Using Warranty Prediction to Make a Better Product
Throughout the years, I have had the opportunity to travel throughout the world. Some of those travels brought me to Asia, and in high-velocity manufacturing settings. I have a special interest in product warranty analysis partly from my Six Sigma Black Belt training, but also from a business financial point of view. Warranty prediction is critical to any manufacturer. From a financial perspective, warranty prediction drives warranty provisioning. In other words, how much money do we need to budget for returned product? Apple iPhone warranty prediction is based on some of the tools mentioned below. So is car warranty. Car warranties are not guessed.
Of the great ideas I hear from the many people I get to meet I often break their hearts when I ask them what their returns plan is. They look at me puzzled and say, “Well I don’t think of that.”
Unfortunately, many investors want to understand your risks and one of the key ones is about product returns. This does not necessarily implying that your product will fail, but that your customers will return products for various reasons. This could cause revenue erosion by increasing additional costs such as returns (RMA) processing.
How to predict your product returns count and how to set a reasonable warranty period based on a warranty prediction analysis
As more and more people become independent and start small companies, I also recognize that there are many that do not incorporate reliability engineering into their designs or products. The reason is simple: more and more entrepreneurs are OEM’ing products that are increasingly available and simply rebranding and repacking many of them after getting bulk pricing from sites like alibaba.com or even ebay.
Minitab’s Warranty Prediction Tool Many warranty prediction software tools are available but my favourite is in Minitab. Minitab offers you the triangular matrix warranty analysis which allows you to enter the number of units shipped and the number of units returned for a predetermined future period. This of course, is assuming you have historical data for the future analysis. For example, how do you figure out what the warranty prediction will be if you have never shipped product? Setting a 20% failure in the first year is high as most product designs strive to keep this number at 2% or lower, but by setting this failure rate, you are positioning your product for the consumer market. 20% returns in a consumer market is not unheard of and probably 60-80% of those returns are not defect-related, but I digress.
Let’s suppose you have some historical returns data. You’ve shipped 100 units per month for a total of 1200 units shipped in one year with 73 returns. Minitab allows you to enter the number of returns per month, will offer you a warranty model for future periods. Remember that this will be for future periods.
So whatever failure rates you have for the first twelve months, the future period will highlight the number of failures for the next twelve months. If you intend to offer a 24 month warranty, then you must factor in the year 1 costs and the future year 2 costs.
In this example, it’s estimated that by the end of the 24 month period (future 12 month period) 263 returns would be expected on 2400 units shipped.
Let’s break this down: 1200 units are shipped for each year with a total of 2400 units shipped in two years. 73 units are returned in year 1 and 190 returns are expected in year 2. 263 units returned on 2400 units shipped would suggest 10.9% return rate over two years.
After analyzing the actual returns, consider whether there are design or documentation improvements to better demonstrate how the product should work, thus reducing the number of returns.