Systems Engineering

- Overview

Systems engineering is defined as a methodical, multi-disciplinary approach for the design, realization, technical management, operations, and retirement of a system. 

A “system” is the combination of elements that function together to produce the capability required to meet a need. The elements include all hardware, software, equipment, facilities, personnel, processes, and procedures needed for this purpose; that is, all things required to produce system-level results.

The results include system-level qualities, properties, characteristics, functions, behavior, and performance. The value added by the system as a whole, beyond that contributed independently by the parts, is primarily created by the relationship among the parts; that is, how they are interconnected. 

It is a way of looking at the “big picture” when making technical decisions. It is a way of achieving stakeholder functional, physical, and operational performance requirements in the intended use environment over the planned life of the system within cost, schedule, and other constraints. 

It is a methodology that supports the containment of the life cycle cost of a system. In other words, systems engineering is a logical way of thinking.

Artificial intelligence (AI) offers a wide range of capabilities that can significantly improve the efficiency, accuracy, and innovation of systems engineering. As AI technology continues to evolve, its role in systems engineering is likely to become even more prominent.

In essence, the objective of systems engineering is to create a system that is not only technically sound but also cost-effective, user-friendly, and capable of meeting the needs of all stakeholders throughout its entire lifecycle. 

Please refer to the following for more information:

- Core Areas of Research in Systems Engineering

Systems engineering research and applications focus on developing and applying engineering principles to design, analyze, and manage complex systems, encompassing both natural and engineered systems. 

This field explores how these systems behave, are designed and operated, and the societal and environmental impacts they have. Research often involves interdisciplinary collaborations and the development of new methods and tools for managing complex systems.

• System Design and Analysis: Focuses on understanding how systems work, identifying their strengths and weaknesses, and designing new systems to meet specific needs.
• Model-Based Systems Engineering (MBSE): Develops and uses models to represent systems, allowing for simulation, analysis, and communication of design decisions.
• Requirements Management: Ensures that systems meet the needs of stakeholders by managing and tracking requirements throughout the system lifecycle.
• Risk Analysis and Mitigation: Identifies and assesses potential risks associated with system development and operation, and develops strategies to mitigate those risks.
• System Optimization: Finds ways to improve system performance, efficiency, and cost-effectiveness.
• Human-Machine Interaction: Considers the interaction between humans and machines within a system, ensuring usability and safety.
• Interdisciplinary Collaboration: Works with diverse disciplines, including engineering, healthcare, social sciences, and more, to address complex problems.
• Sustainment and Legacy: Focuses on maintaining and improving systems over their entire lifecycle, including upgrades, repairs, and eventual retirement.

- Examples of Applications in Systems Engineering

Systems engineering research and applications are crucial for addressing complex challenges in a variety of fields. The field emphasizes a holistic approach to system design and management, considering all aspects of the system and its interactions with its environment. 

Research and development in systems engineering are constantly evolving, with new methods and tools being developed to address emerging challenges.

• Energy Systems: Designing and optimizing energy production, distribution, and consumption.
• Transportation Systems: Improving the efficiency and safety of transportation infrastructure and vehicles.
• Healthcare Systems: Developing and improving systems for diagnosis, treatment, and patient care.
• Aerospace Systems: Designing and developing aircraft, spacecraft, and related systems.
• Defense Systems: Developing and managing military systems and technologies.
• Software Systems: Designing and managing large-scale software systems.
• Cyber-Physical Systems: Developing and managing systems that combine software and physical components, such as smart grids and autonomous vehicles.
• Pharmaceutical Manufacturing: Optimizing pharmaceutical production processes.

- Systems Engineering Objectives

Systems engineering aims to develop and manage complex systems efficiently and effectively, ensuring they meet user needs and stakeholder expectations while balancing cost, performance, and risk. It focuses on integrating all system elements to create a cohesive and functional whole, considering the entire lifecycle from concept to disposal. 

Here's a breakdown of the objectives:

1. Meeting Customer Needs:

• Systems engineering prioritizes understanding and fulfilling the needs of users and other stakeholders.
• It involves eliciting requirements, defining specifications, and ensuring the final product meets those needs.

2. System Integration and Cohesion:

• The primary goal is to create a system where all components work together seamlessly.
• This involves defining interfaces, managing dependencies, and ensuring compatibility between different parts of the system.

3. Balancing Cost, Performance, and Risk: 

• Systems engineering seeks to find optimal solutions that balance cost, performance, schedule, and risk.
• This often involves trade-off studies to find the best combination of features and capabilities within budget and constraints.

4. Managing Complexity: 

• Systems engineering provides a structured approach to managing complex systems with multiple components and interactions.
• It uses various tools and techniques, such as modeling and simulation, to analyze and understand the system's behavior.

