The Operational Process Dashboard for Manufacturing

AbstractAgility is a critical success factor for manufacturers in today's volatile global environment and requires employees monitoring their performance and reacting quickly to turbulences. Thus, comprehensive information provisioning on all hierarchy levels is neces- sary. Yet, existing IT systems, e. g., Manufacturing Execution Systems, scarcely address information needs of workers on the shop floor level. This causes uncoordinated waiting times, inflexibility and costly communication. To address these issues, we present the Operational Process Dashboard for Manufacturing (OPDM), a mobile dashboard for shop floor workers. We identify process- oriented information needs, develop technical dashboard services and define IT requirements for an implementation

Framework for Context-Sensitive Dashbords Enabling Decision Support on Production Shop Floor

The advancing digitalization of production means that a large amount of data and information is being collected. Used correctly, these represent a significant competitive advantage. Decision support systems (DSS) can help to provide employees with the right information at the right time. Context-sensitive dashboards in the sense of decision support have the potential to provide employees on the shopfloor with information according to their needs. Within the scope of this work, a framework for the determination of the context-sensitive information needs of the staff on the shopfloor was developed. The goal was to reduce the development and adaptation effort of a context-sensitive application by classifying activities with similar information needs in advance. According to the methodology, the information needs of the employees are first analyzed and activities are summarized in terms of their general information needs. Subsequently, the information needs are weighted in order to prioritize them with regard to the processing and selection of information. The context-sensitive dashboard was then implemented using a user-centric approach to achieve a high level of user acceptance. The developed prototype, including architecture and design, was then tested and evaluated by experts. Three scenarios were compared in which experts were asked to assess the information requirements for employees in production. These results were then compared with the results of the framework. The comparison showed that for two of the three scenarios, the weighting determined in the framework matched the experts' assessments to a high degree. These general scenarios show that it is possible to generate context-sensitive dashboards based on demand using the developed framework. If the activities become more specific, it became apparent that further developments of the framework are necessary to cover the corresponding information needs. For this purpose, an iterative application to further scenarios and subsequent implementation in the framework seems to be purposeful

THE EFFECT OF ADVANCED ANALYTICS REAL-TIME DASHBOARDS ON COGNITIVE ABSORPTION AND TASK LOAD OF HUMAN END USERS

Advanced analytics can be used to gain comprehensive insights into real-time production processes. They are commonly subdivided into predictive and prescriptive analytics. While previously descriptive analytics only reflected current states of machinery, predictive analytics enables statements about the future. Going one step further, prescriptive analytics cover automated recommendations for action based on these predictions. On the one hand, humans as central stakeholders in production shall benefit from the information gained through advanced analytics to make better decisions. On the other hand, predictive and prescriptive analytics dashboards contain additional information and may potentially overwhelm human decision-makers in contrast to descriptive dashboards. In this study, we investigate the perception of human end users on information presented in descriptive, predictive, and prescriptive dashboards and investigate their cognitive absorption and task load. Our results show that more advanced predictive and prescriptive dashboards increase mental demand rather than lowering intellectual requirements. However, we found that prescriptive dashboards reduce user frustration by making decision alternatives and consequences more tangible

Towards Flexible and Cognitive Production—Addressing the Production Challenges

Globalization in the field of industry is fostering the need for cognitive production systems. To implement modern concepts that enable tools and systems for such a cognitive production system, several challenges on the shop floor level must first be resolved. This paper discusses the implementation of selected cognitive technologies on a real industrial case-study of a construction machine manufacturer. The partner company works on the concept of mass customization but utilizes manual labour for the high-variety assembly stations or lines. Sensing and guidance devices are used to provide information to the worker and also retrieve and monitor the working, with respecting data privacy policies. Next, a specified process of data contextualization, visual analytics, and causal discovery is used to extract useful information from the retrieved data via sensors. Communications and safety systems are explained further to complete the loop of implementation of cognitive entities on a manual assembly line. This deepened involvement of cognitive technologies are human-centered, rather than automated systems. The explained cognitive technologies enhance human interaction with the processes and ease the production methods. These concepts form a quintessential vision for an effective assembly line. This paper revolutionizes the existing industry 4.0 with an even-intensified human–machine interaction and moving towards cognitivity

The role of big data analytics in industrial Internet of Things

Big data production in industrial Internet of Things (IIoT) is evident due to the massive deployment of sensors and Internet of Things (IoT) devices. However, big data processing is challenging due to limited computational, networking and storage resources at IoT device-end. Big data analytics (BDA) is expected to provide operational- and customer-level intelligence in IIoT systems. Although numerous studies on IIoT and BDA exist, only a few studies have explored the convergence of the two paradigms. In this study, we investigate the recent BDA technologies, algorithms and techniques that can lead to the development of intelligent IIoT systems. We devise a taxonomy by classifying and categorising the literature on the basis of important parameters (e.g. data sources, analytics tools, analytics techniques, requirements, industrial analytics applications and analytics types). We present the frameworks and case studies of the various enterprises that have benefited from BDA. We also enumerate the considerable opportunities introduced by BDA in IIoT.We identify and discuss the indispensable challenges that remain to be addressed as future research directions as well

