Overview

The Label Failover Dashboard displays failover activity in the Shipium carrier selection system, showing when the system had to use a second-best carrier option because of issues preventing the first-choice carrier label from being produced (e.g., carrier API outages or similar problems).

The dashboard provides comprehensive visibility into failover volume trends, cost impacts, and carrier/service method patterns. It is organized into five key sections:

Volume Overview shows overall failover counts and rates
Cost Overview displays the financial impact of using fallback carriers
Carrier Analysis breaks down which carriers are involved in failovers
Service Method Analysis details specific carrier service methods experiencing failovers
Carrier Service Method Failover Matrix shows the exact paths from original to fallback carriers.

Filters

Filter Name	Filter Description
Effective Ship Date	A range of time must be selected for the effective ship date of shipments (e.g., when each was expected to be handed off to a carrier). Defaults to the range 6 weeks ago to now.
Carrier	One or more carriers can be selected to show the metrics on the dashboard for only those carriers. Defaults to all carriers.
Carrier Method	One or more carrier methods can be selected to show the metrics on the dashboard for only those carrier methods. Defaults to all carrier methods.
Fulfillment Name	One or more fulfillment names (fulfillment contexts) can be selected to show the metrics on the dashboard for only those fulfillment names. Defaults to all fulfillment names.
Tenant Name	One or more tenant names can be selected to show the metrics on the dashboard for only those tenant names. Defaults to all tenant names.
Timezone	A timezone for reporting must be selected for use when doing time calculations (such as the effective ship date). Defaults to your organization's default timezone for reporting.

Sections and metrics

Volume Overview

This section provides high-level metrics on failover volume and rates, showing both current values and trends over time to help identify patterns and anomalies.

Label Failovers Volume - Current vs Prior Period

For the set of filters that have been selected, this metric shows the total count of label failovers in the trailing week compared to the previous week. The metric is displayed as a KPI with week-over-week comparison. Users should interpret significant increases as potential signs of carrier issues or system problems requiring investigation.

Failover Percentage - Current vs Prior Period

For the set of filters that have been selected, this metric shows the failover rate (percentage of labels that failed over) in the trailing week compared to the previous week. The failover rate is calculated as the sum of failover counts divided by the sum of total shipment counts, expressed as a percentage. This normalized metric helps users understand the relative impact regardless of overall volume changes.

Total Failover Labels

For the set of filters that have been selected, this metric shows the total count of failover labels aggregated at the monthly level. The value is displayed as a KPI showing the current calendar month's failover count.

Failover Percentage of Total Labels

For the set of filters that have been selected, this metric shows the overall failover rate across all selected data, expressed as a percentage. The rate is calculated by dividing the sum of all failover counts by the sum of all shipment counts. This provides a single summary statistic representing the percentage of labels that experienced failover for the entire filtered dataset.

Failover Volume Over Time

For the set of filters that have been selected, this metric displays the total failover count broken down by week in a column chart. The visualization shows trends over time, allowing users to identify spikes, patterns, or seasonal variations in failover activity. Each bar represents one week's total failover count.

Failover Percentage of Total Labels Over Time

For the set of filters that have been selected, this metric shows the failover rate as a percentage over time in a weekly area chart. The rate is calculated for each week by dividing failover counts by total shipment counts. Users can identify trends in failover rates independent of volume changes, helping to spot deteriorating or improving carrier performance.

Cost Overview

This section quantifies the financial impact of label failovers by comparing the cost of originally selected carriers versus the fallback carriers actually used.

Avg. Failover From CPP

For the set of filters that have been selected, this metric shows the average cost per package (CPP) of the originally selected carrier that could not be used. It is calculated by dividing the total cost in USD of all "from" carriers by the total failover count. This represents the expected cost before failover occurred.

Avg. Failover To CPP

For the set of filters that have been selected, this metric shows the average cost per package (CPP) of the fallback carrier that was actually used after failover. It is calculated by dividing the total cost in USD of all "to" carriers by the total failover count. Comparing this to the From CPP reveals whether failovers generally increase or decrease shipping costs.

