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Opening a new store is a gamble on the future—and daily production planning is the part of that gamble that immediately costs money if it goes wrong. The fundamental problem: There is no sales history for the new location. No one knows how many croissants the location sells on a Tuesday.
Most businesses make do with estimates: figures from a similar store, a safety margin, and a lot of gut feeling. In the first few weeks, this almost always leads to one of two scenarios—massive returns because planning was too optimistic, or empty shelves at the grand opening if the rush of customers was underestimated. Both are particularly costly precisely when first impressions matter most.
Why the opening phase is so difficult to plan
The opening rush distorts everything. Curiosity brings in more customers in the first few days than the location will be able to sustain in the long run. If you use the opening week as a benchmark, you’ll end up producing too much for weeks afterward.
The location behaves differently than expected. Even two stores in similar locations can have completely different demand patterns: different commuter flows, different competition, and different peak times of day.
The product lineup still needs to take shape. It will take weeks to see which products are selling well at the new location. Until then, every decision about the product lineup is based on assumptions.
How Data-Driven Planning Works Without Historical Data
AI forecasts require data—but not necessarily data from the new location. For new store openings, GoNina uses the sales patterns of comparable stores as a starting point: similar location, similar product assortment, similar customer base. This results in an initial forecast that is significantly more reliable than a manual estimate.
The real benefit becomes apparent afterward: As soon as the first sales data from the store itself becomes available, the model learns quickly. After just a few days, the actual patterns from the new location are incorporated into the forecast, and the predictions become more accurate from week to week. The “opening effect” is automatically recognized as a special circumstance and does not skew long-term planning.
For production management, this means that instead of spending months making adjustments, planning for the new location will stabilize within a few weeks—with order recommendations for each item that feed directly into the usual workflow.
Practical Tips for the First Few Weeks
Start small, then ramp up quickly. It’s better to bake twice on the first day than to throw away three baskets in the evening. A deliberately conservative initial batch size with the option to produce more later is better than a large safety margin.
Track sales accurately from the very beginning. Every receipt from the first few days serves as training data for forecasting. Mark special promotions and grand-opening discounts separately so they don't skew the patterns.
Review the data weekly. During the initial phase, it’s worth taking a regular weekly look at returns and clearance sales by item—that way, every mistake becomes a lesson for the following week.
Frequently Asked Questions
How long does it take for the forecast for a new store to become reliable?
The initial forecast, based on comparable locations, is usable from day one. As your own sales data is added, it becomes increasingly accurate—typically reaching the level of established stores after just a few weeks.
Does this also work for a company's very first store?
Yes, with some limitations: Without comparable stores of its own, the initial forecast is based on general industry trends. The rapid learning effect from the first few days of sales remains the same.
Conclusion
Planning a new store opening without sales history remains challenging—but it doesn't have to be a shot in the dark. Comparative data provides the starting point, and quickly learning from the first few days of sales takes care of the rest. This turns weeks of uncertainty into a short, controlled ramp-up phase.
You can read about how AI sales forecasts work in general in our comprehensive guide for bakeries.
