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Decision Modelling For Business Analytics MIS775

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Subject Code – MIS775
Subject Name – Decision Modelling for Business Analytics
University Name – Deakin Business School, Australia

Decision Modelling for Business Analytics MIS775 subject is taught to students who are pursuing with Master’s degree at Deakin Business School in Australia. This subject is designed to provide students with an opportunity to upgrade their knowledge and understanding to build complex decision models and use advanced quantitative as well as network modeling techniques to develop effective solutions to business problems.

The subject Decision Modelling for Business Analytics MIS775 includes a wide range of concepts such as – sensitivity analysis, linear programming applications, non-linear programming models, integer linear programming models, network modeling, decision analysis, building stochastic decision models, simulation modeling, quantitative risk analysis, and many more.

There is no denial that Decision Modelling for Business Analytics is a complex subject including a variety of technical terms that requires a huge amount of time and dedication from students to study for their online exam. However, not all students can devote long hours and as a result, seeks online exam help services.

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More About Decision Modelling for Business Analytics

Non-Linear Programming Models

Nonlinear programming is a mathematical technique used for finding optimal solutions to optimize problems with nonlinear functions as the objective function or as some of the constraints.

The main types of Non-linear programming models are-

  • Pricing model
  • Advertising response and selection models
  • Facility location models
  • Portfolio optimization models
Linear Programming Models

Linear programming is a special case of mathematical programming. Linear programming is the challenge of maximizing or minimizing a linear function under linear constraints. Different types of Linear programming models are-

  • Advertising models
  • Employee scheduling models
  • Aggregate planning models
  • Blending models
  • Production process models
  • Financial models
Integer Linear Programming Models

Integer linear programming expresses the optimization of a linear function to a set of linear constraints over integer variables.

3 types of Integer Linear Programming Models are-

  • Capital budgeting models
  • Fixed cost models
  • Cutting stock models
Networking Models

A network model is a database model which is designed to represent objects and their relationships. A unique feature of the network model is its schema, which is viewed as a graph where relationship types are arcs and object types are nodes.

A network model is comprised of nodes (locations, either origins or destinations) connected by arcs (arrows representing shipping routes between locations), and amounts (unit shipping costs) associated with the arcs and/or nodes.

Types of Networking models are-

  • Random network
  • Scale-free network
  • Hierarchical network
Decision Analysis

Decision analysis is a method for making strategic business decisions that is systematic, quantitative, and visual. Decision analysis involves components of psychology, management practices, and economics, as well as a number of instruments.

Steps in Decision Analysis

  1. Identify the decision alternatives (different strategies available to decision-maker)
  2. Identify the states of nature (future chance outcomes which may occur – these are not under the control of the decision-maker)
  3. Determine the payoff (consequence) for each combination of decision alternatives and possible outcomes
  4. Assess the probabilities of the states of nature
  5. Develop a decision tree
  6. Select decision criteria for choosing the best decision alternative
  7. Apply the criteria and make your decision

Types of Business Decision Models

Deterministic Decision Models

  • Deterministic models are built on the assumption that all inputs to a model, apart from the decision variables, are fixed.

Stochastic Decision Models

  • Some inputs to these models are stochastic, i.e. they exhibit random behavior
  • Instead of making a single “best guess” for each of these random inputs, we
  • incorporate uncertainty into these models via probability distributions
  • This in turn produces random variation in model outputs, which we quantify in
  • terms of probabilities and statistics
  • This information is then used to guide our decision making


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