Demand planning, on your premises
A forecast you can stand behind.
Stratos turns the sales exports your team already produces into a reviewed, auditable twelve-month plan. The models compete, the planner decides, and every number can explain itself.
I — The problem
Every month, one planner faces two hundred products and a spreadsheet that answers to no one. The busiest weeks of the year arrive on a calendar the spreadsheet has never heard of; the formula that sets next quarter’s stock was written by someone who left; and when a number is challenged in the room, there is nothing behind it to show.
So the plan is negotiated instead of read. Sales pads it, production discounts it, finance re-does it — and the company quietly runs on three private versions of the truth.
Two hundred products. Twelve months. One person answerable for every number.
II — How it works
One cycle, every month.
Stratos does not replace the planner’s judgement. It gives that judgement a place to stand, a record, and a way to be checked next month.
Ingest
The same export your team already emails. Stratos maps it to a clean monthly picture, proposes the cut-off, and waits for the planner to confirm.
Review
Months that broke pattern are flagged with their evidence — a collapse, an event, a spike. The planner rules on each. Nothing is corrected quietly.
Simulate
Seven model families re-forecast months they never saw. The winner must beat doing nothing at all, or it does not win.
Publish
One plan is signed and becomes the record. Every publication is revisioned; nothing is overwritten, and amendments carry a reason.
Grade
Next cycle, last cycle’s promise is measured against what actually happened — in kilograms and in money, at every level of the portfolio.
Roll up
The published plan loads the production lines, sizes the buffer, and feeds the executive view of volume, revenue and gross profit.
Inside steps two and three: the pass that runs twice.
The first scan for unusual months knows nothing about the model. The second one is the model — and what it did not see coming comes back to you.
III — The instruments
Six instruments.
Each does a single job, shows its work, and leaves a record a reviewer can follow. Every one of them exists to help a planner make one decision they could not make from a spreadsheet.
Forecast Simulation.
Before a model is allowed to forecast your next twelve months, it has to prove itself on the twelve it was never shown.
A model that fits your history perfectly has proved nothing; it has only memorised. So Stratos cuts each product’s history once, twelve months back. Every candidate family is trained on everything before the cut and then asked to forecast the twelve months it has never seen — the same twelve months, for all of them, so the comparison is fair. Each month’s gap between forecast and reality is measured, and the families are scored on the total weighted error and on bias, which says whether a model leans high or low rather than merely missing. The winner is the one that actually forecast best, with a preference for models a planner can explain when the margin between them is thin. One competitor is always in the race: the seasonal naïve, which simply repeats last year. Anything that cannot beat that is not worth running.
Outlier scanning, with a Kalman innovations filter.
Not “which month looks odd” — which month actually surprised the model that is doing the forecasting.
Most tools flag a month because it is far from an average. That catches every Ramadan and misses the quiet disasters. Stratos asks a sharper question: given everything the model knew a month ago, how surprising was what actually arrived? The filter carries a running expectation for each product and a running measure of that product’s own noise, and scores each month’s surprise in units of that noise. The tolerances are deliberately lopsided — a sudden collapse is more often a stockout or a delivery that slipped than a real fall in demand, so it is questioned sooner than an equally large jump. Inside a known event the bar is raised, because a surprise during Ramadan is usually the calendar rather than the market. What breaks tolerance is flagged, with its evidence, and handed to the planner. Nothing is ever corrected on its own.
Calendar intelligence.
The biggest weeks of your year do not sit still. Stratos knows where they will be, per product, for every year ahead.
The Hijri year is about eleven days shorter than the civil one, so Ramadan and both Eids march steadily backwards through the Gregorian months. A model that only knows “March” sees a peak that arrives early, then earlier, then splits across two months — and reads all of it as noise. Stratos places the events themselves on the calendar for every year ahead, along with the weeks that lead into them, the school terms and the paydays. It then measures, per product, how much each of those actually moves demand: the run-up to Eid matters enormously for gifting lines and hardly at all for a staple. The result is a forecast that expects a February peak to become a January peak, because it knows why.
