Best in Class Widgets

This guide explains why labor performance, utilization, and effectiveness matter, and how the right widgets work together to tell a clear labor story across your operation.

You’ll learn why real-time visibility is important compared to next-day reporting, and how to build the most useful widgets in your own environment. Each section covers what the widget does, what data it needs, and how to set it up.

Understanding Labor Performance Metrics

Labor metrics tell a labor story. From certain measurements, you can determine where problems originate and get notified early on about changes in the effectiveness of your workflow. This allows you to fix issues as soon as they start.

Metric	Definition	Why it Matters
Network Performance	The comparison between the expected time a task or data transfer should take and the actual time it takes to complete.	When performance is low, it could indicate equipment problems, workflow inefficiencies, or the need for associate coaching.
Network Utilization	The proportion of total available working time that is actually spent performing productive, measurable tasks.	If utilization drops, associates may be waiting for work, spending time on indirect tasks, or be impacted by bottlenecks in the workflow.
Network Effectiveness	A combined measure that reflects how well the network is performing and how efficiently it is being used.	Effectiveness is an early warning indicator of issues with your labor. If performance is high but utilization is low, the drop in effectiveness shows that people work at a good pace but spend too little time on productive tasks. If utilization is high but performance is low, the drop in effectiveness shows that people are engaged but the work itself is slow or inefficient. If both are low, there is a combined issue with both utilization and performance.

Take the following scenario:

1. Warehouse A’s Effectiveness (a metric represented on the dashboard as a card widget) drops from 83% to 57%, which triggers an alert.

2. Both Utilization and Performance are displayed on the dashboards as line graphs that show the past week’s data. You can see a distinct drop that started yesterday in the Performance graph.

3. You drill-down in this graph to see a grid that displays the expected time per activity and the actual time spent per activity. The Case Picking activity has a significant discrepancy between its expected and actual time.

4. If you drill-down further into that activity, you can see that the high numbers can be attributed to two specific employees, who were both hired within the past month.

5. To resolve the issue, you schedule extra training for these employees and review the case picking workflow at Warehouse A. The next week, effectiveness is back up to normal averages and no alerts are triggered.

Setting Up Dashboards for Labor Monitoring

Let’s look at how dashboards can be structured differently for supervisors, managers, and analysts. Supervisor, manager, and analyst dashboards form a connected map that tell your facilities’ labor story.

Supervisors detect issues as they appear.

Managers look for patterns and assess impact across multiple teams or sites.

Analysts uncover root causes and refine metrics or logic.

When dashboards are consistently designed and role appropriate, users can go easily from identification, to diagnosis, to action. You can then turn labor data into measurable performance improvement.

Design Principles to Follow

Use these foundational principles that can apply to all roles using dashboards:

• Start with outcomes, not data. Each dashboard should answer a specific question. For example, Are we staffed correctly today? Or, Why did productivity drop yesterday?

• Use a top down flow. Begin with summary indicators, then allow users to drill into causes and details.

• Be consistent. Use the same metric names, colors, thresholds, and time logic across dashboards to reduce confusion.

• Prioritize visuals over reading. Cards, gauges, and simple charts should communicate status at a glance.

Supervisor Dashboard

Supervisors’ dashboards should monitor real-time performance and let them act quickly to resolve problems. Recommended dashboard structure:

• 4–6 key card or gauge widgets showing current‑state metrics such as performance, utilization, effectiveness, backlog, or on‑time percentage.

• Simple line or bar charts showing today’s data vs. prior shifts or days.

• Cascading widget groups to reveal details.

Manager Dashboard

Managers’ dashboards should track KPIs, compare data across multiple sites or teams, and identify root causes over time. Recommended dashboard structure:

• A summary level that contains high‑level KPI cards organized by company, region, or warehouse.

• A comparison level that contains bar or column charts comparing warehouses, shifts, or processes with drill-down paths to individual teams, tasks, and associates.

Analyst Dashboard

Analysts’ dashboards should explore data quality and trends on a diagnostic level and build reusable components. Dashboard Structure:

• Grouped, aggregated, and sortable grids that expose drivers behind metrics.

• Widgets arranged by analytical theme (cycle time, outbound performance, inventory), across multiple dashboards grouped under sub-menus.

• Drill down paths ending in detailed transaction grids.

Performance Overview Widgets

Create three widgets that show high-level performance, utilization, and effectiveness trends at the warehouse level.

Widget Types

Bar chart

Data Needed

Below is the data needed to create each of the widgets that show you a performance overview of your warehouses.

Performance	Utilization	Effectiveness
Goal Time, Measured Time	Measured Time, Total Time	Measured Time, Goal Time, Total Time

Steps

For each widget:

1. Navigate to Widget Builder and click New.

2. Fill in basic widget details and select a data source that includes the required data from the table above, depending on whether you’re building a widget to display Performance, Utilization or Effectiveness.

3. Filter the data at the Filter stage. Include a date field.

4. Assign fields at the Fields stage, then design your chart using Chart Designer.

Performance	Utilization	Effectiveness
Create a calculated field for Performance. For example: divide(GOAL_HOURS, MEASURED_HOURS). Configure a Bar Chart in Chart designer with Performance as the series and Warehouse as the X-Axis.	Create a calculated field for Utilization. For example: divide(MEASURED_HOURS, TOTAL_TIME). Configure a Bar Chart in Chart designer with Utilization as the series and Warehouse as the X-Axis.	Create a calculated field for Effectiveness. For example: multiply( divide(GOAL_SECONDS, MEASURED_SECONDS), divide(MEASURED_SECONDS,TOTAL_TIME) ). Create a bar chart that uses Effectiveness as the series and Warehouse code as the X axis.

5. Add all three widgets to a dashboard to have an easy-to-read performance overview of your warehouses.

6. Optional: set up an alert to notify you when one of these widgets goes above a pre-determined threshold. You can use alerts to keep supervisors on the floor rather than at a computer constantly monitoring dashboards. See Alerts for more information about how to set them up.

How to Interpret Performance Overview Widget Results

These three widgets (Performance, Utilization, and Effectiveness) are designed to give you a warehouse-level snapshot of how labor is performing across sites. Together, they help answer and evaluate your data 3 ways:

Performance tells you how closely labor is meeting goal time. How to interpret values:

• > 100% means teams are performing better than goal (be cautious about inaccurate goal times or under-reporting).

• = 100% is exactly on goal (ideal).

• < 100% is underperforming vs goal (requires investigation).

Utilization tells you how much of paid time is spent in measured (productive) work vs non-productive time. How to interpret values:

• 80–95% means strong utilization (varies by operation and how indirect time is expected to be used based on your personal operations)

• < 70% suggests too much time is going unmeasured (indirect time, idle time, coding issues, etc.)

• Very high (near 100%) can indicate that indirect activities are not being captured accurately.

Effectiveness tells you the big picture story of your operation by combining Performance and Utilization. How to interpret values:

• High Effectiveness = people are productive and spending most time on measured activities

• Low Effectiveness could mean either: performance is low, utilization is low, or both

Productive vs Indirect Time Widget

Indirect time is a major source of avoidable labor cost. A Productive vs Indirect Time widget can help managers see what portion of the day is spent on non–value-add tasks. This widget dives deeper into the Utilization metric to help visualize where indirect hours are taking up the most space This can be done at the warehouse, shift, activity or user level.

Widget Type

Pie chart, Stacked Bar

Steps

1. Navigate to Widget Builder and click New.

2. Fill in basic widget details and select a data source that includes Measured time and Unmeasured time for warehouse, activity, or user. For this example, we’ll create the widget at the warehouse level.

3. Filter the data at the Filter stage. Include a date field.

4. Assign fields at the Fields stage.

5. Create a Pie chart in Chart Designer. Choose from the following options:

• Set your Warehouse Code as the argument and Unmeasured time as the series to see which warehouses have the highest percentpercentage of indirect hours. If one warehouse has a disproportionate number of Indirect hours, you can then figure out why. In this example, Warehouse 4 has more indirect hours than any other warehouse, and warehouse 3 has the least.

  

• Create a stacked bar chart that shows a percentage of unmeasured time vs measured time per warehouse. We can see that Warehouse 4 had more Indirect hours than any other warehouse and has the highest disparity between productive and indirect activities. However, this chart also tells us that while Warehouse 3 does have the least number of indirect hours, Warehouse 8 has the best ratio.

  

How to Interpret Productive vs Indirect Time Widget Results

This widget helps you visualize how labor is being spent between Productive time (direct work contributing to output) and Indirect time (required but non–value-add or not captured in standards).

Watch for:

• One warehouse with a much larger indirect portion than others. This indicates a local issue worth investigating.

• Large swings day-to-day. This indicates inconsistency in staffing, downtime, or workload distribution.

• Indirect time appears low, but performance/utilization is poor. This indicates misclassification of time or missing labor tracking.

Applying Labor Performance Widget Data to Real Life

If Performance is lowIf Utilization is lowIf Effectiveness is lowIf Indirect time is high

• Check the top labor-intensive activities for issues

• Validate whether goal times reflect the specific day’s reality (extra travel, staffing issues, etc.)

• Confirm employees are trained and issues aren’t tied to a specific, low-performing employee

Labor Performance by Warehouse

Chart Type

Drill-Down Bar Chart

Overview

This widget pulls real-time data across multiple sites and allows drilling down into individual performance. You can easily compare warehouse performance, then drill into shifts and individuals to quickly identify strengths and bottlenecks.

Data Requirements

• Warehouse-level performance data

• Activity and/or Shift level data

• Individual user-level performance data

Setup Procedure

Let’s set up a drill-down widget that starts at the Warehouse performance at the top level, then by Shift, then by Activity, then by User. You can click on individual warehouse data to show the shift data for that warehouse, then click on each shift to show the individual activity data, and finally click on an activity to show data for each user.

This requires 4 separate widgets for each dataset we want represented. The first three should be bar charts, with the final one (user data) being a grid. Each widget in this example requires their own set of parameters, plus the parameters from the widget level above it. Look at the table below to see an example of what parameters are needed.

Widget	Widget Type	Parameters
Warehouse	Bar	MIN_STOP_DATE_LOCAL, WAREHOUSE_CODE
Shift	Bar	MIN_STOP_DATE_LOCAL, WAREHOUSE_CODE, SHIFT
Activity	Bar	MIN_STOP_DATE_LOCAL, WAREHOUSE_CODE, SHIFT, ACTIVITY
User	Grid	MIN_STOP_DATE_LOCAL, WAREHOUSE_CODE, SHIFT, ACTIVITY, USER_NAME

1. Navigate to Widget Builder. Build 4 widgets with the respective parameters listed above. For details on how to create widgets, refer to the Creating Widgets section of the Help Center.

2. Create a Drill-Down path with the Warehouse data at the top level. Refer to Drill Down Paths.

3. Add the widget to a dashboard to track performance.

Metrics Summary by Employee

Chart Type

Cascading Grid Chart

Overview

This is a cascading widget that provides leaders with an employee-level performance summary. This enables leaders to coach employees effectively with clear performance comparisons.

Data Requirements

• Warehouse-level performance data

• Employee performance data

• Team or group data

• Benchmarks for expected performance

Setup Procedure

Let’s set up a cascading widget that has warehouse- level metrics in the root widget, and employee data in its child widgets. Leaders open the dashboard and see:

• Warehouse-level KPIs (root widget)

• Employee performance summaries (child widgets)

When you click a warehouse, all child widgets instantly filter to employees in that warehouse.

Widget	Widget Type	Parameters
Warehouse Performance Overview	Grid	MIN_STOP_DATE_LOCAL, WAREHOUSE_CODE
Shift Summary	Bar	MIN_STOP_DATE_LOCAL, WAREHOUSE_CODE, USER_NAME, USER_GROUP, BASE_SHIFT
Individual User Performance	Bar	MIN_STOP_DATE_LOCAL, WAREHOUSE_CODE, USER_NAME, USER_GROUP, BASE_SHIFT

1. Navigate to Widget Builder. Build the Warehouse-level root widget and the Employee-level child widgets using the parameters outlined in your design. Ensure each child widget includes all parameters of the root widget. For details on how to create widgets, refer to the Creating Widgets section of the Help Center.

2. Create a Cascading Widget Group with Warehouse data as the root widget. Go to Admin Tools> Widgets >Cascading Widget Configuration, and create a new group. Assign the Warehouse Performance widget as the root and add the Employee-level widgets as child widgets. Refer to the Cascading Widget Groups procedure in the Help Center.

3. Add the cascading widget group to a dashboard to track performance.

Indirect Hours by Warehouse

Chart Type

Drill-Down pie chart

Overview

This pie chart is a view into how many hours are being attributed to different indirect tasks. It informs the team where there is waste and barriers in the operation and keeps these issues from lowering performance KPIs. Reminder that an indirect hours are time spent by employees on non-essential, non-productive activities. E.g., meetings, breaks, lunch time.

