systems Engineering


Specialization in (Artificially) Intelligent Systems.

In the not-so-distant future there will be an increased focus on the use of various “soft computing” techniques (e.g., fuzzy logic, machine learning, artificial neural networks, rules-based logic) encoded in “black box” models vice traditional “hard computing” techniques (e.g., hardcoded logic in source code) to create “intelligence” of various forms (along the spectrum of Assisted AI, Augmented AI, and Autonomous AI), enabling the creation of intelligent systems.

Based on our experience over the last ten years, these intelligent systems have unique properties – such as uncertainty, unpredictability, variability, error, and non-determinism –- that require specialized SE techniques throughout the development lifecycle. It is expected that future SE will require a hybridization of traditional “hard” SE techniques with newer “soft” techniques.

We have defined the Axiologic Solutions Systems Engineering Methodology – ENGINEERai™ to correctly engineer intelligent systems and explicitly address all of these unique properties.

ENGINEERai practices are organized per the standard Axiologic Solutions systems engineering approach (defined above), but focuses on the unique activities required to define, design, develop, validate, use, implement, and operate knowledge and intelligence at enterprise scale. When appropriate, additional focus is given to specific knowledge approaches, such as machine learning, semantics, and rules-based techniques. ENGINEERai discusses how to take an idea and a series of knowledge models developed by a knowledge engineer or ML engineer, integrate them with other types of models, other software, and other hardware, and deploy it as part of reliable, secured, scalable and maintainable intelligent system.

Service System Engineering.

The government is selectively shifting its acquisition focus from “staff augmentation” models to a (quasi-) managed services model. Some of these services may be operated by a third party and some are operated by the government.

Managed services can be quite complex to engineer. To address the typical complexity of services and offer a mature differentiable methodology for designing services, we have defined the Axiologic Service Systems Engineering Methodology – ASSEM™. ASSEM is an engineering-centric methodology, reusing practices from classic engineering domains, business operations, and customers, as well as from different domains such as management science, behavioral science, social science, systems science, network science, computer science, decision informatics, etc. ASSEM incorporates specialized techniques such as:

  • Demand forecasting
  • Anticipating usage patterns
  • Dependency management
  • Modeling and simulation
  • Supply chain management
  • Financial analysis
  • Value stream analysis

Digital Engineering.

Recently, the DoD has issued its Digital Engineering Strategy. The strategy is intended to guide the planning, development, and implementation of the digital engineering transformation across the DoD. The DoD vision for digital engineering is to modernize how the department designs, develops, delivers, operates, and sustains systems in the digital age. DoD defines digital engineering as an integrated digital approach that uses authoritative sources of system data and models as a continuum across disciplines to support lifecycle activities from concept through disposal.

Axiologic Solutions is ready to support the DoD by leveraging digital engineering techniques when performing systems engineering, taking advantage of computational technology, modeling (MBSE), analytics, and data science.

Use of Data Science and Modeling in Systems Engineering.

People often talk about “data-driven decision making” or “evidence-based decision making” when doing SE, but then do not use any data, observations, metrics, or analytical techniques to support these processes. Axiologic Solutions injects data analysis techniques, data science (including the use of SE AI infused tools) and model-based techniques, such as PBSE, MBSE, ModSim, and others, to significantly improve the quality of systems engineering selections.