Department of Systems Engineering & Engineering Management

This is a taught programme, a student is required to take a minimum of 8 postgraduate courses (24 credits in total) within a normal period of two years (Part-time) or one year (Full-time), of which 4 should be required courses and 4 elective courses. An exemption from a required course may be sought provided that the student has a sufficient background and knowledge in the required course. The exempted course must be replaced with an approved elective course. Other MSc courses from the Faculty of Engineering may be taken as electives with the approval of the Division Head. A student shall be required to discontinue studies in the programme, if he has received failure grades in two or more courses.

The degree of Master of Science will be conferred upon students who have completed the prescribed coursework with a cumulative grade-point average of 2.0 or above.

Students are normally required to complete their studies within 24 months.

This course is designed to provide fundamental principles of managing engineering and industrial organizations. The focus is on the application of quantitative and qualitative approaches in the practice of engineering management. Quantitative modelling and solution techniques for strategic and operational problems are discussed. The role of strategic management, strategy formulation, and strategy implementation are covered. Other strategic issues involving innovation and ethics are also addressed.

The challenges, techniques and technologies associated with the management of information technology (IT) in a competitive environment. The linkage of IT to business strategy and business process re-engineering. Different types of information systems: MIS, DSS, TPS. Information technology concepts: networking, database, batch and distributed processing. Development Process. Information system planning. Systems project management and control. IT acquisition, budgeting and deployment. Performance evaluation and audit. Operations management, privacy and security.

Models of risks. Utility functions, and mean-variance theory. Probability models and price dynamics of securities. Geometric Brownian motion, Ito's lemma, Black-Scholes model. Capital asset pricing. Risk hedging. Optimization techniques. Applications to investment and portfolio management. The emphasis is on mathematical modelling, analysis, and computation.

Working knowledge of different financial markets. Equity risk, bond risk, FOREX risk, commodity risk and their corresponding risk management practices. The use of REUTER's, Bloomberg, Dow Jones, and TIBCO traders' terminals.

4 courses from the following areas; no more than 2 from each area.

Area I: Engineering Management

Topics in manufacturing operations management: forecasting, aggregate planning, inventory theory, manufacturing resource planning, Just-In-Time (JIT) production. Topics in service operations management: the framework to analyze service operations, methods for designing, evaluating and delivering services, locating service facilities, allocation of service resources, workforce management and quality management.

Accounting income. Cash flow modeling. Depreciation and taxation. Overview of utility theory. Analysis of economic risk. Risk simulation. Decision tree analysis. Procedures for replacement analysis. Activity-based costing. Analytical hierarchy process. Economic optimization under constraints. Strategic investment analysis.

Review of basic probability concepts and statistical tools. Measuring and predicting part and system reliability. Quality function deployment. Failure mode and effect analysis. Statistical process control charts. Lot acceptance sample plans. Quality improvement processes. Design of experiments. ISO 9000.

Project screening and selection. Multiple-criteria methods for evaluation. Project structuring scheduling and budgeting. Resource management. Life-cycle costing. Project control. Computer support for project management. Forecasting of technology. Strategic and operational consideration of technology.

Logistics planning. Integrated logistics management concept. Customer service. Channels of distribution systems. Order processing and information systems. Logistics network design , location and layout planning. Distribution and delivery planning. Transportation systems. Storage and material handling systems. Warehousing. Global logistics. Third-Party Logistics.

This course introduces the key models and concepts in supply chain management. Topics include: demand forecasting, aggregate planning, supply management, inventory management, matching supply with uncertain demand, information distortion and demand management , information technologies for supply chain co-ordination, e-business models, etc.

Fundamentals in Game Theory, Introduction of Games with Asymmetric Information, Concept of Information Rent, Principal-Agent Models, Adverse Selection, Signaling Model, Moral Hazard， Incentive Schemes, Mechanism Design, Applications in Regulation and Procurement, Contract Design and Coordination in Supply Chain Management.

Area II: Information Systems

Overview of management support systems. Data and model management in decision support systems. Group decision process. Group decision support systems and distributed group decision support systems. Executive information and support systems. Applications of artificial intelligence methodologies in decision support. Integration of decision support technologies. Design and development of management support systems. Organizational and societal impacts.

Introduction to distributed computing. Client/Server theory and practice. Overview of major protocols and distributed system concepts. Management aspects: vision, priority and transition strategies, operational challenges.

Introduction to open system standards and protocols. Transaction protocols. Electronic commerce applications using open system and artificial intelligence technologies. Application of intelligent agents for automated transaction processing. Integration of HTML and JAVA with information and communication systems.

Area III: Financial Engineering

The focus of the course is on various stochastic models that support investment decisions. Overview of investment problems: pricing, hedging, portfolio selection, investment vs. consumption. Asset dynamics, binomial trees, Ito processes. Introduction to option pricing, Black-Scholes formula. Term structure, interest-rate derivatives. Portfolio optimization, optimal control models: Bellman equation and necessary conditions.

Characteristics of financial data, patterns, and models, time-series representation and prediction. Limitations of classical systems identification for prediction problems. Nonlinear system modeling and learning using neural networks, multiple models, and chaotic pattern detection. Heuristic approaches of AI, genetic programming, and data mining in an integrated knowledge-based system for financial engineering applications.

This course emphasized on econometrics modeling and inference techniques. Topics include: OLS, GLS, maximum likelihood estimation, statistical hypothesis testing, GMM, ARFIMA model, GARCH model and Stochastic Volatility model, cointegration, common factors and common features, switching regime model and other nonlinearities, simulation and estimation of continuous diffusion process, the use of S-plus.

The course emphasizes the implementation of numerical algorithms applied to financial problems. The numerical methods include: binomial trees, Monte Carlo simulation, finite difference methods, among others. These methods will be applied to basic options, exotic options, futures, term structure, fixed income securities, dynamic trading strategies, and financial risk management.