Documentation Index
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The Issue Summary table aggregates production issues by type for the selected week and line scope, giving operations teams a fast read on which problem categories — tool wear, material shortages, sensor faults, and so on — are driving the most impact in a given week. A total issue count below the table provides an at-a-glance health signal before drilling into individual lots.
What it shows
The columns rendered depend on whether the Group summary by line checkbox is checked.
Ungrouped (checkbox unchecked):
| Column | Description |
|---|
week_label | ISO week label for the selected week (e.g. 2026-W03) |
issue_type_name | Name of the issue category |
issue_count | Number of issues of that type across all selected lines |
| Column | Description |
|---|
week_label | ISO week label for the selected week |
line_name | Name of the production line |
issue_type_name | Name of the issue category |
issue_count | Number of issues of that type on that specific line |
Group by line toggle
The group_by_line value from st.checkbox("Group summary by line", value=True) is passed directly to the service:
issue_summary = service.get_issue_summary(
selection=selection,
group_by_line=group_by_line,
)
group_by_line=True the underlying SQL query groups by (week_label, line_name, issue_type_name) and orders by line_name, issue_type_name. When False, it groups by (week_label, issue_type_name) and orders by issue_type_name alone.
Total issues
After the table, the app sums the issue_count values across all returned rows and writes the total beneath the table:
summary_total = sum(int(row["issue_count"]) for row in issue_summary)
st.write(f"Issue summary rows: {len(issue_summary)}")
st.write(f"Total issues: {summary_total}")
Example output — ungrouped
week_label | issue_type_name | issue_count
2026-W03 | material_shortage | 1
2026-W03 | tool_wear | 1
Example output — grouped by line
week_label | line_name | issue_type_name | issue_count
2026-W03 | Line 1 | material_shortage | 1
2026-W03 | Line 1 | tool_wear | 1
2026-W03 | Line 4 | sensor_fault | 1
Issue types in seed data
The following issue type names appear in the default seed dataset:
| Issue type name | Description |
|---|
tool_wear | Tooling has degraded past acceptable tolerances |
material_shortage | Insufficient raw material to complete the run |
sensor_fault | Sensor malfunction during production |
changeover_delay | Line changeover took longer than the scheduled window |
operator_training | Issue attributed to operator skill gap |
quality_hold | Lot held pending quality inspection outcome |
Use the Group summary by line toggle to compare issue distributions across production lines side by side. Ungrouped mode is useful for a site-wide total; grouped mode reveals whether one line is disproportionately responsible for a particular issue type.
Dataframe display options
The table is rendered with two options that keep the UI clean regardless of row count:
st.dataframe(rows, hide_index=True, use_container_width=True)
hide_index=True suppresses the default integer row index, and use_container_width=True stretches the table to fill the available page width. If the query returns no rows, st.info("No issues found for the selected scope.") is shown in place of the table.