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The Systems Engineering Experience Accelerator (SEEA) is a new approach to developing the systems engineering and technical leadership workforce. The project is aimed at accelerating experience assimilation through immersive, simulated learning situations where learners solve realistic problems. A prototype of the technology infrastructure and experience content has been developed, piloted, and evaluated. While the prototype has proved useful, its ability to support a community of educators and developers is limited by the challenges in creating or changing experiences. This paper proposes an initial taxonomy of experience archetypes to help development and reuse of experiences, and describes the experience design and development process using the SEEA tool suite. This is followed by an application case study: an experience developed by the United Kingdom Ministry of Defence to accelerate the maturity of reliability engineers and their role in identifying and resolving safety issues.

Quality, affordable healthcare remains a contentious, complex and urgent societal problem. Given the scale and complexity of healthcare, systemic changes addressing emergent cost escalation and quality deficiencies are difficult to study, evaluate, and implement. In particular, it is difficult to empirically study alternative means of delivery that do not yet exist. To enable the study of such issues, we designed and developed Health Advisor, a web-based game using organizational simulation. Players are tasked to manage people through the healthcare system by making various information, cost, and quality of care trade-offs with score based on health outcomes and costs incurred. This paper reports on a series of evaluations of Health Advisor and the insights gained from these studies. In particular, results show that people's perceptions of the usability and usefulness of information sources have a strong impact on the use of these sources, and a significant impact on their subsequent performance in diagnoses and referrals.

Systems engineering increasingly addresses the system lifecycle, as opposed to its more traditional role focusing on design and development. This new situation results in part from the recognition that upstream design and deployment decisions have potentially significant cost and performance implications post-deployment. For military systems, the role that typically addresses post-deployment issues is the logistician. Over the system lifecycle, it is important that the traditional roles of systems engineer and logistician understand issues faced by one another, as well as joint cost and performance implications. This paper presents the design of a role-based experiential learning environment for logisticians involved in military sustainment. This design leverages the generic components of an existing single-learner technology base, the Experience Accelerator, for presenting and controlling the learner experience, plus simulating program outcomes resulting from learner decisions. This technology base has been used to create a learning experience for a lead systems engineer in charge of designing and developing a new unmanned aerial vehicle (UAV) system. In this new environment, the logistician learner interacts with systems engineers during UAV system acquisition and sustainment, learns about systems engineering issues and their effect on logistics, tries to influence upstream systems engineering decisions, and also performs logistics functions.

Systems engineering educators are struggling to meet the workforce development demand for senior systems engineers. The workforce challenge is to shorten the time it takes for a systems engineer to reach the senior level. The Systems Engineering Experience Accelerator research project was conceived as a critical response to these needs and challenges. The purpose of this paper is to provide a description of the Experience Accelerator architecture which will provide the foundation for post prototype development. While the prototype is only intended as a research vehicle for conceptual design and evaluation, this architectural description should address the foreseen issues for a deployable future version of the system.

Systems engineering educators are struggling to meet the workforce development demand for senior systems engineers. Systems engineers are critical for addressing a broad set of increasingly complex systems problems faced by industry and government. However, the discipline is experiencing an outflux of senior systems engineers reaching retirement age with no ready source of systems engineers available to replace them, at a time when the demand for systems engineers is increasing (NDIA, 2010). The workforce challenge is to shorten the time it takes for a systems engineer to reach the senior level. The Systems Engineering Experience Accelerator (SEEA) research project was conceived as a critical response to these needs and challenges. The SEEA focuses on a solution that leverages technology to create an experience intended to accelerate the learning of systems engineering related competencies. This paper summarizes the operation of the preliminary version of a prototype SEEA simulator after the first year of the SEEA project. A review of the plans for future research is also included.

This article describes the development of a modeling hierarchy for complex enterprise networks. Drawing on the extant modeling literature, these network models are elaborated in terms of 4 salient problem characteristics: conversions, flows, controls, and social/organizational relationships. The authors relate these 4 characteristics to phenomena, representations, micromodels, macromodels, and modeling tools. The roles of information and incentives in complex enterprises networks are considered. Examples of 2 domains, global manufacturing and healthcare delivery, are woven through these discussions of alternative representations and models. The authors conclude by providing a structured comparison of these 2 domains, discussing theoretical and practical implications, and presenting opportunities for future enterprise transformation research.

