FIR e. V. an der RWTH Aachen
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Wachsende Informationssysteme (IS) gehen oft einher mit einer wachsenden IT-Komplexität, weil sich heterogene IT-Landschaften über Jahre hinweg zu einem Flickenteppich entwickeln. Diese Entwicklung bringt steigende IT-Kosten und Abhängigkeiten mit sich, die die Wartung und Entwicklung der IT-Landschaften behindern. Der Artikel beleuchtet die aktuelle Literatur über Methoden zum Management von IT-Komplexität mit der Methodik eines Literaturreviews. Da die Definitionen von IT-Komplexität in der Literatur weit auseinandergehen, wird eine Herleitung und eine Definition aufgestellt. Außerdem werden die Ergebnisse des Literatur-Suchprozesses vorgestellt. Danach folgt eine Diskussion und Synthese der Literatur sowie ein Ausblick auf weitere Forschungsfelder.
Bedingt durch den Ausbau der dezentralen Energieversorgung steigen die Redispatch-Maßnahmen durch die Übertragungsnetzbetreiber zur Netzstabilisierung. Je nach Szenario werden in 2030 allerdings 62 - 82 % der Bruttostromnachfrage direkt in das Verteilnetz eingespeist, sodass die Verteilnetzbetreiber die Netzbilanz bereits auf NS- und MS-Ebene ausgleichen müssen. Eine Lösungskomponente dazu ist das Industrial Smart Grid; dazu sind tiefere Einblicke in die Produktionsplanung von Industrieunternehmen nötig, um das Netz proaktiv zu regeln. Die Grundlage für diese nötige Transparenz wird unternehmensintern durch die Etablierung von Energiemanagement und dazu nötigen Energieinformationssystemen geschaffen. Um eine zielgerichtete Systemauswahl für produzierende Unternehmen zu ermöglichen, ist ein grundlegender Funktionskatalog eines Energieinformationssystems zu erstellen. Dieses Paper beschreibt die Erstellung eines solchen Funktionskatalogs in Form eines übersichtlichen Funktionsbaums und einer exemplarischen Funktionsdarstellung in einem Use-Case-Diagramm.
Im Strommarkt 2.0 wird ein Paradigmenwechsel von einer verbrauchsorientierten Erzeugung zu einem erzeugungsorientierten Verbrauch durchlaufen. Dies erfordert in Zukunft ein gesteigertes Angebot von Flexibilität. Die Idee und die für das Energiesystem dienlichen Potentiale eines Flexibilitätsmarktes für die Industrie sind bekannt, dennoch werden diese Möglichkeiten zurzeit nur eingeschränkt genutzt. Es stellt sich somit die Frage, durch welche Anreize und Dienstleistungen es gelingen kann, Unternehmen zur Bereitstellung von Flexibilität zu motivieren. Hierfür werden im vorliegenden Paper die bestehenden Energiedienstleistungen und neue Flexibilitätsdienstleistungen strukturiert beschrieben und Potenziale herausgestellt.
Thanks to the challenges of the imminent energy turnaround, the power market faces a revolution regarding the energy distribution. In future, energy will not only be distributed from a limited number of large, centralized power plants but also from small, decentralized power generators, e.g. households. This also affects manufacturing companies, which are confronted with developing an energy management strategy. As those companies usually have not set high priorities on their energy management, there is a lack of a structured procedure to build an energy management strategy. Consequently, this creates the need for supporting methods to develop and implement an energy management strategy. This paper tackles the first step in the development of an energy management strategy. For this purpose, a target map is developed and possible use cases are systematized. The next steps of the implementation are presented using the example of load management.
Personal user data is collected and processed at large scale by a handful of big providers of Internet services. This is detrimental to users, who often do not understand the privacy implications of this data collection, as well as to small parties interested in gaining insights from this data pool, e.g., research groups or small and middle-sized enterprises. To remedy this situation, we propose a transparent and user-controlled data market in which users can directly and consensually share their personal data with interested parties for monetary compensation. We define a simple model for such an ecosystem and identify pressing challenges arising within this model with respect to the user and data processor demands, legal obligations, and technological limits. We propose myneData as a conceptual architecture for a trusted online platform to overcome these challenges. Our work provides an initial investigation of the resulting myneData ecosystem as a foundation to subsequently realize our envisioned data market via the myneData platform.