Role-Based data visualization for Industrial IoT

The competition among manufacturers in the global markets calls for the enhancement of the agility and performance of the production process and the quality of products. As a result, the production systems should be designed in a way to provide decision-makers with visibility and analytics. To fulfill these objectives, the development of factory information systems in manufacturing industries has been introduced as a practical solution in the past few years. On the other hand, the volume of data generated on the factory floor is rising. To improve the efficiency of manufacturing process, this amount of data should be analyzed by decision-makers. To cope with this challenge, visualization assists decision-makers to gain insight into data. To give a better perspective of collected data to decision-makers, effective visualization techniques should be employed. Adequate data visualization allows the end user to have better understanding of data and make effective decisions faster. Meanwhile, the adoption of the Service-Oriented Architecture (SOA) and Internet of Things (IoT) as state-of-the-art technologies are among the most prominent trends in industrial automation. IoT technology is expected to generate and collect data from various sensors and devices within the production system, and enables enterprises to have real-time visibility into the flow of production process. Moreover, data received from factory floor should be transmitted from back-end side to the front-end side for future analysis. To implement the exchange of data efficiently, the solution should support different communication protocols to make interoperability among heterogeneous devices on shop floor. This study describes an approach for building a role-based visualization of industrial IoT. An extensible architecture was provided by which the future growth of data and emerging new protocols has been anticipated. By using the IoT platform introduced in this thesis, selected KPIs can be monitored by different levels of enterprise. Three prototype IoT dashboards have been implemented for a pilot production line, “Festo didactic training line” located in Seinäjoki University of Applied Sciences (SeAMK) and results have been validated

Melhoria dos fluxos de informação no processo de planeamento da produção: proposta de uma dashboard suportada em KPIs

Technological evolution is currently triggering changes in companies’ practices as well as in the environment they operate. This dynamic has led companies to rethink the way they work, trying to, continuously, improve their processes as a way of gaining efficiency and competitiveness. This report presents a study developed in a sanitary systems multinational manufacturer, more specifically in its production planning and control department, with a focus on improving its information processes. The main goal of this project was to reduce the existing inefficiencies associated to the production planning information flows, using, for that purpose, process mapping tools, as well as waste analyses techniques. While BPMN 2.0 was the language chosen to map the processes, identify the data sources and information flows; Lean tools were used to identify non-value-added activities and, consequently, waste present in the processes. As proof of concept, Microsoft Power BI tool was used as a base for and Information System, guided by Industry 4.0 principles and needs. The main practical contribution of this work should be that this solution, which integrates people, data, flows and processes, may contribute to efficiency gains in the daily tasks of the planners, in the decision-making processes and in the identification and elimination of non-value-added activities, improving the global planning performance.Hoje em dia a evolução tecnológica está a desencadear grandes mudanças no contexto das organizações, bem como no ambiente em que operam. Por sua vez, estas dinâmicas têm contribuído para que as organizações repensem a forma de trabalhar, observando-se uma necessidade contínua de melhorar os processos de maneira a ganhar eficiência e alcançar situações de vantagem competitiva. Este relatório apresenta um estudo que foi conduzido numa multinacional dedicada à produção de componentes sanitários, mais especificamente no departamento de planeamento e controlo da produção, com foco na melhoria dos seus processos de informação. O principal objetivo deste projeto consistiu na redução das ineficiências presentes nos fluxos de informação associados ao planeamento da produção, usando, para o efeito, ferramentas de mapeamento de processos, bem como técnicas de análise de desperdícios. Enquanto o BPMN 2.0 foi a linguagem escolhida para mapear os processos, identificar as fontes de dados e os fluxos de informação, as ferramentas Lean foram usadas para identificar as atividades de não valor acrescentado e, consequentemente, os desperdícios presentes naqueles processos. Por forma a fazer a prova de conceito utilizou-se a ferramenta Microsoft Power BI, vindo esta a constituir um Sistema de Informação orientado às necessidades da Indústria 4.0. Como contributo prático deste projeto espera-se que esta solução, que integra pessoas, dados, fluxos e processos, venha a potenciar ganhos de eficiência nas tarefas diárias dos planeadores, nos processos de tomada de decisão e na identificação e eliminação de atividades de não valor acrescentado, melhorando, assim, o desempenho global do planeamento.Mestrado em Engenharia e Gestão Industria

Enabling IoT in Manufacturing: from device to the cloud

Industrial automation platforms are experiencing a paradigm shift. With the new technol-ogies and strategies that are being applied to enable a synchronization of the digital and real world, including real-time access to sensorial information and advanced networking capabilities to actively cooperate and form a nervous system within the enterprise, the amount of data that can be collected from real world and processed at digital level is growing at an exponential rate. Indeed, in modern industry, a huge amount of data is coming through sensorial networks em-bedded in the production line, allowing to manage the production in real-time. This dissertation proposes a data collection framework for continuously collecting data from the device to the cloud, enabling resources at manufacturing industries shop floors to be handled seamlessly. The framework envisions to provide a robust solution that besides collecting, transforming and man-aging data through an IoT model, facilitates the detection of patterns using collected historical sensor data. Industrial usage of this framework, accomplished in the frame of the EU C2NET project, supports and automates collaborative business opportunities and real-time monitoring of the production lines