Chain Bakery: Automating Planning Across Multiple Locations

Planning a single store is challenging. Planning five stores is a full-time job. And with ten or more locations, daily production and order planning becomes a system that is virtually impossible to manage without digital support.

The problem isn't a lack of experience. Most chain bakeries have skilled production and store managers who know their operations well. The problem is the sheer number of decisions that must be made every day—for every product, at every location, under constantly changing conditions.

What makes planning in chain bakeries so complex

Every store is different. The store at the train station has different peak hours than the one in the neighborhood. The store in the shopping center sells different products than the one on Main Street. The holiday season affects each store differently—some lose customers, while others gain walk-in customers from tourists.

What’s more, while the product selection is often the same, the demand isn’t. 80 croissants for Store A and 30 for Store B—this allocation works on Tuesday, but not on Saturday. And next week, when school breaks begin, it won’t work again.

Anyone who tries to plan this manually faces a combinatorial problem that grows exponentially with every location and every product. With 10 stores, each carrying 100 items, that amounts to 1,000 individual decisions every day. No human being can handle that systematically and without error.

In practice, this leads to typical problems:

Flat-rate allocation. Headquarters distributes the merchandise according to fixed formulas—each store receives the same percentage of total production. This ignores location-specific differences and systematically leads to overstocking or understocking at individual stores.

Stores place their own orders. Each store reports its own needs independently. This sounds logical, but it often results in store managers who order conservatively receiving too little, while those who order aggressively receive too much. The total quantity is not optimized; it is simply added up.

One person keeps everything running smoothly. In many companies, there is someone who “keeps track of everything”—the production manager, the owner, or the experienced scheduler. If that person is unavailable, planning immediately becomes more uncertain.

How AI Forecasts Are Changing Store Planning

AI-based sales forecasts tackle the problem at its root: they replace manual estimates with data-driven predictions for each item and each location.

To do this, GoNina directly to the bakery’s point-of-sale system—such as HS Soft, ProtecData, or Lightspeed. Using sales data from all branches, the AI learns how demand behaves at each individual location. In doing so, it automatically takes into account weather, holidays, school breaks, the day of the week, the season, and location-specific factors.

The result is a daily order recommendation for each store and product. Not “a total of 500 croissants distributed across all stores,” but “Train Station Store: 95, Neighborhood Store: 35, Shopping Center Store: 72”—based on projected sales for that specific day at that specific location.

This proposal can be adopted as is or adjusted manually. The final decision rests with the company.

What's changing in practice

Distribution becomes more equitable. Each store receives exactly what it needs—not what the store manager orders or what a flat-rate allocation dictates. This reduces both returns at overstocked stores and empty shelves at undersupplied ones.

Headquarters gains a clear overview. Instead of compiling and verifying orders from individual stores, production management can see at a glance what is needed overall. Total production is derived directly from store forecasts.

Planning new stores becomes easier. When a new location opens, there is no historical data to draw on. AI can generate an initial forecast based on similar locations and their sales patterns, and continuously adjust it as soon as the store’s own data becomes available.

Seasonal variations are automatically detected. The store in the tourist area performs differently in summer than in winter. The store in the business district sees slower sales during the Christmas holidays. The AI identifies these patterns from the data without anyone having to enter them manually.

Bakery chains that GoNina achieve a reduction in excess inventory of up to 52% and a sales increase of up to 6%. This effect is particularly pronounced for businesses with multiple locations, as the complexity—and thus the potential for optimization—increases with each branch.

A typical scenario

Monday, 5:30 a.m. The production manager of a bakery with eight locations logs into the system. GoNina calculated the forecasts for the day overnight. She can see at a glance: The Old Town location needs 15% less bread today than last week—school vacation, fewer office workers. The Lakeside location, on the other hand, needs 20% more sandwiches—it’s going to be 24°C and sunny.

She reviews the proposals, adjusts one item because she knows that a street is closed today near the Marktgasse branch, and approves production. Ten minutes instead of an hour.

Frequently Asked Questions

Will this work even if my stores are very different from one another?Especially then. The AI creates a separate model for each store based on its specific sales data. A store located at a train station is forecast completely differently than one in a residential neighborhood—automatically and without any manual configuration.

What happens if a store undergoes renovations or temporarily closes?Such special situations can be handled manually. As soon as the store is back to normal operations, the AI automatically adjusts to the current sales data.

How are the suggestions sent to the stores?The suggestions are fed directly back into the existing system—as order suggestions or production lists. Depending on the POS system and inventory management system, this happens automatically or via export. No additional tools are required in the stores.

Conclusion

The biggest challenge in chain bakeries isn’t the baking—it’s daily planning. How much of each product should go to which location? AI-based sales forecasts answer this question using data, individually for each store and each product. This saves time, reduces excess inventory, and makes planning less dependent on individual staff members.

You can find a complete overview of AI sales forecasts for bakeries in our guide.