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ScienceDirect Procedia Computer Science 61 (2015) 240 – 245

Complex Adaptive Systems, Publication 5 Cihan H. Dagli, Editor in Chief Conference Organized by Missouri University of Science and Technology 2015-San Jose, CA

Application of System Design for Operational Effectiveness for Architectural Modeling of the SoS Relationship Between Primary and Enabling Systems Dr. Marilyn T. Gaska*, Joseph S. Bobinis, Vincent Galluzzo Lockheed Martin, 10530 Rosehaven Street, Fairfax, VA 22030, USA

Abstract Government mandates to control costs throughout the life cycle of a product necessitate a comprehensive architecture and methodology from design through operations and sustainment. The Systems Design for Operational Effectiveness (SDOE) model developed by Stevens Institute has become the basis of the Office of the Secretary of Defense (OSD) guidance with a focus on increasing reliability and reducing logistics footprint. The complexity of systems of systems approaches and model based systems development increases when both on-board and off-board enabling systems are considered. Models for both should be included in any comprehensive digital tapestry. There are multiple digital threads for life cycle data for the enabling systems to represent the necessary causal relationships between primary and enabling systems that determine operational effectiveness. To address this challenge, a reusable architecture and methodology for addressing this complexity is the first step. This framework can support definition and advanced application of data analytics and big data approaches to the digital threads that define the interaction between primary and enabling system, the industrial enterprise, and the deployed environment, providing the variables which contribute to operational outcomes, and effectiveness. This includes analysis of feedback from actual operations compared with planned suitability analysis during the design phase.

 2015 The byby Elsevier B.V. This is an open access article under the CC BY-NC-ND license © 2015 TheAuthors. Authors.Published Published Elsevier B.V. (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer-review under responsibility of scientific committee of Missouri University of Science and Technology. Peer-review under responsibility of scientific committee of Missouri University of Science and Technology

Keywords: System Design for Operational Effectiveness (SDOE); Complex System of Systems; Sustainment; Logistics; Lifecycle; Primary; Enabling;

1. Comprehensive Architecture Approach to Manage Life Cycle Affordability The Government mandates to control costs throughout the life cycle of a product necessitate a comprehensive architecture and methodology from design through operations and sustainment. The evolution of the Office of the Secretary of Defense (OSD) Acquisition, Technology, and Logistics (AT&L) Better Buying Power campaign to the

1877-0509 © 2015 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer-review under responsibility of scientific committee of Missouri University of Science and Technology doi:10.1016/j.procs.2015.09.204

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most recent 3.0 release continues to include focus on life cycle costs for weapon systems [1, 2]. Figure 1 summarizes the focus of this paper to describe the systems of systems methodology to the enabling systems, models, and digital tapestry environment, and comprehensive data analytics. .

Fig. 1. Achieving Better Buying Power life cycle affordability necessitates addressing complexity

2. System of Systems Definitions The following box summarizes the systems of systems nomenclature used throughout this paper. These definitions are from Systems Engineering Chapter of the OSD Defense Acquisition Guidebook [3]. The context of the system is also important to consider operational suitability for the external environment for the system of systems. The current focus on big data and data analytics needs to take into consideration external environment feedback for an adaptive system. Nomenclature from Defense Acquisition Guidebook System of Systems Definitions [3]: System: An aggregation of system elements and enabling system elements to achieve a given purpose or to provide a capability. System Elements: Also referred to as configuration items, subsystems, segments, components, assemblies, or parts. (These elements are also called the primary system in subsequent discussions.) Enabling System Elements: Provide the means for putting a capability into service, keeping it in service, or ending its service, e.g., processes or products used to enable system development, test, production, training, deployment, support, and disposal. Note: Each system element or enabling system element may include but is not limited to hardware, software, people, data, processes, facilities, and tools.

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3. Systems Design for Operational Effectiveness (SDOE) Model The SDOE model provides a framework for the integration of primary and enabling systems. The SDOE model was initially developed by Stevens Institute. It is the basis of OSD guidance as early as 2003 for designing and assessing supportability of weapon systems. Figure 2 illustrates the System Design for Operational Effectiveness (SDOE) model from the Defense Acquisition Guidebook and courses taught by Defense Acquisition University [4]. The model has annotations for primary and enabling systems. Bobinis and Herald [5] further describe the enterprise framework for operationally effective system of systems design based on the SDOE model.

Fig. 2. Systems design for operational effectiveness model incorporates primary and enabling system across the life cycle [4]

4. Digital Tapestry/Environment Management Since the 1990s, efforts such as the Defense Advanced Research Projects Agency (DARPA) Initiative in Concurrent Engineering (DICE) focused on cross discipline collaboration and lifecycle considerations. The IEEE has continued the Workshop on Enabling Technologies: Infrastructure for Collaborative Enterprises (WETICE) series, which reached the 23rd in 2015[6]. Sharing of data and maintaining history have been focus areas to support the collaborative environment. More recently, the National Institute of Standards and Technology (NIST) Digital Thread for Smart Manufacturing project “enables the repurposing, reuse, and traceability of information throughout the product lifecycle.” [7] Figure 3 depicts the lifecycle scope of the integrated data management system needed to affordably support a system over its lifecycle. Lockheed Martin has applied the term digital tapestry to the integration of the multiple threads of data and models that make up the digital environment. The initial focus of the integration of the digital threads of data has been on the design to manufacturing part of the lifecycle. Extension of integrated data management throughout the operations and sustainment phase is even more important with the focus on additive manufacturing of parts for repair of the primary system. An integrated view of the digital environment is key for the cost effective development and lifecycle management of the enabling systems required to support a primary system.