Enterprises are complex adaptive socio-technical systems. They exhibit nonlinear, dynamic characteristics, resulting in unpredictable system behavior. Enterprises tend to consist of many independent agents. The needs and desires of these agents are not homogenous; their goals and behaviors often conflict, requiring agents to dynamically adapt to external events. At the same time, agents are intelligent and learn. The overall enterprise system thus changes over time. Given that no single agent is in control, complex enterprise system behaviors are typically not directly controllable, but can be influenced and managed. Successful transformation of such systems is consequently quite challenging. Traditional modeling and analyses approaches fail to consider these complex issues and are therefore inadequate for supporting enterprise transformation efforts. New methods and tools are therefore needed. This chapter argues for the use of multi-level computational methods in pursuit of this endeavor. Enterprise components and behaviors can be conceptualized as consisting of multiple levels. We find using the levels of people, processes, organizations and eco-systems to be a useful framework. Within this framework, computational modeling augments and amplifies human decision-making and facilitates exploration of a wide range of possibilities, thereby enabling the early discarding of bad ideas and refinement of good ones. Also, decomposition of an enterprise into multiple levels allows stakeholders to interact with those phenomena of interest to them. The goal of this work is to enable decision makers to “drive the future” before “writing the check”.

A manufacturing system consists of entities that relate to one another through some type of transaction. Entities exhibit specific behaviours. The essential modelling problem is to reproduce the aggregate effect of individual behaviours, typically by representing all the relevant entities, their behaviours, and their transactions. Simulation software provides the means for creating, executing, and observing these representations. While modern simulation languages provide many useful modelling constructs, they suffer from several limitations in modelling manufacturing systems. These include having incomplete or inconsistent mapping of software abstractions to the system being modelled, having limited modules for representing decision-making, and not being able to explicitly accommodate human supervisory control. In this article we discuss the development and application of an object-oriented architecture for manufacturing modelling and simulation. The architecture is derived from an explicit domain analysis of discrete-part manufacturing systems from a material flow perspective, and provides software patterns for representing entities and their interactions. The architecture also supports the development of real-time, interactive simulations of manufacturing systems with a high degree of fidelity, incorporating supervisory control, and it provides software abstractions for representing manufacturing decision-making and control.

Object-oriented programming (OOP) has been revolutionizing software development and maintenance. When applied to simulation of manufacturing systems, OOP also provides an opportunity for developing new ways of thinking and modeling. In this paper, we identify existing large-scale, persistent OOP-based research efforts focusing on manufacturing system simulation, and present an integrating framework for discussing the associated modeling abstractions, implementation strategies, common themes, and distinctive features. The goal is to identify the fundamental research and application issues, assess the current state of the art, and identify key research needs.

There is an enormous range of computational models and simulations for addressing a variety of analysis and design issues in complex systems. This report addresses systems where behavioral and social phenomena are significant elements of system performance. The report begins with an overview of the state of the art for multi-level modeling of such socio-technical systems. It then reports on an interview study of how four non-defense industries address computational modeling of complex aerospace, automotive, building equipment, and semi-conductor systems. The report concludes with several observations on the overall study of which this work was an element.

With much of the current systems engineering workforce reaching retirement age, there is a critical need to develop work experience among new systems engineers. Increasingly, educators and employers are turning to educational technology-based approaches to aid in this process. One such effort is the Experience Accelerator, an on-going research project aimed at maturing program-level systems engineers in Department of Defense acquisition programs. In this experiential learning approach, learners assume the role of a lead program systems engineer for development of a new unmanned aerial system (UAS), and they must make appropriate decisions and trade-offs at key points during the development lifecycle to keep the program on track and recover from problems that occur. Problems can occur in such areas as requirements, schedule, quality, cost, and customer expectations. The acquisition program is represented with a set of system dynamics simulation models that include such activities as sub-system development, system integration and system test. This paper focuses on the simulation-based approach used to provide internal decision support for learners as they engage in a learning experience within the Experience Accelerator. We present findings to date involving simulation modeling of acquisition programs and effectiveness of simulation results for decision support.

