Enabling Big Data Analytics at Manufacturing Fields of Farplas Automotive

Digitization and data-driven manufacturing process is needed for today's industry. The term Industry 4.0 stands for today industrial digitization which is defined as a new level of organization and control over the entire value chain of the life cycle of products; it is geared towards increasingly individualized customer's high-quality expectations. However, due to the increase in the number of connected devices and the variety of data, it has become difficult to store and analyze data with conventional systems. The motivation of this paper is to provide an overview of the understanding of the big data pipeline, providing a real-time on-premise data acquisition, data compression, data storage and processing with Apache Kafka and Apache Spark implementation on Apache Ha-doop cluster, and identifying the challenges and issues occurring with implementation the Farplas manufacturing company, which is one of the biggest Tier 1 automotive supplier in Turkey, to study the new trends and streams related to topics via Industry 4.0.Comment: 8 page

MOMIS Dashboard: a powerful data analytics tool for Industry 4.0

In this work we present the MOMIS Dashboard, an interactive data analytics tool to explore and visualize data sources content through several kind of dynamic views (e.g. maps, bar, line, pie, etc.). The software tool is very versatile, and supports the connection to the main relational DBMS and Big Data sources. Moreover, it can be connected to MOMIS, a powerful Open Source Data Integration system, able to integrate heterogeneous data sources as enterprise information systems as well as sensors data. MOMIS Dashboard provides a secure permission management to limit data access on the basis of a user role, and a Designer to create and share personalized insights on the company KPIs, facilitating the enterprise collaboration. We illustrate the MOMIS Dashboard efficacy in a real enterprise scenario: a production monitoring platform to analyze real-time and historical data collected through sensors located on production machines that optimize production, energy consumption, and enable preventive maintenance

How SMEs can participate in the potentials of Big Data within Industry 4.0

Through digital interconnection along the value chain, the concept of Industry 4.0 aims to generate data transparency that results in the generation of Big Data. To approach the expected potentials, several barriers exist in the context of Small and Medium-Sized Enterprises (SMEs). On the one hand, large enterprises are much better prepared to profit from the value of big data in comparison to SMEs, which are often suppliers in global value chains and do not have contact to end customers. Further, the data generation within SMEs is often too small and unstandardized in order to generate sufficient input for Big Data. On the other hand, large enterprises require their suppliers, often SMEs, to share their data so that Big Data can be generated in the first place. This paper builds on a literature review on extant research in the field of Industry 4.0, Big Data, and SMEs, and describes insights from an industrial case study. Condensing the findings of literature review and case study, the paper shows approaches how SMEs can be integrated within the concept of Industry 4.0, providing benefits for both, SMEs, and their often larger customers. Thereupon, implications for future research and managerial practice are derived

Archetypes for data-driven business models for manufacturing companies in Industry 4.0

The Industry 4.0 phenomenon, internationally known as the Industrial Internet of Things, is expected to enable data-driven business models across the manufacturing sector. While data-driven business models in business-to-customer (B2C) markets are flourishing, driven by trends such as on-demand services, improved resource allocation, niche advertising and the sustainability movement at large, business-to-business (B2B) data-driven business models and the corresponding literature are less pervasive. While scholars have begun exploring firm-specific cases analyzing the introduction of new data-driven business models, e.g. on automotive shop floors, along manufacturing value chains, or in areas such as rail mobility, a comprehensive overview is missing. In response, this paper condenses extant research on data-driven business models in Industry 4.0 and develops several archetypes. These refer to (a) the types of data, which enable new B2B business models, (b), the forms of data-driven business models, e.g., building on sensor data for predictive purposes, and (c) new monetization forms for data-driven business models. The paper further distinguishes the accelerating and decelerating forces, which influence the implementation of data-driven business models in organizational ecosystems. In doing so, the paper intends to create a framework for future research and for practitioners on data-driven business model innovation in Industry 4.0

An Overview of the Rising Challenges in Implementing Industry 4.0

Industry 4.0 is the fourth industrial revolution that was first introduced in Germany which then becomes a trend of future manufacturing industries. The Industry 4.0 also referred as the umbrella concept for new industrial paradigm which consists of a number of future industry characteristics, were related to cyber-physical systems (CPS), internet of things (IoT), internet of services (IoS), robotics, big data, cloud manufacturing and augmented reality. By adopting these technologies as the key development in more intelligent manufacturing processes including devices, machines, modules, and products, the process of information exchange, action and control will stimulate each other, subsequently to an intelligent manufacturing environment. However, in order to fully utilize the advantages of industry 4.0, there are some challenges that need to be overcome. This paper reviews the challenges in implementing Industry 4.0. The literatures found in this paper mainly from Google Scholar, Science Direct and Emerald. In short, the challenges can be imparted into seven major categories. There are data management and Integration, knowledge-driven, process, security, capital, workforce, and education

Algoritmos de machine learning y su aplicación al mantenimiento industrial en el sector agroalimentario

Las aplicaciones de Machine Learning, o aprendizaje automático, son soluciones que, tras su implementación, continúan mejorando con el tiempo y con una mínima intervención humana, lo que las hace muy adecuadas para ayudar en las labores de mantenimiento de cualquier industria. Se han analizado 10 algoritmos, de los más utilizados, para comprender los conceptos básicos del aprendizaje automático, los problemas que solucionan y seleccionar el mejor algoritmo para la aplicación al mantenimiento predictivo en una industria agroalimentaria española: Solán de Cabras

Data-driven Context Awareness of Smart Products in Discrete Smart Manufacturing Systems

Abstract Traditionally, smart-connected products are predominantly utilized during the usage phase of the product lifecycle. However, we argue that there are distinct benefits of system-integrated sensor systems during the beginning of life, more specifically in manufacturing and assembly. In this paper, we analyze the ability of a smart-connected product with an integrated sensor system to recognize and label different manufacturing processes, generating a distinct process fingerprint within a discrete smart manufacturing system. The ability of the smart-connected product to detect distinct manufacturing process patterns ('process fingerprint') enables the production planner and operator, e.g., to optimize the scheduling, improve part quality, and/or reduce the energy footprint. The experimental setup is based on a FestoDidactics CPlab with eight different manufacturing processes. The smart-connected product is equipped with a sensor system providing data from eight different sensors (e.g., temperature, humidity, acceleration). We used an Artificial Neural Network (ANN) algorithm to create a model to detect specific events/patterns within the dataset after labelling it manually over the course of a complete production cycle. The focal manufacturing process was the heating tunnel where the smart-connected product was exposed to a heat treatment process and sequence. The results of this prototypical implementation indicate that a smart-connected product can reliably recognize specific process patterns with a system-integrated sensor system during a simulated manufacturing process. While this work is only a first step, the potential applications and benefits are promising and further research should focus on the potential quality implications within smart manufacturing of product-integrated sensor readings compared to machine tool-based sensors, both of which monitored during the beginning of life. Smart products' integrated sensor systems provide the means to obtain measurements relevant for smart manufacturing systems that are not obtainable with common external sensor systems today