Systems Engineering and Management
Lead Authors: Jeffrey Carter and Caitlyn Singham
The SEBoK Part-3: Systems Engineering and Management [SE&M] materials provide system lifecycle best practices for creating and executing interdisciplinary processes to ensure that customer needs are satisfied with a technical performance, schedule, and cost compliant solution. The figure below depicts the context of SE&M processes and practices guidance within the SEBoK. The SE&M materials are currently being updated to provide system design practitioners with Model-Based Systems Engineering [MBSE] implementation guidance employing the Systems Modeling Language [SysML®].
Knowledge Areas in Part 3
Each part of the SEBoK is divided into knowledge areas (KAs), which are groupings of information with a related theme. Part 3 contains the following themes:
- Systems Engineering STEM Overview
- Model-Based Systems Engineering (MBSE)
- Systems Lifecycle Approaches
- System Lifecycle Models
- Systems Engineering Management
- Business and Mission Analysis
- Stakeholder Needs Definition
- System Architecture Definition
- Detailed Design Definition
- System Analysis
- System Realization
- System Implementation
- System Integration
- System Verification
- System Transition
- System Validation
- System Operation
- System Maintenance
- Logistics
- Service Life Management
- Systems Engineering Standards
See the article Matrix of Implementation Examples for a mapping of case studies and vignettes included in Part 7 to topics covered in Part 3.
Systems Engineering & Management Overview
Systems Engineering [SE] conducts behavioral and structural design analyses applying interdisciplinary processes and practices to integrate System, Electrical, Mechanical, Software, and Specialty Engineering [EE, ME, SW, and SP] disciplines. The design objective is to develop a holistic technical solution that satisfies specific customer application criteria. SE has traditionally applied intuitive domain-specific practices emphasizing processes and procedures with good writing skills to manually organize information in a disparate collection of documents. The traditional SE deliverables are textual system requirement specifications, analysis reports, system design descriptions and interface specifications.
Figure 2 depicts the “Digital Trinity” of innovative design practices to transform traditional system development including the adoption of Agile System / Software Development, a Modular Open Systems Approach [MOSA], and Digital Engineering.
The governing principle of Digital Engineering is development of system design models with high-fidelity simulation capabilities to realistically emulate systems in virtual computing environments. The design model includes functional, logical, and physical system design representations with high-fidelity simulation capabilities that are integrated with EE, ME, SW, and SP design disciplines for system functional and performance assessments. The integrated simulations provide a digital twin with digital threads of critical system characteristics to evaluate design alternatives in virtual computing environments to discover and resolve design defects before the expense of producing physical prototypes.
- Digital threads are analytical frameworks providing end-to-end system simulation representations to evaluate logical operations and key performance parameters in virtual environments by exchanging information between different modeling tools across the lifecycle.
- Digital twins are authoritative representations of physical systems including the digital thread end-to-end connections with all the data, models, and infrastructure needed to create and optimize a system’s lifecycle digitally. Digital twins enable project team collaboration, system simulation functional performance assessments, design change impact evaluations, and product-line management reuse libraries
The Digital Engineering transformation includes the adoption of Model-Based Systems Engineering [MBSE] as an alternative approach to traditional document-based systems engineering methods. INCOSE defines MBSE as the formalized application of graphical modeling with specific semantic definitions throughout the system lifecycle. MBSE includes the creation, development, and utilization of digital design models with domain product-specific [e.g., aerospace, automobile, software, consumer, …] analyses to define system requirements and behavior / structure characteristics.
The design model provides a Single Source of Truth [SSoT] for the system technical baseline enabling project team and stakeholder collaboration. The design model contains diagrams and meta-data depicted with a graphical modeling language [e.g., Systems Modeling Language [SysML®]. The modeling language has precise semantic definitions for depicting systems design characteristics. There are several commercially available tools compliant with the Object Management Group’s [OMG’s] industry SysML® standard.
MBSE enhances the ability to capture, analyze, share, and manage authoritative information associated with the complete specification of a product compared to traditional document-based approaches. MBSE provides the capability to consolidate information in an accessible, centralized source, enabling partial or complete automation of many systems engineering processes, and facilitating interactive representation of system components and behaviors.
The legacy SE&M materials are all impacted by the adoption of MBSE practices, and the SEBoK is updating its materials accordingly to reflect best practices and principles in an integrated model-based engineering environment. The updated materials to specify system behavior and structure characteristics with traceability to the associated requirements are organized in accordance with the ISO/IEC/IEEE-15288:2015 Systems Lifecycle Processes Standard shown in the figure below.
Value of Ontology Concepts for Systems Engineering
Ontology is the set of entities presupposed by a theory (Collins English Dictionary 2011). Systems engineering, and system development in particular, is based on concepts related to mathematics and proven practices. A SE ontology can be defined considering the following path/rationale.
