Singapore University of Social Sciences

Quantitative Methods (BUS107)


BUS107e Quantitative Methods introduces the essential concepts of quantitative methods that are commonly practiced in business and management for decision-making and resource planning purposes. It examines a series of quantitative techniques that are of interest and relevance to practitioners and researchers in this field. The underlying theme behind each quantitative technique is the formulation of an appropriate quantitative model. Students studying this course will learn the technique of quantitative model formulation and processing, using relevant computer software to solve practical business problems. The critical skills of data analysis and interpretation for decision making will also be taught in this course. Students will learn to work in teams to solve cases as well as present the findings in class.

Level: 1
Credit Units: 5
Presentation Pattern: Every semester
E-Learning: BLENDED - Learning is done MAINLY online using interactive study materials in Canvas. Students receive guidance and support from online instructors via discussion forums and emails. This is supplemented with SOME face-to-face sessions. If the course has an exam component, this will be administered on-campus.


  • Introduction to Quantitative Analysis
  • Introduction to Linear Programming
  • Linear Programming Sensitivity Analysis
  • Linear Programming Applications
  • Time Series & Smoothing Methods in Forecasting
  • Trend Projection and Seasonal Components
  • Problem Formulation & Decision-Making with/without Probabilities
  • Decision Analysis with Sample Information
  • Simulation Modelling and Applications
  • Network Modelling

Learning Outcome

  • Describe the management science/operations research approach to decision making.
  • Apply linear programming models for simple problems.
  • Interpret the solution of a linear programming problem for business decision-making.
  • Employ the techniques of classical time series modelling.
  • Use classical time series modelling to predict future aspects of business operations.
  • Discuss a simple decision analysis problem from both a payoff table and decision tree point of view as to develop a risk profile and interpret its meaning for business decision-making.
  • Define what simulation is and explain how it aids in the analysis of a problem.
  • Develop network and linear programming models for the minimal-spanning tree, the maximum-flow and the shortest-route problems.
  • Use a suitable computer software to construct and process quantitative models for result generation and reporting. Relevant software include the following QM software applications like Management Scientist, Solver with Excel, TreePlan with Excel, CrystalBall with Excel and GoalSeek with Excel.
  • Identify alternatives to decision-making problems through data analysis and interpretation of the results derived from the quantitative model.
  • Develop decision alternatives in a logical and concise manner.
  • Develop the essential knowledge and interpersonal skills to work effectively in a team.
  • Illustrate the results of various areas related to Quantitative Methods in class or through video recordings.
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