A Business Cycle is the upward and downward movement of a Market around a long-term growth trend.

In 1946, the economists Arthur Burns and Wesley Mitchell defined business cycles:

Business cycles are a type of fluctuation found in the aggregate economic activity of nations that organize their work mainly in business enterprises: a cycle consists of expansions occurring at about the same time in many economic activities, followed by similarly general recessions, contractions, and revivals which merge into the expansion phase of the next cycle; in duration, business cycles vary from more than one year to ten or twelve years; they are not divisible into shorter cycles of similar characteristics with amplitudes approximating their own.

Most economists generally believe that Business Cycles are a macro-economic phenomenon which impact the GDP – not a micro-economic phenomenon. According to Arthur Burns:

Business cycles are not merely fluctuations in aggregate economic activity…

But as every business executive and sales manager will tell you, Business Cycles are found within individual Markets.

This Market Simulation models the operation of a single consumer-durables Market over time. After purchasing a Product, the Customer’s demand for that Product immediately drops. But the Customer’s Willingness To Pay (WTP) will soon start to creep up until it is necessary for the Customer to again enter the Market and Purchase another Product from the same Category. The emergent behavior of the Market is that sales appear to be cyclical.

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.

This Market Simulation models the sales trend for jeans. Customers leave the Market after purchasing a pair of jeans as their demand for all jeans will drop — not just for the pair of jeans they purchased but for all brands of jeans. However, over time, the Customer’s WTP will increase to the point where they will once again want to buy another pair of jeans.

Each Customer has a different Willingness To Pay (WTP) for each brand of jeans. Each Customer will also wear out their old pair of jeans at a different rate — hence the rate at which their WTP will again increase will also vary.

See also the Market Simulation workflow: MS-193 Depreciation and Replacement

There are 1,000 Customers in this Market and 8 brands of jeans:

1. Armani,
2. Calvin,
3. Diesel,
4. Eagle,
5. Guess,
6. Levi,
7. Tommy, and
8. Wrangler.

The Price of all jeans is $40 and the average starting WTP of all is jeans is normally distributed around $20.

But the model has come into this Market at a midpoint where most Customers are not in urgent need of buying new jeans. Customer demand is still increasing towards their Maximum WTP for each brand (calculated according to a randomly generated Maximum WTP Ratio).

Customers wear out their jeans at different rates, and their WTP Increase Demand Rate is normally distributed around 20% per time-period.

The Market loops over 30 iterations before Customer buying patterns are analyzed.

Maximum WTP

The Math Formula calculates the Maximum WTP by Product.

Within each iteration of the 30 x Market Loops, the following things happen:

1. Customers decide whether they want to purchase a new pair of jeans or stick with their old pair (‘No Sale’).
2. If a Customer purchases a new pair, their WTP for all jeans is reset to zero.
3. As Customers wear out their old jeans, their WTP for all brands increases at their personalized rate.

Increase WTP

Raise WTP towards Maximum at personalized rate.

After the 30 loop iterations, the total Quantity of jeans purchased is counted for each period. The results are then plotted over time.

What emerges is clearly a Business Cycle.

While Customers need to buy jeans at different times, and wear out their jeans at different rates, the overall result is not a smooth average. Demand fluctuates from lows of 150 pairs per time-period up to highs of 450 pairs (a 3x swing).

There are only 1,000 Customers in this Market Simulation, and increasing the number of Customers will tend to smooth out demand.

But other real-world factors will counter-act the smoothing effects and drive more cyclical effects. For example, most Customers shop periodically on holidays and weekends, and their demand will vary by season. Market Shocks will also reset demand for many Customers – pushing those Customers into similar buying cycles.

The next Market Simulation, MS-202 Market Shock, explores the impact of these Market Shocks on Business Cycles.