Doctor of Philosophy (Ph.D.) Major in Engineering Management (Entering with Master's Degree) Online
Prepare to lead research, innovation, and technology-driven transformation across engineering-intensive industries. Gain specialized expertise and management strategies in this online Ph.D. program.
Apply by:
7/22/26
Start class:
8/19/26
Program Overview
Rise to the top with this online engineering management doctorate program
Take your engineering career to the next level without interrupting it. Designed for working professionals, the Ph.D. in Engineering Management (Entering with Master's Degree) online program helps you develop advanced expertise in research, experimentation, analytics, data-driven decision-making, AI, machine learning, data mining, and automation.
Aligned with manufacturing, sustainability, systems design, and product development, the online engineering management Ph.D. curriculum spans computational modeling, supply chain systems, operations improvement, and engineering technology. Tailor the program to your professional goals by adding business management courses. Plus, benefit from flexible block scheduling and the support of dedicated faculty to guide you through dissertation completion.
Engineering management Ph.D. research areas
Doctoral students in Engineering Management engage in advanced, interdisciplinary research focused on optimizing systems, processes, and innovation across engineering-driven organizations. Areas of study include:
- Data-Driven Decision-Making & Analytics: Apply data science and analytics to improve engineering and operational performance
- Experimental Design & Engineering Research Methods: Develop rigorous research frameworks to test, validate, and optimize engineering systems
- Computational Modeling & Simulation: Use simulation and computational tools to analyze complex systems and predict outcomes
- Artificial Intelligence & Machine Learning in Engineering: Explore AI-driven approaches to automation, optimization, and intelligent systems
- Data Mining & Forecasting: Extract insights from large datasets to support predictive decision-making and planning
- Advanced Manufacturing & Automation: Study smart manufacturing, robotics, and automated production systems
- Sustainable Engineering & Industrial Ecology: Design systems that balance performance with environmental and resource considerations
- Lean Systems & Quality Improvement: Improve efficiency and reduce waste through lean methodologies and continuous improvement
- Supply Chain & Operations Systems: Optimize logistics, supply networks, and operations in complex engineering environments
- Product Design & Development: Advance processes for designing, testing, and launching innovative engineering solutions
- Engineering Leadership & Innovation Management: Examine leadership strategies and innovation practices in technical organizations
- Technology & Information Systems Management: Manage digital infrastructure, information systems, and technology integration in engineering contexts
- Data-Driven Decision-Making & Analytics: Apply data science and analytics to improve engineering and operational performance
- Experimental Design & Engineering Research Methods: Develop rigorous research frameworks to test, validate, and optimize engineering systems
- Computational Modeling & Simulation: Use simulation and computational tools to analyze complex systems and predict outcomes
- Artificial Intelligence & Machine Learning in Engineering: Explore AI-driven approaches to automation, optimization, and intelligent systems
- Data Mining & Forecasting: Extract insights from large datasets to support predictive decision-making and planning
- Advanced Manufacturing & Automation: Study smart manufacturing, robotics, and automated production systems
- Sustainable Engineering & Industrial Ecology: Design systems that balance performance with environmental and resource considerations
- Lean Systems & Quality Improvement: Improve efficiency and reduce waste through lean methodologies and continuous improvement
- Supply Chain & Operations Systems: Optimize logistics, supply networks, and operations in complex engineering environments
- Product Design & Development: Advance processes for designing, testing, and launching innovative engineering solutions
- Engineering Leadership & Innovation Management: Examine leadership strategies and innovation practices in technical organizations
- Technology & Information Systems Management: Manage digital infrastructure, information systems, and technology integration in engineering contexts
As a student in this online Ph.D. in Engineering Management program, you will learn how to:
- Apply advanced mathematical, analytical, and computational engineering methods to solve complex technical and organizational challenges
- Design and conduct independent, data-driven engineering research that advances technology, process innovation, or applied industrial systems
- Develop sustainable, efficient, and scalable engineering systems, integrating Lean principles, quality systems, and environmentally conscious design
- Integrate leadership, ethics, communication, and engineering management principles into decision-making within technical organizations
- Synthesize undergraduate, graduate, and doctoral-level learning into a cohesive research agenda that culminates in a defended dissertation
- Apply advanced mathematical, analytical, and computational engineering methods to solve complex technical and organizational challenges
- Design and conduct independent, data-driven engineering research that advances technology, process innovation, or applied industrial systems
- Develop sustainable, efficient, and scalable engineering systems, integrating Lean principles, quality systems, and environmentally conscious design
- Integrate leadership, ethics, communication, and engineering management principles into decision-making within technical organizations
- Synthesize undergraduate, graduate, and doctoral-level learning into a cohesive research agenda that culminates in a defended dissertation
Also available:
We offer a variety of student-centered online degrees that can help you advance. If you’re entering with a bachelor’s degree, learn more about our Ph.D. in Engineering Management pathway designed for direct entry. You can also explore other Ph.D programs in construction and engineering management.
