Sales Demand Forecasting in Cin7 Core is an estimate of the quantity of goods and services which will be sold over the forecast period. The estimation is done based on the historical data about actual fulfilled sales, and there are multiple forecast models for the user to select. This report can also be used to view quantities of planned sales (quotes and orders that have not yet been fulfilled) for the forecast period.
Generated forecasts can then be exported and fed into the Material Requirements Planning (MRP) feature, to make sure forecast and actual sales demand is taken into account when planning future purchasing, transfers, and production.
Prerequisites:
- This feature will only work if you have a good amount of historical sales data (i.e. 6-12 months+)
- See Managing Cin7 Core Reports
Table of Contents:
Access the Sales Demand Forecast Report
Access the Sales Demand Forecast Report by going to Reports → Sales Reports → Sales Demand Forecast Report.
Report templates
Sales Demand Forecast settings can be saved under different templates to be used again easily. Select templates from the dropdown menu. Clicking Delete Template will remove the current template from the system.
Add a new template by clicking + to open a popup window. Enter a name, Save and Close to save the new template.
Forecast models
The first step is to select a Forecast Method for the sales demand forecast. These will calculate the demand in different ways depending on your previous sales. Different forecast methods allow different input parameters, which will be explained below. Available models are:
- Last Year to This Year
- Naive
- Moving Average
- Weighted Moving Average
- Planned Sales
NOTE: Only sales that have been fulfilled count towards sales demand forecast calculations.
NOTE: Fractions of units will be displayed in the results even if the item can only be sold as a whole unit.
Last Year To This Year
When Forecast Method: Last Year to This Year is selected, sales for this year will be assumed to be the same as last year (January 2021 sales = January 2022 sales, Week 43 2021 sales = Week 43 2022 sales).
Growth % allows you to apply positive or negative growth to this forecast. Growth = 0 means that there will be no positive or negative change from last years sales.
NOTE: Growth is applied to sales per month averaged over the whole year, not to each individual month.
Period indicates the period to forecast. Available options are This Month, Next Month, This Quarter, Next Quarter, Next Three Months, This Year, and Custom date range.
Interval is the forecast interval. Available options are Monthly or Weekly.
Forecast can be applied to every location or to specific locations only, and all products or only selected products.
In this example with 0 growth, we assume that week by week sales for this year will be the same as last year. Sales in week 16 (10 sales) will be the same as in week 68. (NOTE: This is an example graph with example numbers, it is not part of the Cin7 Core report).
Next, we want to calculate the forecast with 10% growth. The average sales per week over the year in this example is 14.5. Calculating Last Year to This Year forecast method with 10% growth would increase forecast sales by 1.45 per week compared to last year's sales (for weekly intervals).
Naive
When Forecast Method: Naive is selected, forecast sales for this month (or next month) are assumed to be the same as the previous month.
Growth % allows you to apply positive or negative growth to this forecast. Growth = 0 means that there will be no positive or negative change from last month's sales.
Period indicates the period to forecast. Available options are This Month, and Next Month.
Interval is the forecast interval. Available options are Monthly or Weekly.
Forecast can be applied to every location or to specific locations only, and all products or only selected products.
In this example with 0 growth, we assume that week by week sales for this month will be the same as the last month, no matter what happened during the rest of the year.
Next, we want to calculate the forecast with 10% growth. The average sales per week over the last month in this example is 21.8. Calculating Naive forecast method with 10% growth would increase forecast sales by 2.18 per week compared to last month's sales (for weekly intervals).
Moving Average
When Forecast Method: Moving Average is selected, sales for the historical data period will be used to calculate sales for the period to forecast. This is useful to smooth out spikes and troughs in the sale and give values closer to the average over the historical data period.
Growth % allows you to apply positive or negative growth to this forecast. Growth = 0 means that there will be no positive or negative change from the sales in the historical data period. Growth % is applied to sales during the historical data period.
Include Seasonal Index is useful if your business experiences seasonal fluctuations in sales. If sales in summer are higher than your yearly average of sales, and your forecast period takes place in summer, checking this box will adjust the forecast to take into account seasonal fluctuations. Leaving this box unchecked will simply use the historical data period average for the forecast, regardless of seasonal changes.
- There are 4 seasons applied to calculate the seasonal index:
- Winter: December, January, February
- Spring: March, April, May
- Summer: June, July, August
- Autumn: September, October, November
Seasonal Index is calculated based on all years where a full year of data is available.
- If there is only one full year of data, the seasonal index is calculated using sales data from only one year.
- If there are several full years of data data, the seasonal index is calculated based on sales data from all available full years.
- If there is less than one full year of data, the seasonal index cannot be applied.
Period indicates the period to forecast. Available options are This Month, Next Month, This Quarter, Next Quarter, Next Three Months, This Year, and Custom date range.
Interval is the forecast interval. Available options are Monthly or Weekly.
Historical Data Period is the number of historical periods that will be used to calculate the moving average. Historical Data Period will be the same as Interval: Month or Week.
- If Historical Data Period = 3 and Interval = Month, Historical Data Period will be equal to 3 months.
- If Historical Data Period = 6 and Interval = Weeks, Historical Data Period will be equal to 6 weeks.
Forecast can be applied to every location or to specific locations only, and all products or only selected products.
