SIMULATING EXOGENOUS SHOCKS IN COMPLEX SUPPLY NETWORKS USING MODULAR STOCHASTIC PETRI NETS

Almost all major companies are embedded in complex, global supply networks, consisting of multiple nested supply chains, and building up a high level of complexity. Exogenous shocks on these networks (e.g. natural disasters) can directly and indirectly impact companies and even cause their entire supply network to fail. However, today it is extremely difficult for a company to predict the actual impact of an exogenous shock on its supply network. Hence, companies are not able to identify adequate counteractive measures. Therefore safeguarding measures are oftentimes insufficient or even counterproductive. This paper deals with modelling, analyzing and quantifying impacts of exogenous shocks on supply networks using Petri Nets. It provides means to simulate the vulnerability of different network constellations regarding exogenous influences. In order to evaluate the proposed method, we simulate different intensities of an exogenous shock delaying the delivery for an exemplary supply network. We thereby illustrate which results could be yielded from a real-world application. For our exemplary network we find that the marginal effect of a disruption declines with an increasing intensity of shock. Moreover, the impact of shocks can be mitigated by appropriate counteractive measures like in this example by an increased safety margin of stock

The Cost of Knowing: An Economic Evaluation of Context Acquisition in DSA Systems

Much of the research in Dynamic Spectrum Access (DSA) has focused on the details of the enabling technologies. While this has been quite useful in establishing the technical feasibility of DSA systems, it has missed an important aspect of the overall DSA problem space: in order for operators, regulators and users to be interested in deploying DSA based networks, the expected costs should be in proportion to what the users are realistically willing to pay for services. Consequently, it is important to conduct cost estimates for different DSA approaches in parallel with the technical research.\ud \ud In this paper, we will explore how the cost experienced by primary and secondary users can influence their incentives for participation in DSA. To do this, we compare the costs and cost structures of four context awareness approaches from each of them. The costs we will consider are incremental capital costs over a basic software radio using four different context acquisition approaches (sensing, databases, sensor networks, and cooperative sharing). Since DSA is still a relatively new research field, there is a lot of uncertainty associated with incremental cost analyses. As a result, the cost analysis is parameterized to allow for explicit reasoning about the bounds of cost components

A manufacturing model to support data-driven applications for design and manufacture

This thesis is primarily concerned with conceptual work on the Manufacturing Model. The Manufacturing Model is an information model which describes the manufacturing capability of an enterprise. To achieve general applicability, the model consists of the entities that are relevant and important for any type of manufacturing firm, namely: manufacturing resources (e.g. machines, tools, fixtures, machining cells, operators, etc.), manufacturing processes (e.g. injection moulding, machining processes, etc.) and manufacturing strategies (e.g. how these resources and processes are used and organized). The Manufacturing Model is a four level model based on a de—facto standard (i.e. Factory, Shop, Cell, Station) which represents the functionality of the manufacturing facility of any firm. In the course of the research, the concept of data—driven applications has emerged in response to the need of integrated and flexible computer environments for the support of design and manufacturing activities. These data—driven applications require the use of different information models to capture and represent the company's information and knowledge. One of these information models is the Manufacturing Model. The value of this research work is highlighted by the use of two case studies, one related with the representation of a single machining station, and the other, the representation of a multi-cellular manufacturing facility of a high performance company

Value Creation with Extended Reality Technologies - A Methodological Approach for Holistic Deployments

