- 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
Required datasets for Rebuy & Replenishment in the UiPath Merchandising solution, including product, location, stock, sales, and hierarchy data.
This page describes the customer data required for Rebuy & Replenishment, part of the UiPath Merchandising solution. For details on how to connect and ingest data into the Peak platform, see Data ingestion.
The Rebuy & Replenishment capability of the UiPath Merchandising solution relies on customer-provided data to generate inventory insights and recommendations. This data represents business information that already exists in the customer's enterprise systems, such as sales, products, inventory positions, and planning parameters.
Data is integrated into the solution during onboarding. The specific data sources, formats, and ingestion methods depend on the customer environment and are configured outside the scope of this guide.
Datasets for Rebuy & Replenishment
Rebuy & Replenishment is designed for retail environments selling to end consumers. Note the following about its data requirements:
- Demand history is provided as aggregated sales rather than order-line customer orders.
- A products parent child mapping dataset captures product hierarchies and substitutions (for example, size or colour variants).
- Manufacturing orders and forecast datasets are not part of these requirements.
Required datasets
To deploy and use Rebuy & Replenishment, the following datasets are required:
| Business data category | Description | Dataset(s) in this guide |
|---|---|---|
| Product data | Defines the items that are planned and managed by the solution. | Products dataset |
| Location data | Defines the locations where inventory is held, replenished, or planned. | Locations dataset |
| Inventory position data | Provides current on-hand inventory quantities by product and location. | Stock dataset |
| Demand history | Provides historical sales data used to understand demand patterns over time. | Sales dataset |
| Replenishment parameters | Defines planning inputs such as lead times, reorder policies, or replenishment constraints. | Order parameters dataset |
| Service-level targets or business objectives | Defines target service levels or objectives used to evaluate trade-offs between availability and inventory cost. | Order parameters dataset (service_level field) |
| Inbound supply data | Represents incoming stock from suppliers or internal transfers used to project future inventory availability. | Purchase orders dataset Transfers dataset |
| Financial context data | Provides cost and price information used to assess the financial impact of inventory decisions. | Pricing dataset |
| Supplier metadata | Defines suppliers associated with replenishment and sourcing activities. | Suppliers dataset |
| Product hierarchy data | Defines parent-child relationships between products for substitution and hierarchy modelling. | Products parent child mapping dataset |
| Additional product attributes | Optional. Tenant-specific product attributes in key-value format. | Product extra dataset |
| Product lifecycle | Optional. Active date ranges for products at locations, including discontinuation dates. | SKU calendar dataset |
Data sources and implementation details
These datasets are typically sourced from existing enterprise systems, such as enterprise resource planning (ERP), planning, or retail data platforms. The specific systems, data structures, and ingestion methods depend on the customer environment and are configured during onboarding.