Option to pursue a dissertation project in lieu of two courses.

Students on the Dissertation option of study typically require 1.5 years to complete the full programme.

MAE Graduate Scholarship

The MAE Graduate Scholarship is awarded to exceptional applicants applying for admission to MAE's Master of Science programmes. The applicant must be able to demonstrate significant potential to enhance the academic rigor and reputation of the programme.

On top of the admission requirements of each programme, applicants will be assessed based on multiple factors that include academic record, working experiences, past achievements and awards, etc. Shortlisted applicants may be invited for interviews, and successful applicants will be informed of the outcome shortly after the offer of admission.

Each Scholarship amounts to 100% of the total Tuition Fees for the programme, not including miscellaneous fees. The amount cannot be used to offset the SGD50 application fee and the SGD5000 acceptance of offer deposit payment. Partial scholarships (50% of the total tuition fees) may also be awarded at the discretion of the Scholarship Evaluation Committee.

Recipients are expected to maintain a CGPA of 3.50 each Semester to maintain the eligibility for the Scholarship.

If you are interested, please complete and submit the MAE Graduate Scholarship Application Form to MAE Graduate Studies Office ([email protected]). Deadline for submission [August 2025 intake] will be 28 February 2025.

Candidates must possess 

(A) A good bachelor’s degree in Mechanical or Industrial engineering or a related discipline with mathematical and production training, or 

(B)  A bachelor's degree in engineering or a related discipline with mathematical training and at least 2 years relevant industry experience, and

(C) A good TOEFL score (iBT = 85 or more, PBT = 563 or more, CBT = 223 or more) or IELTS score (6.0 or more) for graduates of universities in which English is not the medium of instruction.
Please ensure that you upload a scanned copy of TOEFL/IELTS along with your application (hardcopy is not required).

Related disciplines include but are not limited to bachelor's programmes offered by the College of Engineering, Nanyang Technological University, Singapore. Business graduates will also be considered if they have relevant specialisation and/or relevant experience; relevant experience needs to be supported by a letter from the employer.

For this programme, applicants must provide two letters of reference from academic or professional supervisors and a clear statement of purpose in support of their application.

Credit Transfer of PaCE@NTU courses

• Transfer of credits is by application and the application will be assessed and approved by the School and Graduate College.
• The academic units earned through PaCE@NTU are valid for five (5) years for transfer of credits to a relevant/adjoining Master’s by Coursework programme. 
  
• The academic units that are approved for transfer of credits are not included in the computation of Grade Point Average (GPA) for the Masters programme. 
• The minimum Grade Point Average (GPA) eligible for transfer of credits is 2.5 (C+) for the candidates who complete PaCE courses by May 2022 or earlier. 
• The minimum Grade Point Average (GPA) eligible for transfer of credits is 3.0 (B-) for each short course for the candidates who start PaCE courses from May 2022 onwards and the mapping of the courses can be referenced here: PaCE Credit Transfer_Mapping Courses _ Updated as at July 2024

Full-Time (min. 1 year, max. 2 years) and Part-Time (min. 2 years, max. 4 years);

30 AUs coursework or 24 AUs coursework and a dissertation 

^#Full-time students choosing the dissertation option typically require 1.5 years instead of 1 year to graduate.

*Please note that ALL students will automatically be assigned the default Option 2 - Coursework Only. If you wish to apply for Option 1: Coursework and Dissertation, you must apply using the "Application for Conversion of Option of study" form during your first Semester.

CORE COURSES

ELECTIVE COURSES

Students are to select at least 2 electives from each basket (Analytical, Functional)

Analytical Electives

(Select at least 2)

Functional Electives

(Select at least 2)

Please note that course offerings are subject to review every academic year.

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Profile of Faculty Members, click here

Course Synopsis

CORE COURSES

MA6701 Quantitative Methods for Operations Analysis

This course will train students to conduct rigorous data analysis in the decision-making processes. It is designed to equip students with fundamental quantitative techniques that will help them in making more informed managerial decisions concerning capacity planning, production, logistics, network optimization etc. Students are expected to use analytical and simulation models to understand the fundamental concepts and theories, as well as apply the techniques for practical problem solving in supply chain, logistics, manufacturing, and service operations.

