- Overview
- Platform setup and administration
- Platform setup and administration
- Platform architecture
- Data Bridge onboarding overview
- Connecting a Peak-managed data lake
- Connecting a customer-managed data lake
- Creating an AWS IAM role for Data Bridge
- Connecting a Snowflake data warehouse
- Connecting a Redshift data warehouse (public connectivity)
- Connecting a Redshift data warehouse (private connectivity)
- Reauthorizing a Snowflake OAuth connection
- Using Snowflake with Peak
- SQL Explorer overview
- Roles and permissions
- User management
- Inventory management solution
- Commercial pricing solution
- Merchandising solution
Sales dataset containing aggregated daily sales per product-location, used by the Rebuy & Replenishment capability of the Merchandising solution to understand demand patterns over time.
The sales dataset contains aggregated daily sales for each product at each location. This dataset represents actual customer demand and is used by the Rebuy & Replenishment capability of the Merchandising solution to understand demand patterns over time.
For Rebuy & Replenishment, sales provide the demand history signal in place of order-level customer orders.
Purpose
The Rebuy & Replenishment capability uses sales data to analyze demand volatility at the product and location level. This analysis is used to calculate inventory parameters and support replenishment and stock optimization recommendations.
During presales, this dataset is also used to assess historical inventory performance and understand the factors influencing demand and service levels.
Required fields
| Field | Description | Type | Use | Notes |
|---|---|---|---|---|
product_id | Unique identifier for the product SKU. | string | Application / Presales | N/A |
location_id | Unique identifier for the store or distribution centre. | string | Application / Presales | N/A |
day_date | Date corresponding to the reported sales. | datetime | Application / Presales | N/A |
sales_units | Number of units sold for the given product, location, and date. | float | Application / Presales | N/A |
sales_value | Revenue value of the units sold for the given product, location, and date. | float | Application / Presales | Should correspond to the value of sales_units. |
Usage notes
- A daily historical record of sales is recommended to support accurate demand analysis.
- Historical data should ideally cover up to two years, where available.
- The dataset should reflect actual sales, not forecasts.
- Consistent identifiers (
product_id,location_id) must align with the corresponding Products and Locations datasets.