The Master of Science in Engineering and Innovation Management (MEIM) at Ajman University is a future-oriented graduate program designed to prepare professionals who can lead innovation, manage technology-driven organizations, optimize engineering operations, and drive digital transformation across industries.
The program bridges the gap between engineering, technology, business, innovation, and management. It is specifically designed for engineers, technology professionals, and graduates who aspire to move beyond purely technical roles into strategic leadership, innovation management, operations optimization, and technology-driven decision-making.
To develop future-ready leaders who leverage emerging technologies, drive innovation, and transform organizations to create sustainable value in an increasingly digital and interconnected world.
Graduates of the MEIM program will:
Prof. Omar Ghrayeb (Profile)
1. Academic Qualifications
Applicants must hold a Bachelor’s degree from a university recognized by the UAE Ministry (or equivalency by CAA) of Education in one of the following disciplines:
• Engineering
• Engineering Technology
• Computer Science
• Information Technology (AI, DA, Cybersecurity, IS)
• Business Administration
• Applied Sciences
• Mathematics, Statistics, or related quantitative disciplines
• Other closely related fields approved by the College
The Bachelor’s degree must be completed with a minimum cumulative GPA of 3.0 on a 4.0 scale or equivalent for regular admission.
Applicants with a CGPA between 2.5 and 2.99 may be considered for conditional admission subject to UAE CAA regulations. Such students may register for a maximum of 6 credit hours in the first semester and must achieve a minimum GPA of 3.0 in the first semester to continue in the program.
Applicants with backgrounds outside engineering or quantitative disciplines may be required to complete remedial or bridge courses as determined by the department.
2. English Language Proficiency
Since the program is taught in English, applicants must demonstrate English language proficiency through one of the following:
• IELTS Academic: minimum overall band score of 6.0
• TOEFL iBT: minimum score of 79
• TOEFL ITP: minimum score of 550
• EmSAT English: minimum score of 1400
• Equivalent standardized English proficiency test approved by the UAE Ministry of Education
Applicants may be exempted from the English proficiency requirement if they satisfy AU exemption criteria in accordance with CAA regulations.
For further information, please refer to the university admissions policy.
The completion requirements of the Master’s degree in Engineering and Innovation Management shall be:
The Master of Science in Engineering and Innovation Management at Ajman University prepares graduates to become future-ready leaders capable of driving innovation, managing technology, leading digital transformation, and creating value in increasingly complex and technology-driven organizations.
Organizations increasingly seek professionals who can:
Career Opportunities
Graduates may also pursue graduate studies (PhD) and professional certifications in areas such as industrial engineering, systems engineering, engineering management, operations research, supply chain management, data analytics, and artificial intelligence-driven operations systems.
Graduates of the Master of Engineering and Innovation Management (MEIM) are uniquely positioned at the intersection of engineering, technology, business, innovation, and leadership. The UAE and GCC economies are actively investing in digital transformation, advanced manufacturing, sustainability, artificial intelligence, smart cities, and innovation-driven growth, creating strong demand for professionals with both technical and managerial expertise.
On successful completion of this Program, the graduate will be able to:
PLO1: Apply advanced engineering management knowledge and analytical tools to solve complex industrial and organizational problems.
PLO2: Apply project management principles and methodologies to plan, execute, monitor, and successfully deliver engineering and industrial projects within scope, time, cost, and quality constraints.
PLO3: Utilize data analytics, artificial intelligence, and optimization techniques to support informed decision-making and improve systems and processes.
PLO4: Evaluate engineering systems and projects economically and strategically, incorporating sustainability, quality, and continuous improvement principles.
PLO5: Conduct applied research or innovation-driven projects using appropriate methodologies to address real-world engineering management challenges.
