BED4 Decision Modelling and Analysis

• Topics

  This course provides an introduction to how business decision problems, focusing on efficient and sustainable use of resources, can be analyzed using mathematical models and computer tools. Some examples of such decision problems are:

  • How to find an optimal product mix when resources are scarce?'
  • How to optimally select suppliers in a tender auction?
  • How to construct an optimal work schedule when demand varies over time?
  • How to create an optimal investment plan when supply of capital is limited?
  • How to determine an optimal portfolio of stocks with different levels of return and risk?
  • How to forecast demand based on historical data?
  • How to utilize a marketing budget efficiently?
  • How to determine an optimal inventory policy, i.e., how often and how much should we refill the inventory?
  • How much should we order when demand for a product is uncertain?
  • How to determine the sequence and allocation of tasks along a production line with multiple work stations?
  • How to determine an optimal transportation plan for a supply chain?
  • How to determine the optimal location of production and storage facilities in a supply chain?

  The choice of models in a particular situation depends on the properties of the decision problem that we are analyzing. All the models that will be studied can be analyzed using computer tools, and in this course we will be using Analytic Solver, which comes as an add-in in Excel. We will start with linear models (linear programming), which can be solved and analyzed relatively easily. Later in the course we will study problems with either/or-decisions, which requires models with integer variables, as well as non-linear decision models. Throughout the course we will emphasize interpretation of analysis results, as well as practical implications for businesses.

  An important topic in our course is handling of uncertainty in decision situations. We will look at how we can make good forecasts, as well as how simulation models can be used to evaluate consequences of different decision alternatives under uncertainty. We will also study how risk attitudes can affect the choice between decision alternatives, e.g., how risk averse decision making can be modeled. Finally, we will look at how the value of additional information can be computed when future outcomes are uncertain, and how decision trees can be used to structure complex decision situations.

• Learning outcome

  Knowledge

  The students:

  • know how to analyze different business decision problems using mathematical models and computer tools
  • know different ways of formulating decision problems, e.g. as linear/non-linear models or integer models, and the computational challenges associated with these
  • has knowledge of the most central decision criteria for the analysis of decision problems with uncertainty, including models that take into account different types of risk attitudes

  Skills

  The students:

  • can formulate decision problems mathematically, implement the models and solve them using computer tools. Simple problems can be solved graphically.
  • can perform sensitivity analyses and apply results from sensitivity reports for LP problems, including shadow prices and slack values, in economic analyses
  • can decide when it is necessary to use integer models or nonlinear models
  • can use simulation models to quantify the consequences of decisions under uncertainty and evaluate the results
  • can use decision trees to analyze simple decision problems with expected value as a decision criterion and calculate the value of perfect/imperfect information

  General competence

  The students can:

  • communicate with experts on modeling and analysis of various decision problems
  • contribute to modeling and analysis for efficient and sustainable resource use in realistic decision situations

• Teaching

  Most of the learning in this course will take place through dialogue groups, where students will work in small groups on relatively simple tasks, and teaching assistants and/or lecturers will be available to discuss and answer questions as needed. General theory that will be useful in the dialogue groups will be presented in videos that will be published at the start of each thematic block, and students will be able to get answers to any questions via the oracle service. More advanced computational exercises from the textbook will be set up for each thematic block, and these exercises will be reviewed in plenary at the end of the thematic block.

• Recommended prerequisites

  Mandatory courses in 1-4 semester, especially BED and MET courses.

• Credit reduction due to overlap

  None.

• Compulsory Activity

  Approval of 3 case assignments.

• Assessment

  4 hour written school exam (pen and paper).

  An assessment will be organised in the non-teaching semester (spring) for students with a valid course approval.

• Grading Scale

  A - F

• Computer tools

  No specific software is required beyond standard tools.

• Literature

  Recommended literature will be provided on Canvas at the start of the semester

• Permitted Support Material

  Calculator

  One bilingual dictionary (Category I)

  All in accordance with Supplementary provisions to the Regulations for Full-time Study Programmes at the Norwegian School of Economics Ch.4 Permitted support material https://www.nhh.no/en/for-students/regulations/ and https://www.nhh.no/en/for-students/examinations/examination-support-materials/