Modelle und Strategien zur Einführung des Computer Integrated Manufacturing (CIM) – Ein Literaturüberblick

Deutsch: Im Rahmen der seit einigen Jahren verstärkten Aktivitäten von Wissenschaft und Wirtschaft im Bereich der digital vernetzten Produktion kann eine Auseinandersetzung mit den Erfahrungen und Erkenntnissen von Computer Integrated Manufacturing (CIM) hilfreich sein. Hierfür werden in diesem Beitrag 37 CIM-Modelle kurz beschrieben, weiterführende Literaturhinweise angegeben und die Visualisierungen der Grundstrukturen abgebildet. Darüber hinaus werden Literaturhinweise zu einigen Planungs- und Umsetzungsstrategien im Kontext CIM gegeben sowie der Stand verfügbarer CIM-Reifegradmodelle dargestellt. English: Within the framework of the activities of science and business in the area of digitally networked production, which has been intensified for a number of years, an examination of the experiences and knowledge of Computer Integrated Manufacturing (CIM) can be helpful. For this purpose, 37 CIM models are briefly described in this article, additional literature references are given and the visualisations of the basic structures are depicted. In addition, there are references to some planning and implementation strategies in the CIM context, as well as the status of available CIM maturity models

A guide to develop competency-oriented Lean Learning Factories systematically

In the last decade, various lean learning factories were established in industry and academia around the globe. They are used for experience-based training, education, and practice-oriented research. Learning factories provide a reality-conform production environment as a learning environment. Processes and technologies of the learning factory are based on real industrial sites. Learning factories doesn't only contain single workplaces or machines, but changeable multilink value added chains. Trainees can test and discover lean approaches in this environment and experience the holistic range of technological, organizational, and social issues linked to the approaches. The main goal of learning factories is an effective competency development, i.e. the development of the participants’ ability (including motivational and emotional aspects) to master complex, unfamiliar situations. In order to reach this goal a systematic approach for the competency-oriented design of learning factory courses and systems is needed. Such a competency-oriented approach for the development of lean learning factories is presented, integrating the conceptual design levels ‘learning factory’, ‘teaching module’, and ‘learning situation’. This approach addresses issues of intuitively designed learning factories and therefore enables an effective development of intended competencies. As a result lean learning factories including teaching modules and learning situations meeting the requirements of industry can be designed with a reduced effort and an increased success in the transfer to real problem situations. Among others, a case study of designing a learning module in the environment of the process learning factory CiP in the field of "Lean Quality" is presented in detail

Old-Fashioned Pep Rallies to Spotlight Football, Soccer; UD Grad, Bar Code Inventor Returns to Alma Mater; JFK Statue Comes Home in Time for Homecoming; 300 Runners Expected for Homecoming 5k Race

News release announces that two pep rallies will be held during Homecoming Weekend; Paul McEnroe will talk about his work to develop the bar code; The restored JFK statue will be unveiled; The annual Thomas J. Frericks 5K run/walk will be held

Traceability System’s Impact On Process Mining in Production

From the perspective of manufacturing companies, data handling is gaining more attention as it is becoming a strategic resource in digital ecosystems. Market forces such as rising amounts of product variants and decreasing batch sizes lead to higher complexity in manufacturing processes. Therefore, production management’s demand for data-based process transparency is growing continuously as well as the number of companies turning to process mining to address these challenges. The increased use of process mining has uncovered many documented data quality issues that hamper output quality. In response to data usage and quality problems, research in the field of Big Data has turned to sophisticated data value chains as a promising approach to optimize data usage. This paper presents the application of the data value chain concept on a manufacturing use case, delivering an assessment of traceability systems and their effect on data quality issues. This assessment reviews commonly known quality issues and investigates how traceability systems can influence and facilitate better data quality. The results support manufacturing companies in their use of traceability systems to improve the reliability of their process mining input data and, hence, their output performance indicators to meet the demand for more data-based process transparency

An Approach For Analysis Of Human Interaction With Worker Assistance Systems Based On Eye Tracking And Motion Capturing

Human behavior in production systems influences productivity, product quality, work safety and overall process performance. To guide human behavior, digital worker assistance systems can be used to support cognitive decision tasks and sensory perception tasks. In doing so, the design of the assistance systems affects user experience and work results. To optimize and develop human-centric productions systems, data on human behavior and interaction with manufacturing equipment must be collected and analyzed. This analysis is expected to yield benefits regarding process monitoring, quality assurance, user experience and ergonomics. In addition, the results could be used for training purposes to monitor skill improvements. This paper presents a framework for data acquisition and analysis of human interaction with digital worker assistance systems. In addition to the overall system architecture, the individual development steps are discussed. An eye tracking device and a motion capturing camera are used for data collection and provide live information about human behavior in conjunction with a digital worker assistance system. The data is stored in a database and analyzed by custom analysis algorithms. The results are displayed in a dashboard application and show that the presented framework with eye tracking and motion capturing is suitable for the analysis of human interaction with worker assistance systems

Weiterbildung und Wertschöpfung an einem Ort: Neues betriebliches Lernen für KI-Anwendungen in der Produktion