The Master of Science in Engineering and Innovation Management requires the completion of 33 Cr. Hrs., classified as follows:
|
Components |
Credit Hours |
|
|
Project option |
Thesis option |
|
|
Core Courses |
18 |
18 |
|
Elective Courses |
9 |
6 |
|
MSc Seminar |
0 |
0 |
|
MSc Project |
6 |
- |
|
MSc Thesis |
- |
9 |
|
Total |
33 |
33 |
Program Structure: List of courses
|
|
Course Code |
Course Name |
Credit Hours |
|
Core Courses |
24/27 |
||
|
1. |
MBA 601 |
Accounting and Finance for Decision Making |
3 |
|
2. |
MAI602 |
Artificial Intelligence |
3 |
|
3. |
MAI601 |
Data Mining |
3 |
|
4. |
MEIM 565 |
Engineering Project Management |
3 |
|
5. |
MEIM 550 |
Lean Six Sigma & Process Improvement |
3 |
|
6. |
MEIM 570 |
Analysis and Design of Supply Chain Systems |
3 |
|
7. |
MEIM 698/699 |
Masters Project/Theses |
6/9 |
|
Electives (either 3 or 2 courses) |
9/6 |
||
|
1. |
MEIM 620 |
Economic Analysis of Industrial Projects |
3 |
|
2. |
MEIM 630 |
Statistical Analysis for Engineering Managers |
|
|
3. |
MEIM 670 |
Linear Programming and Network Flows |
3 |
|
4. |
MEIM 640 |
Quality Engineering Management |
3 |
|
5. |
MEIM 650 |
Simulation Modeling and Analysis |
3 |
|
6. |
MEIM 655 |
Service Operations Management |
3 |
|
7. |
MEIM 535 |
Decision Analysis for Engineers |
3 |
|
8. |
MBA 602 |
Business Analytics for Decision-Making |
3 |
|
9. |
MSEM 680 |
Special Topics in Engineering Management |
3 |
|
10. |
MBA 604 |
Sustainability and Strategic Decision-Making |
3 |
|
11. |
MBA 603 |
Leadership and Organizational Behavior |
3 |
|
12 |
MEIM 511 |
Systems Engineering Management |
3 |
|
Total |
33 |
||
This master level course offers students with the foundation of how financial information drives strategic decision-making within organizations. In today's complex and dynamic business environment, financial judgement is essential for effective leadership and managerial decision-making. This course equips students with the knowledge and skills required to navigate the financial aspects of business operations, enabling them to make informed and strategic choices. This course is more than just a theoretical exploration of financial principles; the assigned trainings empower students with practical skills.
Pre-requisite: ---.
Credit hours: 3 Theory: 3 Lab: 0 Prerequisite: None
The aim of this course is to provide graduate students with in-depth knowledge of AI principles, algorithms and techniques. Topics covered include Knowledge Representation schemes and Automated Reasoning, uncertain knowledge and probabilistic reasoning, search strategies, intelligent agents, machine learning, planning, and ethical and societal issues relating to artificial intelligence. Students also work on a course project individually or in pairs.
Pre-requisite: ---.
Data mining is the process of discovering patterns and knowledge from huge amount of dataset. This course aims to equip students with the necessary skills and knowledge that allow them to develop models using data mining techniques that include association, clustering, outlier, web mining, text mining, and pattern mining approaches. Students will also learn to collate, filter, clean, transform, and sort data using established contemporary tools. Validation and performance assessment is applied to compare test data with training data and assess accuracy of processes and models.
Pre-requisite: ---.
This course introduces the principles and methodologies of Six Sigma and continuous process improvement in manufacturing and service systems. Topics include DMAIC methodology, process mapping, statistical process control, root cause analysis, quality improvement tools, lean principles, process capability analysis, and performance measurement. Emphasis is placed on reducing variability, eliminating waste, improving quality, and enhancing operational efficiency through data-driven decision-making and continuous improvement practices.
Pre-requisite: ---.
This course introduces quantitative methods and analytical techniques for engineering decision-making under certainty, risk, and uncertainty. Topics include elementary decision-making methods when random factors are present, decision trees, expected utility analysis, influence diagrams, value of information, sensitivity analysis, and multi-criteria decision-making. Emphasis is placed on evaluating alternatives and supporting effective decision-making in engineering and industrial systems using analytical and probabilistic models.
Pre-requisite: ---.
