After clicking on the Forecast menu option, select the required location and click on the Filter button. 

In this example, location A10 has been selected.

This screen displays to available enter the criteria to hone in on the forecast group you wish to review/amend:

1. Specify the criteria to filter
2. Click the Apply filter button

In this example, the sales and forecast summary will display for items with:

• Group 1 =  STI
• Classification = Stocked

To use the multi-item manual forecast adjustment

Click the Adjust items button to review the individual forecasts,  make any required amendments, and apply them. This will navigate directly to the multi-item manual forecasting adjustment functionality so that changes can be made to individual item forecasts.

Prior to navigating to the multi-item manual forecasting adjustment functionality, you may want to make (but not apply) a global forecast adjustment for the selected group of items. If so:

1. First, click the Adjust totals button, make the required change to the month(s) in question, and model the change.
2. Then, click the Adjust items button to review the resultant individual adjusted forecasts before applying changes.

List of all items for review

The list of filtered items is displayed, enabling you to scroll down reviewing each item, and making manual forecast adjustments where appropriate.

This screen contains:

• The column headings, each of which are clickable to sort the list of items in that order
• The list of individual items
• A summary of the items listed (bottom right), including the net result of any adjustments made (shown in green)
• A selector enabling you to view the summary by Cost price, Sell price, Margin, or Units
• The forecasts can be downloaded and adjusted in a spreadsheet and
• The adjusted forecasts can be uploaded
• The buttons to Apply the manual forecast adjustments, or to Undo the changes made.
  
  

Click on the column headings to sort the list in that order, for example sorting the list by:

• Risk to highlight items with significant forecast risk. Focusing on improving the forecasts of these items will reduce forecast risk and optimise safety stock levels.

Status so you can review forecasts for items that have stock-outs and potential stock-outs, to ensure the forecasts are accurate and the extent of the stock-out or potential stock-out is realistic.

For each item, information is provided to assist with reviewing the forecast and deciding on whether a manual adjustment is necessary, including:

• Sorted by Risk
• Product and grouping information
• An indicator of the importance of the item (CL)
• An indicator of the Status category (Excess stock)
• The sales and forecast graphic
• Sales and forecast averages, period to date sales, the age of the item
• Two years worth of sales history, the computer forecast and a place to make your forecast adjustment

When forecast changes are made, the cell background colour changes. Additionally, the summary table at the bottom (bottom right) reflects the impact of those changes.

Please note: In these screenshots the optional “Extended Planning Horizon” module has been enabled to show 24 months of forecasts.

Any forecast changes made are not “live” until you click the Apply button, which applies the adjusted forecasts to the live data.

A final step to re-process the data is required to have those forecast adjustments impact ordering, classification and status determination.

The existence of group seasonal factors being used to generate the seasonal forecast on an individual item in a location is shown by the Demand type on the Demand panel (top right) displaying the words Seasonal group.

The seasonal group factors will be used to generate an individual item forecast when:

• The item is in a group that is clearly seasonal.
• And either:
• The item’s limited sales history means that seasonality can’t be detected.
• The item has sufficient history but seasonality thresholds have not been met.

In the example shown in the image, the item has an age of 26 in the #AUCK Warehouse. The sales history did not meet the seasonality criteria thresholds. Because it is in a group that is seasonal, it received a Seasonal Group forecast.

Group seasonal profile

Clicking on the words Seasonal group in the Demand panel will display the group seasonal profile used to generate the forecast. See example below.

The group seasonal profile is created by:

• Aggregating the sales history of all items in the group
• Generating a forecast based on the aggregate sales
• Identifying the resultant forecast profile as seasonal

The item forecast is created by:

• Forecasting the item to get a forecast “level”
• Applying the seasonal forecast profile to the item’s “level” if it does not attract a seasonal forecast based on its sales

Clicking on the words Seasonal group in the Demand panel displays the group seasonal profile used to generate the forecast.

The Seasonal Group graphic displays:

• The selected group; in this case Purpose
• The code and description for the group; in this case Construction: Description for Construction
• Charts; showing multiple years of sales history and the forecast for this group
• Stats; the row underneath the chart includes the following:
• Year-on-Year; a view of the seasonal profiles across multiple years stacked on top of each other
• Comparison; showing any trends, either sales growth or decline, for the group
• Item count; the number of items included in this group
• Avg age; the average age of the items in this group
• Max age; the oldest item in this group
• Age distribution; showing where items fall between maximum age and minimum age
• Min age; the youngest item in this group

Step 1: Download items to a CSV file

Once you have filtered the items in the multi-forecasting page (1) to get to a manageable list (2), simply click the Download button (3) to download all of those items to a CSV file.

Step 2: Modify forecasts in your spreadsheet app

To make the required changes to the CSV file:

1. Open the CSV file in Excel, Google sheets, etc
2. Manually review and amend the forecasts, if required
3. Save the CSV file

Note: it is important to retain column A (important identifiers) and the forecast columns (FC:1 through FC:12) - or (FC: 1 through FC:24 if the extended planning horizon optional module is enabled). Without ALL of these columns the upload of your forecast changes will fail.

Step 3: Upload the CSV file

Click the Upload button (4) in the image above, then Choose file, locate the CSV file you just saved and the upload will automatically start. Upon completion you will either be notified:

• Of any errors processing the file
• That the file has been imported successfully

After a successful import click the Reload now button to refresh the page with the updated forecasts.

Errors are displayed as follows:

Step 4: Apply the forecast changes

Any forecast changes made are not “live” until you click the Apply button, which applies the adjusted forecasts to the live data.

In addition to the Apply button, you can also click Undo to disregard the changes.

After reloading the page, any forecasts that you changed will be highlighted with a green background.

In this case 2 months of forecast have been changed/increased.

The changes are reflected against each item and in the summary totals at the bottom of the page.

By clicking Apply, all of the changes you made to any forecast in the spreadsheet will be applied and the forecasts will be frozen.

You are able to enter a comment during this process and this comment will be added against the products with amended forecasts only. This is useful in keeping a record of when and why forecast changes were made.

Step 5: Perform a re-process

A final step to re-process the data is required to have those forecast adjustments impact ordering, classification and status determination.

The forecast disaggregation feature resolves these issues by the creation of a region consisting of stores/locations. This enables the:

• Aggregation of sales history into the region
• Creation of a more reliable forecast based on the aggregate sales at the regional level. Aggregated sales are more likely to produce a quality forecast, and where appropriate, a seasonal profile is used
• Distribution of the regional forecast back down to the stores/locations

The benefits of this approach are:

• Better forecasts at the store/location level
• Easier management of forecasts

The Demand type for the item at the location level is shown as “Disaggregated”. This can be seen on the Stock Enquiry.

Selecting the hyperlink “Disaggregated” displays the average monthly forecast generated at the regional level, and the share of the average forecast that each location was allocated. 

This provides visibility on the disaggregation calculation.

In this example, the regional average forecast for this item is 106.8. The proportions of how this forecast has been shared amongst the locations are apparent.

The information button shows the basis of how the forecast was disaggregated.

The location link opens a Stock Enquiry for the item in that location.