Applying model-based systems engineering to architecture optimization and selection during system acquisition

2018 Fall.Includes bibliographical references.The architecture selection process early in a major system acquisition is a critical step in determining the overall affordability and technical performance success of a program. There are recognized deficiencies that frequently occur in this step such as poor transparency into the final selection decision and excessive focus on lowest cost, which is not necessarily the best value for all of the stakeholders. This research investigates improvements to the architecture selection process by integrating Model-Based Systems Engineering (MBSE) techniques, enforcing rigorous, quantitative evaluation metrics with a corresponding understanding of uncertainties, and stakeholder feedback in order to generate an architecture that is more optimized and trusted to provide better value for the stakeholders. Three case studies were analyzed to demonstrate this proposed process. The first focused on a satellite communications System of Systems (SoS) acquisition to demonstrate the overall feasibility and applicability of the process. The second investigated an electro-optical remote sensing satellite system to compare this proposed process to a current architecture selection process typified by the United States Department of Defense (U.S. DoD) Analysis of Alternatives (AoA). The third case study analyzed the evaluation of a service-oriented architecture (SOA) providing satellite command and control with cyber security protections in order to demonstrate rigorous accounting of uncertainty through the architecture evaluation and selection. These case studies serve to define and demonstrate a new, more transparent and trusted architecture selection process that consistently provides better value for the stakeholders of a major system acquisition. While the examples in this research focused on U.S. DoD and other major acquisitions, the methodology developed is broadly applicable to other domains where this is a need for optimization of enterprise architectures as the basis for effective system acquisition. The results from the three case studies showed the new process outperformed the current methodology for conducting architecture evaluations in nearly all criteria considered and in particular selects architectures of better value, provides greater visibility into the actual decision making, and improves trust in the decision through a robust understanding of uncertainty. The primary contribution of this research then is improved information support to an architecture selection in the early phases of a system acquisition program. The proposed methodology presents a decision authority with an integrated assessment of each alternative, traceable to the concerns of the system's stakeholders, and thus enables a more informed and objective selection of the preferred alternative. It is recommended that the methodology proposed in this work is considered for future architecture evaluations

Cloud computing : developing a cost estimation model for customers

Cloud computing is an essential part of the digital transformation journey. It offers many benefits to organisations, including the advantages of scalability and agility. Cloud customers see cloud computing as a moving train that every organisation needs to catch. This means that adoption decisions are made quickly in order to keep up with the new trend. Such quick decisions have led to many disappointments for cloud customers and have questioned the cost of the cloud. This is also because there is a lack of criteria or guidelines to help cloud customers get a complete picture of what is required of them before they go to the cloud. From another perspective, as new technologies force changes to the organizational structure and business processes, it is important to understand how cloud computing changes the IT and non-IT departments and how can this be translated into costs. Accordingly, this research uses the total cost of ownership approach and transaction cost theory to develop a customer-centric model to estimate the cost of cloud computing. The Research methodology used the Design Science Research approach. Expert interviews were used to develop the model. The model was then validated using four case studies. The model, named Sunny, identifies many costs that need to be estimated, which will help to make the cloud-based digital transformation journey less cloudy. The costs include Meta Services, Continuous Contract management, Monitoring and ITSM Adjustment. From an academic perspective, this research highlights the management efforts required for cloud computing and how misleading the rapid provision potential of the cloud resources can be. From a business perspective, proper estimation of these costs would help customers make informed decisions and vendors make realistic promises.Cloud Computing ist ein wesentlicher Bestandteil der Digitalisierung. Es bietet Unternehmen viele Vorteile, wie Skalierbarkeit und Agilität. Cloud-Kunden sehen Cloud Computing als einen Zug, auf den jedes Unternehmen aufspringen muss. Das bedeutet, dass Einführungsentscheidungen schnell getroffen werden, um mit dem neuen Trend Schritt zu halten. Solche Schnellschüsse haben zu vielen Enttäuschungen bei Cloud-Kunden geführt und die Kosten der Cloud in Frage gestellt. Dies ist auch darauf zurückzuführen, dass es keine Kriterien oder Leitlinien gibt, die den Cloud-Kunden helfen, sich ein vollständiges Bild davon zu machen, was von ihnen erwartet wird, bevor sie in die Cloud gehen. Aus einem anderen Blickwinkel ist es wichtig zu verstehen, wie Cloud Computing IT- und Nicht-IT-Abteilungen verändert und wie sich dies auf die Kosten auswirkt, da neue Technologien Veränderungen in der Organisationsstruktur und den Geschäftsprozessen erzwingen. Dementsprechend werden in dieser Forschungsarbeit der Total Cost of Ownership-Ansatz und die Transaktionskostentheorie verwendet, um ein kundenorientiertes Modell zur Schätzung der Kosten von Cloud Computing zu entwickeln. Die Forschungsmethodik basiert auf dem Design Science Research Ansatz. Zur Entwicklung des Modells wurden Experteninterviews durchgeführt. Anschließend wurde das Modell anhand von vier Fallstudien validiert. Das Modell mit dem Namen Sunny identifiziert viele Kosten, die geschätzt werden müssen, um die Reise zur digitalen Transformation in der Cloud weniger wolkig zu gestalten. Zu diesen Kosten gehören Meta-Services, kontinuierliches Vertragsmanagement, Überwachung und ITSM-Anpassung. Aus akademischer Sicht verdeutlicht diese Forschung, welcher Verwaltungsaufwand für Cloud Computing erforderlich ist und wie irreführend das schnelle Bereitstellungspotenzial von Cloud-Ressourcen sein kann. Aus Unternehmenssicht würde eine korrekte Einschätzung dieser Kosten den Kunden helfen, fundierte Entscheidungen zu treffen, und den Anbietern, realistische Versprechungen zu machen