How a fluctuating demand forecast works
When the replenishment method for an item or an item-warehouse combination is Demand forecast by fluctuating values, Sage Intacct uses the replenishment fluctuating demand forecast for the item to determine the forecasted demand of the item during the vendor lead time per warehouse.
The examples in this topic show how the entries in a fluctuating demand forecast are used to determine the forecasted demand of an item from the As of date specified in the Replenish Inventory page through the vendor lead time. (The vendors for an item and their lead times are specified in the item.)
All the examples assume that the item is Small Widgets that can be bought from two vendors in Texas for stocking in warehouses in Dallas, Houston, and San Francisco. The Vendor entries table in the Vendor history tab for Small Widgets has the following vendor information entered:
| Vendor ID | Lead time (days) | Economic order quantity | Vendor minimum order qty | Units of measure |
|---|---|---|---|---|
| Acme Widgets | 4 | 10 | 20 | Each |
| Widget Store | 10 | 20 | 60 | Each |
Example 1
In the example, a fluctuating demand forecast was created for Small Widgets with the needed quantities forecasted on a daily basis. Sales from the Dallas and Houston warehouses are about the same, but sales from the San Francisco warehouse are brisker. So, two entries were created for each day—one with no warehouse specified and one with the San Francisco warehouse specified—because the quantity needed per day is greater for the San Francisco warehouse from which more of the item is sold.
Portion of the fluctuating demand forecast for Small Widgets with daily forecast quantities
| Effective date | Forecast quantity | Warehouse |
|---|---|---|
| June 11 | 2 | |
| June 11 | 5 | San Francisco |
| June 10 | 2 | |
| June 10 | 8 | San Francisco |
| June 9 | 4 | |
| June 9 | 8 | San Francisco |
| June 8 | 5 | |
| June 8 | 10 | San Francisco |
| June 7 | 10 | |
| June 7 | 20 | San Francisco |
| June 6 | 10 | |
| June 6 | 20 | San Francisco |
| June 5 | 8 | |
| June 5 | 17 | San Francisco |
| June 4 | 8 | |
| June 4 | 17 | San Francisco |
| June 3 | 8 | |
| June 3 | 17 | San Francisco |
| June 2 | 7 | |
| June 2 | 12 | San Francisco |
| June 1 | 5 | |
| June 1 | 15 | San Francisco |
If the As of date in the Replenish Inventory page is June 1, the system would calculate the demand forecast as shown in the following table. The system runs a query against the fluctuating demand forecast for Small Widgets to find the forecast quantity entered for each day for each warehouse during the demand forecast period (As of date + vendor lead time) and then adds up the daily forecast quantities to arrive at the total demand forecast.
| Vendor ID | Lead time | Demand forecast period* | Demand forecast | Warehouse |
|---|---|---|---|---|
| Acme Widgets | 4 | June 1 to June 4 | 5 + 7 + 8 + 8 = 28 | Dallas |
| 5 + 7 + 8 + 8 = 28 | Houston | |||
| 15 +12 + 17 + 17 = 61 | San Francisco | |||
| Widget Store | 10 | June 1 to June 10 | 5 +7 + 8 + 8 + 8 + 10 + 10 + 5 + 4 + 2 = 67 | Dallas |
| 5 +7 + 8 + 8 + 8 + 10 + 10 + 5 + 4 + 2 = 67 | Houston | |||
| 15 + 12 +17 + 17 + 17 + 20 + 20 + 10 + 8 + 8 = 144 | San Francisco | |||
| *Note: Demand forecast period is As of date + vendor Lead time (days) | ||||
Example 2
The first example showed a fluctuating demand forecast with daily forecasted quantities. Your organization might forecast on a weekly or monthly basis. In this example, entries were entered by week. Like Example 1, sales from the Dallas and Houston warehouses are similar, but sales from the San Francisco warehouse are brisker. So, two entries were created for each week—one without an associated warehouse and one associated with San Francisco.
Portion of the fluctuating demand forecast for Small Widgets with weekly forecast quantities
| Effective date | Forecast quantity | Warehouse |
|---|---|---|
| June 29 | 20 | |
| June 29 | 50 | San Francisco |
| June 22 | 20 | |
| June 22 | 60 | San Francisco |
| June 15 | 30 | |
| June 15 | 70 | San Francisco |
| June 8 | 40 | |
| June 8 | 80 | San Francisco |
| June 1 | 25 | |
| June 1 | 65 | San Francisco |
If the As of date in the Replenish Inventory page is June 7, the system would calculate the demand forecast as shown in the following table. The system runs a query against the entries and uses the full forecast for each week, where the date of the week is on or after the As of Date and within the demand forecast period, to find the total demand forecast.
| Vendor ID | Lead time | Demand forecast period* | Demand forecast during lead time | Warehouse |
|---|---|---|---|---|
| Acme Widgets | 4 | June 7 to June 10 | Week of June 8 is used. 40 | Dallas |
| Week of June 8 is used. 40 | Houston | |||
| Week of June 8 is used. 80 | San Francisco | |||
| Widget Store | 10 | June 7 to June 16 | Week of June 8 and 15 are used. 40 + 30 = 70 |
Dallas |
| Week of June 8 and 15 are used. 40 + 30 = 70 |
Houston | |||
| Week of June 8 and 15 are used. 80 + 70 = 150 |
San Francisco | |||
| *Note: Demand forecast period is As of date + Lead time (days) | ||||