With big data-technologies on the rise, new fields of application appear in terms of analyzing data to find new relationships for improving process under-standing and stability. Manufacturing companies oftentimes cope with a high number of deviations but struggle to solve them with less effort. The research project BigPro aims to develop a methodology for implementing counter measures to disturbances and deviations derived from big data. This paper proposes a methodology for practitioners to assess predefined counter measures. It consists of a morphology with several criterions that can have a certain characteristic. Those are then combined with a weighting factor to assess the feasibility of the counter measure for prioritization.
Manufacturing companies are facing an increasingly turbulent market – a market defined by products growing in complexity and shrinking product life cycles. This leads to a boost in planning complexity accompanied by higher error sensitivity. In practice, IT systems and sensors integrated into the shop floor in the context of Industry 4.0 are used to deal with these challenges. However, while existing research provides solutions in the field of pattern recognition or recommended actions, a combination of the two approaches is neglected. This leads to an overwhelming amount of data without contributing to an improvement of processes. To address this problem, this study presents a new platform-based concept to collect and analyze the high-resolution data with the use of self-learning algorithms. Herby, patterns can be identified and reproduced, allowing an exact prediction of the future system behavior. Artificial intelligence maximizes the automation of the reduction and compensation of disruptive factors.
Influenced by the high dynamic of the markets the optimization of supply chains gains more importance. However, analyzing different procurement strategies and the influence of various production parameters is difficult to achieve in industrial practice. Therefore, simulations of supply chains are used in order to improve the production process. The objective of this research is to evaluate different procurement strategies in a four-stage supply chain. Besides, this research aims to identify main influencing factors on the supply chain’s performance. The performance of the supply chain is measured by means of back orders (backlog). A scenario analysis of different customer demands and a Design of Experiments analysis enhance the significance of the simulation results.
Electro mobility provides the basis for a fundamental, worldwide change when it comes to individual mobility. This change goes far beyond just using vehicles with an electric engine. The transformation also holds the opportunity of developing and providing new kinds of service solutions that can determine the success of electro mobility significantly. A good service offering can function as a mediator between the technology on the one hand and the market and its potential buyers on the other hand. So far, the number of sold and used electric vehicles in the world does not meet the expectations as potential buyers hesitate to purchase the cars. Currently existing scientific work researching the development of electro mobility is primarily focused on technological aspects of e-mobility, such as the range electric vehicles can reach – an in-depth analysis of all the other influence factors effecting the development and success of electro mobility does not exist yet. It is necessary, however, in order to not only implement suitable service solutions successfully but also influence the development of electro mobility in a positive way. Specific criteria that affect the success of e-mobility can only be met if they are clearly identified. Therefore, the aim of this research paper is to identify relevant key influence factors for the future development of electro mobility and researching which requirements need to be considered in order to increase the acceptance of electric mobility.
The topics Internet of Things and Industry 4.0 increasingly lead to the fact that the customer is increasingly focused on manufacturing companies. He wants to know delivery date of the product, wants to make changes at short notice, get an individualized product and much more. Technologically, these requirements have already been met, but the structures within the company as well as the operational processes are not yet or only partially prepared to cope with the increasing complexity and dynamics of production. This leads to many deviations with which the production controller must deal, whether they are complex or trivial.
In order to counteract the increasing number and frequency of deviation situations which are currently encountered with complex manual interventions, it is necessary to systematically evaluate deviations and then to allocate them a dominant reaction strategy (manual, partially automated, automated) from which a suitable reaction measure can be derived. This relieves the production controller, since assistance systems partially eliminate deviations independently.
As a result, the production controller gets more time to deal with the cause of deviations so that a new occurrence of deviations can be avoided and the number of deviations can be reduced sustainably. The following paper provides a solution for the assessment of deviations. In addition, it includes differentiation logic to allocate one of the three different reaction strategies to the identified deviation.