Best in Class Widgets

This article covers how to create useful widgets in your own environment. Each section covers how the widget works, what data it needs, and how to build it.

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.