Friday, June 2, 2017

No Data, No History, No Event

Root cause analysis is to find the single cause of why an unplanned event happened, or a link in the process where a different decision would have made a different outcome. This does not necessarily imply that a different outcome would have avoided the unplanned event, but it may have happened at a different time or location and with a different outcome. The expectation of a different outcome is that the unplanned event would not happen.

When analyzing for the root cause the 5-Why process is often applied. Unless there is an unbiased process applied to the answers of the 5-Why process, the desired answers could be established prior to initiating the process and the answers are tracked backtracked from this desired answer. The fact of this is that most 5-Why processes only allows for one option for the root cause. Since the organization is determined to establish a root cause, the root could be established without applying the 5-Why process. This is the “checkbox” syndrome of establishing a root cause by applying the approved root cause analysis. The assumption is that as long as the paperwork looks good it must be the right root cause and operations must be safe, correct? No, this is not correct. An incorrect root cause is more unsafe than a know, but non-effective root cause, since the new and incorrect root cause has not been tested and the outcome is unknown. Assuming that the new root cause is effective is to assume that opinions are facts.

Find the roots that feeds life into the process.
A root cause analysis must include data from prior documented events. If there is no data, no history or no documented event a root cause analysis cannot be based on past experiences. A onetime event is not a trend and applying a root cause analysis to one event defeats the purpose for the safe operation of an airport or aircraft. If there is no data, there is no trend and are no prior events to compare to the analysis to. The key to success is to establish data and trends to determine the root cause and make changes to the processes to reduce or eliminate another unscheduled event or failure. E.g. should a runway edge light fail and there is no data of prior failures, the short term fix is to replace the lightbulb. This might not be what the regulators wants to see, but the fact is there is no data to justify a root cause, and, in addition, there is no data to justify that the burnt our light is not an acceptable risk. Over time an airport may track the burnt out lights (which is data) and over a period of 3-7 years establish a pattern of malfunctioning lights. With this information the airport may establish the root cause and change the lights at a reasonable time prior to the bulbs are expected to burn out. It’s as simple as that.

Another option is to apply best-practices or continuous safety improvement by collecting data from the light manufacturer of how many hours or cycles a runway edge light is expected to last. If this was done, a process to change these lights prior to lights burn out could be reduced from 7 years to 6 months and their safety goal to minimize burnt out lights achieved in a short time. By applying the data supplied by the manufacturer a 5-Why analysis may not even be necessary to establish the root cause.

When only one option of questions for a root cause the question must be answered first.
Let’s assume that an airport took the best-practices route and established a lifetime for runway edge lights. However, the lights still burned out before expected and became a frustration to airport management and an inconvenience for their customers. The next step is to collect data for a root cause analysis. In the process to decide what approach to take to collect data the 5-Why Matrix was applied. The result from this matrix was to mount wildlife cameras at the airport to see if there is any wildlife connection to the burnt-out lights. Over time it was discovered that the coyotes came and chewed the power cable and that the lights therefore burned out about 2 days later. This data could now be applied to a root cause analysis, or the location of the fork in the road, and the process of transferring power could be improved. In other words, but burying the cable underground and cover it up to the light, the long term corrective action had extended the intervals of replacing the lights.

Without data, there is no event, only opinions of events. Applying a straight 5-Why does not necessarily establish the correct root cause, since the answer is locked in after the first question is answered. For the 5-Why process to be more effective the application of a matrix moves the process out-of-the-box for a nonbiased result.