- 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
Supply Chain & Retail Solutions user guide
Customer orders dataset
The customer orders dataset contains historical customer order-level sales data for each product and location. This dataset represents actual customer demand and is used by the inventory management solution to understand demand patterns over time.
If customer order data is not available at order-line level, an aggregated sales dataset can be provided instead.
Purpose
The inventory management solution uses customer order 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 |
|---|---|---|---|---|
order_id | Unique identifier for each order. | string | Application / Presales | N/A |
order_line_id | Unique identifier for each line in the order. | string | Application / Presales | Optional. Can be the same as order_id if each order contains only one item. |
order_date | Date and time the order was placed. | datetime | Application / Presales | N/A |
product_id | Unique identifier for the ordered product. | string | Application / Presales | N/A |
location_id | Identifier of the location where the order was fulfilled. | string | Application / Presales | N/A |
customer_id | Unique identifier for the ordering customer. | string | Presales | Optional. Enables presales analysis at a customer level. |
ordered_units | Quantity ordered by the customer. | float | Application / Presales | N/A |
shipped_units | Quantity actually shipped to the customer. | float | Application / Presales | Used to support fill-rate and service-level analysis. |
ordered_value | Revenue value of the ordered quantity. | float | Application / Presales | Should correspond to the value of ordered_units. |
shipped_value | Revenue value of the shipped quantity. | float | Application / Presales | Should correspond to the value of shipped_units. |
requested_ship_date | Date by which the order must be shipped to meet the customer's requested delivery date. | datetime | Application / Presales | Optional. Enables analysis of historical OTIF service levels. |
expected_ship_date | Date the order was planned to be shipped. | datetime | Application / Presales | N/A |
actual_ship_date | Date the order was actually shipped. | datetime | Application / Presales | N/A |
status | Current status of the order (for example, finished, cancelled, complete). | string | Application / Presales | Optional. Allows cancelled or incomplete orders to be excluded. |
updated_at | Date and time (UTC) when the record was created or last updated. | datetime | Application | Optional. |
Usage notes
- Historical data should ideally cover up to two years, where available, to support accurate demand analysis.
- The dataset should reflect actual customer demand, not forecasts.
- Consistent identifiers (
product_id,location_id) must align with the corresponding Products and Locations datasets. - If orders can be partially fulfilled, both ordered and shipped quantities should be provided to support service-level analysis.