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At the Intersection of Teams and Technology

Nationally recognized for excellence in online graduate engineering education in 2021 by U.S. News & World Report.

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Engineering Management OVERVIEW

The Online Master of Engineering in Engineering Management provides students with a strong understanding of the technology involved in engineering projects and the management process through which the technology is applied. Students evolve into engineers who can interface between the technical and business aspects, and contribute the most value to their respective enterprises. As an Engineering Management student, you will hone the skills you need to:

  • Leverage advanced techniques and analysis to estimate and use cost information in decision making

  • Form and manage an effective engineering design team in a business environment

  • Handle and process information using Python

  • Master the fundamental of systems dynamics and build system dynamic models

By the Numbers

In U.S.
For graduates with the highest mid-career salaries (2020-2021 PayScale College Salaries Report)
In U.S.
Top 20
For the best 20-year return on investment for graduates, 2020 PayScale College ROI Report
In U.S.
Recognized for "Best Career Placement" on the Princeton Review's Best Value Colleges list (2020)
STEM Colleges
Top 25
Named among the “Top 25 STEM Colleges 2018” by Forbes
In N.J.
Best engineering and science graduate programs by U.S. News & World Report (2022)


Online Master of Engineering in Engineering Management students take coursework to build proficiency in the leading engineering technology and management techniques. Students choose between two program concentrations: Managerial Analytics or Supply Chain and Logistics Management.**

Term 1

This course presents advanced techniques and analysis designed to permit managers to estimate and use cost information in decision making. Topics include: historical overview of the management accounting process, statistical cost estimation, cost allocation, and uses of cost information in evaluating decisions about pricing, quality, manufacturing processes (e.g., JIT, CIM), investments in new technologies, investment centers, the selection process for capital investments, both tangible and intangible, and how this process is structured and constrained by the time value of money, the source of funds, market demand, and competitive position.

This project-based course exposes students to tools and methodologies useful for forming and managing an effective engineering design team in a business environment. Topics covered will include personality profiles for creating teams with balanced diversity; computational tools for project coordination and management; real-time electronic documentation as a critical design process variable; and methods for refining project requirements to ensure that the team addresses the right problem with the right solution.

Term 2

This course brings a strong modeling orientation to bear on the process of obtaining and utilizing resources to produce and deliver useful goods and services so as to meet the goals of the organization. Decision-oriented models such as linear programming, inventory control, and forecasting are discussed and then implemented utilizing spreadsheets and other commercial software. A review of the fundamentals of statistical analysis oriented toward business problems will also be conducted.

This course enables the engineering management student to acquire the knowledge and skills they will need to handle the variety and volume of information encountered in today’s workplace. The course uses Python, which is rapidly becoming the language of choice for information handling and data analysis. Students will work with both structured and semi-structured data.

Term 3

This course emphasizes the development of modeling and simulation concepts and analysis skills necessary to design, program, implement, and use computers to solve complex systems/product analysis problems. The key emphasis is on problem formulation, model building, data analysis, solution techniques, and evaluation of alternative designs/ processes in complex systems/products. An overview of modeling techniques and methods used in decision analysis, including Monte Carlo and discrete event simulation is presented.

This course is a study of analytic techniques for rational decision-making that addresses uncertainty, conflicting objectives, and risk attitudes. This course covers modeling uncertainty; rational decision-making principles; representing decision problems with value trees, decision trees, and influence diagrams; solving value hierarchies; defining and calculating the value of information; incorporating risk attitudes into the analysis; and conducting sensitivity analyses.

Term 4

Project success depends, largely, on the human side. Success in motivating project workers, organizing and leading project teams, communication and sharing information, and conflict resolution, are just a few areas that are critical for project success. However, being primarily technical people, many project managers tend to neglect these "soft" issues, assuming they are less important or that they should be addressed by direct functional managers. The purpose of this course is to increase awareness of project managers to the critical issues of managing people and to present some of the theories and practices of leading project workers and teams.

EM 622 Data Analysis and Visualization Techniques for Decision Making

This course provides a hands-on introduction to the modern techniques for visualizing data and leverages such techniques with the corresponding problem solving skills necessary to complement data visualization into specific strategic decision making. The student will first learn to use the latest off the shelf software for data visualization. Specifically, the student will learn the following languages: R, D3, Google refine and Spot fire.

SYS 640 System Supportability and Logistics

The supportability of a system can be defined as the ability of the system to be supported in a cost effective and timely manner, with a minimum of logistics support resources. The required resources might include test and support equipment, trained maintenance personnel, spare and repair parts, technical documentation and special facilities. For large complex systems, supportability considerations may be significant and often have a major impact upon life-cycle cost. It is therefore particularly important that these considerations be included early during the system design trade studies and design decision-making.

