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Management Science


In this Operations Management course we explore and develop skills and techniques that help students become effective decision makers. The emphasis is on analytical modelling of business problems from marketing, finance, production, and logistics. The main objective is to improve managerial decision making in the functional and cross-functional business areas. We introduce a number of modelling concepts that are used behind the screen of many Decision Support Systems, including (integer) linear programming, Monte Carlo simulation, and decision trees. We point out how these concepts can be used to model and effectively help to solve a wide range of business problems. Through examples, class discussion, case studies and computer tutorials the students become familiar with the concepts, the solution approaches, their limitations and underlying assumptions, and practical use. We also teach you to use some Excel-based decision support tools to analyze business problems.Complexity in decision making comes from many factors. In this course we focus on the difficulties arising from uncertainty and risk and the multitude of interrelated options. We present methodologies to deal with them. The emphasis is on analytical modelling of business problems. The methodologies studied in depth include linear programming, decision trees, and simulation. For many decisions, it is not possible to evaluate all potential alternatives, since the number of ossible solutions is huge and many decisions interact. Linear and integer programming is a useful technique for modelling and solving such problems. We discuss modelling aspects and the economic interpretation of solutions. Further we show how these problems can be solved using Excel’s Solver. As most decisions are made in an uncertain environment, a large part of the course is devoted to decision making under uncertainty and risk analysis. Under uncertainty, even good decisions may sometimes result in bad outcomes – and vice versa. Therefore it is important to distinguish between the decision process and the outcome. This course helps you improve the quality of your decision-making process. It provides an introduction to the main concepts in decision analysis. You will learn how to structure decisions, using decision trees and to analyze risk, using simulation. We will also pay attention to the value of additional information. You will practice these techniques, using Excel-based software (PrecisionTree for decision analysis and @Risk for simulation). Throughout the course, we show how different approaches are integrated in the solution of real-life problems. This course is relevant for anyone who needs to make decisions. We show how analytical modelling can help solving problems in many functional areas. Most importantly, it will make the student aware of the possibilities of these approaches so that he/she can identify opportunities for improved decision making. The hands-on practice with the available software will further enable the student to initiate and manage Management Science projects in their companies.

Learning objectives

The aim of the course is:

  • to develop analytical skills to structure and model business decision problems;

  • to master the language related to analytical decision making;

  • to introduce and extend methodologies and solution techniques for quantitative modelling and interpret solutions;

  • to discuss applications in many functional areas (operations and logistics, finance, strategy, marketing, …);

  • to develop practical skills in solving these problems by hands-on experience with Excel-based decision support tools.