Singapore University of Social Sciences

Quantitative Methods

Quantitative Methods (BUS107)

Applications Open: 01 May 2024

Applications Close: 15 June 2024

Next Available Intake: July 2024

Course Types: Modular Undergraduate Course

Language: English

Duration: 6 months

Fees: $1391.78 View More Details on Fees

Area of Interest: Business Administration

Schemes: Alumni Continuing Education (ACE)

Funding: SkillsFuture

School/Department: School of Business


BUS107 Quantitative Methods introduces management science techniques and their potential applications in various business challenges. Students will learn and practise the use of quantitative methods for various purposes, such as linear programming for optimisation problems and simulation for estimating performance measures. This course will cover linear programming, forecasting, decision analysis, simulation, and network flow problems. In addition to manual methods, this course also presents software tools for performing computing tasks. At the end of the course, students will learn how to transform data into better decisions.

Level: 1
Credit Units: 5
Presentation Pattern: EVERY REGULAR SEMESTER


  • An overview of management science techniques and their applications in business operations
  • Linear programming models
  • Linear programming: A graphical solution procedure and a computer solution
  • Linear programming: Sensitivity analysis
  • Time series analysis
  • Forecasting methods
  • Decision analysis tools
  • Approaches to decision making with or without probabilities
  • Monte Carlo simulations
  • Discrete-event simulations
  • Network flow problems
  • Network optimisation algorithms

Learning Outcome

  • Apply linear programming models for optimisation problems.
  • Use historical data to build forecasts for future business developments.
  • Identify a recommended decision alternative in uncertain settings.
  • Interpret software output for business insights.
  • Demonstrate the use of a simulation model to estimate measures of performance of operational situations.
  • Implement iterative algorithms to find the solutions for network flow problems.
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