Mit zunehmender Rechenkapazität und Übertragungsleistung von Informationstechnologien wächst die Anzahl möglicher Anwendungs-szenarien für Extended Reality (XR)-Technologien in Unternehmen. XR-Technologien sind Hardwaresysteme, Softwaretools und Methoden zur Erstellung von Inhalten, um Virtual Reality, Augmented Reality und Mixed Reality zu erzeugen. Mit der Möglichkeit, Nutzern Inhalte auf immersive, interaktive und intelligente Weise zu vermitteln, können XR-Technologien die Produktivität in Unternehmen steigern und Wachstumschancen eröffnen. Obwohl XR-Anwendungen in der Industrie seit mehr als 25 Jahren wissenschaftlich erforscht werden, gelten nach wie vor als unausgereift. Die Hauptgründe dafür sind die zugrundeliegende Komplexität, die Fokussierung der Forschung auf die Untersuchung spezifische Anwendungsszenarien, die unzu-reichende Wirtschaftlichkeit von Einsatzszenarien und das Fehlen von geeigneten Implementierungsmodellen für XR-Technologien. Grundsätzlich wird der Mehrwert von Technologien durch deren Integration in die Wertschöpfungsarchitektur von Geschäftsmodellen freigesetzt. Daher wird in dieser Arbeit eine Methodik für den Einsatz von XR-Technologien in der Wertschöpfung vorgestellt. Das Hauptziel der Methodik ist es, die Identifikation geeigneter Einsatzszenarien zu ermöglichen und mit einem strukturierten Ablauf die Komplexität der Umsetzung zu beherrschen. Um eine ganzheitliche Anwendbarkeit zu ermöglichen, basiert die Methodik auf einem branchen- und ge-schäftsprozessunabhängigen Wertschöpfungsreferenzmodell. Dar-über hinaus bezieht sie sich auf eine ganzheitliche Morphologie von XR-Technologien und folgt einer iterativen Einführungssequenz. Das Wertschöpfungsmodell wird durch ein vorliegendes Potential, eine Wertschöpfungskette, ein Wertschöpfungsnetzwerk, physische und digitale Ressourcen sowie einen durch den Einsatz von XR-Technologien realisierten Mehrwert repräsentiert. XR-Technologien werden durch eine morphologische Struktur mit Anwendungsmerk-malen und erforderlichen technologischen Ressourcen repräsentiert. Die Umsetzung erfolgt in einer iterativen Sequenz, die für den zu-grundeliegenden Kontext anwendbare Methoden der agilen Soft-wareentwicklung beschreibt und relevante Stakeholder berücksich-tigt. Der Schwerpunkt der Methodik liegt auf einem systematischen Ansatz, der universell anwendbar ist und den Endnutzer und das Ökosystem der betrachteten Wertschöpfung berücksichtigt. Um die Methodik zu validieren, wird der Einsatz von XR-Technologien in zwei industriellen Anwendungsfällen unter realen wirtschaftlichen Bedingungen durchgeführt. Die Anwendungsfälle stammen aus unterschiedlichen Branchen, mit unterschiedlichen XR-Technologiemerkmalen sowie unterschiedlichen Formen von Wert-schöpfungsketten, um die universelle Anwendbarkeit der Methodik zu demonstrieren und relevante Herausforderungen bei der Durch-führung eines XR-Technologieeinsatzes aufzuzeigen. Mit Hilfe der vorgestellten Methodik können Unternehmen XR-Technologien zielgerichtet in ihrer Wertschöpfung einsetzen. Sie ermöglicht eine detaillierte Planung der Umsetzung, eine fundierte Auswahl von Anwendungsszenarien, die Bewertung möglicher Her-ausforderungen und Hindernisse sowie die gezielte Einbindung der relevanten Stakeholder. Im Ergebnis wird die Wertschöpfung mit wirtschaftlichem Mehrwert durch XR-Technologien optimiert

A Machine Learning Approach to Customer Needs Analysis for Product Ecosystems

Creating product ecosystems has been one of the strategic ways to enhance user experience and business advantages. Among many, customer needs analysis for product ecosystems is one of the most challenging tasks in creating a successful product ecosystem from both the perspectives of marketing research and product development. In this paper, we propose a machine-learning approach to customer needs analysis for product ecosystems by examining a large amount of online user-generated product reviews within a product ecosystem. First, we filtered out uninformative reviews from the informative reviews using a fastText technique. Then, we extract a variety of topics with regard to customer needs using a topic modeling technique named latent Dirichlet allocation. In addition, we applied a rule-based sentiment analysis method to predict not only the sentiment of the reviews but also their sentiment intensity values. Finally, we categorized customer needs related to different topics extracted using an analytic Kano model based on the dissatisfaction-satisfaction pair from the sentiment analysis. A case example of the Amazon product ecosystem was used to illustrate the potential and feasibility of the proposed method.https://deepblue.lib.umich.edu/bitstream/2027.42/153965/1/A Machine Learning Approach to Customer Needs Analysis for Product Ecosystems.pd

Computer science: Key to a space program renaissance. The 1981 NASA/ASEE summer study on the use of computer science and technology in NASA. Volume 2: Appendices

Adoption of an aggressive computer science research and technology program within NASA will: (1) enable new mission capabilities such as autonomous spacecraft, reliability and self-repair, and low-bandwidth intelligent Earth sensing; (2) lower manpower requirements, especially in the areas of Space Shuttle operations, by making fuller use of control center automation, technical support, and internal utilization of state-of-the-art computer techniques; (3) reduce project costs via improved software verification, software engineering, enhanced scientist/engineer productivity, and increased managerial effectiveness; and (4) significantly improve internal operations within NASA with electronic mail, managerial computer aids, an automated bureaucracy and uniform program operating plans

Service identification for business process management.

Over the years Service Oriented Architecture (SOA) has gained momentum and is becoming the standard for providing systematic business solutions. Likewise, the requirements for identifying business services are fast changing and a solution to the service identification problem needs a robust approach. It is known that this task of identifying candidate services is the first and the most important step in developing service-oriented business systems. The recent approaches of identifying candidate services have some shortcomings (defined data type size, unrepeatable approach, inapplicable to all enterprise information system and unadaptable to business factor change). Some approaches focus on fixed cases or certain types of organizations (single or collaborating organizations) neglecting the enterprise systems which are either (open or closed) single or collaborating enterprise information system, which makes some past approaches not applicable to some real-life business cases. This thesis focuses on solving the headline issues and introduces a new approach for service identification applicable to different organization’s business processes. The thesis also proposes a new step-by-step algorithm and methodology that identify business services derived from data-set from any given business case

Annual Report Of Research and Creative Productions by Faculty and Staff, January to December, 2014

Annual Report Of Research and Creative Productions by Faculty and Staff from January to December, 2014