Case Study

End of Local Monopoly

This detailed Case Study and Market Simulation workflow follows the overview called: CS-101 Rise of the Microbrew.

Part 03 of the Market Simulation models the USA Beer Industry from around the mid-1800’s when railroads were extended across the whole country and reached many mid-sized and small cities.

Rail, along with cooled and insulated boxcars, meant that beer could travel long distances to reach remote Customers. Customers no longer needed to travel to the source of the beer – beer came to them.

As with Part 02, it is assumed that all Customers live along a single road which joins two towns. Each town has its own brewery, and all of the Customers live at evenly distributed locations along the road.

But in this case, the “Lost Value” suffered by Customers due to Geographic distance from the breweries steadily declines until it is an insignificant part of the Customer’s Willingness To Pay (WTP).

This Case Study provides a high-level overview of the workflow without detailed explanation. It assumes you are already somewhat familiar with KNIME and Market Simulation. If not, start by reviewing the Building Blocks and Community Nodes.


Market Simulation of the early beer industry when trains and refrigerated boxcars eliminated Geographic Differentiation and ended the local monopolies of brewers.

The video contains a brief presentation covering the 2nd economic phase of the beer industry along with an explanation of the KNIME workflow. More detail can be found in the explanation below.

#1 Value of Beer

The distribution of part-worth values that Customers place on Beer is generated with a ‘Customer Distributions’ node. The Mean part-worth value for Beer is $2.00 with a Standard Deviation (SD) of $0.20. The ‘Column Rename’ node is used to rename this Customer Distribution to “Beer”.

As before, there are two nearby towns that make and sell beer. All 10,000 Customers live evenly along the single road that joins the two towns. Customer C00001 lives immediately next door to Brewery 01, while Customer C10000 lives immediately next door to Brewery 02.

Each additional step taken to the town makes it increasingly inconvenient to buy beer. To travel the entire distance from the first town to the second would decrease the value of the beer by $2.00. Hence Customer C05000 living mid-way between both towns would suffer a decrease in value of $1.00 regardless of which town they traveled to and how much they like beer.

Two part-worth Customer Distributions have been set up to reflect the Lost Value due to the Geographic distance from each town: “Geography 01” and “Geography 02”.

Two Beer Products are created: “Product 01” and “Product 02”. Each Beer Product is made up of the part-worth value of “Beer” and the Lost Value due to the Geographic distance from each town. It is assumed both towns make precisely the same type of beer.

  • Product 01 = Beer + Geography 01
  • Product 02 = Beer + Geography 02

The ‘Simulate Market’ node is then used to predict the Market Share of each Product.

Click on an icon to see and scroll through the enlarged version of the images.

#2 Decreasing Inconvenience

The ‘Lost Value’ suffered by the Customers due to the Geographic location of the breweries start to decrease when railroads and insulated boxcars spread across the USA.

The maximum ‘Lost Value’ inconvenience starts out to be $2.00 (the monetary equivalent of walking from the first town to the second). Over the next 51 years this ‘Lost Value’ inconvenience steadily drops to $0.00.

The ‘Loop Start’ node (light blue) makes 51 iterations. At each iteration the node takes the next (decreasing) ‘Maximum_Inconvenience’ value and converts it into a Flow Variable. The Flow Variable then overrides the ‘Default Input Parameter’ in the Customer Distribution nodes when creating the “Geography” Lost Value.

After 51 iterations, the “Geography” Lost Value is zero. At this point, all Customers consider Product 01 to be equivalent to Product 02. Beer is now a commodity, and all Market Share will go to the seller offering the cheapest Price.

#3 Trend Charts

The trends for Quantity sold, Revenue, and Profit can be charted over the 51 iterations for Product 01 versus Product 02.

At the beginning, Quantity sold, Revenue and Profitability briefly increases for Product 01. This is due to additional Customers entering the Market and buying beer due to the increased convenience.

But this windfall is short lived as the cheaper Product 02 quickly becomes more attractive to Customers. Very soon, Product 02 has captured 100% of the Market Share. In a commodity Market, Product 01 has no differentiating qualities that can attract Customers.

Economic Theory

Economic Theory

The population of the United States in 1880 was still growing rapidly, and yet the number of breweries starts to shrink. The amount of beer being consumed is not dropping, so what’s happening?

As discussed above, the answer is “technology”. At that time the railroad network was expanding nationally, and new insulated boxcars were able to distribute beer long distances without spoilage.

In economic terms, the value curves were flattening – causing Geographic Differentiation to decrease. Monopoly power dissipated as consumers now had the realistic option of purchasing other product choices.

Market Simulation

Market Simulation has demonstrated how cheaper commodity beer started dominating the attention of Customers. Breweries which did not have a cost advantage were forced out of the Market.