Staying ahead of demand is crucial for maintaining optimal stock levels and maximizing profitability. The introduction of a new SKU-level setting, which allows users to apply a historical sales curve to forecast demand, is intended to help businesses better manage their inventory, particularly for seasonal products using Kapoq.
Here’s why this new setting is a game-changer and how it can add value to your inventory management strategy.
First, it's important to understand the Historical Sales Curve:
The historical sales curve is a powerful tool that leverages past sales data to predict future demand. By analyzing trends and patterns from previous years, upwards of 3 years of inventory data, this setting provides a more accurate forecast of demand and trends, helping avoid the pitfalls of overstocking or understocking. For seasonal products, where sales can fluctuate dramatically based on time of year, holidays, and other factors, this level of precision is invaluable.
How to Implement the Historical Sales Curve Setting
From the Inventory Module:
Navigate to the Manage inventory section
Select the SKU you wish to edit using the zoom feature next to the item name (+)
Navigate to Daily Demand > Demand Settings > Sales Curves
Adjust the Sales Curve assigned (none are assigned by default) based on your historical data and desired forecast period. Or Adjust via the Bulk Upload Template and upload the completed template back into the system.
As for the details around the sales curves, the Forecast Groups offer different lookback periods and aggregation of data. Last Year for this SKU is the historical unit sales data for the selected SKU for the previous year. Last Year, Last Two Years, and Last Three Years contain data across the account for those periods. A role model SKU is a SKU that had at least one unit of sales for 18 of the past 24 months. Note that the values of 18 and 24 are settings in the inventory module that you are able to change if desired. Correlation is calculated by comparing Last Year for this SKU to all of the forecast groups to determine which most closely fits.
The introduction of the historical sales curve setting offers a more advanced and flexible option to leverage across our demand planning solutions. This setting works alongside your preferred base demand setting (either weighted or flat) to more accurately predict future demand.
By leveraging past sales data to forecast future demand, each SKU's history and sales trends across larger lookbacks (last 3 years) can be incorporated into the restock qty calculations, better predicting upcoming demand in future orders. This will help you achieve greater ordering accuracy, optimizing inventory levels as you lead into and out of seasons, helping you account for the complexities of demand forecasting.