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Technological advancements are giving rise to the fourth industrial revolution - Industry 4.0 -characterized by the mass employment of smart objects in highly reconfigurable and thoroughly connected industrialproduct-service systems. The purpose of this paper is to propose a theory-based knowledgedynamics model in the smart grid scenario that would provide a holistic view on the knowledge-based interactions among smart objects, humans, and other actors as an underlyingmechanism of value co-creation in Industry 4.0. A multi-loop and three-layer - physical, virtual, and interface - model of knowledge dynamics is developedby building on the concept of ba - an enabling space for interactions and theemergence of knowledge. The model depicts how big data analytics are just one component inunlocking the value of big data, whereas the tacit engagement of humans-in-the-loop - theirsense-making and decision-making - is needed for insights to be evoked fromanalytics reports and customer needs to be met.
Faced with the increasing needs of companies, optimal dimensioning of IT hardware is becoming challenging for decision makers. In terms of analytical infrastructures, a highly evolutionary environment causes volatile, time dependent workloads in its components, and intelligent, flexible task distribution between local systems and cloud services is attractive. With the aim of developing a flexible and efficient design for analytical infrastructures, this paper proposes a flexible architecture model, which allocates tasks following a machine-specific decision heuristic. A simulation benchmarks this system with existing strategies and identifies the new decision maxim as superior in a first scenario-based simulation.
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.
Cyber-physical systems (CPS) have shaped the discussion about Industry 4.0 (I4.0) for some time. To ensure the competitiveness of manufacturing enterprises the vision for the future figures out cyber-physical production systems (CPPS) as a core component of a modern factory. Adaptability and coping with complexity are (among others) potentials of this new generation of production management. The successful transformation of this theoretical construct into practical implementation can only take place with regard to the conditions characterizing the context of a factory. The subject of this contribution is a concept that takes up the brownfield character and describes a solution for extending existing (legacy) systems with CPS capabilities.
The digital transformation sets new requirements to all classes of enterprise systems in companies. ERP systems in particular, which represent the dominant class of enterprise systems, are struggling to meet the new requirements at all levels of the architecture. Therefore, there is an urgent need to reconsider the overall architecture of the systems and address the root of the related issues. Given that many restrictions ERP pose on their adaptability are related to the standardization of data, the database layer of ERP systems is addressed. Since database serve as the foundation for data storage and retrieval, they limit the flexibility of enterprise systems and the chance to adapt to new requirements accordingly. So far, relational databases are widely used. Using a systematic literature approach, recent requirements for ERP systems were identified. Prominent database approaches were assessed against the 23 requirements identified. The results reveal the strengths and weaknesses of recent database approaches. To this end, the results highlight the demand to combine multiple database approaches to fulfill recent business requirements. From a conceptual point of view, this paper supports the idea of federated databases which are interoperable to fulfill future requirements and support business operation. This research forms the basis for renewal of the current generation of ERP systems and proposes to ERP vendors to use different database concepts in the future.
Wandlungsfähigkeit von Informationssystemen ist zu einem wesentlichen Wettbewerbsfaktor geworden. Die derzeit unzureichende methodische Unterstützung zur Umsetzung von Wandlungsfähigkeit führt in Unternehmen häufig zu ungenutzten Potentialen einer leistungsfähigen Struktur durch die eingesetzte Informationstechnologie. Ziel des Forschungsprojektes CHANGE ist es, Methoden und Vorgehensmodelle zu entwickeln, die eine dauerhafte Wandlungsfähigkeit von Informationssystemen unterstützen. Dazu wird im Rahmen dieses Beitrages ein Verfahren vorgestellt, welches der Forderung zur Ermittlung der notwendigen Wandlungsfähigkeit unter Einbeziehung des Unternehmensumfeldes nachkommt. Als wesentliches Ergebnis wird ein Kennzahlensystem entwickelt, das zum einen die Umweltsituation als Indikator für den Wandlungsdruck eines Unternehmens beschreibt. Im nächsten Schritt werden Kriterien zur Ermittlung des Wandlungspotentials der eingesetzten IT herangezogen. Abschließend werden beide Dimensionen zusammengeführt und in ihrer Bedeutung für die IT Strategie eines Unternehmens interpretiert.
The concept of adaptability has been widely recognised as research field in recent years. Business information systems play a key part in terms of business performance. Adaptability of information systems therefore is a primary goal of vendors and end-users. However, so far concepts that help to determine the adaptability of Information Systems are missing. Based on research results of the project CHANGE1 this contribution presents an integrated process model addressing the problem and a possible solution.
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.
This contribution presents an approach for requirement oriented team building in industrial processes like product development. This will be based on the knowledge modelling and description language (KMDL(R)) that enables the modelling and analysis of knowledge intensive business processes. First the basic elements of the modelling technique are described, presenting the concept and the description language. Furthermore it is shown how the KMDL(R) process models can be used as a basis for the team building component. Therefore, an algorithm was developed that is able to propose a team composition for a specific task by analyzing the knowledge and skills of the employees, which will be contrasted to the process requirements. This can be used as guidance for team building decisions.
Im Beitrag wird dargestellt, wie der Einsatz von Wissensmanagementwerkzeugen einen wichtigen Beitrag zur Gestaltung eines erfolgreichen ERP-Betriebes leisten kann. Da diese Aufgabe einen hohen Anteil an wissensintensiven Prozessen aufweist, wird dazu eine Methode benötigt, welches die Prozesse strukturiert erfasst, durch die Modellierung analysiert und geeignete Maßnahmen des Wissensmanagements vorschlägt. Die Methode KMDL® ermöglicht durch die Einführung einer Wissensebene die Spezifikation, wann Wissen welchen Inhalts und in welcher Form im Prozess benötigt oder erzeugt wird. Dabei wird auch darauf eingegangen, wie durch die Anwendung der KMDL® der optimale Schulungsbedarf für ERP-Anwender ermittelt werden kann.
Die teilweise sehr kurzfristig notwendige Reaktion auf Veränderungen erfordert von Unternehmen ein hohes Maß an Flexibilität und Reaktionsgeschwindigkeit. Anwendungssystemarchitekturen, die im Wesentlichen aus alten und selbst entwickelten Systemen bestehen, erfüllen häufig diese Anforderungen nicht. Investitionsmittel für neue Software sind jedoch begrenzt, daher müssen Prioritäten in der Ablösung von Altsystemen gesetzt werden. Eine effiziente Analysemethode zur Planung der Erneuerung der Anwendungssystemlandschaft stellt die Wandlungsfähigkeitsanalyse dar. Dieser Beitrag beschreibt Vorgehen und Ergebnisse am Beispiel eines international tätigen Automobilzulieferers.