Model-Based Systems Engineering Foundations and Applications to the Production Enterprise

A multidisciplinary team of faculty, staff, and students at Purdue University has developed a series of online instructional modules on Model-Based Systems Engineering (MBSE) under a Production Engineering Education and Research grant from the National Science Foundation. Module content was selected to help meet the industry's needs for a workforce prepared to perform efficiently in a model-based digital enterprise, especially in a production engineering context. The Purdue program consists of a set of seven modules that can be taken as a group or as a subset consisting of only those modules that align with an individual's job responsibilities and/or interests. The modules are asynchronous but instructor-facilitated, providing learners with an opportunity to interact with both the instructor and other students.

The seven modules are listed below. Registration is also available to purchase all seven modules together. Links to registration pages for individual modules can be found in the table at the bottom of this webpage.

Introduction to Systems Engineering (SE) and Model-Based Systems Engineering (MBSE) for Production Systems

This first module uses videos, case studies, written materials, guided discussions, and team projects to introduce learners to fundamental concepts in SE and MBSE. Concepts include the importance of SE and MBSE, natural and engineered systems, simple and complex systems, models of systems, the foundations of systems engineering, and the ongoing transition to model-based systems in production and manufacturing engineering.

Engineering a System with SysML

This module is designed for learners who want to prepare to work in an environment where MBSE is used. Topics include an overview of systems modeling with SysML, foundations of modeling preparation and organization, systems engineering using SysML, options for presenting results, and an introduction to MBSE tools that can be used for managing the life cycle of a system.

SysML Implementation and Applications

This module is for learners who will be directly involved in developing models used in MBSE. Learners will get hands-on experience building SysML models according to the principles learned in the previous module. Topics include learning to use an MBSE tool demonstrating the ability to form and use models for system requirements analysis, physical, functional, and allocated architecture, system integration and verification, model validation, and documentation.

Quantitative Statistical Methods Supporting MBSE

Designed for learners who want to develop or refresh analytical skills for data analysis, this module provides instruction on interpreting simulation results and making effective design and business decisions. The module presents statistical foundations such as distributions and probability, hypothesis testing, and confidence intervals. It also covers applications and decision-making, including value/objective functions and optimization, simulation (including Monte Carlo), and decision trees.

Production Engineering and MBSE

This module provides an overview of production engineering processes, systems, and key performance indicators. Modeling and simulation in production engineering are discussed along with their benefits. Examples of the use of simulations in both a manufacturing process and a production system are explored. At the end of this module, learners will be able to identify manufacturing characteristics that should be considered in creating SysML diagrams and understand how SysML diagrams provide information on manufacturing domain models and vice versa.

Digital Engineering and the Model-based Enterprise

Learners completing this module will be able to explain the importance of integrating and connecting MBSE to the Digital Enterprise to enable collaboration and how MBSE data and models flow and are shared throughout the different stages of the product lifecycle. Topics explored in the module include the state of MBSE tools and technology with respect to the digital enterprise infrastructure, the digital thread, MBSE and PLM integration, traceability and change management.

MBSE Capstone Project

The capstone project provides learners with an opportunity to apply the ideas and concepts learned in prior modules to a real-world problem and demonstrate the business value of the project using quantitative methods. In the capstone project, learners will be able to identify stakeholders, write a set of requirements, and develop a top-level architecture for a particular system. Groups of learners from the same organization may propose projects of particular interest to their group. Individuals taking the module will be able to select from a set of pre-defined projects.

Schedule, Cost, and Credentials

Each module is expected to require 10-15 hours of active participation, and each module is available for three weeks, except for the capstone module which will be available longer. Learners will watch short videos, read materials provided, participate in guided discussions, and complete online quizzes and other assignments. The instructor will participate in the discussions and provide feedback on assignments. Assignments will have specific due dates. A certificate of satisfactory completion will be awarded to each learner who receives a score of at least 80% on all required assignments. Learners are encouraged and expected to participate in discussions and other class activities regularly.

Model-Based Systems Engineering Foundations and Applications to the Production Enterprise Flyer

Below please find the schedule for each module using the Registration link for the module. 

*The standard cost per module is $500 or $3,150 for all seven. For employees of organizations on the Advisory Board for this project taking the modules during this first year and willing to complete a short survey on their experience, the cost is $400 per module or $2,520 for the suite of seven. Enrollment in each module during the first offering will be limited to 40 learners.

For additional information, contact proed@purdue.edu.

Acknowledgment

This work was made possible by a grant from The National Science Foundation (#1935683). Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author and do not necessarily reflect the views of The National Science Foundation.