Distribution & Trade

Your challenges

Challenges that slow growth

Field sales without visibility

Sales reps make the rounds without knowing who to visit first, what to pitch, or what's in stock. Routes are guesswork. The real potential of every account stays hidden.

Dead stock, shortage stock

Excess inventory on some SKUs, stockouts on others. Inventory reduction happens by gut feeling, not algorithm. Cash gets locked in unsellable items.

Quotes and orders still manual

Every quote takes 20 minutes. Data entry errors cost time and credits. Sales reps waste time on admin instead of closing deals.

Customers leaving without notice

No visibility into disengagement signals. Inactive customers are re-engaged too late. Customer churn is discovered when it's too late to react.

The deployment flow

How we integrate with your systems

1

Data audit and cleanup (Week 1-2)

We analyze your product catalog, cost structure, sales history. We identify and fix duplicates, incorrect margins, incomplete descriptions. Your team continues operations normally—no disruption.

2

ERP integration (Weeks 3-4)

Supply Chain connects to your ERP (Sage, Cegid, Odoo). Historical sales data is ingested. Supplier data and minimum orders are mapped. The system begins learning patterns.

3

Prediction calibration (Weeks 5-6)

Machine learning models are trained on your 12-24 months of history. First recommendations appear week 4. You validate and adjust parameters. Week 6: recommendations are tuned to your business.

4

Live operation and optimization (Week 7+)

Daily recommendations in your dashboard. Purchase team reviews and approves orders. System learns from accepted/rejected recommendations. Continuous improvement in prediction accuracy.

Prerequisites that block

What must be in place first

Robust product data and pricing

If cost prices are wrong, calculated margins are wrong. If products lack descriptions or are duplicated, demand prediction loses its bearings. 2-3 weeks of cleanup equals real gain afterward. No shortcuts.

Accessible sales history

Prediction needs at least 12 months of real data. Ideal: 24 months. If data is in PDFs or paper, we digitalize first. If in your ERP: we extract directly.

Access to your supplier network

We need to know: real delivery times, minimum orders, MOQ (quantity), discounts. If this changes monthly or you have 50 different suppliers, it's more complex. But we handle it.

Commercial acceptance of AI recommendations

AI proposes—your team decides. A good inventory manager will accept 80-85% of recommendations, hold 15-20% for special cases (future promo, loyal customer). That saves enormous time without breaking the business.

What you really measure

KPIs that make the difference

1

Stockout rate by product family

Stockouts cost: immediate lost sale, lost customer, lasting friction. With well-tuned prediction, you drastically reduce stockouts. Every improvement in this rate translates directly to additional revenue through customer retention.

2

Inventory turnover and days of coverage

You have articles that sit long in inventory, generating significant storage costs. Prediction plus automated markdown trigger equals freed cash and space for fast-moving items. Result: improved working capital.

3

True distribution margin

Gross margin minus stockout costs minus overstock costs minus storage costs equals net margin. Do you see it really? Or guess? With Supply Chain dashboards, you know by product, by customer, by period.

4

Replenishment time and supplier reliability

Who delivers on time? Who delivers late? By how much? History often reveals a reliable supplier is worth 2 euros less per item than an unreliable one. You reinvest in good partners.

Objections we hear

Straight answers

"Our product data is really poorly organized."

Normal. Distribution equals 5-30k products. No one has time to fill everything perfectly. We spend 2-3 weeks deduplicating, fixing cost prices, completing missing descriptions. Cost-benefit: one week of effort for 2 years of optimized stock.

"My ERP doesn't connect to anything."

If Sage 100, Cegid, Odoo, or standard ERP: we connect directly. If legacy proprietary: we use daily exports. Slower, but works. Additional cost depends on complexity.

"This will overload purchasing—too many prediction emails."

No email spam. One dashboard you check daily. Critical alerts (imminent stockout) are flagged. Other recommendations are in the table. You control the flow, not the other way around.

"We can't order from suppliers—MOQs too high."

That's a real constraint and we incorporate it. If Supplier X requires 500 minimum and you sell 10 monthly, we handle it: grouped orders, leveled stock, or pivot to alternate supplier. Prediction includes these constraints.

"Our customers make erratic, unpredictable orders."

AI detects outliers and isolates them. It doesn't try to predict surprise promos or large unexpected deals. But it detects regular anomalies (stronger orders at certain times). You'll never get 100%, but 85% is already massive.

Frequently asked questions

Direct answers

How long before I see stockouts avoided?

Once we have 6-8 weeks of history. First alerts start week 4. First measurable gains: months 2-3. Complete stable gains: months 4-6 (enough to see full cycles).

What do we do with products that never sell?

AI flags them automatically. You decide: clearance, destruction, donation. Supply Chain shows storage cost—you can compare with liquidation cost. Often, selling 50% of cost-price beats holding the item 18 months.

How do we handle seasonal products?

AI detects seasonality in your history. It proposes stock 60-90 days before peak season. Regular customers: easier. New patterns: AI alerts you for manual validation the first times.

If I cancel, do I get my data back?

Yes, everything. CSV, JSON, XML—your choice. No lock-in. 30-day notice. One week of technical support for transition.

Is there extra cost per product or SKU?

No. Fixed subscription by catalog size and transaction volume. 5k products or 50k: same subscription as long as you stay in the tier. No cost per SKU or per prediction.