Stock dataset

Stock dataset containing aggregated historical inventory levels per product-location, used by Rebuy & Replenishment to understand stock behavior and exposure over time.

The stock dataset contains aggregated historical inventory levels for each product at each location. It represents how inventory has been held over time and is used by the Rebuy & Replenishment capability to understand stock behavior and exposure.

This dataset should provide a daily view of inventory levels, where possible, to support accurate historical analysis.

Purpose

The Rebuy & Replenishment capability uses stock data to analyze historical inventory performance and assess the impact of inventory decisions over time.

By comparing historical stock levels with recommended inventory parameters, the solution can help estimate:

• Capital tied up in overstocked inventory
• Risk and impact of understocked goods
• Opportunities to improve inventory efficiency and availability

During presales, the stock dataset is also used to evaluate potential value and improvement opportunities.

Required fields

About the Constraint column: every field listed must be provided in your data. Non-null means its value cannot be null; Nullable means the value may be sent as null (or left blank in your source). Primary key, Unique, and Foreign key describe the field's role in the data model; a Primary key is always non-null.

Field	Description	Type	Constraint
product_id	Unique identifier for the product SKU.	string	Primary key
location_id	Unique identifier for the store or distribution centre.	string	Primary key
day_date	Date corresponding to the reported inventory level.	datetime	Primary key
opening_stock	Number of units of inventory on hand at the start of the given date, for the specified product and location.	integer	Non-null
closing_stock	Number of units of inventory on hand at the end of the given date, for the specified product and location.	integer	Non-null

Usage notes

• A daily historical 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 and Products).
• Product and location identifiers must align with the corresponding Products and Locations datasets.
• If historical data is incomplete, analysis and recommendations may be less accurate.