Christmas markets, corporate gifts, in-store traffic surges — during the holiday season, business accelerates… and decisions become critical.
Yet many executives still manage inventory and sales using fragmented data, Excel exports, and backward-looking indicators.
What if your Odoo dashboard didn’t just report stockouts after the fact, but alerted you before they happen?
In this article, discover how Smart Analytics transforms your Odoo data into actionable predictions using Artificial Intelligence, helping you secure peak activity without slowing down operations.
ACT 1: IMMERSION – THE CHAOS OF DECEMBER
It’s December 16th, one week before the Christmas peak. The smell of hot chocolate fills the store, the queue stretches onto the sidewalk, and the phone keeps ringing with large “corporate gift” orders.
Céline, the store manager, smiles at customers — but inside, she’s anxious. She hasn’t had time to properly train her seasonal staff. She recognizes loyal customers in line, yet a nagging fear grips her: “Will I still have enough signature gift boxes for them on Christmas Eve?”
In the middle of this whirlwind, her Excel files become a ticking time bomb. The “Accounting” file says one thing, the “Sales” export says another. Manual exports are time-consuming and prone to human error. She spends her evenings reconciling rows instead of resting.
The underlying issue? A severe lack of a single source of truth. She’s managing her business blindly, lost in a fog of contradictory data.
ACT 2: DIAGNOSIS – FROM ANALYSIS TO PREDICTION
The usual reflex would be to run a replenishment calculation directly in Odoo. But at 5 p.m., during peak in-store traffic, this means risking slower checkout operations — standard solutions quickly reach their limits when handling large data volumes.
This is where Smart Analytics comes into play.
The solution connects to Odoo via API to automatically extract sales, purchasing, and inventory data, without changing front-office workflows or impacting operational fluidity.
An architecture designed for performance and reliability
The dashboard is built on an advanced architecture structured around several key pillars:
Checkout performance (operational fluidity)
All computations and analytics are offloaded to a dedicated data warehouse (Google BigQuery). Céline can analyze up to 10 years of historical data while her sales teams process transactions in real time. ERP performance is never impacted.
Security & historical depth
Data is stored securely and incrementally. Critical analyses can be rerun on December 23rd without any risk of system overload or data corruption.
But the real breakthrough is not purely technological — it lies in the intelligence applied to the data.
A dashboard designed to reveal what numbers hide
In the chocolate industry, seasonality is a defining factor. Between Christmas, Valentine’s Day, Easter, and the quieter summer months, demand can vary by a factor of three. For a company like The Chocolate Company, understanding these cycles is no longer optional : it is a strategic lever.
The Smart Analytics dashboard highlights these dynamics through several key indicators:
• Sales history
A clear visualization of month-by-month sales performance. The peaks in November and December stand out immediately, revealing the impact of gift purchases and seasonal traditions.
• Top 10 most profitable months
At a glance, the company can identify the periods generating the highest revenue. December consistently ranks at the top — a strong signal for optimizing production, marketing, and inventory planning.
Intelligent anticipation: the true “superpower”
Beyond descriptive analytics, Smart Analytics integrates proven artificial intelligence algorithms (through Python execution on the extracted data).
The solution analyzes the past three years of sales, incorporates external data (weather, local trends), and detects weak signals invisible to the human eye.
It goes even further with demand forecasting, providing for each period:
- a central forecast (expected value),
- a conservative scenario (lower bound),
- an ambitious scenario (upper bound).
Result: seasonality is no longer endured it becomes actionable.
ACT 3: REVELATION – THE PREDICTIVE SCENARIO
No more managing by looking in the rear-view mirror. Here’s how AI changes the game for Céline:
⛔ BEFORE (Intuition-Based Decisions) Céline used to order “last year’s volume +10%.”
- Result: Stockouts on the 500g gift box (trend underestimated) and massive overstock of dark chocolate (trend overestimated).
- Cost: Disappointed customers and unnecessary cash tied up in inventory.
✅ AFTER (Smart Analytics Anticipation) : Céline now uses her Retail dashboard to visualize the future:
- November 15 – Predictive Alert : Her dashboard triggers an alert based on historical patterns: “Risk of stockout: 500g gift box, week of 12/18. Detected trend: +340% vs last year. Recommended action: Order +60% now.”
- November 20 – Trend Adjustment : Cross-analysis shows declining interest in dark chocolate in her area. Céline reduces her supplier order.
- Financial Outcome: • >90% availability on best-sellers • €4,500 in cash not tied up in dead stock • Operating margin preserved at 42%
In addition, thanks to standard retail KPIs such as Average Basket Analysis and Customer Lifecycle Tracking, she optimizes upselling opportunities in real time.
SMART ANALYTICS: YOUR GROWTH PARTNER
Christmas should no longer be a survival test — it should be a period of controlled performance. Smart Analytics turns your data into decisions, without requiring technical expertise: the interface is designed to be visually maintainable (No-Code).
Out-of-Stock at Christmas: How the Smart Analytics dashboard anticipates demand on Odoo with AI