The verdict in three sentences
An AI demand forecasting model learns from your sales history, seasonality and promotions to recommend order quantities, cutting stockouts by up to 25% and overstock by 15%. The 2026 budget runs 20,000 to 60,000 EUR excl. VAT for delivery in 3 to 5 months, the duration driven by data quality and history depth. ROI comes from lower tied-up working capital and fewer lost sales.
Where the return comes from
Gains concentrate on three areas: less capital tied up in stock, fewer lost sales, less time spent reordering manually.
| Item | Before AI | After AI | Effect |
|---|---|---|---|
| Stockout rate | 8% | 6% | -25% |
| Overstock level | baseline | -15% | Cash freed |
| Lost sales/month | 45,000 EUR | 33,750 EUR | -25% |
| Reorder time/week | 20 h | 8 h | -60% |
| Stock turnover | 6x/year | 7.2x/year | +20% |
Figures are a 2026 order of magnitude and depend heavily on range depth and data quality.
Budget, timeline and prerequisites
| Scope | Cost (EUR excl. VAT) | Timeline | Data prerequisite |
|---|---|---|---|
| 1 category/warehouse | 20,000 - 30,000 | 3 months | 18 months history |
| Multi-category | 30,000 - 45,000 | 4 months | 24 months + promos |
| Multi-site + auto-reorder | 45,000 - 60,000 | 5 months | 24 months + logistics |
Running cost (retraining, hosting, accuracy tracking): 300 to 1,200 EUR/month. A model must be monitored: measure its accuracy monthly (MAPE).
Mini case study
Thomas, supply chain manager at a technical-parts distributor in Lyon, manages 3,500 SKUs. His overstock ties up 1.2M EUR and he loses about 45,000 EUR/month in sales to stockouts.
He deploys a multi-category forecasting AI: 38,000 EUR excl. VAT + 700 EUR/month. Over 12 months: overstock down 15% (180,000 EUR of cash freed) and lost sales down 25% (11,250 EUR/month recovered, i.e. 135,000 EUR/year). Recurring annual gain exceeds 125,000 EUR after running cost. The investment pays back in under 4 months.
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FAQ
How much history is needed for a good model?
At least 18 months, ideally 24 months including promotions and seasonal events. Without enough history, we start with a simple model and refine it.
Does the model handle promotions and seasonal peaks?
Yes, if they are documented in the history. We feed in promo and event calendars so the model does not treat them as noise.
Who stays in charge of ordering?
Your teams. The AI recommends quantities; the buyer approves or adjusts. Auto-reorder can be enabled on stable SKUs only.
How do we know the model stays reliable?
Via an accuracy indicator (MAPE) tracked monthly. If it drifts, the model is retrained. This is included in the running cost.
What if our data is incomplete?
A data-preparation phase is built into the project. That is often where success is decided: cleaning, historization, product reference base.
Let's scope your project. Tell us your number of SKUs, your available history and your sites, and we assess feasibility and budget. Detailed quote within 48 h. WhatsApp +221 77 596 93 33.
Mohamed Bah
Fondateur, Kolonell
Passionate about digital and entrepreneurship in Africa, Mohamed has been helping Sénégalese businesses with their digital transformation since 2020. Founder of Kolonell, he believes every SME deserves a professional and accessible online présence.