Total Net Cost Impact

For the set of filters that have been selected, this metric shows the total additional cost (or savings) resulting from failovers across all selected data. It is calculated by summing the net cost impact in USD for all failovers, where net cost impact equals the difference between the "to" carrier cost and the "from" carrier cost. A positive value indicates failovers increased total shipping costs, while a negative value indicates savings.

Total Net Cost = Actual Failover Cost - Original Intended Cost

Avg. Failover Cost Impact per Package Over Time

For the set of filters that have been selected, this metric displays the net cost impact per package by week in an area chart. For each week, it is calculated by dividing the total net cost impact in USD by the total failover count. This time-series view helps users identify when failovers are most costly and whether cost impacts are trending up or down.

Total Failover Cost Over Time

For the set of filters that have been selected, this metric shows the total net cost impact by week in a column chart. Each bar represents the sum of all net cost impacts for that week. Users can see which weeks had the highest financial impact from failovers and correlate these with volume or carrier performance issues.

Carrier Analysis

This section breaks down failover activity by carrier, showing which carriers are the source of failovers (from) and which are used as fallbacks (to).

Failover From Carrier Mix

For the set of filters that have been selected, this metric shows the distribution of failovers by the original carrier (the carrier that failed) as a pie chart. Users can quickly identify which carriers are responsible for the most failovers, indicating potential reliability issues.

Failover To Carrier Mix

For the set of filters that have been selected, this metric shows the distribution of failovers by the fallback carrier (the carrier actually used) as a pie chart. This reveals which carriers the system relies on most heavily when failovers occur.

Failover From Carrier Volume Mix Over Time

For the set of filters that have been selected, this metric displays a stacked column chart showing failover counts by original carrier (from) over time on a weekly basis. Users to see how the mix of failing carriers changes week to week and identify emerging patterns or carrier-specific issues.

Failover To Carrier Volume Mix Over Time

For the set of filters that have been selected, this metric displays a stacked column chart showing failover counts by fallback carrier (to) over time on a weekly basis. Users can observe shifts in which carriers are being used as backups and whether certain carriers are increasingly relied upon.

Service Method Analysis

This section provides detailed breakdowns at the carrier service method level, showing specific shipping methods (e.g., "FedEx Ground", "UPS Next Day Air") involved in failovers.

Top 10 Failover From Carrier Services

For the set of filters that have been selected, this metric shows the top 10 carrier service methods by failover count as a pie chart, filtered to show only the service methods with the highest failover volumes. Each slice represents one service method's share of total failovers. Users can identify which specific shipping methods are experiencing the most failures.

Top 10 Failover To Carrier Services

For the set of filters that have been selected, this metric shows the top 10 carrier service methods used as fallbacks, displayed as a pie chart. Each slice represents a service method's share of being selected as the failover option. This reveals which specific service methods the system most frequently uses when failovers occur.

Failover From Carrier Service Method Mix Over Time

For the set of filters that have been selected, this metric displays a stacked column chart showing the top 8 carrier service methods (by failover count) that are failing over time on a weekly basis. The visualization is limited to the top 8 to maintain readability. Users can track whether specific service methods are consistently problematic or if issues are transient.

Failover To Carrier Service Method Mix Over Time

For the set of filters that have been selected, this metric displays a stacked column chart showing the top 8 carrier service methods used as fallbacks over time on a weekly basis. The chart is limited to the top 8 service methods by volume. Users can observe which specific fallback methods are being relied upon and whether the mix is changing.

Carrier Service Method Failover Matrix

This section provides a detailed matrix view showing the exact failover paths from specific original service methods to specific fallback service methods.

Carrier Service Method Failover Matrix

For the set of filters that have been selected, this metric shows a detailed table of failover counts organized by "from" carrier service method (rows) and "to" carrier service method (columns). Each cell shows the count of times a specific service method failed over to another specific service method, sorted by total failover count descending. Users can identify the most common failover paths (e.g., "FedEx Ground → USPS Priority") and understand the specific routing patterns when carriers fail. The default display mode is table view, but a heatmap visualization is also available for pattern recognition.