Software Alternatives to Design Learning Activities for Lean Six Sigma in e-learning

[EN] In this article, the authors have designed a workflow compatible with e-learning for the teaching-learning of process control methodologies within the contemporary Lean Six Sigma context, applied to students of industrial engineering. Based on the proposed workflow and e-learning orientation and bibliometric analysis, technical and educational requirements have been established ad-hoc for the choice of the most appropriate computer package. The requirements have been grouped into six categories: usability, power, scalability and efficiency, learning, access, and resources. Each studied software alternative (Matlab®, Minitab®, SPSS®, R, and Python) has been evaluated based on the preset requirements for the same test data sets in a format compatible with all the software evaluated to open a discussion and draw the conclusions of the workS

Análise de Big Data por meio de estatísticas multivariadas na Indústria 4.0: uma revisão da literatura

Com relação ao tema Big Data, observa-se atualmente uma quantidade significativa e um aumento considerável de publicações relacionadas ao assunto, porém ainda os pesquisadores sentem falta de meios para auxiliar na escolha dos referenciais teóricos. Desta forma, o objetivo deste trabalho é demonstrar um processo utilizado para a seleção das publicações relevantes, as quais são o produto de uma revisão sistemática da literatura e que buscam nortear os pesquisadores, agregando conhecimento para conduzir uma pesquisa sobre os métodos analíticos aplicados em Big Data, em ambientes de Manufaturas Inteligentes e apoiadas por abordagens multivariadas. Para buscar esse objetivo foi desenvolvido um roteiro de pesquisa e uma técnica de classificação das publicações mais relevantes. Como principais resultados deste processo, foi possível identificar 14 publicações aderentes, e que permitiram integrar os conceitos sobre Big Data, Indústria 4.0 e abordagens multivariadas, além de demonstrar a análise preditiva de dados como uma dos modelos mais utilizados na análise de Big Data

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

Quality management in the industry 4.0 era

In the current competitive scenario, manufacturing companies are facing various challenges related to an increasing level of variability. This variability means different sets of dimensions such as demand, volume, process, technology, quality, customer behavior and supplier attitude, and transform the industrial systems engineering domain. A new paradigm tries to solve these challenges and solutions such as "the fourth industrial revolution" or "Industry 4.0" refers to new production patterns, including new technologies, productive factors and labor organizations, which are completely changing the production processes and developing high-efficiency production systems that make it possible to minimize production costs and improve production and product quality. Manufacturing companies need to achieve a substantial improvement in performance by manufacturing high-quality products and creating highly flexible systems that make it possible to maintain their efficiency even when demand varies dramatically. Tools for the management and optimization of quality are vitally important. In this way the adoption of highly flexible cyber physical production units permits the implementation of production processes capable of guaranteeing high-quality standards in the finished product, even in the case of small production lots. Industry 4.0 provides promising opportunities for quality management therefore, the purpose of this paper is to focus on the quality management and industry 4.0 concepts and analyze the current state of literature trying to understand the implications and opportunities for quality management in the industry 4.0 era

Technology and quality management: a review of concepts and opportunities in the digital transformation

Purpose - The Digital Transformation brings change to organizations, their processes, and their production systems. Nevertheless, most efforts observed in its context tend to be technology-driven, and it is often argued that Quality Management is inadequately integrated into the discussion. Design/methodology/approach - Surveying the literature, this work reviews, list, and organizes the different technological concepts and integration opportunities that have been explored in the scope of Quality Management in the Digital Transformation. Findings - Findings include the expanded capacity of quality tools and methods for managerial purposes; the reinforced importance of Data Quality; the increased automation and augment resources for Quality control; and the increased process optimization and integration of systems and between organizational areas. Originality/value - It is demonstrated that although scattered in the literature, there are already a number of works exploring the impacts of technology in the management of Quality in the scope of the Digital Transformation. Three main areas for integration arise: (a) Digital Quality Management (application of industry 4.0 technologies to Quality Management itself, its tools, methods, and systems), (b) the management of the Quality of digital products and services, and (c) the management of the Quality of digital product development and production processes.(undefined

Anomaly Detection Methods to Improve Supply Chain Data Quality and Operations

Supply chain operations drive the planning, manufacture, and distribution of billions of semiconductors a year, spanning thousands of products across many supply chain configurations. The customizations span from wafer technology to die stacking and chip feature enablement. Data quality drives efficiency in these processes and anomalies in data can be very disruptive, and at times, consequential. Developing preventative measures that automate the detection of anomalies before they reach downstream execution systems would result in significant efficiency gain for the organization. The purpose of this research is to identify an effective, actionable, and computationally efficient approach to highlight anomalies in a sparse and highly variable supply chain data structure. This research highlights the application of ensemble unsupervised learning algorithms for anomaly detection on supply chain demand data. The outlier detection algorithms explored include Angle-Based Outlier Detection, Isolation Forest, Local Outlier Factor and K-Nearest Neighbors. The application of an ensemble technique on unconstrained forecast signal, which is traditionally a consistent demand line, demonstrated a dramatic decrease in false positives. The application of the ensemble technique to the sales-order netted demand forecast, a signal that is irregular in structure, the algorithm identifies true anomalous observations relative to historical observations across time. The research team concluded that assessing an outlier is not limited to the most recent forecast’s observations but must be considered in the context of historical demand patterns across time

Industrial Cyber-Physical System Evolution Detection and Alert Generation

Industrial Cyber-Physical System (ICPS) monitoring is increasingly being used to make decisions that impact the operation of the industry. Industrial manufacturing environments such as production lines are dynamic and evolve over time due to new requirements (new customer needs, conformance to standards, maintenance, etc.) or due to the anomalies detected. When an evolution happens (e.g., new devices are introduced), monitoring systems must be aware of it in order to inform the user and to provide updated and reliable information. In this article, CALENDAR is presented, a software module for a monitoring system that addresses ICPS evolutions. The solution is based on a data metamodel that captures the structure of an ICPS in different timestamps. By comparing the data model in two subsequent timestamps, CALENDAR is able to detect and effectively classify the evolution of ICPSs at runtime to finally generate alerts about the detected evolution. In order to evaluate CALENDAR with different ICPS topologies (e.g., different ICPS sizes), a scalability test was performed considering the information captured from the production lines domain