Effective inventory management is one of the most critical areas of business, directly impacting profitability. Excess inventory ties up capital, while insufficient stock leads to lost sales.
By using Odoo with the Statistical Orderpoint functionality, inventory planning becomes automated and based on real data rather than guesswork.
This is especially relevant for growing companies, where manual planning becomes too slow and inefficient. An automated solution not only saves time but also reduces the risk of human error.
Statistical Orderpoint is an advanced inventory management method that automatically determines:
• minimum quantity (Min Qty)
• maximum quantity (Max Qty)
for each product and warehouse location.
The system analyzes:
• historical sales
• stock movements
• supplier lead times
• demand fluctuations
This allows you to accurately determine when and how much to reorder.
Unlike traditional methods, this system adapts to real conditions—if demand increases or decreases, stock levels are adjusted automatically.
The Odoo system analyzes data daily and updates orderpoints.
Main steps:
1. Sales data is collected
2. The average daily demand is calculated
3. Demand variability is assessed
4. Supplier lead time is factored in
5. The desired service level is applied
The minimum stock level is calculated using the formula:
Min=Z⋅σlt+μlt
Where:
• Z – Z-score based on the desired service level
• σₗₜ – demand variability during the lead time
• μₗₜ – average demand during the lead time
This means the system not only knows how much you sell, but also accounts for risk.
In practice, this helps avoid situations where products run out unexpectedly or, conversely, the warehouse becomes overstocked with unnecessary inventory.
1. Lower warehouse costs
Automated calculations help reduce excess inventory.
2. Fewer stockout situations
The service level used allows you to control risk.
3. Data-driven decisions
No more guesswork — everything is based on real data.
4. Automated operation
The system automatically updates data without manual intervention.
Odoo provides a clear overview of inventory status:
• 🔴 Out of stock (Stock shortage)
• 🟠 Critical level
• 🔵 Excess inventory
• ⚫ Normal level
One of the most important metrics is Days of Cover, which shows how many days the current inventory will last.
This allows you to quickly understand the situation and make decisions.
This kind of visualization helps managers quickly identify issues and take action before actual stock shortages occur.
Odoo analyzes all min/max changes and detects unusual fluctuations.
If the change is too large, the system flags it as an anomaly.
This helps to:
• identify errors
• identify changes in demand
• respond to market conditions
This allows you not only to react to problems but also to anticipate them in advance, based on data trends.
Using artificial intelligence, Odoo can explain anomalies:
• why demand has changed
• whether it is a one-time event
• what actions to take
This is especially useful for larger businesses with complex logistics.
AI provides an additional layer of insight that helps make faster and more informed decisions.
To enable automated inventory management in Odoo:
1. Mark products and locations as “Use in Orderpoint”
2. Set the analysis period
3. Select the service level (Service Level)
4. Verify supplier lead times
After that, the system starts operating automatically.
At the beginning, it is recommended to monitor the results and adjust the settings if needed according to your business specifics.
This solution is ideal for:
• e commerce
• wholesale trade
• manufacturing companies
• businesses with a large number of SKUs
Especially useful for fast-growing companies where inventory management becomes a critical operational factor.
By using Odoo with the Statistical Orderpoint functionality:
• you will reduce warehouse costs
• you will avoid stock shortages
• you will automate processes
• you will make more accurate decisions
If you are still planning inventory “by eye,” it is limiting your business growth. If you want to automate inventory management and make data-driven decisions, contact us — we will help tailor the solution to your business needs.
Properly configured inventory management not only optimizes processes but also helps better plan cash flow and ensure more stable long-term business growth.
What is a Statistical Orderpoint ?
It is a system that automatically calculates the minimum stock level based on actual sales data in order to reduce the risk of stock shortages.
Do you need to configure everything manually?
No. The system calculates the values automatically; you only need to enable products and locations for the calculation.
When do the calculations take place?
Automatically every night. If needed, it can also be run manually.
How is the minimum quantity determined?
It takes into account:
• average sales
• demand fluctuations
• lead time
• the selected service level
Why is the minimum quantity sometimes too high?
Dažniausiai dėl:
• a high service level
• large fluctuations in demand
• one-time spikes in sales
This can be adjusted in the settings.
Why is the minimum quantity 0 for some products?
Because there were no sales during the selected period.
What do the colors in the replenishment window mean?
• 🔴 out of stock
• 🟠 critical level
• 🔵 excess stock
• ⚫ all good
• ⚪ no data
What is “Days of Cover”?
It shows how many days the current stock will last based on the current consumption rate.
Does the system override manual settings?
No. Manual settings are not affected.
How do you disable automatic calculation for a product?
You just need to disable the product from being included in the calculation or turn off automatic management.
What are anomalies?
These are unusual changes in stock levels that stand out from normal historical patterns.
What are anomalies?
These are unusual changes in stock levels that stand out from normal historical patterns.
What do AI explanations do?
They help explain why an unusual change occurred and what should be checked.
Why do the results appear inaccurate?
Most often due to poor data quality:
• inaccurate inventory movements
• incorrectly configured lead time
• too short analysis period
