The Order Processing dashboard provides a real-time view of order activity and fulfillment performance across all connected sales channels. It is designed to help operations teams monitor order volume, track the lifecycle of orders from receipt to shipment, and identify fulfillment bottlenecks that may impact customer experience.
The dashboard reflects a snapshot of data as of the last daily refresh — counts and metrics shown are not live, but rather represent the state of orders at the most recent data update. By default, the dashboard displays orders with an Order Date within the last 30 days, though this and all other filters can be adjusted to narrow or expand the scope of data shown.
The dashboard is organized into two main areas: Order Status, which summarizes current order counts and trends by status and marketplace, and Order Fulfillment SLA, which measures how quickly orders are being labeled and fulfilled relative to when they were placed.
The filters bar at the top of the dashboard controls which orders are included across all charts and KPI tiles. Filters can be applied individually or in combination. All sections of the dashboard update dynamically when filters are changed.
This table outlines the available configuration filters and how each controls the data scope of the order processing dashboard.
|
Filter |
Description |
|---|---|
|
Order Date |
The date the order was originally placed by the customer. Defaults to the last 30 days. |
|
Label Create Date |
The date a shipping label was created for the order. Useful for focusing on fulfillment activity during a specific period rather than order receipt. |
|
Store |
Filter by a specific connected store or sales channel. |
|
Warehouse Name |
Filter by the warehouse or fulfillment location handling the orders. |
|
Status |
Filter to show only orders in a specific order status (e.g., Awaiting Shipment, Shipped). |
This section provides a snapshot of the current count of orders by their status, as of the last data refresh. These KPI tiles give operations teams an at-a-glance view of pipeline health — how many orders are ready to ship, have shipped, are paused, or are pending payment.
Example value: 17
The total number of orders that have been received and are ready to be fulfilled but do not yet have a shipping label. These orders are in the active fulfillment queue and are the primary workload for the shipping team.
Example value: 816
The total number of orders for which a shipping label has been created and the shipment has been dispatched. This count reflects orders that have exited the fulfillment queue during the filtered time period.
Example value: 34
The total number of orders that have been manually placed on hold. Orders in this status are paused and will not be processed or shipped until the hold is removed. Common reasons include payment issues, address verification, or customer requests.
Example value: 1
The total number of orders that have been received but not yet paid for by the customer. These orders cannot be fulfilled until payment is confirmed and the status advances.
Dashboard Update Frequency
The total number of orders that have been received but not yet paid for by the customer. These orders cannot be fulfilled until payment is confirmed and the status advances.
This stacked bar chart shows the volume of new orders received each day, broken down by their order status. Each bar represents a single day on the Order Date axis, and the colored segments within each bar indicate the status distribution of orders placed on that date.
Cancellations are counted against the original order date, meaning if an order placed on April 16 is later cancelled, it will appear in the April 16 bar in the Cancelled color segment. This allows the chart to reflect total order activity per day regardless of when status changes occurred.
Chart axes:
-
X-axis (Order Date): Each point represents one calendar day within the filtered date range.
-
Y-axis (Orders): The total count of orders stacked within each bar.
Color Legend
|
Color |
Status |
|---|---|
|
|
Awaiting Payment |
|
|
Awaiting Shipment |
|
|
Cancelled |
|
|
On Hold |
|
|
Shipped |
This donut chart provides a percentage breakdown of all orders in the filtered period by their current order status. It gives a proportional view of how orders are distributed across statuses. For example, what share ended up Shipped versus Cancelled.
Example values:
-
Shipped: 81.76%
-
Cancelled: 13.03%
-
On Hold: 3.41%
-
Awaiting Shipment: 1.70%
-
Awaiting Payment: 0.10%
This chart is useful for understanding overall order outcome rates. A high Cancelled percentage may indicate issues with product availability, pricing, or checkout friction, while a persistently high Awaiting Shipment percentage may signal a fulfillment backlog.
This horizontal stacked bar chart displays order volume broken down by the sales channel or marketplace from which each order originated. Each row represents a different marketplace, and each bar is segmented by order status using the same color coding as Section 3.
Marketplaces shown include: Etsy, Shopify, Custom Store, ShipStation, and Walmart.
This chart helps identify which sales channels are driving the most volume and whether fulfillment performance or cancellation rates differ between channels.
Performance Metrics by Channel
The ShipStation marketplace represents orders that were manually created directly inside ShipStation — these are not imported from an external storefront. This is commonly used for phone orders, wholesale orders, or any order entered by a team member on behalf of a customer.
This section measures how quickly orders are being fulfilled — specifically, how much time elapses between when an order is placed and when its shipping label is created. "Order Age" is defined as the number of hours from the order received date to the label create date. This is a key SLA (Service Level Agreement) metric for operations teams monitoring fulfillment speed commitments.
Example value: 0-24 Hours is the dominant bucket
This horizontal bar chart shows the total number of shipments bucketed by how old the order was when its first label was created. The four age buckets are:
|
Age Bucket |
Description |
|---|---|
|
0 - 24 Hours |
Orders labeled within the same day they were received — same-day fulfillment. |
|
24 - 48 Hours |
Orders labeled on the following day — next-day fulfillment. |
|
48–72 Hours |
Orders labeled 2 days after receipt. |
|
72+ Hours |
Orders labeled 3 or more days after receipt — late fulfillment. |
A heavily weighted 0–24 Hours bar (as seen in the example data) indicates that the majority of orders are being fulfilled on the same day they are placed, which is a positive indicator of fulfillment efficiency.
Example value: Stacked area chart with 0-24 Hours dominating daily
This stacked area chart plots the same four age buckets over time, with the X-axis showing First Label Create Date and the Y-axis showing Total Shipments per day. Each color band represents one fulfillment speed tier:
|
Color |
Age Bucket |
|---|---|
|
Dark Blue |
0 - 24 Hours |
|
Light Blue/Purple |
24 - 48 Hours |
|
Cyan / Teal |
48 - 72 hours |
|
Red |
72 + Hours |
This chart allows teams to spot patterns. For example, spikes in the 72+ Hours (red) band on specific dates could indicate a fulfillment delay event, such as a warehouse closure or a surge in order volume that outpaced capacity.
Example value: 19.85
This KPI tile displays the average number of hours between the order-received date and the label-create date across all orders in the filtered period. This is the headline SLA metric: the lower this number, the faster orders are being fulfilled on average.
Example value: Daily line chart ranging from ~10 to ~63 hours, with a 20-hour average reference line
This line chart plots fulfillment time by Order Date, showing two data series:
|
Series |
Description |
|---|---|
|
Fulfillment Time (Hours) (dark line) |
The actual average fulfillment time in hours for orders placed on each date. |
|
Average Fulfillment Time (Hours) (blue horizontal line) |
The overall period average, shown as a flat reference line for easy comparison. |
Days where the dark line rises significantly above the blue reference line indicate dates where orders took longer than average to be labeled. It is useful for post-incident analysis and identifying recurring delay patterns by day of week or specific event dates.