Suggest quantities based on historical demand¶
For a straightforward push-based replenishment strategy, the Suggest feature recommends quantities to order on requests for quotations (RFQs) based on historical demand.
Key parameters¶
Replenish for: future coverage window (days).
Based on: period that defines historical demand: last 7 days, 30 days, 3 months, 12 months, or the same month or quarter the previous year.
Factor: growth or decline factor (default 100%). After obtaining the total from the period, multiply the historical demand by this percentage to determine how much of the demand should be replenished. (e.g., input
120%if sales are projected to grow 20% more than the previous period)
Demand calculation¶
To find the average daily demand, Odoo sums all validated deliveries, components consumed in manufacturing orders (MOs), or used to resupply subcontractors in the Based on period and divides that total by the number of days in the Based on period. Lastly, that value is multiplied by the Factor to determine volume.
Tip
In a multi-warehouse setup, an In field appears. Choose a specific warehouse or leave blank to use all warehouses to calculate demand accordingly.
Recommended quantity¶
To find the suggested quantity, Odoo multiplies the average by Replenish for days to get the recommended quantity.
Example
In example 1, Odoo recommends 19 units to
Replenish for 14 days Based on the past month’s 40 delivered units.
Prerequisite setup¶
Purchase and Inventory apps must be installed.
Validate at least one delivery order for each product.
Ensures there is a past delivery record so the system can calculate average daily demand.
Add a vendor to the vendor pricelist with a purchase price for each product.
The Suggest feature is vendor-specific, so each product needs a matching vendor for accurate purchase quantity and price calculations.
Set the Product Type to Goods and ensure the product is Tracked by quantity.
Ensures the system can manage stock levels and calculate recommended replenishment quantities for tangible items.
Suggest quantities to order¶
To suggest quantities based on past sales, navigate to the app. Create a New RFQ or select an existing one.
In the RFQ, set the Vendor field to the chosen supplier.
In the Products tab, click the Catalog button to view that vendor’s items.
Important
Verify that each product in the catalog is configured with the chosen vendor.
Tip
By default, products listed in the product catalog are filtered by vendor.
Remove the filter in the search bar to view all items or use the built-in Group By for Product Category.
Inside the Catalog, click Suggest in the upper-left corner to open the Suggest Quantities based on Sales & Demands pop-up window. Complete its fields as follows:
Replenish for: Number of days intended to stock products.
Based on: There are two inputs:
Period: select the time frame that represents historical demand (e.g., Last 30 Days, April 2024).
Growth factor %: scale the demand up or down (e.g., 120% for 20% growth, 30% for 70% drop).
The total in the lower-right corner shows the order value. Odoo multiplies the vendor’s Unit Price by the suggested quantity.
Once the parameters are confirmed, click Compute to calculate recommended quantities, which are auto-filled in each product’s quantities in the catalog. Adjust amounts if needed, then click Back to Quotation to confirm the final numbers on the RFQ.
Recommend at 100% growth¶
A company needs to replenish orchids for 14 days, referencing the last 30 days of historical data, assuming the revenue growth is the same this month, at 100%.
Delivered/consumed within the period:
20 units delivered 15 days ago in a
WH/OUToperation.20 units delivered 1 day ago
Total: 40 units in the last 30 days
Variables¶
Replenish for: 14 days
Based on: 30 days
total delivered/consumed in the period: 40 units
Factor: 100%
Suggested quantity¶
Suggestion to purchase 19 orchids. Since the Unit Price is $3, \($3 \times 19 = $57\), which is the total amount displayed in the Suggest Quantities based on Sales & Demands pop-up window.¶
Recommend at 120% growth¶
To plan for ordering roses this month, the company reviews the previous week’s sales. Since a local event is coming up, the company expects 120% growth.
Variables¶
Replenish for 30 days
Based on: 7 days
total delivered/consumed in the past week: 166 units
Factor: 120%
Suggested quantity¶
Suggestion to purchase 854 roses. Each rose costs $4.58 with the chosen vendor, so \($4.58 \times 854 = $3911.32\).¶
Recommend from specific warehouse¶
When there are multiple warehouses in a company, analyze delivered or consumed quantities in a specific warehouse to narrow the results. This is particularly helpful when multiple warehouses serve different communities, franchises, or branch stores.
To do that, ensure multiple warehouses are set up and deliveries or MOs are validated in each warehouse.
Navigate to the suggestion window by going to the app, clicking the desired RFQ, clicking the Catalog button in the product line, and then clicking Suggest in the upper-left corner.
With multiple warehouses set up, the In field becomes available, where the specific warehouse can be selected to analyze quantities consumed only in the specific warehouse, or leave the field blank to observe quantities across all warehouses.
Best practices¶
Validate historical data
Forecasts are based on validated delivery orders, manufacturing orders, and other inventory actions that consume quantities. For delivery orders, the Effective Date field is considered the date the quantities were consumed.
Maintain accurate vendor pricelists
Review and update vendor pricelists to reflect the latest pricing and supplier information to ensure correct suggestions.
Test sales projections based on seasonality
Reference prior months or quarters to capture seasonal fluctuations and experiment with growth and decline factors to project sales.
Review suggestions critically
Although the tool provides a baseline recommendation, always apply business judgment. Market changes, promotions, and upcoming events can affect actual demand.