The Continuous Improvement Workflow¶
Purpose¶
Harro Digital is not just a set of dashboards — it is a tool for continuous improvement. Its job is to turn machine data into targeted action, so you spend your work hours where they have the most impact.
This page shows the typical end-to-end way to use the app: a repeatable loop that takes you from "where is my biggest loss?" to "the fix is standardized and shared." Each step links to the app features you use for it.
The loop at a glance¶
| Step | The question to answer | Where in the app |
|---|---|---|
| 1. Benchmark & prioritize | Where is the biggest loss? | Dashboard, OEE |
| 2. Diagnose the main loss driver | Which OEE pillar is weak? | Availability · Performance · Quality |
| 3. Root cause analysis (RCA) | Why exactly is the loss happening? | Scrap Analysis, Error Pareto, Analyze Message |
| 4. Check existing measures | Was this already solved somewhere? | Guidance search |
| 5. Define & implement measures | What fix delivers the highest impact? | Create guidance, AI Predictions |
| 6. Validate | Did it actually work? | KPIs Over Time |
| 7. Standardize & scale | How do we keep and share the learning? | Guidance backup |
Step by step¶
1. Benchmark & prioritize¶
Where is the biggest loss? Don't optimize at random — start where it pays off most.
- Compare the machines of a line on the Dashboard and focus on the main bottleneck.
- Use the OEE widget to see which machine or line loses the most.
2. Diagnose the main loss driver¶
Which OEE pillar is weak? OEE combines three pillars — find the one that is dragging the number down.
- Check Availability, Performance and Quality for the selected machine and period.
- The weakest pillar tells you where to dig deeper in the next step.
3. Root cause analysis (RCA)¶
Why exactly is the loss happening? Move from "what" to "why".
- Quality losses → Scrap Analysis and Scrap Over Time to find the top scrap reasons and when they spike.
- Availability/performance losses → Error Pareto / Warning Pareto for the most impactful faults, and Error History for the sequence of events.
- Drill into a specific message with Analyze Message to see its occurrence history and durations.
- Look for patterns: top sensors, batches, downtime, and trends over time.
4. Check existing measures¶
Was this already solved somewhere? Reuse before you reinvent.
- Search the knowledge base in Guidance for the machine, category, or symptom.
- Reuse validated solutions instead of starting from scratch.
5. Define & implement measures¶
What fix delivers the highest impact? Turn the root cause into a concrete action.
- Derive targeted actions from your RCA (parameter tweaks, upgrades, training, spare parts).
- Prioritize by impact vs. effort — do the high-impact, low-effort fixes first.
- Use AI Predictions and its Help action to find matching guidance for likely upcoming faults.
- Capture the procedure as a guidance entry so the fix is repeatable: Create and Edit Instructions.
6. Validate¶
Did it actually work? Confirm the improvement with data, not gut feeling.
- Re-check the same KPIs for a comparable period using KPIs Over Time and the over-time widgets.
- Ask: is the trend stable, and is the root cause really eliminated?
7. Standardize & scale¶
Turn the learning into knowledge. A fix that only lives in one person's head does not scale.
- Keep the troubleshooting steps as guidance and link cause ↔ fix.
- Share the solution across lines and machines, and back it up: Backup (Import and Export).
- Then start the loop again on the next biggest loss.
From a manual loop to an automated one¶
As your process matures, the same loop can run more and more automatically. Instead of stepping through it by hand, production data is collected and processed continuously, deviations are detected as they happen, root causes and corrective actions are surfaced, actions are deployed, and the impact feeds back into your KPIs — a closed optimization loop.
You do not have to automate everything at once: the manual loop above and the automated one use the same steps, so anything you standardize today makes the next level of automation easier.
Where to go next¶
- Dashboard — your operational overview and widgets.
- Guidance — capture and reuse solutions.
- AI Predictions — early warning for likely upcoming faults.