.0 implementation: the case of small and medium enterprises and Lean organizations

In recent years, industries have undergone several shifts in their operating and management systems. Alongside to the technological innovation, rapid market changes and high competitiveness; growing customer needs are driving industries to focus on producing highly customized products with even less time to market. In this context, Industry 4.0 is a manufacturing paradigm that promises to have a great impact not only on improving productivity but also on developing new products, services and business models. However, the literature review has shown that research on Industry 4.0 implementation is still characterized by some weaknesses and gaps (e.g., topics such as the implementation of Industry 4.0 in SMEs and its integration with Lean Management approach). Motivated by so, this thesis sought to answer four key questions: (RQ1) What are the challenges and opportunities for SMEs in the Industry 4.0 field? (RQ2) What are the resources and capabilities for Industry 4.0 implementation in SMEs? (RQ3) How can these resources and capabilities be acquired and/or developed and (RQ4) How to integrate Industry 4.0 and Lean Management? To deal with the first research question, a semi-systematic literature review in the Industry 4.0 field was conducted. The main goal is to explore the implementation of Industry 4.0 in SMEs in order to identify common challenges and opportunities for SMEs in the Industry 4.0 era. To face with the second and third research questions, a multiple case study research was conducted to pursue two main aims: (1) to identify the resources and capabilities required to implement Industry 4.0 in Portuguese SMEs. Furthermore, based on mainstream theories such as resource-based view (RBV) and dynamic capability theory, it sought empirical evidence on how SMEs use resources and capabilities to gain sustainable competitive advantage; (2) to shed light on how those SMEs acquire and/or develop the Industry 4.0 resources and capabilities. Finally, this thesis employed a semi-systematic literature review methodology to deal with the fourth research question. As such, it explored the synergistic relationship between Industry 4.0 and Lean Management to identify the main trends in this field of research and, ultimately, the best practices. The analysis and discussion of the best practices revealed a set of potential relationships which provided a more clear understanding of the outcomes of an Industry 4.0-LM integration.Nos últimos anos, as indústrias têm passado por várias mudanças tanto nos seus sistemas operacionais, como de gestão. Juntamente com a inovação tecnológica e alta competitividade; as mudanças nas necessidades dos clientes levaram as indústrias a se concentrarem na produção de produtos altamente personalizados e com tempo de lançamento no mercado cade vez menores. Nesse contexto, a Indústria 4.0 é um paradigma de manufatura que promete ter um grande impacto não só na melhoria da produtividade, mas também no desenvolvimento de novos produtos, serviços e modelos de negócios. No entanto, a revisão da literatura mostrou que a investigação sobre a implementação da Indústria 4.0 ainda é caracterizada por algumas lacunas (por exemplo em tópicos como a implementação da Indústria 4.0 em pequenas e médias empresas (PMEs) e sua integração com a filosofia de gestão Lean Management). Diante disso, esta tese procura responder à quatro questões-chave: (RQ1) Quais são os desafios e oportunidades para as PMEs no campo da Indústria 4.0? (RQ2) Quais são os recursos e capacidades necessários para a implementação da Indústria 4.0 nas PMEs? (RQ3) Como esses recursos e capacidades podem ser adquiridos e/ou desenvolvidos e (RQ4) Como integrar os paradigmas de manufatura, Indústria 4.0 e Lean Management? Para responder à primeira questão de investigação, este trabalho empregou uma revisão semi-sistemática da literatura. O objetivo principal foi explorar a implementação da Indústria 4.0 nas PMEs, a fim de identificar quais são os desafios e oportunidades para as PMEs na era da Indústria 4.0. Para fazer face à segunda e terceira questões de investigação, foi realizado um estudo de caso em 5 PMEs localizadas em Portugal a fim de atingir os seguintes objetivos: (1) identificar os recursos e capacidades necessários para implementar a Indústria 4.0 nas PME portuguesas; (2) esclarecer como essas PMEs adquirem e/ou desenvolvem esses recursos e capacidades. Além disso, com base nas teorias resourcebased view (RBV) e dynamic capabilities, buscar evidências empíricas sobre como as PMEs usam recursos e capacidades para obter vantagem competitiva sustentável. Finalmente, para lidar com a quarta questão de investigação, este estudo explorou a relação sinérgica entre a Indústria 4.0 e a filosofia de gestão Lean Management (LM) para identificar as principais tendências neste campo de investigação e promover as melhores práticas. A análise e discussão das melhores práticas revelaram um conjunto de potenciais relações, o que contribuiu para um entendimento mais claro sobre a integração da Indústria 4.0 com LM

