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 estimate demand, Odoo sums all validated deliveries, components consumed in manufacturing orders (MOs), or used to resupply subcontractors coming from the warehouse specified on the RFQ in the Based on period. The average daily demand is this sum of outgoing moves divided by the number of days in the Based on period, multiplied by Factor. The estimated demand is the average daily demand multiplied by Replenish For days.

\[\begin{split}Average~Daily~Demand = \frac{Delivered~or~Consumed~Items}{Based~on~Days} \times Factor \\ \\ Estimated~Demand = Average~Daily~Demand \times Replenish~for~Days\end{split}\]

Note

Only Assigned or Confirmed moves are taken into account in the average daily demand calculations, sales quotations or manufacturing orders in Draft are not taken into account until validated.

Tip

In a multi-warehouse setup, internal deliveries are also counted in demand estimation. In the case of a central warehouse dispatching products to individual stores, the average daily demand of the central warehouse will include internal transfers to individual stores.

Recommended quantity

To find the suggested quantity, Odoo deducts the estimated demand from the current stock available quantity and all incoming shipments.

\[Recommended~Quantity = Estimated~Demand - (Available~Stock + Incoming~Stock)\]

Example

In example 1, Odoo recommends 19 units to Replenish for 14 days Based on the past month’s 40 delivered units.

Prerequisite setup

1. Purchase and Inventory apps must be installed.

2. Validate at least one delivery order for each product.

   Ensures there is a past delivery record so the system can calculate average daily demand.

3. 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.

4. 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 Purchase 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 and that the Purchase Order is in the RFQ stage

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, toggle Suggest in the left sidebar to activate the feature. Complete its fields as follows:

• Replenish for: Number of days intended to stock products.

• Based on: There are two inputs:

  1. Period: select the time frame that represents historical demand (e.g., Last 30 Days, April 2024).

  2. Growth factor %: scale the demand up or down (e.g., 120% for 20% growth, 30% for 70% drop).

• The total in the bottom shows the order value. Odoo multiplies the vendor’s Unit Price by the suggested quantity.

Once the parameters are confirmed, click Add All to add all suggestions to the order. Adjust amounts if needed, then click Back to Quotation to confirm the final numbers on the RFQ.

Example Workflow

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/OUT operation.

• 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%

\[Average~Daily~Demand = \frac{40}{30} \approx 1.33 \text{ units/day}\]

Suggested quantity

\[Suggested~Quantity = 1.33 \times 14 \approx 18.67 \text{ (rounded to 19 units)}\]

Suggestion to purchase 19 orchids. Since the Unit Price is $3, \($3 \times 19 = $57\), which is the total amount displayed.

Planning for Mother’s Day

To better plan for the upcoming Mother’s day week, the company changes Based on to the same month last year (May 2024). As the business has grown since then, they also decide to add a 120% growth factor.

Variables

• Replenish for 7 days

• Based on: May 2024,

  • total delivered/consumed in the entire May 2024 month: 361 units

• Factor: 120%

\[Average~Daily~Demand = \frac{361}{30} \times 1.20 \approx 14.44 \text{ units/day}\]

Suggested quantity

\[Suggested~Quantity = 14.44 \times 7 \approx 101.08 \text{ (rounded up to 102 units)}\]

Suggestion to purchase 102 orchids. Each orchid costs $3 with the chosen vendor, so \($3 \times 102 = $306\).

Best practices

1. 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.

   

2. Maintain accurate vendor pricelists

   Review and update vendor pricelists to reflect the latest pricing and supplier information to ensure correct suggestions.

3. 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.

4. Review suggestions critically

   Although the tool provides a baseline recommendation, always apply business judgment. Market changes, promotions, and upcoming events can affect actual demand.