The use of this forecasting tool is at the user’s own risk. Lightspeed cannot guarantee the accuracy and reliability of the forecasts generated. Actual results may differ. The tool is provided for informational purposes only and should not be considered as financial, investment, or legal advice. Users are encouraged to exercise caution before making business decisions based on the tool’s output.

Demand forecasting is the process of estimating future demand for products over a specific time period based on historical data and identified trends. Retail POS analyzes historical data like products sold and stockouts to help predict the quantity of stock needed for upcoming sales periods, streamlining inventory planning to decrease the cost of goods sold and increase profit margins.

If your plan includes access to forecasting features, demand forecasting metrics will be integrated into your Inventory replenishment report. You can also adjust the forecast period and factor in missed sales for enhanced reporting.

Before setting up your demand forecasting report, learn more about how demand forecasting works and the historical sales and forecast demand measures are used to build recommendations in your report.

Understanding demand forecasting

Demand forecasting is based on historical sales data. In your Inventory replenishment report, you can use the Missed sales and Estimated sales measures to:

• Identify and better understand periods when products go out of stock.
• Ensure you have the right amount of stock during busy periods.
• Minimize manual calculations and get a clear picture of your sales potential.

These measures work best with products that:

• Sell consistently.

• Have long-term historical sales records.

  Products that sell infrequently and special order items will not have enough data to support accurate forecasting recommendations.

You can also add the Forecasted demand and Suggested reorder quantity measures to your report to:

• Avoid under- or over-stocking products.
• Get suggested order amount recommendations and create purchase orders.
• Leverage in-product forecasting calculations, instead of manually exporting inventory and sales reports.

These measures work best if you:

• Reorder existing catalog items from suppliers throughout the year or season.

• Have historical sales data.

  New products purchased for an upcoming season or year will not have the historical sales data needed for demand forecasting.

Factors that affect demand forecasting

The accuracy of demand forecasting recommendations can be affected by multiple factors, including:

• Sales volume: Products that sell more provide more data points, improving forecasting accuracy.

• Stockouts: Out-of-stock products can lead to missed sales, which affects the accuracy of historical sales data.

  Optionally, you can factor in missed sales estimates in your reports for a more comprehensive forecast.

• Seasonality: Products affected by seasonality (recurring seasonal spikes across two or more years) need year-over-year data to identify trends.

Lightspeed cannot guarantee the accuracy and reliability of the forecasts generated. The tool is provided for informational purposes only.

Forecasting methods

The forecasting methods used in Retail POS are based on recent demand, seasonality, or a combination of both.

• Recent demand methods, based on the last 6 weeks of sales:
  • Moving average (with missed sales): Used for fast-moving inventory to help capture lost sales due to out-of-stock issues.
  • Moving average (actual sales only): Used when demand is steady, and stock levels are consistent.
  • Recent (Mixed): Used with multi-location businesses where demand patterns differ across outlets.
• Seasonal methods, based on year-over-year sales patterns:
  • Seasonal Naïve: Used for repeating seasonal trends like seasonal gear or holiday items.
  • Seasonal + Recent (Mixed): Used with multi-location businesses where some outlets have steady demand, and others have seasonal patterns.

When a report is formatted by Show totals only, some cells will have a grey triangle in the top-right corner. You can click them to see how demand was calculated for a specific SKU and the forecast method used. Multi-location merchants can format the report By outlet to see how each outlet’s method contributes to the total forecast.

Understanding historical sales calculations

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Demand forecasting calculations are calculated using historical sales metrics, including the amount of items sold per day and missed sales due to stockouts:

Items sold per day (excluding stockouts)

The Items sold per day measure shows the average number of products sold per day over a period of time.

• How it’s calculated: Number of units sold in a 6 week period, minus the number of days the item was out of stock.

  For example, 50 units sold in the last 6 weeks (42 days) minus 10 days out of stock = 32 days. 50 units divided by 32 days = 1.5 items sold per day (excluding stock outs).

• Used to calculate: Missed sales, Forecasted demand, Suggested order quantity

You can decrease the cost of goods sold and increase profit margins with the Forecasted demand and Suggested reorder quantity measures. These, when added to the Replenishment report, can help you plan your required ordering of seasonal and non-seasonal items.

You can use these measures to:

• Access accurate suggested order amounts, accounting for out-of-stock periods.
• Minimize manual tasks by accessing these details in the platform, rather than exporting inventory and sales reports to determine replenishment.
• Avoid under-stocking or over-stocking products.

These features are especially helpful if you reorder existing catalog items from suppliers throughout a year or season, rather than purchase new items for an upcoming season or year.

The use of this forecasting tool is at the user’s own risk. Lightspeed cannot guarantee the accuracy and reliability of the forecasts generated. Actual results may differ. The tool is provided for informational purposes only and should not be considered as financial, investment, or legal advice. Users are encouraged to exercise caution before making business decisions based on the tool’s output.

To access the forecasting columns:

1. Navigate to Reporting > Inventory reports

2. Click on the Replenishment tab.

   

3. Select the Date range dropdown.

4. Select the Forecast tab. The range will be set to forecast the next four weeks by default, but can be reconfigured to suit your needs.