Application of lean scheduling and production control in non-repetitive manufacturing systems using intelligent agent decision support

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.Lean Manufacturing (LM) is widely accepted as a world-class manufacturing paradigm, its currency and superiority are manifested in numerous recent success stories. Most lean tools including Just-in-Time (JIT) were designed for repetitive serial production systems. This resulted in a substantial stream of research which dismissed a priori the suitability of LM for non-repetitive non-serial job-shops. The extension of LM into non-repetitive production systems is opposed on the basis of the sheer complexity of applying JIT pull production control in non-repetitive systems fabricating a high variety of products. However, the application of LM in job-shops is not unexplored. Studies proposing the extension of leanness into non-repetitive production systems have promoted the modification of pull control mechanisms or reconfiguration of job-shops into cellular manufacturing systems. This thesis sought to address the shortcomings of the aforementioned approaches. The contribution of this thesis to knowledge in the field of production and operations management is threefold: Firstly, a Multi-Agent System (MAS) is designed to directly apply pull production control to a good approximation of a real-life job-shop. The scale and complexity of the developed MAS prove that the application of pull production control in non-repetitive manufacturing systems is challenging, perplex and laborious. Secondly, the thesis examines three pull production control mechanisms namely, Kanban, Base Stock and Constant Work-in-Process (CONWIP) which it enhances so as to prevent system deadlocks, an issue largely unaddressed in the relevant literature. Having successfully tested the transferability of pull production control to non-repetitive manufacturing, the third contribution of this thesis is that it uses experimental and empirical data to examine the impact of pull production control on job-shop performance. The thesis identifies issues resulting from the application of pull control in job-shops which have implications for industry practice and concludes by outlining further research that can be undertaken in this direction

Implementing Industry 4.0 in SMEs

This open access book addresses the practical challenges that Industry 4.0 presents for SMEs. While large companies are already responding to the changes resulting from the fourth industrial revolution , small businesses are in danger of falling behind due to the lack of examples, best practices and established methods and tools. Following on from the publication of the previous book ‘Industry 4.0 for SMEs: Challenges, Opportunities and Requirements’, the authors offer in this new book innovative results from research on smart manufacturing, smart logistics and managerial models for SMEs. Based on a large scale EU-funded research project involving seven academic institutions from three continents and a network of over fifty small and medium sized enterprises, the book reveals the methods and tools required to support the successful implementation of Industry 4.0 along with practical examples

Designing pull production control systems:Customization and robustness

In this dissertation we address the issues of selecting and configuring pull production control systems for single-product flowlines. We start with a review of pull systems in the literature, yielding a new classification. Then we propose a novel selection procedure based on a generic system that we test on a case also studied in the literature. We further study our procedure for a variety of twelve production lines. We find new types of pull systems that perform well. Next, we raise the issue of designing pull systems under uncertainty. We propose a novel procedure to minimize the risk of poor performance. Results show that risk considerations strongly influence the selection of a specific pull system

Project Management in the Fourth Industrial Revolution. Beer production project

El objetivo de este documento es encontrar soluciones a los problemas de gestión de proyectos que surgen como consecuencia de la cuarta revolución industrial, que está cambiando la industria tal y como la conocemos y nos sitúa en un punto crítico de adaptación a una nueva realidad que traerá consigo grandes oportunidades y también grandes riesgos. Además, la gestión de los nuevos proyectos 4.0 supondrá un reto de comunicación entre expertos en tecnologías y lenguajes informáticos muy diferentes, por lo que este documento destaca los elementos a tener en cuenta en la revolución tecnológica y estudia cómo gestionar un proyecto en una Smart factory.The aim of this document is to find solutions to the project management problems that arise as a result of the fourth industrial revolution, which is changing industry as we know it and places us at a critical point of adaptation to a new reality that will bring great opportunities as well as great risks. In addition, the management of new 4.0 projects will pose a challenge for communication between experts in very different technologies and computer languages, which is why this document highlights the elements to be taken into account in the technological revolution and studies how to manage a project in a Smart factory.Hochschule Albstadt-SigmaringenGrado en Ingeniería en Organización Industria