Net-centric enterprises increasingly are found in government and industry contexts. In this research, a net-centric enterprise consists of a number of semi-autonomous organizations that collaborate within the context of a federated structure. Such collaborations may be temporary and of known duration, temporary and of unknown duration, or permanent and known to be permanent. When such semi-autonomous organizations collaborate, they typically have information technology needs to support their collaboration. In the information technology (IT) domain, such needs are called requirements. From a business or organizational perspective, these needs are called capabilities or functions. In designing and developing IT systems to support high-level capabilities, capabilities are decomposed to functions and then to requirements. From requirements, software architectures are derived and then implemented. This process occurs in the context of integrating or interoperating systems. The fundamental problem is how to manage the process of proceeding from capabilities to systems, i.e., requirements management in the netcentric enterprise. This is a socio-technical problem involving inter-organizational socio issues, as well as technical system integration issues. This report provides a methodology for addressing the requirements management problem that includes component methods, processes and tools for addressing subproblems. This methodology is evaluated via application to case studies of system integrations that have strong net-centric enterprise characteristics. In addition, case studies are used to elucidate effective practices with respect to socio issues. Validation of the concepts and results of the research is done via interaction with subject matter experts. Finally, recommendations for future research and technology transfer are provided.

This document is a summary of the work that was completed in the second increment year of the SERC Research Topic 16 Developing Systems Engineering Experience Accelerator (SEEA) Prototype and Roadmap . The major research activities in Increment 2 are as follows: 1.Update Documentation and Planning 2.Prototype Evaluation (cont.) 3.Pilot System Development: 4.Pilot System Evaluation 5.Open Source Preparation and Deployment 6.External Developers Engagement 7.Develop Multi-Learner Technology 8.Write Final Report In addition to the work activities, the following five top program risks were identified and tracked throughout Increment 2 of the program: 1.Project Management - Inability to support known and evolving customer/user feedback with current staff, budget and timeframe. 2.Configuration Management Inability to successfully manage the large number of files,configuration variables,present in the Experience Accelerator. 3.Technology Development - Inability to tradeoff long-term architecture and technology objectives (leading to successful open source support) vs. short-term prototype goals. 4.Content Development - Inability to produce a prototype that provides a compelling experience,supports the desired learning and is seen to be authentic. 5.Evaluation - Inconclusive results due to threats to validity of Experimental design(inability to generalize results),limited availability of suitable subjects and insufficient literature to support development of evaluation instruments. A set of lessons learned was compiled and categorized as noted below: Competencies, Learning and Content Complexity/Effort vs. Authenticity/Learning Technology R&D Processes

Net-centric enterprises increasingly are found in government and industry contexts. In this research, a net-centric enterprise consists of a number of semi-autonomous organizations that collaborate within the context of a federated structure. Such collaborations may be temporary and of known duration, temporary and of unknown duration, or permanent and known to be permanent. When such semi-autonomous organizations collaborate, they typically have information technology needs to support their collaboration. In the information technology (IT) domain, such needs are called requirements. From a business or organizational perspective, these needs are called capabilities or functions. In designing and developing IT systems to support high-level capabilities, capabilities are decomposed to functions and then to requirements. From requirements, software architectures are derived and then implemented. The fundamental problem is how to manage the process of proceeding from capabilities to systems, i.e., requirements management in the net-centric enterprise. The preceding simple linear process description is useful, but inadequate to address the complexity of the net-centric enterprise. This complexity manifests itself in the following forms -- the need to join existing IT systems belonging to the organizations involved in the collaboration to support the desired capabilities, the perhaps unknown durations of such collaborations, the presence of legacy systems, and the evolving needs and missions of the various organizations. This research uses case study analysis of business mergers and other types of IT integrations to aid in the specification of a methodology to address the requirements management problem. This report provides the results of a Phase 1 effort of this research, including case study analysis methodology specifications and recommendations for future research.

