How to Make Better Decisions in Complex Systems Engineering Projects

An unfortunate fact of life is that it can sometimes be complicated - and for many engineers, so are today's modern systems. What makes these modern systems complicated is their complexity, and one reason behind that is the recent rise of autonomous systems.

Autonomous systems can provide intelligent responses to their environment with minimal human input, helping to solve complex problems such as increasing efficiency within transportation network systems. But what defines a complex system?

For systems engineers, a complex system is an open system with continually cooperating and competing elements that continually evolve and change according to its condition and external environment (White, 2012). However, it can be hard to manage such complex systems because of these same qualities.

When designing and creating systems that are autonomous or rely on autonomous technology, it is critical that systems engineers are able to overcome any complex systems engineering challenges by making informed decisions to ensure project success.

Challenges of Complex Systems

According to A Complexity Primer for Systems Engineers (2015) created by INCOSE, these are the common challenges of complex systems engineering projects:

Constant Change to Requirements & Scope

Constant change makes it difficult to define stable goals.

Technological Uncertainty

Rapid technological change and advancements can make systems obsolete quickly.

Risk Management

Relationships between components can lead to subtle bugs, surprising dynamic and emergent behavior, and unintended consequences.

Complex System Descriptions

Difficulties describing system structures at a single level or view can cause challenges, often requiring multi-scale descriptions.

Data Overload

Describing the system's behavior might require unobtainable amounts of information to predict behavior given finite resources.

Complex System States

A complex system may have multiple stable states, transient states, or even no lasting stable states, exhibiting continuous evolution.

Complex Dynamic Behavior

Simulating complex system behavior often cannot be achieved with simple averages due to opaque key variables, indeterminate boundaries, and weak ties, disproportionately affecting system behavior.

In large and complex systems engineering projects, there are many teams made up of different individuals, organizations, and disciplines. On programmatic and technical levels, these teams must either be coordinated, consulted, or informed during each project phase. Sometimes they also across different project phases in anticipation of future project activities, such as testing and analysis.

These teams are considered stakeholders, or groups that have interests or relevance to the project. Stakeholders can be either internal or external to the project, such as the organization's decision-makers, technical experts, supporting business units, and even the future users envisioned to use the system.

Stakeholders often have conflicting interests or goals - a result of the differences between them or because of the type of relationships with each other. Similarly, on a deeper technical level, the system and its components also have physical and logical interactions with each other that must be managed.

Both of these levels create an environment where conflicting requirements and constraints can and will occur, often leading to necessary trade-offs and even communication breakdowns amongst teams.

Strategies for Better Decision-Making for Complex Systems

Complexity doesn’t mean things have to be complicated. To navigate this chaos, systems have a number of strategies that they can use for better decision-making.

The first is to approach problems with a systems thinking mindset, recognizing both parts and the whole, as well as the relationships between each other that make up an interconnected holistic system.

Next, we have the rise of Model-Based Systems Engineering (MBSE), which is an approach that seeks to overturn the traditional document-based approach to systems engineering. MBSE emphasizes using models and diagrams to capture, analyze, share, and manage information for systems (Walden, 2023).

These strategies also help us maintain stakeholder engagement by allowing engineers to more easily involve stakeholders early and frequently in the systems engineering process with visuals and the latest information. Consistent stakeholder engagement can reduce chaos and improve the alignment of multiple teams in a project environment.

Lastly, leveraging technology and tools can significantly speed up the process of applying one or more of these strategies. One tool that can do it all is SPEC Innovations' flagship tool, Innoslate.

Innoslate

Within Innoslate, there are a multitude of tools and features that support systems engineers by enabling modeling and simulation in both Lifecycle Management Language (LML) and Systems Modeling Language (SysML), which are all contained in a centralized cloud database environment. Such examples include Innoslate’s Discrete Event and Monte Carlo Simulators, capable of simultaneously checking, modeling, and executing real-time system processes to gain useful insights for projects.

These models provide powerful benefits by being easily exportable, quickly executable, and scalable in the cloud. Innoslate’s models also feature customizable scripts and are available for integration with tools such as MATLAB and Ansys Systems Toolkit (STK).

Innoslate also allows users to leverage data analytics and artificial intelligence on its cloud database to process large data sets to gain better insights and pattern recognition. Algorithms are used to check whole requirements documents to provide quality scores, an Intelligence View checks project databases to ensure that all project entities are correct and complete, and an AI Assistant is available to help users navigate Innoslate itself along with providing suggestions and images for project use. 

Digital tools such as Innoslate empower users to gain improved decision-making abilities during all aspects of your project’s systems engineering lifecycle, leading to successful project outcomes. 

Sopatra

If you have existing processes that are documented in Standard Operating Procedures (SOPs), SPEC Innovation’s Sopatra is capable of automatically converting those documents into process diagrams upon import. By quickly converting SOPs into diagrams, project teams can focus on analyzing existing processes for new opportunities to improve and gain new insights, reducing time spent laboriously building existing processes beforehand.

SPEC Innovations holds a steadfast commitment to advancing the field of systems engineering and supporting all systems engineering professionals within academic, commercial, and government organizations. Through innovative tools, comprehensive training, and a focus on collaboration, we empower engineers to enhance their skills and streamline complex processes.

By developing tools such as Innoslate and Sopatra, SPEC Innovations aims to play a critical role in shaping a more efficient and effective engineering landscape, directly contributing to the success of projects and the growth of the industry as a whole.

Sources

Sheard, S., Cook, S., Honour, E., Hybertson, D., Krupa, J., McEver, J., McKinney, D., Ondrus, P., Ryan, A., Scheurer, R., Singer, J., Sparber, J., & White, B. (2015, July). A Complexity Primer for Systems Engineers. 

Walden, D. (2023). Modeling, Analysis, and Simulation. In Systems Engineering Handbook (pp. 219-220). Wiley. 

White, B. E. (2012). NDIA 15th Annual Systems Engineering Conference. In NDIA Conference Proceedings. DTIC. Retrieved October 3, 2024, from chrome-extension://efaidnbmnnnibpcajpcglclefindmkaj/https://ndia.dtic.mil/wp-content/uploads/2012/systemtutorial/14626.pdf.