In this example with 0 growth, the average sales over the historical data period (6 weeks) is used to forecast the sales for the following week. The average sales for the 5 weeks of the historical data period + 1st forecast week is used for the 2nd forecast week, average sales for 4 weeks of the historical data period + 1st forecast week + 2nd forecast week is used for the 3rd forecast week, and so on.
If growth is applied, e.g. growth of 20%, this is calculated from the average sales of the historical sales period and applied to the forecast period.
Weighted Average
When Forecast Method: Weighted Average is selected, average sales for the historical data period will be used to calculate sales for the period to forecast. This is useful to smooth out spikes and troughs in the sale and give values closer to the average over the historical data period. However, the periods closest to the current date will given a higher weight in the calculation than periods further in the past, which makes it better at following sales trends. The other settings are the same as far Forecast Method: Moving Average.
Growth % allows you to apply positive or negative growth to this forecast. Growth = 0 means that there will be no positive or negative change from the sales in the historical data period. Growth % is applied to sales during the historical data period.
Include Seasonal Index is useful if your business experiences seasonal fluctuations in sales. If sales in summer are higher than your yearly average of sales, and your forecast period takes place in summer, checking this box will adjust the forecast to take into account seasonal fluctuations. Leaving this box unchecked will simply use the historical data period average for the forecast, regardless of seasonal changes.
- There are 4 seasons applied to calculate the seasonal index:
- Winter: December, January, February
- Spring: March, April, May
- Summer: June, July, August
- Autumn: September, October, November
Seasonal Index is calculated based on all years where a full year of data is available.
- If there is only one full year of data, the seasonal index is calculated using sales data from only one year.
- If there are several full years of data data, the seasonal index is calculated based on sales data from all available full years.
- If there is less than one full year of data, the seasonal index cannot be applied.
Period indicates the period to forecast. Available options are This Month, Next Month, This Quarter, Next Quarter, Next Three Months, This Year, and Custom date range.
Interval is the forecast interval. Available options are Monthly or Weekly.
Historical Data Period is the number of historical periods that will be used to calculate the moving average. Historical Data Period will be the same as Interval: Month or Week.
- If Historical Data Period = 3 and Interval = Month, Historical Data Period will be equal to 3 months.
- If Historical Data Period = 6 and Interval = Weeks, Historical Data Period will be equal to 6 weeks.
Forecast can be applied to every location or to specific locations only, and all products or only selected products.
For moving average, the weighting factor is the same for every period. Weighting moving average is calculated according to the following formula.
In an example where Historical Data Period = 2 months, and forecast period = 1 month, this will affect the forecast sales like so:
| Month | Sales | Weighting Factor (Moving Average) | Weighting Factor (Weighted Average) |
| June | 23 | 1/2 | 1/3 |
| July | 15 | 1/2 | 2/3 |
When Moving Average is used to calculate forecast sales for August, we get: (23x0.5 + 15x0.5) = 19
When Weighted Average is used to calculate forecast sales for August, we get (23x0.333 + 15x0.666) = 17.65
In an example where Historical Data Period = 5 months, and forecast period = 1 month, this will affect the forecast sales like so:
| Month | Sales | Weighting Factor (Moving Average) | Weighting Factor (Weighted Average) |
| March | 12 | 1/5 | 1/15 |
| April | 18 | 1/5 | 2/15 |
| May | 25 | 1/5 | 3/15 |
| June | 23 | 1/5 | 4/15 |
| July | 15 | 1/5 | 5/15 |
When Moving Average is used to calculate forecast sales for August, we get: (12x0.2 + 18x0.2 + 25x0.2 + 23x0.2 + 15x0.2) = 18.60
When Weighted Average is used to calculate forecast sales for August, we get (12x0.066 + 18x0.133 + 25x0.2 + 23x0.27 + 15x0.333) = 19.33
Planned Sales
The final option, Planned Sales, generates a forecast for all sales that have not yet been fulfilled, where Required By Date is after the current date and during the forecast period.
Growth is not applicable for this forecast method.
Period indicates the period to forecast. Available options are This Month, Next Month, This Quarter, Next Quarter, Next Three Months, This Year, and Custom date range.
Interval is the forecast interval. Available options are Monthly or Weekly.
Forecast can be applied to every location or to specific locations only, and all products or only selected products.
Show only selected products
Sales Demand Forecast can be applied to all products, groups of products, or specific products only. Use All to forecast demand for all products in your inventory, or Selected to filter products further.
Groups of products can be filtered by selecting one or more product families, product tags, product categories or product brands. In the example below, this will filter products to show products in the Apparel category, Adidas Shoe product family and MMA gloves product family.
One or more specific products can be filtered by selecting them from the dropdown Select Product menu. This filter cannot be applied at the same time as the group filter.
Forecast Errors
The Errors tab of the report will show products not shown in the report for various reasons - this will almost always be due to a lack of sales data for that product. Products where no sales have been made during the historical sales period will not be shown in the report and will be shown in the Errors tab.
Customise Report View
Available fields are displayed along the top of the report screen, where they can be dragged and dropped to change the layout of the report. Column order can be changed again by dragging and dropping – the report will automatically adjust the column data.
Sales Forecasts and MRP
Material Requirements Planning (MRP) and supply chain management features allows our users to incorporate their supply chain strategies into Cin7 Core management. See Material Requirements Planning (MRP) for more information on this feature.
Sales forecasts generated using the Sales Demand Forecast Report can be exported In CSV format using Export.
This forecast CSV can then be be uploaded during an MRP Run (Inventory → MRP) to be included as demand when planning.