Total Tuition
$27,025*
Duration
As few as 3-4 years
Credit Hours
54
Tuition
Pay-by-the-course tuition gives you budget flexibility
Tuition for the online Ph.D. degree program in engineering management is affordable and paid by the course, so you can achieve your academic goals while remaining within your budget.
| Program | Per Credit Hour | Per Course | Per Program |
|---|---|---|---|
| Ph.D. Engineering Management | $500 | $1,501 | $27,025* |
*Students in Accelerated Online Programs are not eligible for payment plans or emergency loans. Tuition and fees are subject to change by legislative or Board of Regents action; changes become effective on the date of enactment. BookSmart fees are automatically added each semester but are not included in program costs; students may opt out by the start of each term. Registrations after the tuition deadline require full payment at registration. Cost of Attendance details are available through TXST One Stop.
Tuition breakdown
Total Tuition
$27,025*
Per Credit Hour
$500
Ready to take the next step
toward earning your degree?
Calendar
Take note of these key calendar deadlines
The Texas State Ph.D. in Engineering Management program is delivered in a convenient online learning format that offers maximum flexibility for working adults like you. Choose the start date that fits your goals.
| Term | Start Date | App Deadline | Document Deadline | Registration Deadline | Tuition Deadline | Class End Date | Term Length |
|---|---|---|---|---|---|---|---|
| Fall 1 2026 | 8/19/26 | 7/22/26 | 7/22/26 | 8/19/26 | 8/13/26 | 10/7/26 | 8 weeks |
Now Enrolling
Apply by
7/22/26
Start Class
8/19/26
Admissions
We’ve streamlined the admission process for this Ph.D. in Engineering Management
Apply to the Ph.D. in Engineering Management online program quickly and easily with our streamlined admission process. Review the admission requirements below and take the next step toward your professional goals.
To be eligible to earn your Ph.D. in Engineering Management degree online from Texas State University, you must have the following:
- Completed online application
- Nonrefundable application fee of $55 ($90 for international*)
- Baccalaureate degree in engineering, computer science, physics, technology, or a closely related field from a regionally accredited university. (Non-U.S. degrees must be equivalent to a four-year U.S. bachelor’s degree. In most cases, three-year degrees are not considered. Visit our International FAQs for more information.)
- Master’s degree in engineering, computer science, physics, technology, or a closely related field from an accredited college or university. (Non-U.S. degrees must be equivalent to a U.S. master’s degree. Master’s degrees following a three-year bachelor’s degree may not be equivalent; please contact Graduate Admissions if you are unsure.)
- A copy of an official transcript from each institution where course credit was granted.
- Minimum 3.3 GPA overall
- Statement of purpose (two pages) conveying your research interests, plans for graduate study, and professional aspirations
- Three letters of recommendation evaluating your skill and potential in this degree program, preferably from academic sources
- Resume/CV outlining education, work experience, scholarships/grants, publications/presentations, and other accomplishments
- Top-ranked applicants who meet the minimum preferred credentials may be interviewed by the Ph.D. program director and other committee members via online tools such as Zoom or Microsoft Teams
- GRE not required
For applicants without a U.S. bachelor’s degree (or equivalent)**:
You must submit an approved English proficiency exam score that meets the minimum program requirements.
- Official TOEFL iBT scores required with a 78 overall and minimum individual module scores of
- 19 listening
- 19 reading
- 19 speaking
- 18 writing
- Official PTE scores required with a 52 overall
- Official IELTS (academic) scores required with a 6.5 overall and minimum individual module scores of 6.0
- Official Duolingo scores required with a 110 overall
- Official TOEFL Essentials scores required with an 8.5 overall
**Exemption: Applicants who have earned a bachelor’s degree or higher from a regionally accredited U.S. institution or an equivalent degree from a country on our exempt countries list are not required to submit an English proficiency exam score.
