General Michael V. Hayden
General Michael V. Hayden
Jerry Fisher, ESEP
The purpose of this tutorial is to acquaint the attendees with the INCOSE Systems Engineering Professional Certification Program, how to apply, how to prepare for the exam. Discussions will include:
ASEP, CSEP and ESEP Program Overview
Discussion of the INCOSE Systems Engineering Handbook content and expected changes, including study tips
Discussion of the exam, including: Where to take the exam and scheduling it, What to expect on test day, Preparing for the Exam, Sample questions
A line-by-line tutorial on filling out the application
Howard Eisner, Ph.D.
Building large and complex systems continues to be a significant challenge for today’s systems engineers and integrators. This tutorial sets forth new ways of thinking that could lead to improvements in how systems engineering and integration are carried out. In particular, nine new perspectives are suggested for “”thinking outside the box””. Examples are provided for each along with a discussion of their potential benefits. In addition, thinking in groups as well as historical thinking suggestions are examined. A summary provides an overview of all suggested notions that represent departures from current mainstream approaches.
Kathy Laskey, Ph.D., et. al.
Data analytics (i.e., the process of acquiring, extracting, integrating, transforming, and modeling data with the goal of deriving useful information) is increasingly important across a wide variety of applications. The need for data analytics is driven by the massive accumulation of “Big Data” in a variety of industries such as healthcare, finance, government (federal, state, and local), and cyber defense. The ultimate goal of data analytics is to derive value by suggesting effective actions for the future. Prescriptive analytics focuses on methods for deciding on the best course of action, while taking into account constraints and risks. This tutorial in prescriptive analytics will introduce methods to drive effective decision making and to identify and select optimal courses of action. Techniques are discussed to analyze both structured and unstructured data to derive meaningful knowledge, which will be useful for developing effective strategies and making optimal decisions. The use of prescriptive analytic methods in computerized decision support systems is also discussed. The tutorial emphasizes both analytical and practical aspects of prescriptive analytics. Hands-on exercises stress the practical aspects of modeling, optimization, and risk analysis. Students are also expected to demonstrate proficiency in decision making, design of decision support systems, and risk analysis.
This tutorial discusses the layered approach to system design and improvement. It shows the way to a process that is more agile than the traditional plan driven methods and, at the same time, maintains the disciplined system view avoiding the pitfalls of component engineering. We will discuss the concept of systems thinking and approach. We will see the importance of understanding the business process components of the system. We will explore the delivery of capability that is value-adding for the customer in a meaningful time frame without losing sight of the system context.
Gina Guillaume-Joseph, Ph.D. Candidate
Software Project failures are costly and often result in an organization losing millions of dollars due to termination of a poor quality project (Jones, 2012). Software engineering is a risky endeavor whose outcome often cannot be predetermined. Software Testing is a critical component of mature software engineering; however, project complexities make it the most challenging and costly phase of the Systems Engineering Lifecycle (SELC) (Jones, 2012).
Predictive Analytics is a data driven technology used to predict and influence the future. We develop a Predictive Model that determines failure points in the SELC and relates them to specific causal factors of testing. Our work attempts to optimize project data and information to provide informed and real-time decisions that combat financial risks incurred with failed projects.
Ewusi-Mensah, 2003 offers an empirically grounded study on software failures and proposes a framework of abandonment factors that highlight risks and uncertainties present in the SELC phases of a software project. Takagi et al, 2005 analyzed the degree of confusion of several software projects using logistic regression analysis to construct a model to characterize confused projects.
This work introduces the Project Testing Confidence Metric (PtcM) and the corresponding Predictive Model. The Model developed from data of software project failures and successes is based on a framework that identifies significant influencing failure factors and impact on the four major phases of the SELC in table 1.0. The failure factors in the testing phase have the greatest impact on software project failure as demonstrated in figure 1.0. The variables in Table 1.0 and Figure 1.1 are used to develop the Model.
