OBJECTIVES

  • Learn specific techniques to analyse business context and strategy, to support decision- and model design.
  • Create awareness of issues to consider before embarking on a full model build.
  • Create models which are flexible, transparent, and robust.

DESCRIPTION

  • OverviewThis course is a natural extension of the Level I course “Decision Support and Model Planning”. It covers some specific techniques that can help to plan and design models, and also addresses in detail issues that should be considered before (and during) the building of a model, in order to maximise the effectiveness, transparency, flexibility and robustness of the model.
  • Practical work and exercises. Students are required to conduct numerous hands-on modelling exercises and can also follow along by replicating other examples that are shown in the text.
  • Assessment tests. There are several assessment tests which test the key concepts and require one to do practical exercises in Excel.

KEY TOPICS

  • Frameworks and tools for contextual analysis of a decision. Frameworks and tools to analyse decision context and business strategy. Macro economics, five forces analysis, portfolio composition analysis. Introduction to risk, uncertainty and optimisation, and their role in decision-making and model design.
  • In-depth model planning. Mapping the decision to a model. Criteria and model outputs. Data versus formula-driven structures. Data sources and structures. Defining logic, variables, flow and granularity. Historical versus forecasted analysis. Role of inflation. Updating requirements. Horizontal or vertical time axis. Structural limitations versus parameter flexibility. Benefits of modularity. Approaches to link modules. Decision sequences. Logic reversals.
  • Best practices in model building. Principles for design, layout, structure, and formatting. Principles of transparency. Complexity reduction and optimisation. Circularities and other issues.

III.b Model Planning, Design and Best Practices

The issues to consider when planning and building models, in order to maximize their effectiveness.

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