Transcripts may be sent electronically to [email protected] or mailed to:
TXST One Stop
Texas State University – Graduate Admissions
601 University Dr.
San Marcos, TX 78666
*Texas State defines an on-campus international student as anyone with a nonimmigrant visa status, including H-1B visa holders, or those seeking a visa to enroll. If you are not a U.S. citizen, permanent resident, refugee, or asylee, you will be classified as an international applicant.
An online international student is someone who holds citizenship in another country, is not a U.S. permanent resident, and resides outside the U.S. while enrolling in an online program.
Students who are not on a visa but are graduating from a Texas high school after three years in residence are considered domestic applicants.
If you are a U.S. citizen, permanent resident, refugee, or asylee, you are considered a domestic applicant.
Admission Requirements
- Minimum 3.3 GPA on any completed graduate course work
- No GMAT/GRE required
- All official transcripts
Courses
Explore your online engineering courses
To be admitted with a master’s degree and graduate from the Ph.D. in Engineering Management online degree program, you must complete a total of 54 credit hours, including 21 credit hours of core courses, nine credit hours of electives, and 24 credit hours of dissertation courses.
Core Courses
EMGT 7300: Research Methods
Duration: 8 Weeks weeks
Credit Hours: 3
This course will provide students with the essential knowledge and practical skills to conduct rigorous and systematic applied research in the field of engineering management. Through a combination of theoretical discussions and hands-on exercises, students will gain a comprehensive understanding of the systematic research process in the context of engineering management applications. Specific topics covered are problem formulation, literature search, research methods, data analysis, data management, data privacy, statistical approaches to analyze data, technical writing, academic integrity, presenting, and publishing.
EMGT 7310: Risk Management and Resiliency Analysis
Duration: 8 Weeks weeks
Credit Hours: 3
This course will provide the students with the necessary knowledge and skills to identify and understand various types of risks faced by organizations, manage and mitigate risk, and conduct resiliency analysis based on various quantitative methods. Specific topics covered includes risk identification, response, monitoring and control through the use of good data input, and systematic approach and quantitative risk and resilience evaluation.
EMGT 7315: Industrial Sustainability and Circular Economy
Duration: 8 Weeks weeks
Credit Hours: 3
This course will provide students with the knowledge and skills needed to manage and grow a sustainable business by incorporating circular economy principles and strategies. The course will use systems thinking to understand the technological, economic and policy implications of circular economy transitions. The focus of the course will be on real-world applications of sustainable management with the objective of achieving zero waste and circular outcomes in various industries.
EMGT 7320: Engineering Data Analytics
Duration: 8 Weeks weeks
Credit Hours: 3
This course will provide the students with the knowledge and practical skills required to leverage data analytics in decision-making and problem-solving processes. Emphasis will be placed on real-world applications and use cases across various engineering management areas. The topics include data preprocessing and cleaning, exploratory data analysis, descriptive statistics for decision making, predictive modeling techniques, and supervised and unsupervised machine learning.
EMGT 7325: Decision Making Under Uncertainty
Duration: 8 Weeks weeks
Credit Hours: 3
This course will provide students with the essential skills and knowledge to effectively handle and quantify uncertainty in engineering projects and decision-making processes. In addition to theoretical foundations for decision-making under uncertainty, practical techniques for uncertainty analysis will be extensively covered and industrial case studies will be discussed. Specific topics covered include sources and types of uncertainties in engineering systems, uncertainty propagation, robustness analysis, uncertainty reduction, reliability analysis, and design optimization with uncertainty.
MSEC 7301: Practical Skills in Commercialization and Entrepreneurship
Duration: 8 Weeks weeks
Credit Hours: 3
This course is the first of a two-course series to impart business and commercialization skills by producing a business plan. Key areas covered include intellectual property law, technology transfer and licensing strategies, business plan development, business finance strategies, management structures, project management methods, statistical quality and process control.
MSEC 7302: Leadership Skills in Commercialization and Entrepreneurship
Duration: 8 Weeks weeks
Credit Hours: 3
This course is the second of a two-course series to impart business and commercialization skills by producing a business plan. Key areas covered include intellectual property law, technology transfer and licensing strategies, business plan development, business finance strategies, management structures, project management methods, statistical quality and process control. Prerequisite: MSEC 7301 with a grade of "B" or better.
