- 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
Products dataset for the Rebuy & Replenishment capability of the Merchandising solution, containing product metadata used across inventory analysis and reporting as a reference dataset.
The Products dataset contains metadata for the products managed by the Rebuy & Replenishment capability of the UiPath Merchandising solution. It defines the items that are planned and provides descriptive and categorical information used across inventory analysis and reporting.
This dataset acts as a reference dataset and is joined to other datasets, such as Sales, Stock, Pricing, and Order parameters, using the product_id.
Purpose
The Rebuy & Replenishment capability uses the Products dataset to:
- Identify the products included in inventory planning
- Enable analysis at both individual product and aggregated levels
- Support reporting and categorization across product hierarchies
- Ensure consistent product identifiers across all datasets
During presales, this dataset is also used to validate product coverage and support category-level analysis.
Required fields
| Field | Description | Type | Use | Notes |
|---|---|---|---|---|
product_id | Unique identifier for the product SKU. | string | Application / Presales | Required. Must match across all related datasets. |
product_name | Name of the product. | string | Application / Presales | Optional. Improves readability and reporting. |
product_description | Description of the product. | string | Application / Presales | Optional. Provides additional context. |
product_category | Category or type of the product. | string | Application | Optional. Enables aggregated analysis at a category level. |
source_id | Identifier for the supplier or internal distribution centre associated with the product. | string | Application | Optional. |
Usage notes
- Every product referenced in other datasets (for example, Sales, Stock, Pricing, Purchase orders, or Transfers) must exist in the Products dataset.
- The
product_idmust be consistent across all datasets. - Product category information is optional but strongly recommended to support higher-level analysis and reporting.
- Units of measure associated with products must be consistent with related datasets (for example, Stock and Sales).
- If supplier relationships vary by location, this relationship should be reflected in the relevant datasets (for example, Order parameters), not encoded solely in the Products dataset.
Why this dataset matters
The Products dataset defines the scope of inventory planning. Without a consistent product reference dataset, the solution cannot reliably join demand, stock, pricing, and replenishment data or generate meaningful inventory insights.