Buying out of habit
You order the same as last month because reviewing product by product would take two days. That's how what doesn't turn over piles up and what does runs out.
Both happen at the same time and for the same reason: nobody has time to review eight thousand products one by one. The agent reviews all of them, every week, and tells you which ones matter today.
“You have $68,400 in 214 products that haven't moved in over four months. Eighty of them are from a single brand.”
“Twelve class A products will stock out in less than three weeks if they aren't ordered today. Two are from a vendor that takes 45 days.”
“Of the slow movers, Ferretería R bought 38 last year and stopped ordering them. Worth a call before clearing them out.”
Your buyer knows the top hundred sellers by heart. The problem is the other seven thousand nine hundred: that's where idle capital piles up and where stockouts slip through.
Your system already calculates almost all of this. The report exists, it's right there, and nobody opens it — because it's eight thousand rows and nobody has a free morning to go through them.
You order the same as last month because reviewing product by product would take two days. That's how what doesn't turn over piles up and what does runs out.
Nobody looks at those products until there's a physical count or the close has to be reconciled. By then they've been there a year and have already lost value.
The buyer who knows how much to order of each item keeps it in their head. If they get sick, go on vacation or quit, that information leaves with them.
And it's not just a Panama problem: 81% of companies worldwide still plan their supply in Excel, even though they have systems that already do the math. The obstacle was never the math.
We didn't invent a forecasting method. We use the ones the industry has already validated, and add on top what no tool includes: the reading in plain language and the concrete action.
It classifies each product by what it represents in money and how predictable its sales are. An item that sells every day isn't the same as one that sells three times a year, and they aren't managed the same way.
Different models depending on the pattern: steady sellers are projected one way and sporadic sellers another. That distinction is what makes the suggestions useful in a catalog of thousands of items.
Reorder point and quantity per product, adjusted to each vendor's real lead time and to the service level you decide for each item class.
This is the part that isn't in any book: turning those numbers into a short, prioritized list, with the reason for each recommendation, sent to the person who can act on it.
It works on top of whatever system you already have. No need to switch ERPs or migrate anything — it connects and reads.
The big platforms that do this exist, and they're excellent. They also cost hundreds of thousands of dollars a year and take six to eighteen months to implement.
It does all this and more. Designed for chains with dozens of warehouses and a dedicated planning team. Six to eighteen months before it starts paying off.
Free, flexible and understood by whoever built it. It doesn't warn, doesn't prioritize, doesn't cross-check against sales, and it leaves the day that person leaves.
The same math, on the system you already have, in weeks and not a year. And it alerts you on its own, which is the difference between a calculation and a decision.
If your operation justifies an international platform, we'll tell you in the assessment and we won't sell you this. This is for whoever is on Excel today and knows money is slipping away there.
Three figures you already know. We give you the order of magnitude of what can be freed up and what it would cost.
How often you review slow movers
Where that number comes from
The math assumes that in a catalog without periodic review, between 15% and 30% of inventory is sitting idle. If you already review slow movers every month, your gain here is smaller and we'll tell you so. The real number comes from looking at your inventory, not this slider.
The price depends on the size of the catalog, not the value of the inventory. If you handle manufacturing, the raw materials module is quoted separately because it requires connecting the production plan.
It works on top of the one you already have. It connects and reads your sales history, inventory, purchases and each vendor's lead times. No need to migrate anything or switch ERPs. If your information lives in two or three different systems, even better: that's exactly the case where no tool on the market works for you, because they all assume you have everything on a single platform.
Ideally twenty-four months of transactions. Twelve is workable, but the forecast won't capture full seasonality and first-year suggestions will be more conservative. With less than twelve months we tell you and we don't start: it would be guessing dressed up as calculation.
That's the most common case and we review it in the assessment. Duplicate codes, wrong units, items nobody ever deactivated. Cleaning it up is part of the setup, and if the mess is bigger than usual it's quoted separately before we start.
Not without someone authorizing it. It suggests what to order, how much and from whom, with the reason for each suggestion. Your buyer reviews and approves. Autonomy can be expanded later, with an amount limit you define in writing.
Not at all, as far as the math goes: they use the same methods the industry validated decades ago. The difference is the price, the time to go live and the fact that those platforms assume you have a dedicated planning team to interpret their dashboards. Here the interpretation comes done and reaches whoever has to act.
It will be wrong, and that's normal: no forecast is always right. That's why the system doesn't give a bare number but a range, with the margin of error measured against your own history, and that's why the buyer approves before anything is executed. During implementation we measure that error against past months and show it to you before going live.
We look at your catalog, your sales history and how clean your item master is. We tell you how much is tied up and whether it's worth doing something — even if the answer is that the data needs to be cleaned up first.
We review your inventory, measure the forecast error against your own history and adjust the number to your case. Setup in 20 business days.
Request assessmentYou're talking to Génesis, Valtriom's AI