5. Lifecycle Management: 

• Systems engineering considers the entire lifecycle of the system, from initial concept to disposal.
• It involves planning, development, deployment, operation, maintenance, and eventual retirement of the system.

6. Continuous Improvement and Learning:

• Systems engineering promotes a continuous improvement mindset, encouraging feedback and learning throughout the lifecycle.
• This helps to identify areas for optimization and ensure the system remains relevant and effective over time.

[University of Chicago]

- The Evolution of Systems Engineering

Systems engineering is the art and science of developing an operable system capable of meeting requirements within often opposed constraints. 

Systems engineering is a holistic, integrative discipline, wherein the contributions of structural engineers, electrical engineers, mechanism designers, power engineers, human factors engineers, and many more disciplines are evaluated and balanced, one against another, to produce a coherent whole that is not dominated by the perspective of a single discipline. 

Systems engineering seeks a safe and balanced design in the face of opposing interests and multiple, sometimes conflicting constraints.

Systems engineers are well-versed in the art and science of balancing organization, cost, and technology interactions in complex systems. Systems engineers and support organizations are critical to providing accurate, timely cost and schedule information for technical activities to support planning and project planning and control (PP&C). 

Systems engineering is about trade-offs and compromises; it uses a broad cross-cutting view of the system rather than a single-disciplinary view. Systems engineering is about looking at the "big picture" and not only making sure they get the design right (meeting requirements), but also making sure they get the right design (achieving operational goals and meeting stakeholder expectations). 

The twenty-first century provides an exciting opportunity for systems engineering. New advances in our understanding of the traditional discipline continue to emerge. At the same time, new forms of systems engineering have developed to address the engineering challenges of systems-of-systems (SoS) and enterprise systems. 

Even at this point in their evolution, these new forms display their own principles, processes, and practices. Some are different in degree than engineering at the system level, while others are different in kind. 

While it's impossible to predict how the traditional and new forms of systems engineering will evolve, however, a robust future lies ahead. Increases in technological complexity result in new challenges in architecture, networks, hardware and software engineering, and human systems integration. 

At the same time, the engineering scale for systems exceeds levels that could have been imagined only a short time ago. As a consequence, all forms of systems engineering will be needed to solve future engineering challenges, sometimes separately - yet increasingly - in combination.

- V-Model of Systems Engineering Lifecycle

The V-model is a graphical representation of a systems development lifecycle. It is used to produce rigorous development lifecycle models and project management models. The V-model falls into three broad categories, the German V-Modell, a general testing model and the US government standard.

The V-model summarizes the main steps to be taken in conjunction with the corresponding deliverables within computerized system validation framework, or project life cycle development. It describes the activities to be performed and the results that have to be produced during product development.

The left side of the "V" (the diagram above) represents the decomposition of requirements, and creation of system specifications. The right side of the "V" represents integration of parts and their validation. However, requirements need to be validated first against the higher level requirements or user needs. 

Furthermore, there is also something as validation of system models. This can partially be done at the left side also. To claim that validation only occurs at the right side may not be correct. 

The easiest way is to say that verification is always against the requirements (technical terms) and validation always against the real world or the user needs. 

- AI in Systems Engineering

AI is increasingly playing a crucial role in systems engineering, enhancing various aspects of the process from initial design to maintenance. It can be used for tasks like automating repetitive work, optimizing designs, making informed decisions based on data analysis, and even assisting in testing and validation. 

AI offers a wide range of capabilities that can significantly improve the efficiency, accuracy, and innovation of systems engineering. As AI technology continues to evolve, its role in systems engineering is likely to become even more prominent.

Here's a more detailed look at AI's role: 

1. Enhanced Decision-Making: 

AI algorithms can analyze vast datasets to provide insights that help engineers make more informed decisions about system design, architecture, and implementation. This can lead to better resource allocation, improved performance, and reduced risks.

2. Optimization and Automation:

AI can be used to automate routine tasks, freeing up engineers to focus on more complex and creative aspects of the systems engineering process. It can also optimize system designs and processes, leading to cost savings and efficiency improvements.

3. Simulation and Analysis:

AI can be used to create and analyze simulations of complex systems, allowing engineers to understand the behavior of the system under various conditions and to identify potential problems early on. This can be particularly useful for systems with complex interactions or dynamic behaviors.

4. Testing and Validation:

5. Predictive Analytics:

AI can analyze historical data to predict future problems or failures, helping engineers to take preventive measures and avoid costly repairs. This can be particularly valuable for systems that operate in harsh environments or that are subject to high levels of stress.

AI can be used to generate alternative designs, solutions, or even code, allowing engineers to explore a wider range of possibilities and to find innovative solutions to complex problems. This can be especially helpful for systems that are highly customizable or that need to adapt to changing requirements.

7. Examples of AI Applications in Systems Engineering:

8. Challenges and Considerations: