QIQO, or how 5 questions can improve your Flowrence uptime

Quality In Quality Out – the golden rule of catalyst testing. But the quality of the “In” is often disturbed by unforeseen events. In a simple world each upset of a test has only one reason. However, as all of us know that identifying the root cause in high-throughput testing equipment can be complicated as multiple and interlinked factors contribute to one potential failure.

From our experience, the immediate cause of e.g. a failed massflow controller is typically not only a single wrong piece of analytical equipment or non-calibrated device, but more often it is the result of a variety of underlying causes each of which result from additional factors. Over the recent years Avantium has applied different tactics to identify root causes to increase our utilization and drive data quality which we briefly want to present to you.

In house we use two techniques to detect root causes – the 5‑Why method and Ishikawa diagrams.

No matter which approach you are using, the key to success is the clear problem statement with measurable items to improve.

The 5-Why

The 5-Why method uses a straightforward questioning approach in order to find root causes. While repeatedly asking the question “Why?”, it aids you in digging deeper and deeper into a problem by eliminating individual causes and then allowing a fair judgment of the contribution to the overall cause.

See below simple example applying this method for the case of the failing massflow controller.

  1. Why did the massflow controller failed?
  2. Why was the calibration off?
  3. Why was the repeated calibration not in the start up procedure?
  4. Why was the start up procedure not up to date?
  5. Why is the start up procedure not checked on a regular basis?

The Ishikawa Diagram

Ishikawa method works with a similar approach as it allows the user to consolidate all possible problem causes and interdependencies in a clearly structured diagram. The parts of the diagram should be able to explain how the failure happened, while a single item in the diagram does not necessarily lead directly to the failure.

Below you can find an example for a high mass balance in a test, where the analysis of the various factors often reveals connections that no one would have thought of initially.  Causes like the equipment, materials, methods, man, and analytics are obvious but especially things like lack of a single owner might come as a surprise but should be equally taken into account as this is easy to fix and eliminate.



In conclusion, problem-solving in high-throughput experimentation is complex. A structured root cause analysis can help you to improve your uptime and by this ultimately the data quality. Within Avantium we have applied these over the recent years and are able to increase our utilization by over 10%. Involving all stakeholders and a clear communication is the key to success.

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