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As the complexity of learning task requirements, computer infrastruc- tures and knowledge acquisition for artificial neuronal networks (ANN) is in- creasing, it is challenging to talk about ANN without creating misunderstandings. An efficient, transparent and failure-free design of learning tasks by models is not supported by any tool at all. For this purpose, particular the consideration of data, information and knowledge on the base of an integration with knowledge- intensive business process models and a process-oriented knowledge manage- ment are attractive. With the aim of making the design of learning tasks express- ible by models, this paper proposes a graphical modeling language called Neu- ronal Training Modeling Language (NTML), which allows the repetitive use of learning designs. An example ANN project of AI-based dynamic GUI adaptation exemplifies its use as a first demonstration.
Industry 4.0, based on increasingly progressive digitalization, is a global phenomenon that affects every part of our work. The Internet of Things (IoT) is pushing the process of automation, culminating in the total autonomy of cyber-physical systems. This process is accompanied by a massive amount of data, information, and new dimensions of flexibility. As the amount of available data increases, their specific timeliness decreases. Mastering Industry 4.0 requires humans to master the new dimensions of information and to adapt to relevant ongoing changes. Intentional forgetting can make a difference in this context, as it discards nonprevailing information and actions in favor of prevailing ones. Intentional forgetting is the basis of any adaptation to change, as it ensures that nonprevailing memory items are not retrieved while prevailing ones are retained. This study presents a novel experimental approach that was introduced in a learning factory (the Research and Application Center Industry 4.0) to investigate intentional forgetting as it applies to production routines. In the first experiment (N = 18), in which the participants collectively performed 3046 routine related actions (t1 = 1402, t2 = 1644), the results showed that highly proceduralized actions were more difficult to forget than actions that were less well-learned. Additionally, we found that the quality of cues that trigger the execution of routine actions had no effect on the extent of intentional forgetting.
Die Innovationstätigkeit im industriellen Umfeld verlagert sich durch die Digitalisierung hin zu Produkt-Service-Systemen. Kleine und mittlere Unternehmen haben sich in ihrer Entwicklungstätigkeit bisher stark auf die Produktentwicklung bezogen. Der Umstieg auf „smarte“ Produkte und die Kopplung an Dienstleistungen erfordert häufig personelle und finanzielle Ressourcen, welche KMU nicht aufbringen können. Crowdsourcing stellt eine Möglichkeit dar, den Innovationsprozess für externe Akteure zu öffnen und Kosten- sowie Geschwindigkeitsvorteile zu realisieren. Bei der Integration von Crowdsourcing-Elementen ist jedoch einigen Herausforderungen zu begegnen. Dieser Beitrag zeigt sowohl die Potenziale als auch die Barrieren einer Crowdsourcing-Nutzung im industriellen Umfeld auf.
The implementation of learning scenarios is a diversely challenging, frequently purely manual and effortful undertaking. In this contribution a process based view is used in scenario generation to overcome communication, coordination and technical gaps. A framework is provided to identify, define and integrate technological artefacts and learning content as modular, reusable building blocks along a modeled production process. The specific contribution is twofold: 1) the theoretical framework represents a unique basis for modularization of content and technology in order to enhance reusability, 2) the model based scenario definition is a starting point for automated implementation of learning scenarios in industrial learning environments that has not been created before.
The increasing demand for software engineers cannot completely be fulfilled by university education and conventional training approaches due to limited capacities. Accordingly, an alternative approach is necessary where potential software engineers are being educated in software engineering skills using new methods. We suggest micro tasks combined with theoretical lessons to overcome existing skill deficits and acquire fast trainable capabilities. This paper addresses the gap between demand and supply of software engineers by introducing an actionoriented and scenario-based didactical approach, which enables non-computer scientists to code. Therein, the learning content is provided in small tasks and embedded in learning factory scenarios. Therefore, different requirements for software engineers from the market side and from an academic viewpoint are analyzed and synthesized into an integrated, yet condensed skills catalogue. This enables the development of training and education units that focus on the most important skills demanded on the market. To achieve this objective, individual learning scenarios are developed. Of course, proper basic skills in coding cannot be learned over night but software programming is also no sorcery.