Electives
Select three courses from the following list:
MMIE 7305 : Advanced Design of Experiments
Duration: 8 Weeks weeks
Credit Hours: 3
This course teaches students to plan, design and conduct experiments efficiently and effectively, and to analyze the resulting data for obtaining valid conclusions. Students use computer experiments and software tools to optimize manufacturing, energy and service operations based on both deterministic and stochastic models. Topics include full and fractional factorial designs, blocking and confounding design, regression model, response surface method and design, and robust parameter design or Taguchi method. Through the course project, students apply the optimal design methodology to improve a real manufacturing or service process.
MMIE 7317 : Applied Data Science I
Duration: 8 Weeks weeks
Credit Hours: 3
This course covers machine learning tools. Topics include supervised algorithms, techniques for improving model performance, evaluation techniques, and software packages for implementation. Emphasis will be placed on real-world applications across various domains particularly relevant to mechanical, manufacturing or industrial engineering (MMIE) and engineering management (EM) fields. Prerequisite: MMIE 7305 with a grade of "B" or better.
MMIE 7318 : Applied Data Science II
Duration: 8 Weeks weeks
Credit Hours: 3
This course focuses on concepts and techniques in unsupervised machine learning. Students will explore various algorithms and methodologies for extracting meaningful information from unlabeled data. The course covers dimensionality reduction, clustering methods, generative models, and deep unsupervised learning. Emphasis will be placed on understanding and implementation of unsupervised learning models across various domains particularly relevant to mechanical, manufacturing and industrial engineering (MMIE) and engineering management (EM) fields. Prerequisite: MMIE 7317 with a grade of "B" or better.
MMIE 7374 : Applications of Data Science in Multi-Objective Optimization
Duration: 8 Weeks weeks
Credit Hours: 3
This course gives students an in-depth understanding of the intersection between data science techniques and multi-objective optimization. It equips students with the knowledge and skills required to address real-world problems that involve multiple conflicting objectives by leveraging data-driven approaches and optimization techniques. This course assumes a prerequisite solid understanding of data analysis, programming, and optimization concepts. Prerequisite: MMIE 7317 with a grade of "B"or better.
MGT 5380F : Management for Organizational Sustainability
Duration: 8 Weeks weeks
Credit Hours: 3
This course is designed to take a broad look at Sustainability from both Ecological and Organizational perspective.
MMIE 7379 : Modeling and Design of Net Zero Manufacturing and Service Enterprises
Duration: 8 Weeks weeks
Credit Hours: 3
This course teaches students to design and operate carbon neutral and zero-energy manufacturing, transportation, and service infrastructure through the integration of renewable energy. Students use statistics and probability theory, design of experiments, discrete event and agent-based simulation, and stochastic optimization to solve large scale, multi-layer manufacturing supply chain design and operation problems. Through the semester-long team project, students make both strategic and operational decisions on production, warehousing, transportation, and microgrid generation in the nexus of manufacturing, energy and climate.
MGT 7314: Organizational Behavior and Theory
Duration: 8 Weeks weeks
Credit Hours: 3
This course focuses on organizational behavior and structure as influenced by environmental variables and system relationships. Topics include personality, motivation, teams, and leadership. These key concepts and others such as perception, emotions, and culture act interdependently, are influenced by, and in turn influence the environment in which the system operates.
MSEC 7325: Principles of Technical Project Management
Duration: 8 Weeks weeks
Credit Hours: 3
This course includes planning, budgeting, identification of risks and risk mitigation approaches, resource allocation, review of milestones and schedules, and evaluating projects to measure success. Responsibilities of project managers in the areas of problem solving, motivating and managing creative technical staff in project and matrix organizations will be included.
MSEC 7395E: Industrial Ecology and Sustainability Engineering
Duration: 8 Weeks weeks
Credit Hours: 3
This course covers the basic principles of life cycle analysis (LCA) of engineered products, materials, and processes. Topics covered include: biological ecology, industrial ecology, resource depletion, product design, process design, material selection, energy efficiency, product delivery, use, end of life and LCA.