The course covers the following topics: Data analysis and probability; Probability distributions; Sampling and sampling distributions; Confidence interval estimation; Regression analysis; Decision analysis; Linear optimization; Network optimization; Integer linear optimization; Nonlinear optimization; Integrated data analytics and decision making.

MA6702 Corporate Resource Planning

This course focuses on planning and control of inventories and manufacturing capacities, demand management, order fulfillment, and other supply chain issues. The objective of this course is to develop planning and analytical skills useful for demand management, order fulfillment, master production scheduling, and planning and control of capacity and component/sub-assembly requirements. The course relies on latest supply chain systems and MRP-based methodologies, as well as mathematical models, to illustrate the techniques.

The course covers the following topics:  Manufacturing planning and control framework; Enterprise resource planning; Demand management; Sales and operations planning; Master production planning; Material requirements planning; Distribution requirement planning; Capacity planning; Advanced concepts in SOP, MPC system design and strategy.

MA6703 Supply Chain Inventory Planning

A key aim of this course is to inculcate the value of information sharing for effective inventory planning among supply chain partners. Starting with the importance of information sharing in supply chains, the course covers various inventory policies for single echelon and multi-echelon inventory management, before delving into risk pooling, pipeline inventory considerations, and inventory-transportation trade-offs. The course also covers the key aspect of managing dispersed and horizontal supply chains via effective performance measurement.

The course covers the following topics: Supply chain management: issues and challenges; Value of information; Supply chain inventory management: continuous review policies; Supply chain contracts; Supply chain designs; Supply chain inventory management: periodic review policies;  SC performance measurements; SC Game: design and manage a supply network.

MA6704 Management of Logistics Functions

The objective of this course is to provide fundamental and emerging concepts of Logistics Functions. Logistics management is becoming a vital for many industries, especially manufacturing. However, operational managers and industrial engineers who are specialized in logistics management often need to deal with a wide variety of inter-related issues that span across multiple functional departments. The rationale of introducing this course is to give students a broad overview and the fundamental theories regarding various logistics functions and their management. With this course, students will be able to approach logistics management with a holistic view and be able to understand, analyse, and coordinate various functions with a coherent framework.

Topics covered include: The role of Logistics; Customer service and distribution management; Transportation; Logistics information systems; Global logistics; Strategy, systems integration and case studies; Warehousing and materials handling.

ELECTIVE COURSES

Analytical Electives

MA6514 Machine Learning and Data Science

The purpose of this introductory course in Machine Learning is to show how to adopt ML as an important and essential paradigm in advancing a corporation’s operation and decision making processes towards Industry 4.0. Using Python, Numpy, Pandas and Colab Notebook as its development environment, the presentation of outcome of machine learning computations are achieved through visualization tool, Matplotlib. Scikit-Learn, an extensive well-documented open source suite of machine learning algorithms serves as the platform to analyse data for underlying trends, classification, identifying criteria parameters, deriving rules for decision making in real-world problem solving, thus leading to a rapid prototyping of a suitable machine learning system.

Topics included are: Context of machine learning and data science in Smart Manufacturing for Industry 4.0; Types of machine learning; Unsupervised learning; Supervised learning; Neural networks and reinforcement learning; Model evaluation and improvement.

The primary objective of this course is to provide an insight into effective decision-making using simulation modeling. The bulk of the time in the course is spent on discrete event simulation modeling. Simulation model building aspects of discrete systems (such as manufacturing and logistics facilities, supply-chains) are covered in detail. The course also demonstrates the effectiveness of computer simulation to successfully model, analyze and improve systems under study. Simulation software (Arena) is used to demonstrate building and executing the models. Continuous and combined system simulation is also covered in later part of the course. The course also covers the topic of simulation life cycle analysis, and goes over issues such as model verification and validation. Additionally, it looks into the modeling of input data and analysis of model output.

The course covers the following topics: Discrete-event simulation; Basics model-building blocks; Simulation case studies; Simulation modelling of manufacturing facilities; Supply-chain simulation; Simulation workshop; Continuous simulation; Simulation in the process industry; Input-output analysis; Simulation life-cycle analysis; Model verification and validation, Simulation paradigms and languages.

MA6721 Data Analytics for Supply Chain Management

MA6731 System Reliability & Risk Analysis

This course aims to equip graduate students with a solid theoretical foundation in system reliability and risk analysis, which can be applied to address a broad range of design, analysis, and operational issues in various engineering and enterprise systems.