The authors describe research on the development of a high-fidelity simulator using the object-oriented programming paradigm. The simulator models the complex interactions in reentrant flow manufacturing facilities. In addition to potentially providing better estimates on throughput, work-in-progress levels, and bottleneck resources in the modeled system, the simulator serves as a platform to investigate issues of operator cross-training, decision aiding, and the effect of operator decisions in such manufacturing facilities. The authors describe the class hierarchy of objects required for modeling and analysis of semiconductor and thin films fabrication, and discuss their approach to modeling complex supervisory control decisions in reentrant flow lines. >

The object-oriented modeling of manufacturing systems as used in the development of a high-fidelity simulator is described. The separation of control and plant resources in the model allows explicit representation of the factory control system. Different control strategies can be readily tested within the simulation. The application of this approach to deadlock detection and resolution within a flexible manufacturing cell is discussed. The simulator also serves as a platform to investigate the integration of human resources and automated equipment in advanced manufacturing facilities, and to study supervisory control issues in automated systems. >

A well-designed graphical environment that supports model specification has the potential of enabling the modeler to make better use of the modeling constructs and architecture. We describe on-going research in creating a graphical environment that supports model specification in OOSIM (Object-Oriented Simulation in Manufacturing), a high fidelity manufacturing system simulator. >

Control software is acknowledged as the biggest bottleneck in integrating modern manufacturing systems. We argue that understanding the interactions between parts, equipment, and supervisory control processes in the functioning of manufacturing enterprises is critical to the development of control software. We discuss a modeling environment which provides a platform for describing manufacturing control in terms of control entities and their interactions with their clients, and provides a rich representation of the manufacturing world in terms of object-oriented development of system components. The modeling environment is useful for rapidly prototyping manufacturing systems simulation. In this paper, we briefly describe the modeling architecture focusing on the concepts and implementation of the decision making controller entities in the architecture. >

Increasingly, automation and computer-based control play key roles in the operation of manufacturing systems. These two factors are necessary to support trends towards greater product customization and increased emphasis on reduction of lead times. At the same time, though, automation and computer-based control can be enormously expensive to design and implement in a factory. These high costs motivate the need for modeling and analysis. Existing modeling and analysis tools are effective for high level modeling of manufacturing systems. However, they suffer from limitations which are important in detailed analysis of system performance. This research is motivated by the need to develop modeling tools which support rapid development of detailed simulation models to assess system performance. The fundamental idea is to develop such an environment (i.e., a "virtual factory") which one may use to test different system configurations and control policies. We demonstrate our work thus far by presenting a case study involving deadlock avoidance in a semiconductor cluster tool.

As the effective deployment of system production capacity and operational capability becomes critical for competitive semiconductor manufacturing, there is an increasing interest toward the development of a control framework which allows effective and efficient operation of the emerging 300 mm wafer fabs. In the paper, a colored Petri net-based approach is taken to develop such a distributed event-driven 300 mm fab controller. The key element of the proposed controller design is the distribution of control function, to the constituent components of the 300 mm fab, while maintaining the logical correctness and efficiency of the fab behavior. Furthermore, in order to achieve the viability of the resulting control scheme, the proposed approach seeks to incorporate detailed operational characteristics of the 300 mm fabs, including batching, setups, reworks, maintenance, machine failures, and complex resource allocation dynamics due to the routing flexibility and auxiliary resource sharing. The proposed approach is demonstrated through a small-scale example, modeling the operation of a 300 mm fab bay.

An ideal warehousing and distribution system has no inventory and operates with minimal handling. In an attempt to achieve this ideal, expert designers rely on their intuition, together with analytical models and procedures formalized over years of experience. Understanding the processes that experts employ to realize their designs can have great benefits, for instance, in developing computational tools to assist in design. We have conducted an ethnographic study of an expert design team during actual system design. In this paper, we describe our observations and a process model of design.

Semiconductor fabs are capital intensive. The rate of capital return heavily depends on their productivity. Accordingly, simulation has been adopted in many cases as a viable design and analysis tool to achieve better productivity. However, the gap between the simplistic simulation models and real complex systems has limited the confidence to apply simulation results directly to real systems. Ideally, the simulator should support the rapid modeling and fast execution of high-fidelity fab models in order to be able to provide more realistic simulation results. In this paper, we present our current research effort to develop a high-fidelity web-based simulator for the 300 mm wafer fabs. We take a model-view-control approach that allows us to develop modular and reusable fab simulation objects. The proposed approach is implemented as a distributed system that uses a message-based event synchronization mechanism to coordinate the simulation objects as well as to support distributed simulation execution. The presented simulator also provides a high quality VRML animation for visualization of real time paced simulation execution.