ED 7359: Seminar in Quantitative Research
Duration: 8 Weeks weeks
Credit Hours: 3
This course is a small group seminar that focuses on analytic strategies specific to the doctoral student’s dissertation topic. Examples include structural equation modeling, hierarchical linear modeling, log linear modeling, non-parametric analyses, factor analysis, factorial analysis of variance, and other multivariate statistical methods. Prerequisites: ED 7351 and ED 7353, all with a grade of "B" or better.
BA 7310: Organizational Theory and Group Dynamics
Duration: 8 Weeks weeks
Credit Hours: 3
This course examines the structure and behavioral dynamics of complex organizational systems, with emphasis on small groups. Students explore foundational theories, including systems theory and organizational behavior, and analyze stages of group development, power, influence, and conflict. The course addresses change management, group facilitation, and optimization of team performance. Students also assess challenges in managing virtual and dispersed teams and integrate theoretical and applied research to address organizational issues. Emphasis is placed on reflective leadership and evidence-based approaches to improving group effectiveness.
BA 7311: Analysis of Corporate Policy
Duration: 8 Weeks weeks
Credit Hours: 3
This course examines the measurement and analysis of corporate operations through an integrated strategic lens. Students apply advanced systems theory, organizational design, and financial and operational frameworks to evaluate and optimize corporate policies. Emphasis is placed on quantitative analysis, risk management, ESG integration, and governance structures. Through engagement with academic and practitioner research, students assess policy impacts on firm value, behavior, and performance, and ultimately develop data-driven, innovative strategies and original research proposals for sustainable growth.
BA 7306: Experimental Design and Survey Methods
Duration: 8 Weeks weeks
Credit Hours: 3
This course focuses on the methods and applications of survey, quasi-experimental, and experimental research by designing and testing models as appropriate. Topics include construct development, latent variables (formative and reflective), non-latent variables, hypotheses development, different types of research design (e.g. completely randomized design, Latin square design, factorial design), design with covariates, confounding, blocking, power analysis, random effects/mixed effects models, statistical techniques for analyzing data, interpreting results and ethical considerations including the role of IRB approval on different types of research.
BA 7312: Global Business Issues
Duration: 8 Weeks weeks
Credit Hours: 3
This course provides a rigorous foundation for managing an enterprise that operates in a global economy. It integrates global economic issues and global strategic issues to provide a comprehensive understanding of macroeconomic concepts and models for analyzing the global environment in which managers of the multi-national enterprises need to make business decisions and develop strategies for diversification, vertical integration, global expansions, etc. The role of technology, supply chain, and government policies in affecting the economic and business conditions will be assessed.
Dissertation
EMGT 7199: Dissertation
Duration: 8 Weeks weeks
Credit Hours: 1
Original research and writing in Materials Science, Engineering, and Commercialization, is to be accomplished under direct supervision of the PhD Research Advisor. While conducting dissertation research and writing, students must be continuously enrolled each long semester. Repeatable for credit.
EMGT 7299: Dissertation
Duration: 8 Weeks weeks
Credit Hours: 2
Original research and writing in Materials Science, Engineering, and Commercialization, is to be accomplished under direct supervision of the PhD Research Advisor. While conducting dissertation research and writing, students must be continuously enrolled each long semester. Repeatable for credit.
EMGT 7399: Dissertation
Duration: 8 Weeks weeks
Credit Hours: 3
Original research and writing in Materials Science, Engineering, and Commercialization, is to be accomplished under direct supervision of the PhD Research Advisor. While conducting dissertation research and writing, students must be continuously enrolled each long semester. Repeatable for credit.
EMGT 7599: Dissertation
Duration: 8 Weeks weeks
Credit Hours: 5
Original research and writing in Materials Science, Engineering, and Commercialization, is to be accomplished under direct supervision of the PhD Research Advisor. While conducting dissertation research and writing, students must be continuously enrolled each long semester. Repeatable for credit.
EMGT 7899: Dissertation
Duration: 8 Weeks weeks
Credit Hours: 8
Original research and writing in Materials Science, Engineering, and Commercialization, is to be accomplished under direct supervision of the PhD Research Advisor. While conducting dissertation research and writing, students must be continuously enrolled each long semester.
EMGT 7999: Dissertation
Duration: 8 Weeks weeks
Credit Hours: 9
Original research and writing in Materials Science, Engineering, and Commercialization, is to be accomplished under direct supervision of the PhD Research Advisor. While conducting dissertation research and writing, students must be continuously enrolled each long semester. Repeatable for credit.
