From analysis to a testable simulation
Every collection starts with a bet: the pre-order decision is made months before the first customer sees the product. Three stages turn the bet into a calculation.
The fourth stage happens inside the company: after introduction, the simulated trajectory is compared against sell-through, returns and markdowns of the running season. What the measurement refutes gets replaced.
The pre-order quantity and the price of forecast error
Order too much and it runs into the markdown, order too little and demand goes unserved. The simulation weighs both error costs against each other and shows what a smaller forecast error per style is worth.
Every slider is an assumption, not a fact. All initial values are fictional examples: no customer data, no industry figures, not the values of the sample company from the six modules.
Calculation: demand is normally distributed with the set mean and relative spread (assumption). Under-ordering costs selling price minus purchase cost, over-ordering purchase cost minus salvage value. Expected contribution margin per order quantity via the loss function of the normal distribution; the optimum is the maximum of this curve. One season per style, no replenishment. The calculation is meaningful as long as the salvage value sits below the purchase cost and that below the selling price.
After introduction, the forecast error per style is measured on held-back seasons, and the ordered quantity is compared against actual sell-through.
Season contribution margin as a distribution
The same pre-order decision as a distribution instead of a point: 500 simulated seasons per scenario. The random number generator starts from a fixed seed, so every visit shows the same result.
Calculation: demand per style normally distributed, truncated at zero; order quantity per style equal to the forecast, without a safety margin (assumption). Selling price, purchase cost and salvage value come from simulation 01. 500 seasons per scenario, fixed random seed, independent random streams per scenario, so the metric compares self-contained runs.
At the end of the season the measured contribution margin sits inside the band or outside it, and the deviation is reported, not explained away.
What an avoided return is worth
The simplest calculation on this page: returns, addressable share, achievable reduction and the savings per year.
Calculation: returns = orders × rate. Avoided returns = returns × addressable share × reduction. Savings = avoided returns × cost per return. The simulation does not split by return reason and claims no cause shares, so the addressable share and the reduction are your assumptions.
The return rate is measured per category and order cohort before and after the measure; the assumed reduction is replaced by the measured one.
Markdown: early and shallow versus late and deep
The old merchandising dispute as a calculation instead of an opinion. You set the sell-through assumptions of both scenarios yourself; no relationship between markdown depth and demand is claimed, because it would only emerge from your data.
Calculation: before the markdown, each week sells 6 % of the then-remaining stock at full price (fixed value); from the start week onwards the set sell-through rate applies at the reduced price. Revenue = sum of weekly sales × price plus remaining units × salvage value. Selling price and salvage value come from simulation 01. Horizon 14 weeks. The chart shows cumulative sales revenue without the salvage value.
For each markdown step the actual weekly sell-through rate is measured; the assumption yields to the measured curve, and the next season calculates with it.
What this page assumes, fully disclosed
Every number on this page follows arithmetically from the slider values. In addition:
- Example values: all initial values are fictional. They are not customer data, not industry benchmarks and not the values of the sample company from the six modules.
- Normal distribution: demand per style is normally distributed (truncated at zero in simulation 02), with a relative spread of at most 50 %. Real fashion demand has heavier tails, so the band understates extreme seasons.
- Order quantity in simulation 02: per style equal to the forecast, without a safety margin. Fixed random seed, 500 seasons per scenario, independent random streams per scenario.
- Returns: no cause split. The simulation claims no shares of size-related or other return reasons; addressable share and reduction are pure assumptions.
- Markdown: the sell-through rates of both scenarios are your assumptions; no empirical relationship between markdown depth and demand is claimed. A full-price rate of 6 % per week and a horizon of 14 weeks are fixed values.
- Not additive: the four simulations overlap in subject, so their results must not be summed, and this page does not sum them.
- Nominal revenue: without process costs beyond those named, without discounting, without VAT.
This page is a basis for conversation, not an offer. All values are model calculations based on freely adjustable assumptions; no number is a commitment, a forecast for a specific company or an industry figure.
In a real project your data replaces the sliders: your sell-through history provides forecast errors and sell-through rates, your returns data the addressable share. The calculation paths stay the same.