   

5. Under Forecast Period, enter a number in the field, and from the dropdown, select whether you want the forecast period to be for that number of Days, Weeks, or Months. The minimum date range is one day, and the maximum is 25 months. The date range you set will display beside the Apply button.
6. If you want to include missed sales, enable the Factor missed sales into the demand forecast checkbox. If you don't include missed sales, the forecast will only include the average data without factoring in the missed sales.
7. Click Apply when you're done entering the forecast details.
8. Click Search.

Forecasted demand

The Forecasted demand column, by default, measures how much stock you would need for the period you selected. For example, if you want to determine how much stock you might need for the next six weeks, choose a forecast period of six weeks in the date picker. The column will display the amount of inventory of each SKU that it estimates you would need for that period, using historical sales data to calculate expected demand.

Click the Forecasted demand header once to sort the column data from highest to lowest value. Click it a second time to sort the data from lowest to highest value.

If you want to include missed sales, enable the Factor missed sales into the demand forecast checkbox. If you don't include missed sales, the forecast will only include the average data without factoring in the missed sales.

Forecasted demand methods in reports

When a report is formatted by Show totals only, some cells will display a grey triangle in the top-right corner. These are expandable cells you can click to view more information in a pop-up within the report for your convenience.

The information displayed in the pop-up varies depending on the SKU’s forecast and the combination of methods you used:

• Seasonal + recent demand method or methods:
  • Mixed methods: Totalled from outlets using a combination of seasonal and recent demand methods.
  • Format the report By outlet to view more details.
• Recent demand methods:
  • Mixed methods: Totalled from outlets using a combination of recent demand methods.
  • Format the report By outlet to view more details.
• Seasonal naïve:
  • Seasonal method: Seasonal trends have influenced sales for this product over the past 2 years. Calculated forecasted demand based on last year's sales.
• Moving average (using Factor missed sales into the demand forecast):
  • Recent demand method: Calculated based on items sold over the past 6 weeks, factoring in missed sales.
• Moving average (not factoring in missed sales):
  • Recent demand method: Calculated based on items sold over the last 6 weeks.

Suggested order quantity

Suggested order quantity takes forecasted demand and subtracts closing and inbound inventory to reliably predict how much stock you should order for the selected date range.

Click the Suggested order quantity header once to sort the column data from highest to lowest value. Click it a second time to sort the data from lowest to highest value.

Understanding demand forecasting calculations

The demand forecasting calculations are based on the items sold per day and the missed items sold. If you want to include missed sales, enable the Factor missed sales into the demand forecast checkbox. If you don't include missed sales, the forecast will only include the average data without factoring in the missed sales.

Items sold per day (excluding stock outs)

The Items sold per day (excluding stock outs) calculation is based on how many units were sold in a 6 week period minus the number of days the item was out of stock. For example, if a product sold 50 units in the last 6 weeks (42 days) but was out of stock for 10 days, the calculation would be 50 units divided by 32 days (42-10) = 1.5 items sold per day (excluding stock outs). This number is then used to calculate missed items sold, forecasted demand, and suggested order quantity.

Missed sales

The Missed sales column is calculated by multiplying Items sold per day (excluding stock outs) by the number of out of stock days from the Last 6 weeks of historical data. For example, if a product sold 1.5 units per day (excluding stock outs) and were out of stock for 10 days, there would be 15 units that could have been sold if it weren't out of stock.

Click the Missed sales header once to sort the column data from highest to lowest value. Click it a second time to sort the data from lowest to highest value.

Forecasted demand

Forecasted demand identifies SKUs with recurring seasonal spikes across two or more years. For identified seasonal SKUs, the forecast is based on the prior year's sales volume for the corresponding period, rather than recent sales trends. Non-seasonal and new SKUs will be forecasted using the existing Last 6 weeks sales data method

The calculations are based on Items sold per day (excluding stock outs) to determine how much stock you will need for a forecasted time period and how much you should order to meet that need. The system looks to historical sales data to make these calculations.

By analyzing in-stock days, the system ensures there is sufficient historical data to generate accurate sales rates for both high and low volume items, leading to more reliable recommendations.

To determine stock needs, the system multiplies items sold per day (excluding stockouts) by the number of days in the forecasted period. For example, if a product sells 1.5 units per day (excluding stockouts) and the forecasted period is 6 weeks (42 days), you would need 63 units to meet projected demand.

Suggested order quantity

The Suggested order quantity column is calculated by taking the Forecasted demand number and subtracting the closing and inbound inventory from it. This ensures that the recommendation takes into account any inventory you currently hold or are expecting to arrive from suppliers or other outlets. For example, if you need 63 units to meet forecasted demand and have 50 units currently in stock, and no inbound inventory, you would need to order 13 units to meet projected demand. If you want to include missed sales, enable the Factor missed sales into the demand forecast checkbox. If you don't include missed sales, the forecast will only include the average data without factoring in the missed sales.

Formatting results for multiple outlets

If you have multiple outlets, you can format these results to show a breakdown by outlet.

1. Click Format results.
2. Select By outlet.

3. Click Apply.

   

This will show the forecasted demand and suggested reorder quantity per outlet for each SKU. This can help determine which outlets need to restock and identify opportunities to transfer stock between outlets.

What's next?