SE provides engineers with an approach based on a set of concepts (i.e., stakeholder, requirement, function, scenario, system element, etc.) and generic processes (Madni and Sievers, 2018). Each process is composed of a set of activities and tasks gathered logically around a theme or a purpose. A process describes “what to do” using the applied concepts. The implementation of the activities and tasks is supported by methods and modeling techniques, which are composed themselves of elementary tasks; they describe the “how to do” of SE. The activities and tasks of SE are transformations of generic data using predefined concepts. Those generic data are called entities, classes, or types. Each entity is characterized by specific attributes, and each attribute may have a different value. All along their execution, the activities and tasks of processes, methods, and modeling techniques exchange instances of generic entities according to logical relationships. These relationships allow the engineer to link the entities between themselves (traceability) and to follow a logical sequence of the activities and the global progression (engineering management). Cardinality is associated with every relationship, expressing the minimum and maximum number of entities that are required in order to make the relationship valid.
| ‘Cardinality in systems is associated with every relationship, expressing the number of entities that are required to make the relationship stand. As such a relationship can be viewed in one of three ways: One-2-One, One-2-Many or Many-to-Many and described in terms of quantity, pattern and arrangement.’ |
Additional information on this subject may be found in Engineering Complex Systems with Models and Objects (Oliver, Kelliher, and Keegan 1997).
The set of SE entities and their relationships form an ontology, which is also referred to as an "engineering meta-model". Such an approach is used and defined in the ISO 10303:AP233 standard (ISO 2007). There are many benefits to using an ontology including:
- the use of a standardized vocabulary, with carefully chosen names, which helps to avoid the use of synonyms in the processes, methods, and modeling techniques
- the reconciliation of the vocabulary used in different modeling techniques and methods
- the automatic appearance of the traceability requirements when implemented in databases, SE tools or workbenches, and the quick identification of the impacts of modifications in the engineering data set
- the continual observation of the consistency and completeness of engineering data; etc.
Throughout Part 3, there are discussions of the ontological elements specifically relevant to a given topic.
Mapping of Topics to ISO/IEC 15288, System Life Cycle Processes
Figure 2, below, shows the relative position of the KA's of the SEBoK with respect to the processes outlined in the ISO/IEC/IEEE 15288 (ISO 2015) standard.
As shown, all of the major processes described in ISO/IEC/IEE 15288:2015 are discussed within the SEBoK.
The ISO/IEC/IEEE 15288:2015 marked with an * are new or have been renamed and modified in scope for this revision of the standard.
These changes and associated changes to the SEBoK now mean that the two are significantly more closely aligned than before. It should also be noted that the latest update of the INCOSE SE Handbook (INCOSE 2015) is now fully aligned with the 2015 revision of the standard.
Any future evolution of Life Cycle Process knowledge in the SEBoK will be complementary to these standard descriptions of the generic SE process set.
References
Collins English Dictionary, s.v. "Ontology." 2011.
Estefan, J. 2008. A Survey of Model-Based Systems Engineering (MBSE) Methodologies, rev, B. Seattle, WA: International Council on Systems Engineering. INCOSE-TD-2007-003-02. Available at: http://www.omgsysml.org/MBSE_Methodology_Survey_RevB.pdf. Accessed April 13, 2015.
INCOSE. 2015. Systems Engineering Handbook: A Guide for System Life Cycle Processes and Activities, version 4.0. Hoboken, NJ, USA: John Wiley and Sons, Inc, ISBN: 978-1-118-99940-0.
ISO/IEC/IEEE. 2015. Systems and Software Engineering -- System Life Cycle Processes. Geneva, Switzerland: International Organisation for Standardisation / International Electrotechnical Commissions / Institute for Electrical and Electronics Engineers. ISO/IEC/IEEE 15288:2015.
ISO. 2007. Systems Engineering and Design. Geneva, Switzerland: International Organization for Standardization (ISO). ISO 10303-AP233.
Oliver, D., T. Kelliher, and J. Keegan. 1997. Engineering Complex Systems with Models and Objects. New York, NY, USA: McGraw-Hill.
Primary References
INCOSE. 2015. Systems Engineering Handbook - A Guide for System Life Cycle Processes and Activities, version 4.0. Hoboken, NJ, USA: John Wiley and Sons, Inc, ISBN: 978-1-118-99940-0.
ISO/IEC/IEEE. 2015. Systems and Software Engineering -- System Life Cycle Processes. Geneva, Switzerland: International Organisation for Standardisation / International Electrotechnical Commissions. ISO/IEC/IEEE 15288:2015.
Additional References
Bell Telephone Laboratories. 1982. Engineering and Operations in the Bell System. Murray Hill, NJ, USA: Bell Telephone Laboratories.
Fortescue, P.W., J. Stark, and G. Swinerd. 2003. Spacecraft Systems Engineering. New York, NY, USA: J. Wiley.
Madni, A. M. and Sievers, M. 2018. Model‐based systems engineering: Motivation, current status, and research opportunities, Systems Engineering. 2018; 21: 172– 190. https://doi.org/10.1002/sys.21438