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Fig. 3. Digital tapestry and the model based enterprise relies on an integrated data management system [8]

5. Model Based Support for the Life Cycle To manage complexity effectively, important early steps are the use of models during both early conceptualization and design and analysis phases that incorporate system of systems and lifecycle considerations. Models for both the primary and enabling systems should be included in any comprehensive digital tapestry environment. There are multiple digital threads for life cycle data for the enabling systems to represent the necessary causal relationships between primary and enabling systems that determine operational effectiveness. The maximum impact on affordability is achieved when producibility, testability, reliability, supportability, and sustainability are considered early. Oster [9] highlighted the importance of a multi-disciplinary approach to model based systems engineering (MBSE) in support of an integrated model-based digital tapestry. The NIST report from the Model-Based Enterprise Summit held in December of 2012 summarizes the challenges when a “digital three-dimensional (3D) model serves as the authoritative information source for all activities in a product’s lifecycle.” [10] Lander and Bijan described a practical implementation of model based system develop [11] as part of a conference track addressing the transition from modeling and simulation to model based systems engineering. The vision shown in Figure 4 highlights the importance of shared data. The maturity of the data for each of the contributors/users changes over the lifecycle as the concept evolves to production. The maximum impact on affordability and life cycle cost is made when operations and sustainment is considered as part of the design. The challenges and opportunity for the configuration management / data management profession (CM/DM) have also been described [8]. As additive manufacturing application to parts to support the repair process emerges, data access across the supply and maintenance chain will become even more important to the operational community. Early planning of the management of data across the life cycle helps avoid problems in sustainment.

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Fig. 4. Shared data is central to the vision for model based systems development for the life cycle [11]

6. Framework for System of Systems and Context Complexity Complexity is further increased once a system of systems is deployed. The operational framework shown in Figure 5 encompasses the data collection and analytics for the control system in order to be able to affordably manage operations and sustainment across the life cycle. This framework can support definition and advanced application of data analytics and big data approaches to the digital threads that define the interaction between primary and enabling system, the industrial enterprise, and the deployed environment, providing the variables which contribute to operational outcomes, and effectiveness. The analysis of feedback from actual operations compared with planned suitability analysis during the design phase may necessitate system modifications to adapt to changes.

Fig. 5. Operational data analytics for the system of systems and external context

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While the primary system and enabling system components of the system are defined in Section 3, a more detailed description of the external environment components follows. The supply chain for production/sustainment and operational user interaction with the system are two key areas of the external environment. For the external supply chain needed for both the primary and enabling systems, tools have been developed to address predictive analytics [12]. Supply chain risk management includes identification of potential counterfeit parts suppliers as well as to examine global supply chain risk and stability due to economic, social, and political trends [12]. Systems may be operated in different environments and cultures to impact suitability of a system for the intended functions and missions. Changing market and political factors in the environment may also impact a system and its suitability in a changing world. Conclusion This paper summarizes the importance of a comprehensive architecture and methodology from design through operations and sustainment that accounts for the complexity of both primary and enabling systems, as described in the SDOE model and OSD guidance. A digital environment that supports the life cycle needs to address multiple data threads from design through sustainment. A reusable architecture framework can support definition and advanced application of data analytics and big data approaches to the digital threads that define the interaction between primary and enabling system. Interfaces with the industrial enterprise and the operational environment also need to be part of the plan. This includes analysis of feedback from actual external environment operations compared with planned suitability analysis during the design phase. An open systems approach based on a modular architecture and defined data interfaces will support collaboration across the digital threads that connect primary system and product support elements in the enabling systems. The impact of the external environment to include the global supply chain continues to increase the complexity of the system of systems. Acknowledgements The authors acknowledge the participants in workshops and initiatives that shaped the ideas in this paper. References 1. Undersecretary of Defense Acquisition, Technology, and Logistics. Implementation Directive for Better Buying Power 3.0 – Achieving Dominant Capabilities Through Technical Excellence and Innovation. April 9, 2015. 2. Department of Defense (DoD). DoD Instruction (DoDI) 5000.02. Operation of the Defense Acquisition System. January 7, 2015. 3. Defense Acquisition Guidebook. Chapter 4.2.1.2. System of Systems. Figure 4.2.1.2 as of June 1, 2015. 4. Defense Acquisition Guidebook. Chapter 5.2. Applying Systems Engineering to Life-Cycle Sustainment. Fig. 5.2.F.2 as of June 1, 2015. 5. Bobinis J, Herald Jr T, An Enterprise Framework for Operationally Effective System of Systems Design. Journal of Enterprise Architecture, May. 2012. 6. WETICE. 24th IEEE International Conference on Enabling Technologies: Infrastructure for Collaborative Enterprises. Larnaca (Cyprus), Greece. June 15-17, 2015. 7. National Institute for Standards and Technology (NIST). Enabling the Digital Thread for Smart Manufacturing Project. Internet. June 1, 2015. 8. Gaska M. Importance of CM/DM in the Current and Emerging DoD Product Support Environment. Association for Configuration and Data Management Conference. Savannah, Georgia. March 3-5, 2014. 9. Oster C. Digital Tapestry. Model Based Systems Engineering Workshop at INCOSE International Workshop. Jacksonville, Florida. January 21, 2012. 10. Lubell J, Frechette SP, Lipman RR, Proctor FM, Horst JA, Carlisle M, Huang PJ. Model-Based Enterprise Summit Report. NIST Technical Note 1820. November 2013. 11. Landers T, Bijan Y. Practical Implementation of Model-Based Systems Development. NDIA Annual Systems Engineering Conference. Springfield, VA. October 28-30, 2014. 12. Biesecker, C. Lockheed Martin Turning Predictive Analytics Tools to Supply Chain, Counterfeit Risks. Defense Daily. April 16, 2015.

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