Customer orders dataset

Customer orders dataset containing historical customer order-level sales data per product-location, used by Inventory Management to understand demand patterns.

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 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

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
order_id	Unique identifier for each order.	string	Primary key
order_line_id	Unique identifier for each line in the order. Can be the same as order_id if each order contains only one item.	string	Primary key
order_date	Date and time the order was placed.	datetime	Non-null
product_id	Unique identifier for the ordered product.	string	Non-null
location_id	Identifier of the location where the order was fulfilled.	string	Non-null
customer_id	Unique identifier for the ordering customer. Enables presales analysis at a customer level.	string	Nullable
ordered_units	Quantity ordered by the customer.	float	Non-null
shipped_units	Quantity actually shipped to the customer. Used to support fill-rate and service-level analysis.	float	Nullable
ordered_value	Revenue value of the ordered quantity. Should correspond to the value of ordered_units.	float	Non-null
shipped_value	Revenue value of the shipped quantity. Should correspond to the value of shipped_units.	float	Nullable
requested_ship_date	Date by which the order must be shipped to meet the customer's requested delivery date. Enables analysis of historical OTIF service levels.	datetime	Non-null
expected_ship_date	Date the order was planned to be shipped.	datetime	Non-null
actual_ship_date	Date the order was actually shipped.	datetime	Nullable
status	Current status of the order (for example, finished, cancelled, complete). Allows cancelled or incomplete orders to be excluded.	string	Nullable

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.