Lernen mit Assistenzsystemen
(2020)
Der Beitrag beschreibt die Konzeption und Durchführung und bietet einen Einblick in die ersten Ergebnisse einer Untersuchung mit experimentellem Design in einer simulierten Prozessumgebung im Forschungs- und Anwendungszentrum Industrie 4.0 in Potsdam. Im Mittelpunkt stehen Anlernprozesse im Bereich der Einfacharbeit (Helfertätigkeiten) und ihre Gestaltung durch den Einsatz digitaler Assistenzsysteme. In der Arbeitsforschung finden sich Hinweise darauf, dass mit dem Einsatz dieser Systeme Prozesswissen verloren geht, im Sinne einer guten Kenntnis des gesamten Arbeitsprozesses, in den die einzelnen Tätigkeiten eingebettet sind. Das kann sich als Problem erweisen, vor allem wenn unvorhersehbare Situationen oder Fehler eintreten. Um die Rolle von Prozesswissen beim Einsatz von digitalen Assistenzsystemen zu untersuchen, wird im Experiment eine echte Fabriksituation simuliert. Die Probanden werden über ein Assistenzsystem Schritt für Schritt in ihre Aufgabentätigkeit angelernt, einem Teil der Probanden wird allerdings am Anfang zusätzlich Prozesswissen im Rahmen einer kurzen Schulung vermittelt.
Willentliches Vergessen
(2019)
Dieser Beitrag im Journal Gruppe. Interaktion. Organisation. stellt dar, wie willentliches Vergessen die Anpassung an notwendige Veränderungen für Individuen, Gruppen und Organisationen verbessert und wie willentliches Vergessen bewusst und gezielt gestaltet werden kann.
Damit Verhalten in Folge einer notwendigen Veränderung angepasst wird, reicht es nicht aus, dass Menschen wissen was zu tun ist, willens und in der Lage sind ihr Verhalten zu verändern. Eine Veränderung gelingt nur dann, wenn nur noch das neue Verhalten zur Anwendung kommt und nicht mehr das Alte, wenn das alte Verhalten vergessen wird. Der notwendige Prozess des willentlichen Vergessens ist durch Entfernen von Hinweisreizen, die die Erinnerung des zu Vergessenden und durch Platzierung von Hinweisreizen, die die Aktivierung des Neuen auslösen, gestaltbar.
Der vorliegende Beitrag stellt die förderliche Wirkung von Hinweisreizen auf willentliches Vergessen dar, stellt sie im Rahmen des Berichts einer experimentellen Studie unter Beweis und gibt praktische Implikationen, wie für Individuen, Gruppen und Organisationen willentliches Vergessen gestaltet werden kann.
In nowadays production, fluctuations in demand, shortening product life-cycles, and highly configurable products require an adaptive and robust control approach to maintain competitiveness. This approach must not only optimise desired production objectives but also cope with unforeseen machine failures, rush orders, and changes in short-term demand. Previous control approaches were often implemented using a single operations layer and a standalone deep learning approach, which may not adequately address the complex organisational demands of modern manufacturing systems. To address this challenge, we propose a hyper-heuristics control model within a semi-heterarchical production system, in which multiple manufacturing and distribution agents are spread across pre-defined modules. The agents employ a deep reinforcement learning algorithm to learn a policy for selecting low-level heuristics in a situation-specific manner, thereby leveraging system performance and adaptability. We tested our approach in simulation and transferred it to a hybrid production environment. By that, we were able to demonstrate its multi-objective optimisation capabilities compared to conventional approaches in terms of mean throughput time, tardiness, and processing of prioritised orders in a multi-layered production system. The modular design is promising in reducing the overall system complexity and facilitates a quick and seamless integration into other scenarios.
This paper presents an exploratory study investigating the influence of the factors (1) intermediary participation, (2) decision-making authority, (3) position in the enterprise, and (4) experience in open innovation on the perception and assessment of the benefits and risks expected from participating in open innovation projects. For this purpose, an online survey was conducted in Germany, Austria and Switzerland. The result of this paper is an empirical evidence showing whether and how these factors affect the perception of potential benefits and risks expected within the context of open innovation project participation. Furthermore, the identified effects are discussed against the theory. Existing theory regarding the benefits and risks of open innovation is expanded by (1) finding that they are perceived mostly independently of the factors, (2) confirming the practical relevance of benefits and risks, and (3) enabling a finer distinction between their degrees of relevance according to respective contextual specifics.