industry-department-solutions
latest
false
Supply Chain & Retail Solutions user guide
- 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
Stock dataset for the Markdown & Promotions module, containing daily inventory levels per product and location.
The Stock dataset contains daily inventory levels for each product at each location. It provides the availability context used to ensure markdown and promotion actions are operationally feasible.
Purpose
The Markdown & Promotions module uses the Stock dataset to:
- Provide current and historical inventory levels by product and location
- Assess availability and exposure for ageing or excess stock
- Support clearance and markdown decisions
Required fields
| Field | Description | Type | Use | Notes |
|---|---|---|---|---|
location_id | Unique identifier for the store or location. | string | Application / Presales | Required. Primary key. Foreign key to location. |
product_id | Unique identifier for the product SKU. | string | Application / Presales | Required. Primary key. Foreign key to product. |
stock_date_at | Date of the stock snapshot. | date | Application / Presales | Required. Primary key. |
created_at | Timestamp when the record was created. | timestamp | Application | Optional. |
stock_id | Source-system identifier for the stock record. | string | Application / Presales | Optional. |
stock_units | Number of units in stock on the given date. | integer | Application / Presales | Required. |
updated_at | Timestamp when the record was last updated. | timestamp | Application | Optional. |
Usage notes
- A daily record of stock levels is recommended to support accurate analysis.
- Stock quantities should use the same unit of measure as related datasets (for example, Sales).
- Product and location identifiers must align with the Products and Locations datasets.
Why this dataset matters
Stock levels determine which products carry clearance or markdown risk. Without accurate stock data, recommendations may target the wrong products.