Imagine a sky-blue kurta sells steadily, then records almost no orders for a week. A forecasting dashboard interprets the fall as fading interest. The buying team cuts the next order.
But the useful sizes were unavailable.
The dashboard described sales correctly and demand badly. Before asking AI to predict the next season, fashion teams need to make that distinction visible in the data.
Decide what the forecast is meant to predict
Gross orders, shipped units and units retained after returns answer different questions. Choose a target before training a model. Keep the other measures alongside it rather than combining them into one number called “sales.”
A weekly planning table could include style, variant, date, units ordered, cancellations, returns, selling price, promotion status and days available to buy. Link later returns to their original orders when the data allows, so a return recorded this week does not masquerade as this week’s weak customer interest.
This is a proposed planning workflow. It does not describe TRYBUY.IN’s internal forecasting system or disclose its sales performance.
Mark stockouts; do not invent the missing demand
Record when each size was unavailable. A zero during full availability and a zero while sold out should remain distinguishable. Neither a spreadsheet nor a machine-learning model can directly observe the purchases customers would have made when buying was impossible.
If the team estimates lost demand, label it as an estimate and keep the original observations intact. Document the method and compare decisions with and without that adjustment. Silent replacement of zeros can make a model look more certain than the evidence supports.
Use a product such as TRYBUY.IN’s Men’s Sky Blue Regular Fit Cotton Kurta to picture the planning unit: one style can have several size variants, each with its own availability history. The catalog image here illustrates the category, not an actual stockout or sales trend.
Make the AI earn its place
Compare a machine-learning forecast with a simple baseline, such as a recent average or an appropriate seasonal comparison. Complexity is worthwhile only when it improves decisions on data the model has not already seen.
Nixtla’s MLForecast documentation demonstrates time-series cross-validation using successive cutoffs: forecasts at each cutoff use only earlier observations, then are compared with the later outcomes. This is a useful technical foundation for testing a fashion forecast. Nixtla’s cross-validation guide.
In practical terms, pretend you are planning from an earlier date. Hide what happened afterwards, create the forecast, and then reveal the results. Repeat across ordinary weeks and promotional periods. Do not let future return totals or campaign outcomes leak into the historical inputs.
Keep adult and boys’ demand separate
A men’s kurta and a boys’ kurta can share a colour story without sharing a demand curve. Keep their categories, size structures and histories distinct. If you borrow information from related products for a new style, record the relationship and review whether it is commercially sensible.
A newly launched printed shirt also needs different treatment from a long-running solid shirt. With little history, use a cautious test quantity and explicit assumptions instead of presenting a precise forecast as established knowledge.
Turn the forecast into a reviewable decision
Ask the planner to record three things: the expected requirement, the uncertainty around it and the operational constraint that could change the order. Supplier lead time, stock already committed and a minimum production batch belong in the decision even if they are not predicted by the model.
Review the consequences of both error directions. Over-forecasting can leave stock ageing; under-forecasting can leave customers without their size. An average error score should not hide a repeated failure on important variants.
Questions from the planning desk
Can AI recover the exact demand lost during a stockout?
No. Lost demand must be estimated under stated assumptions, not treated as an observed fact.
Does a new style need a complex model?
Not necessarily. A transparent baseline and a small reviewed test may be more useful when history is limited.
Should an AI forecast place purchase orders automatically?
Start with human-approved decisions. Validate both forecast quality and operational limits before considering further automation.
Explore TRYBUY.IN for current product details. Good planning should ultimately help a shopper find the right garment—not merely improve a dashboard.