The course covers the following topics: System reliability and risk:overview; System failure models; System configuration and reliability; Stochastic risk models for complex systems; Reliability of maintained systems; Bayesian reliability analysis.

Functional Electives

MA6712 Procurement & Supplier Development

The aim of this course is to equip the participants with an ability to develop insightful sourcing strategies and supplier relationships in a VUCA (volatile, uncertain, complex, and ambiguous) environment, in alignment with organisational goals. Starting with procurement fundamentals, this course examines real-world strategies, industry practitioners’ perspectives and case studies, with a focus on synthesizing strategies for sourcing, supplier performance management and supplier relationship development. The course will enable participants to gain a broader appreciation of the strategic role procurement and suppliers play within the evolving supply chain and enable them to create value through safeguarding of business continuity and establishing a competitive advantage for organisations.

Students will also learn the following topics: Basics of procurement; Strategic alignment; External and internal integration; Global sourcing; Category management; Inventory and quality management; Supplier performance; Supplier development; Supplier relationship management.

MA6713 Advanced Topics in Supply Chain Management

The aim of this course is to introduce current and potential issues affecting design and management of supply chains. Using case study as a vehicle for discussion, this course delves into contemporary topics and issues such as life-cycle assessment, circular economy, e-commerce, global supply chain risk, supply chain resilience, digital supply chains, and sustainability considerations in global supply chains. The objective of the course is to arm the participants with knowledge of these issues, and strategies global companies have adopted to overcome and manage the associated challenges.

Topics covered are: Supply chain management: issues; Perishable supply chain management; E-commerce supply chains; Global supply chain risk management; Digital supply chains: supply chain 4.0; Closed loop supply chains; Cradle-to-cradle: Life-cycle analysis; Sustainable supply chain management.

MA6714 Specialised Logistics Operations

The objective of this course is to apprise students of the challenges associated with specialized logistics operations and processes for managing such operations. While operations such as warehousing, transport and freight forwarding for commonly transported goods have well-established processes and service providers, specialized logistics operations such as cold supply chains transporting perishables, pharmaceuticals, and vaccines, and hazardous substances require special handling and dedicated service providers and processes.

The course covers the following sections: Specialized Logistics Operations, Airport Infrastructure for Cargo Logistics, Air Cargo Logistics, Multi-modal Transport Management & SEAPort Logistics, Logistics Management of Specialized and Hazardous Cargo, Strategy, Case Studies.

MA6716 Manufacturing and Service Operations Management

The programme is tailored for practicing engineers, logisticians and information technologists employed (or seeking employment) in manufacturing or logistics sectors. The programme relies intensively on industry speakers, site visits, case studies and hands-on computer modeling and game plays to support in-class learning. 

Fees

Please note this MSc programme is a self-financed, non-MOE subsidised programme.

Fees are subject to annual revision.

• NTU Alumni students are entitled to 10% study incentives in the form of reduction in fees.
• Students who are Singapore Citizens and Singapore Permanent Residents will receive a one-time subsidy of $5,000.
• If you are a Singaporean student, you may use up to $5000 of your SkillsFuture credits towards tuition fees. The claim submission has to be completed within 60 days of the start date of the next Semester (e.g. You must submit from November to claim towards Semester 2 tuition fees)

To do so, please follow the following steps:

1. Log in to SkillsFuture portal and click on “Make SkillsFuture Credit Claim”
2. Select NTU MSc Supply Chain Engineering
3. To submit a claim, you should have supporting documents such as letter of offer, matriculation documents etc.
4. In your claim, indicate the course start date to be first day of the upcoming Semester in the Academic Calendar.
5. As your e-bill for the upcoming Semester would not be available yet, take note of your SFC Claim ID.
6. Notify School ([email protected]) and NTU NSS-Finance ([email protected]) with the SFC Claim ID and the amount to be claimed through SFC.
7. When you receive your e-bill for the Semester, leave the SFC amount to be claimed out of your payment.
8. Please refer to Skillsfuture FAQ at this link.

Students will be billed after course registration period each Semester, and payment due date is 2 weeks after billing date.
A student who withdraws or leaves the University after course registration period is liable to pay the fees due for the semester.