MS Engineering
Management
Department
Engineering
Level
Graduate
College
College of Computer Sciences & Information Systems
Course Detail
The MS in Engineering Management program provides students with a strong foundation in engineering and management principles integrated with emerging Artificial Intelligence (AI) technologies to address the evolving needs of modern industries. The program equips students with the knowledge and skills required to enhance efficiency, productivity, quality, and strategic decision-making in technology-driven organizations. It emphasizes the effective planning, organization, allocation of resources, and control of engineering activities while leveraging AI-enabled tools and data-driven approaches.
Students can develop specialized expertise through focused streams in AI for Project Management, AI in Supply Chain and Operations Management, and AI for Quality Engineering and Process Optimization, with opportunities to explore advanced topics such as predictive analytics, smart logistics, digital twins, intelligent automation, and machine learning applications in engineering systems.
The program is designed in accordance with the Higher Education Commission (HEC) guidelines. It spans a minimum duration of two years, comprising at least four semesters, and requires the successful completion of 30 credit hours. To meet the degree requirements, students must complete eight taught courses along with the 6 credit hours of research thesis (for MS by Thesis) or 6 credit hours of Independent Research Studies (IRS – I and IRS – II for MS by Independent Research Study) or 6 credit hours of 2 courses (Elective IV and Elective V where each course is of 3 credit hours for MS by Coursework). The MS by Coursework is subject to the approval of the departmental committee.
MS Engineering Management students learn to:
- Apply advanced engineering management principles, analytical techniques, and emerging AI technologies to solve complex organizational and industrial problems.
- Plan, organize, and manage projects, resources, operations, and quality systems to enhance efficiency, productivity, and organizational performance.
- Utilize data-driven decision-making tools, predictive analytics, and intelligent systems to optimize engineering and business processes.
- Demonstrate leadership, communication, and teamwork skills required to effectively manage multidisciplinary teams and technology-driven organizations.
- Conduct independent research and critically evaluate contemporary issues in engineering management, leading to innovative and sustainable solutions.
Learning Outcomes for MS Engineering Management students include:
- Demonstrate advanced knowledge of engineering management principles and apply Artificial Intelligence and machine learning techniques to solve complex problems in projects, operations, and quality systems.
- Analyze, design, and optimize project management, supply chain, and engineering processes using predictive analytics, intelligent decision-making tools, and emerging digital technologies.
- Assume leadership roles and effectively contribute to multidisciplinary teams in technology-driven organizations, fostering innovation and operational excellence.
- Evaluate and enhance organizational performance through the application of AI-enabled methodologies for forecasting, risk management, quality assurance, process optimization, and continuous improvement.
- Communicate effectively with diverse stakeholders and engage in lifelong learning while upholding professional, ethical, social, and environmental responsibilities expected of engineering leaders and entrepreneurs.
Career Path:
Engineering Manager | Quality Engineering Manager |
AI Project Manager | Process Optimization Specialist |
Operations Manager | Digital Transformation Consultant |
Supply Chain Analytics Manager | Technology and Innovation Manager |
Prospective Employers:
Manufacturing and Process Industries | Healthcare and Pharmaceutical Industry |
Technology and Software Companies | Consulting and Engineering Services Firms |
Supply Chain and Logistics Organizations | Research and Development Institutions |
Automotive Industry | Government and Public Sector Organizations |
Eligibility:
Sixteen years of education in any engineering/Computing discipline with a minimum 55% marks in overall academic career in the Semester/annual system and a CGPA of 2.5 in a semester system or equivalent from HEC-recognized Institutes/Universities
Semester breakup (MS -Engineering Management)
Semester 1
Course Title | Credit Hours |
Research Methodology | 3 |
Project Management | 3 |
AI and Machine Learning for Engineering Managers | 3 |
Understanding of Holy Quran I * | 1 |
Total: | 10* |
Semester 2
Course Title | Credit Hours |
Core Course I | 3 |
Core Course II | 3 |
Elective I | 3 |
Understanding of Holy Quran II * | 1 |
Total: | 10* |
Semester 3
Course Title | Credit Hours |
Elective II | 3 |
Elective III | 3 |
Total: | 6 |
Semester 4
Course Title | Credit Hours |
MS Thesis /IRS – I and IRS – II / Elective IV** and Elective V ** | 6 |
Total: | 6 |
* Muslim students are required to take Understanding of the Holy Quran I (1 credit hour) and Understanding of the Holy Quran II (1 credit hour) to complete the degree requirement as per HEC requirements.
**For MS by Coursework, students are required to take 2 additional courses (Elective IV and Elective V) each of 3 credit hours (subject to the approval of the departmental committee).
Streams:
AI for Project Management
Core Courses | Credit Hours |
Project Management | 3+0 |
Research Methodology | 3+0 |
AI and Machine Learning for Engineering Managers | 3+0 |
AI for Project Planning & Scheduling | 3+0 |
Predictive Analytics for Project Risk and Cost Management | 3+0 |
Elective Courses | Credit Hours |
Generative AI for Project Managers | 3+0 |
AI in Agile and Lean Project Management | 3+0 |
Digital Twin Applications in Project Management | 3+0 |
Big Data Analytics for Projects | 3+0 |
AI in Project Controls | 3+0 |
AI and Machine Learning in Decision-Making | 3+0 |
AI in Supply Chain and Operations Management
Core Courses | Credit Hours |
Project Management | 3+0 |
Research Methodology | 3+0 |
AI and Machine Learning for Engineering Managers | 3+0 |
AI for Demand Forecasting and Inventory Optimization | 3+0 |
Smart Logistics and Transportation Systems | 3+0 |
Elective Courses | Credit Hours |
AI-Driven Procurement and Supplier Analytics | 3+0 |
Blockchain and AI for Supply Chain Transparency | 3+0 |
Resilient and Adaptive Supply Chain Systems | 3+0 |
Warehouse Automation and Robotics | 3+0 |
Predictive Maintenance in Operations | 3+0 |
Intelligent Supply Chain Analytics | 3+0 |
AI for Quality Engineering and Process Optimization
Core Courses | Credit Hours |
Project Management | 3+0 |
Research Methodology | 3+0 |
AI and Machine Learning for Engineering Managers | 3+0 |
AI for Quality Control and Assurance | 3+0 |
Machine Learning for Process Optimization | 3+0 |
Elective Courses | Credit Hours |
Computer Vision for Industrial Inspection | 3+0 |
AI-Driven Six Sigma and Lean Systems | 3+0 |
Digital Manufacturing and Smart Factories | 3+0 |
Process Mining and Intelligent Automation | 3+0 |
Predictive Quality Analytics | 3+0 |
Statistical Learning for Engineering Systems | 3+0 |
AI-Based Root Cause Analysis and Reliability Engineering | 3+0 |