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Systems Modeling & Simulation: A Brief Introduction

When it comes to systems modeling, SMS_ThinkTank LLC knows sustainable innovation is a key differentiator. In order to address many challenges, an open and extensible development platform with integrated modeling and simulation tools is needed. This platform-based approach needs to leverage a common systems engineering and product data model (model-centric systems engineering) that encompasses requirements, platform, program, project, system definition, product structure, lifecycle, and configuration management capabilities. The unified data model needs to support cross-discipline decomposition and aggregation, while maintaining the links, relationships, and rich semantics that exist between the individual artifacts that describe the system or product.

Improved Engineering

Systems engineering and is best described by the traditional "V" model, though perhaps it is not best implemented using this simplistic view. Implementing a successful systems engineering practice is a complex task and must be embraced as it relates to the corporation's processes and product feasibility. For example, too many times projects overrun expectations or fail completely due to the lack of properly defined requirements. Robust downstream tools are only capable of quickly automating errors if the initial data is sparse, incorrect, poorly defined, poorly managed, or non-existent.

80 percent of main engineering decisions are taken in the first 20 percent of a project's lifecycle; up to 50 percent of projects are behind schedule due to poor early systems architecture validation, and more than 40 percent of project failures are due to lack in requirements, management, and traceability1. It's difficult to justify the costs of systems engineering in terms that program managers and corporate managers can relate to. Costs associated with systems engineering are as evident as the costs of resources and schedule time. However, the benefits are less obvious: cost avoidance (e.g. reduction of rework from interface mismatches), risk avoidance (e.g. early risk identification and mitigation), improved efficiency (e.g. clearer organizational boundaries and interfaces), and better products (e.g. better understanding and satisfaction of stakeholder needs)2.

However, studies have been performed to quantify many of the benefits associated with the proper implementation of systems engineering. By employing systems engineering in projects, companies have seen schedule reductions by more than 20 percent and cost savings exceeding 20 percent3. In a recent study, programs deploying the least amount of systems engineering had only 15 percent of the highest level of program performance. Among those deploying the greatest amount of systems engineering, 56 percent delivered the highest amount of program performance. For the most challenging programs, the number of programs delivering high program performance increased from 8 percent to 62 percent, with increased systems engineering deployment3,4. Note: program performance is defined as a combination of cost performance (satisfaction of budget), schedule performance, and technical performance (satisfaction of requirements).

In addition to the hard metrics, consider a scenario where a full product has to be built and tested to identify functional failures. These expensive tests should be done in a less risk-prone virtual environment, incorporating common standards and platforms while utilizing HiL (Hardware in the Loop), SiL (Software in the Loop), and MiL (Model in the Loop) practices. These identify serious gaps earlier in the development cycle.

Another scenario that is even more startling is when a product ships to the field and functional failures are realized between the mechanical, electrical, and software systems. Could this have been prevented by the clear and concise identification of stakeholder needs through a good systems engineering requirements process and/or by developing the product using a platform-based approach leveraging a common systems engineering and product data model? We must also ask: are we constantly putting out fires? Why? And if we don't have time to do it right the first time, how do we find the time to do it over?

Implementing new approaches is challenging. Developing a model-centric system engineering environment means the creation of digital threads within and across the different domains and disciplines involved for the specific design objectives. Simply creating huge amounts of digital data does not guarantee program success. A strong foundational governance around the virtual product development process, along with physical test in combination with system verification and validation, is essential. When combined with a simulation data management system inside the platform, it enables proper capturing and reuse of data, knowledge across disciplines, and makes proper data available to all stakeholders involved in the lifecycle of a product, its system, and the surrounding engineering environment.

The modern reality demands that we make use of the created and available data in real time. We cannot just use the data anymore to provide business analytics that develop new scenarios for the field upfront. We need to be able to run scenarios in real time to make decisions on the spot, based on the situation in the field and operating environment. This real-time collaboration around a visual, model-based design environment will be the catalyst to affect a cultural transformation that achieves the model-centric engineering vision. However, this cultural change cannot be successful without management support at all levels; most importantly, at the highest level.

Change can be uncomfortable and will be resisted by many stakeholders in the process. Therefore, system modeling and simulation is not just about product and process, it is also bringing the surrounding ecosystem with its culture and behavior into play and linking them together. This can only provide sustainable innovation.

1"Overrun Schedule and Cost," INCOSE VDC Analysis; INCOSE Systems Engineering Center of Excellence, Honourcode Inc.
2"A Business Case for SE," October 22, 2012, Carnegie Mellon University
3"The Value of Systems Engineering" by James Elm, Software Engineering Institute, Carnegie Mellon University
4"The Business Case for Systems Engineering Study: Results of the Systems Engineering Effectiveness Study"