Decision Modelling: Introduction

Summary

This video introduces decision modelling, explaining it as a mathematical representation of scenarios to aid in business decision-making. It differentiates between deterministic models, where inputs are certain, and probabilistic models, where inputs are uncertain and estimated with probability. The video outlines the three main steps in decision modelling: formulation, where the problem and model are defined; solution, where the model is solved and tested; and interpretation, where sensitivity analysis is conducted to understand how changes affect outcomes. The summary emphasizes the importance of models in analyzing scenarios and making informed business decisions.

Takeaways

• 🏠 A model is a representation of the real thing, which can be physical or abstract, but in decision modelling, it's a mathematical or quantitative representation of a scenario.
• 🔢 Decision models are mathematical constructs that provide insights into solving decision problems, such as calculating total revenue as 6x where x is the number of units sold.
• 📊 Decision models are classified into two main types: Deterministic, where input values are known with certainty, and Probabilistic, where input values are uncertain and can only be estimated with some probability.
• 💼 Deterministic models are useful when dealing with known quantities like selling price per unit, number of parking spots, or store operating days.
• 🎰 Probabilistic models are essential for dealing with uncertainties such as customer purchase behavior, economic conditions, or government policies.
• 📈 The input values for models can be qualitative, describing non-numeric characteristics, or quantitative, involving numeric data like production hours or units sold.
• 🛠️ Decision Modelling involves three main steps: Formulation (defining the problem and developing a model), Solution (developing a solution and testing its correctness), and Interpretation (analyzing results and performing sensitivity analysis).
• 💡 Formulation involves defining the problem, such as determining profit, and developing a corresponding model like Total Revenue – Total Cost.
• 🔄 Solution involves acquiring input data, solving the model, and testing if the solution meets the objective, with the option to iterate and adjust the model based on the results.
• 🔍 Interpretation includes sensitivity analysis to understand how changes in input values affect the model's output, which is crucial for making informed decisions.
• 🔮 Sensitivity analysis helps in analyzing best- and worst-case scenarios and is useful for adjusting the model to accommodate new assumptions or making strategic decisions.

Q & A

What is a decision model?

A decision model is a mathematical or quantitative representation of a scenario that provides insights into solving decision problems a business might face.

What are the two main types of decision models?

The two main types of decision models are Deterministic models and Probabilistic (or Stochastic) models.

What distinguishes a deterministic model from a probabilistic model?

A deterministic model has input values that are known with certainty, while a probabilistic model has input values that are uncertain and can only be estimated with some probability.

Can you give examples of deterministic model inputs?

Examples of deterministic model inputs include a fixed selling price per unit, a specific number of parking spots for customers, the number of days a store is open, or a set warehouse space.

What are some examples of probabilistic model inputs?

Examples of probabilistic model inputs include the number of units customers might buy in a week, the possibility of an interest rate hike, or the potential for an economic recession.

What are the three main steps in decision modeling?

The three main steps in decision modeling are: Formulation, Solution, and Interpretation.

What is involved in the formulation step of decision modeling?

In the formulation step, the problem is defined and a mathematical model is developed to represent it. For example, a profit model might be created using total revenue minus total cost.

How is the solution step conducted in decision modeling?

In the solution step, the model is used to compute the desired outcomes, and these are tested to see if they meet the objectives. Adjustments can be made if the outcomes are not satisfactory.

What is sensitivity analysis in decision modeling?

Sensitivity analysis, also known as 'what-if' analysis, examines how the model responds to changes in input variables, helping to evaluate best- and worst-case scenarios.

Why might a business decide against maximizing short-term profits according to the script?

A business might choose not to maximize short-term profits to pursue long-term goals, adhere to company values, or comply with government regulations.