This course presents an integrated approach to the management of engineering and high-technology projects throughout the entire project life cycle, including project initiation, organization, planning, implementation, control, and termination. Topics include project evaluation, scheduling, resource allocation, cost control, contract selection, risk management, quality management, and human resource management. Emphasis is placed on the use of quantitative methods and project management tools to successfully manage engineering projects within scope, time, cost, and quality constraints.
Pre-requisite: ---.
This course introduces quantitative methods and analytical techniques for engineering decision-making under certainty, risk, and uncertainty. Topics include elementary decision-making methods when random factors are present, decision trees, expected utility analysis, influence diagrams, value of information, sensitivity analysis, and multi-criteria decision-making. Emphasis is placed on evaluating alternatives and supporting effective decision-making in engineering and industrial systems using analytical and probabilistic models.
Pre-requisite: ---.
This course introduces the fundamental principles of systems engineering and their applications to the development and management of complex engineering systems. Topics include systems thinking, systems definition, requirements analysis, system design and implementation, system life-cycle management, project integration, risk management, configuration management, and performance evaluation. The course also presents modeling and optimization tools used for system architecture evaluation and decision-making. Emphasis is placed on coordinating technical, organizational, and managerial aspects of engineering systems throughout the system life cycle.
Pre-requisite: ---.
This advanced course focuses on the development, analysis, and optimization of sophisticated simulation models for complex engineering, business, and socio-technical systems. Students explore advanced techniques in discrete-event simulation, agent-based modeling, system dynamics, Monte Carlo simulation, digital twins, and simulation-based optimization. The course emphasizes the use of simulation as a strategic decision-support tool for addressing uncertainty, risk, resource allocation, operational efficiency, and innovation challenges. Through real-world case studies and hands-on projects using industry-standard software, students learn to design, validate, and leverage advanced simulation models to improve organizational performance and support data-driven decision-making.
Pre-requisite: ---.
This course provides advanced methods for evaluating the economic feasibility and financial viability of engineering and industrial projects. Students learn to apply engineering economics, financial analysis, risk assessment, and investment appraisal techniques to support strategic decision-making. Topics include cash flow analysis, time value of money, cost estimation, capital budgeting, project financing, sensitivity analysis, risk and uncertainty modeling, life-cycle costing, and economic evaluation of technology and innovation investments. Through case studies and applied projects, students develop the skills needed to assess, justify, and manage investments in industrial, infrastructure, and technology-driven initiatives.
Pre-requisite: ---.
This course focuses on optimization techniques for solving complex resource allocation, planning, logistics, and operational decision-making problems. Topics include linear programming, network flow models, transportation and assignment problems, shortest path algorithms, maximum flow models, integer programming, and sensitivity analysis. Students learn to formulate, analyze, and solve optimization problems using mathematical modeling and industry-standard software, enabling them to improve efficiency and support strategic decision-making across engineering and business systems.
Pre-requisite: ---.
This course explores advanced principles and practices for achieving organizational excellence through quality management and continuous improvement. Topics include quality planning, statistical quality control, reliability engineering, process capability analysis, quality audits, and performance measurement systems. Students learn to design and manage quality-driven organizations, improve operational performance, and foster a culture of continuous improvement and innovation in engineering and technology-intensive environments.
Pre-requisite: ---.
This course examines the design, management, and improvement of service systems in sectors such as healthcare, finance, logistics, hospitality, government, and technology services. Students learn how to analyze service processes, manage capacity and demand, optimize customer experience, and improve operational efficiency. Topics include service strategy, process design, queueing and waiting-line analysis, workforce planning, service quality, performance measurement, digital service delivery, and data-driven service optimization. Through case studies and applied projects, students develop the skills needed to manage complex service operations and lead continuous improvement initiatives in service-oriented organizations.
Pre-requisite: ---.
This course explores emerging trends, advanced concepts, and contemporary challenges in engineering management. Topics vary based on industry developments and faculty expertise and may include artificial intelligence in management, digital transformation, Industry 4.0, smart manufacturing, sustainability, innovation ecosystems, technology commercialization, resilient supply chains, and strategic leadership. Through case studies, research-based projects, and industry engagement, students examine current issues and develop the knowledge and skills needed to lead organizations in rapidly evolving technological and business environments.
Pre-requisite: ---.