Purpose
This playbook helps Caddis users unlock insights from production activity using the built-in tag system. When used properly, tags provide a powerful way to connect performance trends with the people, jobs, and parts involved—supporting faster analysis and smarter improvements.
Outcomes
- Identify which jobs or parts are associated with more downtime or higher output
- Analyze operator performance trends over time
- Improve accountability and visibility into production activities
- Pinpoint which activities or contexts might contribute to interruptions or delays
How Tags Work in Caddis
Tags are metadata labels attached to an active run. These tags are grouped into user-defined categories, such as:
- Operator (e.g., JohnDoe, ShiftA)
- Job Number (e.g., J02345)
- Part Number (e.g., PN-0012)
You assign tags at the beginning of a run. Once the run has started, you cannot retroactively edit tags, so upfront tagging is essential for useful insights.
Tags are not for general documentation—they’re best used to categorize production runs for analysis and filtering.
Step-by-Step: Apply Tags to a Run
Tags must be applied when creating a new active run. You cannot apply or change tags once a run has started, so be sure to complete this step at the beginning.
How to Apply Tags
- Navigate to the Equipment tile
- Start a new active run
- When prompted, apply tags in the available categories (Operator, Job Number, Part Number, etc.)
Tag Examples for Production Analysis
In addition to standard categories like Operator, Job Number, and Part Number, here are additional relevant tag categories you can create within Caddis to enhance production insight and drive improvement:
Additional Useful Tag Categories and Example Tags
- Machine Group: (e.g., "Press_Line_1", "Machining_Cell_A"
- Use to compare performance across similar machine types or cells
- Insight: Identify which machine lines are more prone to downtime or require more maintenance
- Material Type: (e.g., "Aluminum_6061", "Steel_A36")
- Track how different materials impact cycle times or failure rates
- Insight: Discover if specific materials contribute to greater wear or inefficiency
- Shift: (e.g., "Shift_1", "Shift_2", "Weekend_Team")
- Separate from operator name for broader comparison
- Insight: Weekend shifts may require different training or support
- Tooling Setup: (e.g., "Tool_Set_A", "DrillHead_9mm")
- Tag runs with specific tooling configurations
- Insight: Detect if certain setups result in better performance or higher scrap
- Customer Order: (e.g., "Cust001_ORD8793")
- Link runs to specific customer jobs
- Insight: Gain visibility on customer-specific issues or demands
How to Use These Tags for Improvement:
- Trend Analysis: Use filters in the dashboard to break down performance by tag. Spot trends in cycle time, downtime, or quality across categories.
- CI Meeting Insights: Use tag-based reports to fuel discussions in weekly standups or continuous improvement reviews
- Training Gaps: Compare operator tags and shift tags to discover if specific groups need support.
- PM Impact Validation: When paired with downtime tags and notes, track if new PMs improve performance for specific machines or tooling setups.
- Material Troubleshooting: Review how different material types affect run data to guide procurement or scheduling decisions.
The goal is to build a tagging strategy that helps you connect the data dots—so you can take action, not just observe patterns.
Use Case
| Use Cases |
Description |
| Tag Category |
Operator and Compare Downtime Across Shifts |
| Part Number |
Identify parts with long cycle times |
| Job Number |
Spot job-specific quality issues |
Filtering and Reporting with Tags
To analyze tag performance in Caddis, the most effective method is to export run data from the Active Runs History:
- Navigate to Active Runs History
- Select a specific time rang
- Export the data as a CSV or JSON file
- Open the file in Excel, Google Sheets, or another analysis tool
From there, you can:
- Use filters or pivot tables to analyze run data by tag category
- Compare operator or shift performance
- Identify high-downtime jobs or part numbers
- Track productivity across material types or tooling setups
Tip: Save your Excel reports as templates so teams can repeat monthly analysis easily.
Using Notes with Tags for Context
You can add notes during or after events like excessive downtime or PM completions. While tags show "what" and "who", notes capture the "why". Examples:
- "Vibration detected during Shift B—possible bearing issue"
- "Operator unfamiliar with new procedure on Part PN-0045"
When used together, tags + notes allow for:
- Root cause investigation
- More targeted training
- PM planning based on patterns
Tips for Effective Tagging
- Standardize naming conventions (e.g., use "Shift_A", not "Shift A" vs. "A Shift")
- Avoid missing tags by making it a required step before run start
- Audit tag usage monthly to ensure consistency
- Train operators on the importance of accurate tag entry
Success Signals
- You can filter run history by job, operator, or part to uncover trends
- Tag-based reports are used in weekly standups or CI meetings
- Tags help uncover who or what is tied to excessive downtime
- PM adjustments or training decisions are made based on tag data
