Coffee & Bakery is an AI-powered demand forecasting approach that enables predicting sales by dish, day, and location, adjusting production and purchasing to reduce waste and stockouts in HORECA chains. It is 4:30 in the morning. Your production manager enters the bakery and has exactly 90 minutes to decide how many croissants, sourdough loaves, focaccias, and carrot cakes to bake for the next 14 hours. They have no data about the day. They do not know if it will rain and 25% fewer people will come, if there is an event on the street next door that will triple lunch traffic, or if this is one of those Tuesdays that, for no apparent reason, sells 20% less than the previous one. So they do what they always do: produce the same as last week, plus a 15% safety buffer. At 8:00 PM, when the location closes, 19% of the day's production is discarded. It cannot be frozen. It cannot be sold tomorrow. It is thrown away. And tomorrow, at 4:30, the same gamble.
The existential problem of bakery: everything baked today gets thrown away today
The coffee & bakery segment has one characteristic that makes it radically different from any other type of restaurant: the product has a shelf life measured in hours. A croissant baked at 5:30 AM may be below the freshness standard by 11:00. A loaf of artisan bread that does not sell before closing goes in the bin. There is no option to repurpose it into another dish. There is no second day. No freezing saves the margin.
In any other restaurant segment, excess purchasing generates overstock that gets managed. In coffee & bakery, excess production generates direct, irreversible waste on the same day. That turns every production decision into a financial bet that resolves in 14 hours. And that decision is made before dawn, when the bakery manager has not a single data point about the day that is about to begin.
The three traps of producing without forecasting
Producing at dawn without data: the most expensive decision in the business
The bakery starts between 4:00 and 5:00 AM. At that hour there is no sales data for the day. The only reference is the history, but last Tuesday's history does not reflect that today there is rain forecast, that tomorrow is a holiday and today will have less office traffic, or that a sporting event three streets away will generate a 1:00 PM demand spike nobody anticipates. Without a forecasting model integrating weather, calendar, events and traffic patterns, the production decision depends on the baker's memory and "better to have too much than too little."
The "better to have too much" mentality is the direct cause of 15-25% waste. And perversely, it makes sense from the bakery manager's perspective: if they underproduce and run out of croissants by 10:00 AM, they lose sales, lose customers, and get a call from the area manager. If they overproduce, the surplus is silently discarded at closing and nobody asks. The incentive is misaligned. As we explain in overproduction and forecasting, the solution is not to change the baker's incentive: it is to give them data so they do not have to guess.
Closing waste: the cost that destroys operating profit
The cost of waste in bakery is not just the ingredient. It is the ingredient plus production labor, oven energy, preparation time, packaging cost and the opportunity cost of display space. When a location discards 20% of its daily production, it does not lose 20% of flour and butter cost. It loses 20% of the entire operational cost of producing those items. In chains with margins of 8-12% on sales, that waste can represent the entirety of the location's operating profit.
Franchise freshness standards: the window that forces waste
Franchises and bakery chains operate with strict freshness standards essential to the brand promise: product that has been in the display for more than 4-6 hours gets removed. A croissant baked at 5:30 must be sold before 11:30. If morning demand is 15% lower than expected, those croissants are removed even though a 2:00 PM lunch traffic spike arrives. This creates a double-window problem requiring production in staggered batches matched to each time slot's demand -- and that requires forecasting.
Data measured in active Controliza clients.
How Controliza turns production into a science
Controliza combines demand forecasting per SKU, location and time slot with shift-based production planning to solve bakery's core problem: knowing what to bake, how much, and when, before the first oven fires up.
Daily forecasting per SKU, location and time slot
The forecasting engine analyzes each item's sales history at each location, weighted by day of the week, seasonality, weather, holidays and local events. The result is a SKU-level forecast indicating, for tomorrow Tuesday at your Serrano location, how many croissants you will sell before 11:00 AM, how many focaccias between 12:00 and 3:00 PM, and how many carrot cakes in the afternoon. That data reaches the bakery before it starts kneading.
Production plan by shift with adjusted batches
From the time-slot forecast, the system generates a specific production plan for each batch of the day. The pre-dawn shift knows exactly what to bake for opening. The mid-morning second batch adjusts to cover the lunch peak. It is the direct application of forecasting to buy and produce: the prediction feeds directly into the production order.
Intra-day adjustment: correct course before it is too late
As the morning progresses, Controliza compares actual sales with the forecast and adjusts recommendations for subsequent batches. If by 10:00 AM croissant sales are 18% above forecast, the system recommends additional production for the second batch. If the olive focaccia is 20% below, it recommends reducing. This is not a post-mortem data point: it is a real-time correction allowing the bakery to react within the same day, before waste materializes.
Do you know how much product you throw away each day at each location?
Controliza tells you exactly what to produce, how much and when, for each location, each item and each shift. Request a personalized demo and calculate how much margin you can recover with daily SKU-level forecasting.
How Controliza turns production into a science
Controliza combines demand forecasting by SKU, location, and time slot with shift-based production planning to solve the bakery’s core challenge: knowing what to bake, how much, and when, before the production kitchen fires up the first oven.
Daily forecasting by SKU, location, and time slot
The forecasting engine analyzes the sales history of each item at each location, weighted by day of the week, seasonality, weather, holidays, and local events. The result is a forecast by SKU that tells you, for tomorrow Tuesday at your Serrano location, how many croissants you’ll sell before 11:00, how many focaccias between 12:00 and 15:00, and how many carrot cakes in the afternoon. That information reaches the production kitchen before the dough mixing even begins.
Shift-based production plan with optimized batches
Based on the forecast by time slot, the system generates a specific production plan for each batch of the day. The early-morning shift knows exactly what to bake for opening. The second mid-morning batch is adjusted to cover the lunchtime peak. And if the chain produces from a central kitchen for several locations, the plan includes the allocation by location with quantities already calculated. It’s the direct application of what we explain in forecasting to buy and produce: the forecast feeds directly into the production order.
Intra-day adjustment: correcting course before it’s too late
As the morning progresses, Controliza compares actual sales with the forecast and adjusts recommendations for the following batches. If croissant sales are running 18% above forecast by 10:00, the system recommends additional production for the second batch. If olive focaccia is running 20% below forecast, it recommends reducing output. This isn’t post-mortem data: it’s a real-time correction that allows the production kitchen to react within the same day, before waste materializes.
Waste analysis by item, shift, and location
Controliza records waste at the end of each shift, broken down by item and by location. Throwing away 8 croissants is not the same as throwing away 8 cheesecakes: the financial impact is radically different. The system calculates the real cost of waste by SKU, identifies the items with the biggest gap between production and sales, and flags locations with unusual patterns. The operations director can see, in a single dashboard, which location throws away the most, which item has the worst sell-through rate, and which shift generates the most surplus.
Do you know how much product you throw away every day at each location?
Controliza tells you exactly what to produce, how much, and when, for every location, every item, and every shift. Request a personalized demo and calculate how much margin you can recover with daily forecasting by SKU.
