A controlling system for bakeries that joins your sales forecast to your costs. Instead of waiting for the period to close, you see the projected month-end result and know what you can still do about it.
You learn how May went in June. By then it is too late to do anything about it — the costs were incurred, the sales happened, and the decisions that mattered were made in May without those numbers.
In a bakery it is harder still, because revenue swings day to day while a large share of costs is fixed. A weak week in the retail chains is not only lower revenue — it is also the ingredient, production and transport cost that has already gone out.
Questions that are hard to answer mid-month:
Our model predicts demand by store, product and day — for company stores, in-store bakeries, retail chains and individual customers. It is the same forecast that drives production.
Costs pulled automatically from your ERP and assigned to categories and carriers: management, transport (by vehicle), company stores, production per plant, wholesale, financial costs.
The volume forecast translates into revenue and into ingredient, production and transport cost. Fixed costs are forecast separately — from history, contracts and the staffing plan. Together they give a projected result for the month, updated daily.
This is the edge an ordinary report cannot give you. A spreadsheet or a BI module shows the past. We know future sales — and most costs in a bakery follow from them.
We are not building separate programmes. We are building one system for bakeries that we keep extending with new modules — and each one brings fresh data into the shared base.
Demand prediction, cost controlling and route planning all draw on the same set of information about your company. That is why every new element does not merely add a feature: it raises the accuracy of all the others.
What that looks like in practice
Once we know how much you will sell in each channel, we also know the revenue, the ingredient cost and the production cost. Without a sales forecast, forecasting variable costs is guesswork.
When the system knows the real cost of a location, a production batch and a delivery, the forecast stops optimising volume alone and starts optimising margin. That is a different production decision from "bake as much as will sell".
Real cost and time for every delivery turn transport from an averaged line in overheads into an amount attached to a specific customer. Then you can see which customers are profitable.
The compounding effect: the longer we work on your data and the more modules are connected, the more accurate the forecasts get. The system does not stand still from the day it goes live — every month it knows your company better.
Costs and revenue incurred to date, set against the plan for the whole month, split by cost category and sales group. Alongside them — the projected close, EBITDA and operating profit.
Payroll, energy, rent, leasing, financial costs. The system learns from your history and your contract schedule, and accounts for seasonality and planned increases.
An unplanned cost item, budget burning too fast, a falling sales forecast, a weather change affecting demand — the notification reaches you on a day when you can still react.
You never get just a number. You always see where it comes from.
Payroll plan: PLN 100,000. By 20 January, PLN 87,000 spent.
Projected close: PLN 120,000.
Why: expected pay rises in production (+5%), higher wages in the company stores in Gdańsk (+3%), and the last ten days of January historically account for 28% of the monthly cost.
Import the plan from a spreadsheet, budget revenue by sales group and costs by category, and compare plan to actuals on any day of the year.
You state a goal — say PLN 8 million net profit. The system shows at what turnover, margin and cost level it is achievable, and which levers matter most. Plus scenarios: flour up 12%, energy up 20%, one retail chain lost.
A visual link between the components that drive the result — you see how a change in one sales channel travels through costs all the way to profitability.
We work directly on data from your ERP — among our clients that includes AXEL. Access is read-only: we write nothing back to the ERP and we do not change how your team works.
We also pull in the staffing plan, energy tariffs and contract schedules. Implementation starts with a data audit, during which we map your cost structure and check whether the history is sufficient for forecasting.
The data stays in infrastructure agreed with you. We do not share it with third parties and we do not use it to train models for other companies.
A conversation and a data review: what is in the ERP, what the cost structure looks like, where the gaps are. The outcome is a concrete answer on what can and cannot be forecast.
Mapping cost categories and carriers, connecting data sources, importing the plan.
The live month view, alerts, reports. Team training — half a day is enough.
Cost forecasts, the goal planner, scenarios. We add modules at a pace that suits you.
A report shows what happened. We forecast what will happen — because we have a demand model that predicts sales. Without a sales forecast you cannot forecast variable costs, and in a bakery those are what decide the result.
For a sound seasonality forecast we need a minimum of 3.5 years of sales, prices, deliveries and returns. With a shorter history some modules work in a limited scope — we will say so honestly at the audit stage.
No. We connect to your existing environment in read-only mode.
No. It is a management tool. Accounting settles the past according to the regulations — we help you make decisions during the month.
The board, the finance director or chief accountant, production and sales managers. Each role sees its own scope.
All modules are one system and one database. Adding another does not mean starting the implementation again: the integrations, cost structure and history already work. What is more, each new module improves the accuracy of the ones you already have, because it brings new information into the shared model.
Priced individually — a subscription model depending on the number of locations and the scope of modules. We agree the exact scope and terms after the free data analysis.
System development, kept current.
| Module | Status |
|---|---|
| Demand prediction production optimisation, fewer returns | Available, live with clients |
| Cost management controlling and result forecasting | Pilot implementations |
| Route planning delivery and transport-cost optimisation | In preparation |
Clients using one module get access to the next ones on the terms agreed at the first implementation — with no repeat integration and no reconfiguring of data.