The Predictive Model leverages past project performance to predict outcomes of future work. The PtcM uses that data to determine the effectiveness of testing by correlating project failure with inadequate testing to isolate those areas for improvement. The Predictive Model and the resulting PtcM provide leadership insight into determining which projects to embark upon within the project portfolio as outlined in figure 1.2.
The Predictive Model and the PtcM will assist in maturing an organization’s testing and quality assurance capabilities by implementing institutional learning. By predicting the likelihood of project failure during the early planning phase, this work will promote a more successful project portfolio. Our work helps organizations answer the question, “What will happen in the future and how can we act on this insight?”
Chris Ritter, Daniel Hettema, and Steven H. Dam, Ph.D., ESEP
Agile software development has become a staple of the software development community. However, it is a fairly new concept to the systems engineering community. This paper discusses an Agile process that includes the traditional aspects of systems engineering, thus speeding up the overall development cycle. It will discuss the application of these Agile techniques to a commercial software development process and how that could be adapted to DoD systems engineering and software development.
Many organizations are struggling with how to reduce the risk of maliciously inserted functionality in the IT products they purchase. At the same time, there is an increasing buzz about US and International standards for addressing technology risks from the supply chain. The challenge is in understanding how each standard or a combination of standards might be used to reduce risks specific to an organization. Cyber Supply Chain Risk Management (SCRM) seeks to manage and mitigate cyber and supply chain risk throughout an acquisition lifecycle for an element or a system. It is a multi-disciplinary challenge which requires contributions and collaboration among many disciplines. Key areas include systems engineering, information security, application security, supply chain and logistics planning and management, IT resiliency, and risk management. Existing standards development efforts are creating a robust set of standards that can be used to address the various aspects of Cyber SCRM.
Without an understanding of the nuances of the standards, it is challenging for stakeholders to select the standards that mitigate the risk from organizational specific threats. This session will leverage the common supply chain threats that organizations are working to address to determine which anti-counterfeit, acquirer/supplier relationships, software assurance, and product certification standards are offer mitigations to your organization. This session will provide an overview of existing and emerging standards and recommendations for selecting the right standards for an organization.
Warren K. Vaneman and Kostas Triantis
System of Systems (SoS) have been increasingly employed to satisfy operational goals and objectives. Often when a SoS is assembled, little analytical consideration is given to the SoS performance. Instead, decision-makers often rely on antidotal evidence to forecast the expected SoS performance. This approach ignores the emergent behavior of the SoS. While emergence can assume many aspects of SoS performance, one perspective that is most overlooked is resiliency, or the ability to adapt to changing conditions and prepare for, withstand, and rapidly recover from a disruption.
This paper explores resiliency as an emergent behavior of a SoS from a system dynamics perspective. As a result of this model-based approach, SoS performance can be evaluated as disturbances are introduced to the SoS, and forecast how the SoS will recover to a steady-state. Furthermore, this paper will introduce a methodological approach that will identify the key nodes that can be changed during the design process, or through policy, that will increase SoS performance through resiliency.
Bonnie Young and John M. Green
The ability to optimally manage distributed warfare assets for collaborative operation significantly increases our military advantage. The primary results include enhanced situational awareness and improvements in fire control, engagement support, operational planning, combat reaction times, threat prioritization, and the list continues. Bettering the use of sensors and weapons in concert with one another—effectively creating a system of distributed systems—provides major payoffs.
The effectiveness of managing distributed resources depends on the ability to make complex decisions. The complexity is due in part to the circuitous nature of fusing data from multiple sensor sources to provide a representation of the operational environment from which to redirect sensors for further information optimization and from which to base military operations. The “goodness” of such complex decisions depends on the “goodness” of the information available and the understanding of the situation from a “big picture” perspective.
This paper explores distributed resource management from a decision-based perspective. With an objective of enabling a collaborative system of systems (SoS), a systems approach is proposed to implement a decision paradigm that extends from system conception to operations.