This detailed Case Study and Market Simulation workflow follows the overview called: CS-101 Rise of the Microbrew.
Part 02 of the Market Simulation models the USA Beer Industry in the early 1800’s when each city and town needed it’s own brewery. Each brewer enjoyed a local monopoly over consumers living nearby.
The Product Features in this Market include: (a) Beer, and (b) Geographic Inconvenience (a negative value reflecting the inconvenience to the Customer in procuring the beer).
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.
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 in the USA when the Market Power of brewers was based upon Geographic Differentiation.
The video contains a brief presentation covering the 1st 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
A distribution for the part-worth value 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 name this Customer Distribution to “Beer”.
Click on an icon to see and scroll through the enlarged version of the images.
#2 Value of Location
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 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”.
#3 Market Simulation
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 ‘Profit Engine’ node is then used to predict the Market Share of each Product.
Note in the ‘Purchased Products Matrix’ results that Customer C05000 is the last Customer to buy Product 01. Customer C05003 is then the first Customer in the list to buy Product 02 (Customer C05001 and C05002 don’t value Beer enough to travel the distance into town).
#4 Change Price
As both towns produce exactly the same type of beer, this should be a commodity. But the beer is differentiated by Geographic distance. This sets up two local Monopolies – one in each town.
To test this, the Price of beer sold in the first town is increased by 20% to $1.20, while the Price of beer sold in the second town is decreased by 20% to $0.80. The results show that the Profitability of the more expensive beer is barely impacted – in fact, Profit increases. This is the kind of Market Power a firm gets when it controls a Monopoly.
Note that the Customer divide-line has shifted in this branch of the workflow. When the Price of both Products was equal, the Customer divide-line was half way along the 10,000 Customers: C05000 preferred Product 01, whereas C05001 preferred Product 02.
After the Price change, C04000 was the last Customer to prefer Product 01, while Customer C04001 was the first to prefer Product 02.
In this version of the Market, there are 6 Customers evenly distributed along the road. The Customers are called A, B, C, D, E, and F. Customer A has the highest Willingness To Pay (WTP) for the beer located directly next door to them in the first town (Product #1). Customer F has the lowest WTP for that Product #1, but the highest WTP for the beer located directly next door in the second town (Product #2). When these two charts are overlaid it becomes clear that Product #1 is the only practical choice for Customers A and B, while Product #2 is the only practical choice for Customers E and F. Only C and D are swayed by the relative Price of Product #1 versus Product #2.
The ‘Line Chart’ from the Market Simulation workflow comparing the Willingness To Pay (WTP) of Customers for Product 01 (blue) versus Product 02 (red) looks like this. There is a lot of noise in this chart as Customers have different WTP for the part-worth value of Beer. But the overall trend clearly aligns with the theory.