Feature scanning, selection & grid search.
Which signals actually move this product — and what happens if the planner disagrees.
Stratos offers every product the same list of candidate signals and lets the history decide. Each candidate is measured for how much it moves demand and how confident that measurement is; the ones that cannot earn their place are dropped, and the survivors are shown with their strength, not merely their names. Then comes the part that matters in a review meeting. If a planner believes a signal is missing — or believes one that survived is nonsense — the grid search re-fits the alternatives and ranks them side by side, including the option of using no calendar factors at all. The argument stops being a matter of seniority.
How the ranking works, in plain English
The trap. Adding another factor always makes a model fit the past a little better — even a meaningless one. So you cannot choose by fit alone, or you would always choose the biggest set, and the model would be memorising your history rather than learning from it.
The fix. Each combination is scored on how well it explains the history minus a penalty for every factor it used. A factor only improves the score if it earns more than it costs. The penalty gets steeper when history is short relative to the number of factors, which matters here — four years of monthly numbers is not much to spend. Lower is better, and the table shows each set’s distance from the best rather than a raw number, because only the gap means anything.
The honest part. A gap of less than about two points is not a real difference — those sets are statistically indistinguishable from the winner. Stratos draws that band rather than hiding it, so a tie is presented as a tie. When several sets are level, the choice belongs to the planner, and the sensible rule is to take the simplest one you can explain to the room.
Capacity check.
A plan the factory cannot build is not a plan. Stratos says so before the month begins, not during it.
Once the plan is published, Stratos routes it onto the production lines that actually make the products and compares each month’s load against what that line can produce. The result is a single grid: every line, every month, shaded by how hard it is being pushed, with the months that do not fit marked plainly. Where a month overflows, the shortfall is quantified rather than described — this many tonnes, on this line, in this month — and the neighbouring months show their headroom, so the size and the shape of the problem are both visible. What Stratos will not do is decide for you. It does not schedule the factory, it does not move volume between months, and it never quietly trims the forecast to make the arithmetic work. It shows the collision early enough that the people who own the plant can deal with it while there is still time.
Safety stock from measured error.
How a buffer is actually sized — in five moves, each one visible in the application.
Stratos never sizes a buffer from a textbook constant. It collects the shipped model’s real misses over the review window, gathers them into their own distribution, and places each item’s service target on that curve — higher for the A items that carry the business, lower where a miss costs little. The buffer is then capped by the item’s shelf life, because stock that expires is not safety. And the promise is graded in public: the service level the plan committed to, printed next to the service level the shelves actually delivered.
IV — How it is delivered
On your premises. Under your name.
Stratos installs on a machine you control. There is no tenant, no shared cluster, and no copy of your sales history anywhere but your own building.
It runs where you are
A single install on your own hardware. Your sales data is read locally and never sent anywhere for processing.
Every cycle is kept
Each month’s run is archived whole — the data, the decisions, the published plan. Old cycles can be reopened and read, never rewritten.
Configured, not coded
Your product hierarchy, calendar, measures and service policy are settings. Adapting Stratos to your business does not mean a development project.
Plain language throughout
The application explains itself to a planner, not to a statistician. Every recommendation says what it is and why, in words.
V — The demo
Step inside.
The demo is the real application, running over a fictional client — John S Foods, a Gulf biscuits and snacks manufacturer invented for the purpose. Every figure on every screen was computed by the same engine that would run in your building.
- A full planning cycle, from raw export to a published, signed plan.
- The Forecast Simulation with its scoreboard, item by item.
- Last cycle’s promise graded against what actually happened.
- Capacity load and safety stock built on the published plan.
One email at most, to follow up on your visit. No mailing list, no third-party trackers.
Rather talk to a person first? WhatsApp us · sales@elunia.cloud