Importance of Field Data

Customers experience product failures. Understanding these failures that occur in the hands of customers is an essential undertaking. We need this information to identify increasing failure rates, component batch or assembly errors, or design mistakes.

In designing for reliability, various assumptions are made about customer expectations and use stresses. The field performance either validates the reliability design work or illuminates the errors. In selecting suppliers and building a stable assembly processes the goal is to identify the highest risk elements for reliability (and quality) but plenty of assumptions are involved. The field performance again validates the reliability design work or illuminates the errors.

The field reliability performance impacts the business directly, through customer satisfaction, brand loyalty, and warranty expenses. The business objectives hinge on reliability performance. Customers may expect product improvements even if they did not experience the failures personally. The Internet provides many venues for customers to compare notes and discuss failures. Customers increasingly do not simply want a replacement: They may demand an improved design instead.

The Nature of Field Data

Field data are never perfect. However, they are better than anything we can create in the lab though. Field data are actual data. They are a record of how the product performs for those using the product. All the expectations, stresses, and component variation are present. No sample sizes or confidence bounds are necessary.

Part of the issue here is that we the data from customers are inherently noisy. We do not know exactly when the first turn-on or use occurs after purchase, nor do we know exactly and under what circumstances failure occurs. Often, we do not know the exact failure, only that a customer reports a failure. Not all customers even provide a complaint or report. Yet these are the best data we have available.

Finding the Field Data

Your organization most likely gathers information about customer-experienced field failures. Call centers, return authorizations, replacements, repairs, and warranty claims all provide information on field failures. Ideally, you will have date installed, date of failure, use conditions, symptoms or failure mode, plus a root causes analysis of each failure to the specific mechanism. More likely, you have the date the customer reported the failure, which may not be the same as when the product actually failed.

When first looking for useful field data, you should consider data already gathered for other purposes. Databases and records are typically set up to help serve the customer and track costs, not to reveal reliability performance. As you and the organization realize the value of the field data analysis, you’ll be able to establish better data-capture processes.

Another element of data you require for an analysis is the number of units placed in service, both those that have failed and those that have not failed. Shipment data are often a good source. Records of initialization or turn-on would be even better to have. These records may be complicated by delays in shipping and warehousing or with the use of units as spares. Likewise, not all units installed and operated continue to operate indefinitely. Some additional work and investigation may be needed to determine the nominal and range of operating durations. Most often, we simply assume that every unit shipped is still operating unless reported as failed.

Gathering the Field Data

A common mistake is to simply count the number of returns each month and report the count on a month-by-month bar chart. This is easy to do but generally noninformative. Trends are as likely caused by variation in shipments as by any other reason.

Beyond how many failures occur you need to gather the time-to-failure information as a minimum. Knowing when the product was shipped, installed, failed, and reported would be great, yet knowing the month of shipment and month of failure is often the best we can do. The time-to-failure data allow a Weibull analysis or similar analysis to be used to estimate the overall failure rate trend versus the age of the product. Time zero is when each unit was installed (or shipped). Do they show signs of wear out (increasing failure rate) after 3 months?

The conditions of use and reported failure mode provide a way to conduct a Pareto analysis of the issues. Adding the cost to the customer or the manufacturer may provide a way to refine the priorities for improvement work. Plot the various failure modes or failure mechanisms using a Weibull analysis as each one is likely to be on a different failure rate trend. Some will indicate early-life failures and others may show wear-out behavior. At different points in the age of the product the Pareto chart of issues is likely to be different.

The last, often most useful element of the field data is to find out what happened. A root cause analysis should be conducted on as many units as possible. Determine the sequence of events or stresses that leads to the product failure. If it’s not possible to redesign or improve the current product, you can do so during the next design cycle.

We’ll explore how to analyze the data in another article, yet gathering the data is often the difficult part of the exercise. Do you have good data? Where do you find the best source of field data?

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.