# Workflow Overview

The first ‘Building Blocks’ workflow (BB-101 Simple Monopoly) used a bell-shaped Normal Distribution to generate the Customer Willingness To Pay (WTP) matrix. This workflow introduces two new Customer Distributions that may generate more realistic WTP Matricies depending upon the characteristics of the market.

*This Building Blocks example assumes you have already downloaded the open-source KNIME analytics platform and installed the free Market Simulation (Community Edition) plugin. If not, start by returning to Getting Started.*

# Downloads

# New Distribution #1

## Weibull Distribution

A distribution which pushes up all low-value Customers to have a WTP above zero might be better when simulating the market for informational Products. A Weibull Distribution and an Inverse Gaussian Distribution both do this.

#### Define Products

#### Product Details

The ‘Product’ name and Price, along with an optional Cost, are defined in the same way. In more advanced workflows, the ‘Distribution Type’ and ‘Input Parameters’ can also be defined here.

#### Generate WTP

#### Configuration

The Weibull Distribution is selected with a Shape Parameter (A) of 2.0 and a Scale Parameter (B) of 120. Details of these terms can be found in the Market Simulation Node Documentation and on Wikipedia.

#### Product Array

The Output Product Array adds the Type of Distribution (Weibull) and provides simple statistical values for the Mean and Standard Deviation (SD) as part of the Product description.

#### WTP Histogram

#### Normal Distribution

The Normal Distribution from BB-101 looks like this, where Customers have a Mean WTP of $100 with a Standard Deviation (SD) of $50.

#### Weibull Distribution

By comparison, the Weibull Distribution looks like this, with a Mean of $106 and a SD of $56.

#### Profit Engine

#### Product Array

The Output Product Array shows the prediction that 2,153 Customers will buy Sprockets at $150, while the other 7,847 Customers are ‘No Sale’. No attempt was made to tune these results, so no direct comparison can be made to the earlier results.

#### Demand Curve

The Weibull Demand Curve looks similar to the Normal Distribution Demand Curve. But in this case, the Profit Maximizing Price is $115.

# Economic Theory #1

## Weibull Distribution: Demand Curve

And yet, as before, the results from the Market Simulation do closely follow the Linear Demand Curve as described in traditional micro-economic models.

The green linear trend from the Market Simulation closely follows the blue Demand CurveĀ over the majority of Price vs Quantity.

## Profit Maximizing Price

The results from the ‘Profit Engine’ node in the Market Simulation predict that the Profit Maximizing Price for Sprockets is $115.

The theoretical equations for a Linear Demand Curve having a y-intercept (a) and slope (b) are shown to the right. Using the fitted green curve from above (a = 11,438 and b = 63.386) the Profit Maximizing Price is also calculated to be $115.

This shows a perfect match between micro-economic theory and Market Simulation.

# New Distribution #2

## Simple BiModal Distribution

This Simple Bimodal Distribution is, in fact, a combination of two Normal (Gaussian) Distributions where the Standard Deviation (SD) is automatically calculated to be a quarter of the distance between the two Means.

#### Generate WTP

#### Configuration

The Simple Bimodal Distribution is selected to have a First Mean (A) of 50 and a Second Mean (B) of 150. The SD is 25 (a quarter the distance between the two).

#### Product Array

The Output Product Array has statistics similar to the earlier Customer Distributions, with a Mean of 100 and Standard Deviation (SD) of 56.

#### Profit Engine

#### Product Array

The Profit Engine predicts that 2,557 Customers will buy Sprockets at $150, while the other 7,443 Customers are ‘No Sale’.

#### Demand Curve

The Simple Bimodal Demand Curve has a distinct wiggle, but is relatively straight and downward slowing. The Profit Maximizing Price is predicted to be $130.