Gathering Field Failure Data

A common and rather poor technique to gather field data is to count the number of returns by week or month. This can provide a graph showing the number of returns over time. However, it hides useful information you need to understand your field failures.

Let’s take a look at a way to gather the same field failure data and retain the critical information necessary for time-to-failure analysis.

Necessary and Available Data

Most organizations keep track of shipments, for example, counting the number of products shipped to customers on a monthly basis. Let’s say we’re manufacturing bicycles and roughly ship 5,000 bikes per month. We need this information to compare to the number of returns in order to estimate failure rates and more importantly, the number of bikes that have not failed.

From a short conversation with the person tracking shipments we find over the past 6 months the following shipments:

Month Shipments

Jan       3,519

Feb       6,292

Mar      7,132

Apr       5,633

May      4,222

Jun       4,476

Each bicycle has a serial number that includes the month of production. Thus we know which month the unit shipped. This is the next piece of data we need concerning a returned unit: which month was it manufactured.

When a unit is returned we count it as a return for the specific month of production. This allows us to know how many bikes from that month of production have not failed.

We also then know how long this particular retuned unit was with a customer. Of course, we’re making some assumptions about transpiration, time on store shelves, and other variables, yet often we only really know the shipment month and the returned month and thus the time (roughly) with a customer.

Organizing the Returns Data

One way to organize the data as they become available is in a “Nevada chart.” The name comes from the resulting triangle shape of the table, which is reminiscent of the lower part of the state of Nevada (or so they say).

Following the example started above, let’s count the number of failures per month and log the count by month of shipment:

Month    Ship    Jan   Feb   Mar    Apr    May   Jun   July

Jan          3,519     3       6        3         7      10        3

Feb          6,292              4       8         20      35       24

Mar         7,132                        8         14      25       31

Apr          5,633                                    4       13       6

May         4,222                                               6        8

Jun          4,476                                                         6

Thus in January we shipped 3,519 units but 3 from that group were returned in January, another 6 in February, and so on. We also received 4 returns in February from the batch of February shipments. Notice the shape of the table of return counts—looks like Nevada right?

Preparation for Analysis

This chart is only used for gathering the data. It is difficult to draw any conclusion based on this table of data. What we do need to know is how long the units returned were in the field and how many remain right-censored (i.e., haven’t failed yet).

The time to failure is the difference between the return month and the month of shipment. Thus for the returns in February, the units shipped in January we can say were in the field for 2 months. For those shipped in February, they were in the field for 1 month.

Of course, this simple analysis presumes that all shipments are at the start of the month and all returns are at the end of the month. This helps to avoid having a duration in the field of zero, which can cause trouble with some analysis later.

Having day or week of shipment and corresponding day or week of return would be an improvement, yet it seems that monthly shipments and returns is fairly common. Monthly data are still useful.

The other element we need to know is how many units remain and are thus right-censored. We can calculate this for each row by subtracting the total number of returns from the number of units shipped. The time of censoring is the difference between the current month and the month of shipment.

Repeating this calculation for each listed month and shipment row where a return occurred then completes the analysis. Thus, for the January shipment row, we have 32 returns and thus 3,487 units that have not failed. These calculations are then repeated for each row.

Now you have the number of returns and the time to failure for those returns plus the number and duration of units censored. That is the necessary information needed for a time-to-failure analysis.

Bio:

Fred Schenkelberg is an experienced reliability engineering and management consultant with his firm FMS Reliability. His passion is working with teams to create cost-effective reliability programs that solve problems, create durable and reliable products, increase customer satisfaction, and reduce warranty costs. If you enjoyed this articles consider subscribing to the ongoing series at Accendo Reliability.