Data Requirements

• Indirect activity data

• Hours logged per warehouse per task

• Performance benchmarks

Setup Procedure

Let’s build a widget that allows you to see Indirect Hours by warehouse and find which warehouse, specific indirect activities, and even users are contributing the most hours. This configuration creates a drill-down widget that begins with a high-level view of Indirect Hours by Warehouse, then drills into a detailed user-level grid for deeper analysis. You can click on a warehouse segment in the pie chart to reveal the indirect activities, then click again to see individual users contributing indirect hours within that warehouse and review supporting data such as user group and shift.

Widget	Widget Type	Parameters
Indirect Hours by Warehouse	Pie	MIN_STOP_DATE_LOCAL, WAREHOUSE_CODE
Indirect Hours by Activity	Pie	MIN_STOP_DATE_LOCAL, WAREHOUSE_CODE, USER_NAME, USER_GROUP, BASE_SHIFT
Individual User Performance	Grid	MIN_STOP_DATE_LOCAL, WAREHOUSE_CODE, USER_NAME, USER_GROUP, BASE_SHIFT

1. Navigate to Widget Builder. Build 3 separate widgets with the respective parameters listed above. For details on how to create widgets, refer to the Creating Widgets section of the Help Center.

2. Create a Drill-Down path with the Warehouse data at the top level. Refer to Drill Down Paths in the Help Center.

3. Add the widget to a dashboard to track performance.

Indirect Hours by Warehouse

Chart Type

Stacked Bar Chart

Overview

This widget tracks overtime hours per employee. When connected to time and attendance systems, this widget can generate alerts to help limit overtime costs.

Data Requirements

• Time clock or attendance data

• Regular vs. overtime hours

• User-level labor assignments

Setup Procedure

Let’s set up a widget that will show total hours worked per employee. The hours will be grouped into regular and overtime hours. Regular hours are colored green. If an employee works overtime, the extra hours will appear yellow. If an employee works an excessive amount of overtime, the extra hours appear red.

1. Navigate to Widget Builder. Build your widget using employee timesheet data, overtime data, and standard shift data. For details on how to create widgets, refer to the Creating Widgets section of the Help Center.

2. Build a stacked bar chart. To add the different colored sections, add multiple series. Refer to to Setting Up Stacked Bar Charts the Help Center.

3. Add the widget to a dashboard to track performance.

Note

It’s possible to integrate this chart into a drill down from the warehouse or shift level. Refer to Drill Down Paths in the Help Center.

Forecast Demand by Labor Category and Shift

Chart Type

Line Chart

Overview

This widget projects staffing needs by labor category and shift by combining forecasted demand with labor planning inputs. It follows the same process described in Creating Widgets Based on Labor Planning Results, including how to extract values from object arrays and build calculated fields.

Data Requirements

• Forecasted demand data

• Labor categories (e.g., pickers, packers, forklift drivers)

• Planned shift structures

Setup Procedure

Let’s make a widget that predicts the number of hours needed on a daily basis to cover the forecasted workload demand each week. This widget will use previous data metrics to predict future labor demands using the Labor Planning module.

1. Create a new widget in non-guided mode and select LP FORECAST RESULTS as your data source. Apply filters such as date range, warehouse/site, and demand type as needed.

2. Add a Display Fields stage and convert required arrays using the same method described in Converting Object Arrays into Separate Data Columns.

   Convert:

   • QUANTITY_FIELDS for forecasted volume

   • LABOR_OUTPUT_FIELDS for labor category, productivity, and shift

   • DEMAND_OUTPUT_FIELDS only if demand attributes are needed (e.g., warehouse or order type)

   Add another Display Fields stage and extract:

   • Labor Category (from LABOR_OUTPUT_FIELDS)

   • Shift (from LABOR_OUTPUT_FIELDS, if stored there)

   • Forecast Quantity (from QUANTITY_FIELDS)

   • Productivity (from LABOR_OUTPUT_FIELDS or standard field)

3. Create a calculated field that converts forecasted demand into hour requirements per labor category and shift. For example, Required_Hours = Forecast_Volume / Productivity

4. Build the Line Chart:

   • X-axis: Date

   • Y-axis: Required Hours

   • Series: Labor Category

   • Grouping: Shift

   This displays forecasted staffing requirements over time, segmented by labor category and shift.