Term 5


Getting usable information from the vast amount of data we are immersed into requires a combination of methodologies, tool, techniques, algorithms and ingenuity. Creating views, extracting trends, define patterns, identify clusters are all elements we need to manage large data. The field of data mining has evolved from the disciplines of statistics and artificial intelligence. This course will examine methods that have emerged from both fields and proven to be of value in recognizing patterns and making predictions from an applications perspective. Final goal of the course is to provide the students with a “data toolbox” they can use in their activities. This “toolbox” contains methods and tools that students will use themselves during the course for real world applications. The course is hand-on, but no coding is required, using Open Source Data Science tools that are based on Graphical User Interfaces.

Many social-technical systems in healthcare, energy and urban systems can be considered as multi-agent systems where different agents – people, organizations or autonomous technologies- with heterogeneous, and often opposing objectives interact and shape the complex collective behavior and evolving nature of such systems. Analysis, design and governance and design of such systems can be very challenging and require rigorous agent-based thinking rooted in analytical models. This course teaches fundamentals of multi-agent systems, starting from models of single agent decision making and planning under uncertainty, and moves to basic frameworks for multi-agent system analysis tools, with a focus on game theory and complex network analysis . The course will take a combination of analytical and conceptual methods, and will use agent based simulation techniques rooted in these methods. Students will apply the course material to a real-world project, close to their area of dissertation research.


This course illustrates the theory and practice of designing and analyzing supply chains. It provides tool sets to identify key drivers of supply chain performance such as inventory, transportation, information and facilities. Recognizing the interactions between the supply and demand components, the course provides a methodology for implementing integrated supply chains, enabling a framework to leverage these dynamics for effective product/process design and enterprise operations.

This course covers the theory and application of modeling aggregate demand, fragmented demand and consumer behavior using statistical methods for analysis and forecasting for facilities, services and products. It also aims to provide students with both the conceptual basis and tools necessary to conduct market segmentation studies, defining and identifying criteria for effective segmentation, along with techniques for simultaneous profiling of segments and models for dynamic segmentation. All of this provides a window on the external environment, thereby contributing input and context to product, process and systems design decisions and their ongoing management.

**Applicants are not required to select a program concentration during the application process.
Please reach out to your enrollment advisor or for more detail on the above.

Career Outlook

Graduates of the M.Eng. in Engineering Management program bring a blend of interpersonal and technical skills to a variety of engineering and engineering leadership roles.





Architectural and Engineering Managers



Computer and Information Systems Managers



Industrial Production Managers



Industrial Engineers



Source: Emsi Labor Market Data, 2021


  • BMW
  • IBM
  • Exxon
  • Lockheed Martin
  • Goldman Sachs
  • UPS


  • Bachelor's Degree

    Minimum GPA of 3.0 from an accredited institution. Degree required to begin the program; completion not required at time of application.


    You may submit unofficial transcripts during the application process. After admission, you will be required to submit official transcripts.


    Faculty members and/or professional colleagues.


    Required for international students.


    Optional, but strongly recommended.


    Optional, but strongly recommended.


Due to the impacts of the coronavirus (COVID-19) on testing centers around the world, Stevens has made the following accommodations available to all students for the fall 2021 admissions cycle:

  • GRE/GMAT: Test scores are temporarily waived.

  • TOEFL/IELTS/DUOLINGO: Affected applicants may submit Duolingo English Test (DET) results in lieu of TOEFL/IELTS exam results.

Tuition & Cost

Per Credit (30 credits)
Application Fee
Fee waivers available
Enrollment Deposit
*Tuition rates based on Fall 2022 tuition rate effective September 2022. Tuition and fees are subject to change annually.

Key Dates & Deadlines


Early Submit

Priority Submit

Final Submit

Start of Classes

$250 Deposit Waiver* and Application Fee Waiver Available.

Application Fee Waiver Available and Early Application Review.

Fall 2022

May 23, 2022

June 27, 2022

July 25, 2022

September 12, 2022

* Applicants who apply by the early submit deadline and are admitted may be eligible for a $250 deposit waiver. Applicants who receive education assistance from employers or other tuition discounts are not eligible. Other eligibility conditions may apply.

Upcoming Webinars

Wednesday, July 13th
7:00pm ET

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Thursday, August 18th
7:00pm ET

"What School is Right For You?"


The School of Systems and Enterprises (SSE) faculty is made up of world-class educators and groundbreaking researchers who offer industry insights to Engineering Management students.

Chong Ee



A headshot image of Dr. Alparslan Emrah Bayrak.



Teresa Zigh



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GRE/GMAT Not Required