Künstliche Intelligenz (KI) ist nicht aufzuhalten – oder vielleicht doch? Ein Problem in der Praxis ist es, geschultes Personal für den Umgang mit KI zu finden. Mit einer zunehmenden Verbreitung von KI-Anwendungen in der Produktion steigen gleichermaßen die Anforderungen an die betriebliche Weiterbildung der Mitarbeitenden, um die Technologie adäquat verwenden zu können. Lernen außerhalb des Arbeitsplatzes bspw. in Form von Seminaren und Weiterbildungen kann die spezifischen Anforderungen, die bei der Implementierung der neuen Technologie an einem Arbeitsplatz oder Prozess gefragt sind, nur unzureichend erfüllen. Am Mittelstand-Digital Zentrum Darmstadt wird daher an einem kognitiven Assistenzsystem gearbeitet, durch welches Mitarbeitende direkt am Ort der Wertschöpfung die Grundlagen der KI vermittelt bekommen und diese an ihrem Arbeitsplatz unmittelbar nutzen können

Towards a Data-driven Performance Management in Digital Shop Floor Management

Key performance indicators (KPIs) are crucial for measuring and managing the performance of industrial processes. They are used to detect deviations in processes, enabling opportunities to improve manufacturing processes within the three dimensions time, quality, and cost. In this context, the timeliness of information plays a decisive role in the success of measures since delayed information availability can leave decision makers with no time to react. With the introduction of digitization and industry 4.0, increasing amounts of data become available. They can be used to accelerate problem detection and shortening reaction times to define appropriate actions. This paper presents a data-driven performance management approach integrated in digital shop floor management (dSFM). If a deviation is detected in one process, KPIs of subsequent processes (horizontal level) as well as subordinate levels (vertical level) are checked for correlations and, if present, the associated team is notified by an automatic warning through the dSFM system. Based on the identified correlations, the team discusses the deviations and defines suitable countermeasures. The aim of this approach is to identify deviations more quickly and to quantify their impacts, thus giving shop floor managers the ability to react in time

Integrating Assessment Methods in the Development of ML-based Business Models for Manufacturing

The use of machine learning promises great potential along the entire value chain of manufacturing companies. Many companies have already recognized the resulting opportunities for increasing enterprise value and are developing their machine learning applications for the production environment. However, despite these efforts, many of the solutions developed fail in the market. Especially small- and medium-sized enterprises have difficulties developing suitable business models for their technical applications. These difficulties arise because companies do not evaluate their business projects sufficiently during the development phases. As a result, unpromising projects are not recognized until late in the development process and thus cause high sunk costs. This paper presents an approach for integrating assessment methods into developing machine learning- driven business models for production. Due to the diametric evolution of information availability and uncertainty during the business model development process, various methods and tools can be used for the assessment depending on the current phase. For this purpose, existing assessment methods are evaluated and contrasted regarding their suitability concerning machine learning-based business models for production. Afterwards, three approaches for the different planning phases of business model development (strategic, tactical, operational) are presented in this paper

Produktionsplanung und -steuerung (PPS) – ein Überblick der Literatur der unterschiedlichen Einteilung von PPS-Konzepten

Deutsch: Die Produktionsplanung und -steuerung (PPS) plant und realisiert Fertigungsabläufe, um vorgegebene Produktionsziele zu erreichen. Hinsichtlich der theoretischen Abfolge der einzelnen Schritte, die im PPS-Prozess durchlaufen werden, besteht in der wissen-schaftlichen Literatur jedoch kein Konsens. Diese Publikation gibt einen Überblick über bestehende Konzepte zur Einteilung verschiedener PPS-Konzepte und hebt sowohl deren Gemeinsamkeiten als auch deren Unterschiede hervor. Durch einen Vergleich von zwan-zig Definitionen verschiedener Autoren können grundlegend die Schritte der Produkti-onsplanung in eine Gruppe und die Schritte der Produktionssteuerung in drei verschie-dene Gruppen gegliedert werden. Das Ziel der Publikation ist es diese Konzepte wert-neutral aufzulisten. English: Production planning and control (PPC) plans and implements production processes in order to achieve predefined production targets. However, in the scientific literature is no consensus regarding the theoretical sequence of the individual steps that are went through in the PPC process. This publication provides an overview of existing concepts for classifying different PPC concepts and highlights their similarities as well as their dif-ferences. By comparing twenty definitions of different authors, the steps of production planning can be divided into one group, the steps of production control can be divided into three different groups. This publication targets a value-free overview about PPC cat-egories

Knowledge Graphs for Data And Knowledge Management in Cyber-Physical Production Systems

Cyber-physical production systems are constituted of various sub-systems in a production environment, from machines to logistics networks, that are connected and exchange data in real-time. Every sub-system consumes and generates data. This data has the potential to support decision making and optimization of production processes. To extract valuable information from this data, however, different data sources must be consolidated and analyzed. A Knowledge Graph (KG), also known as a semantic network, represents a net of real-world entities, i.e., machines, sensors, processes, or concepts, and illustrates their relationship. KG allows us to encode the knowledge and data context into a human interpretable form and is amenable to automated analysis and inference. This paper presents the potential of KG in manufacturing and proposes a framework for its implementation. The proposed framework should assist practitioners in integrating raw data from multiple data sources in production, developing a suitable data model, creating the knowledge graph, and using it in a graph application. Although the framework is applicable for different purposes, this work illustrates its use for supporting the quality assessment of products in a discrete manufacturing production line