Weibull analysis is a specific type of statistical analysis that attempts to predict how a product will behave over its lifetime, what kinds of parameter changes one can expect from it, and what direction its development will likely take at different stages. When used with a sufficient amount of information to drive the analytical process, this can be an extremely powerful tool with some strong implications for most operations that can benefit from statistical analysis. At the same time, it’s not a universal tool that can work in every situation, and understanding its finer nuances is important for any leader.
Life data is a term commonly used when discussing Weibull analysis, and it refers to the way a product changes over its life cycle in basic terms. This includes the change of any variable related to the product, especially variables indicating its durability, failure time, and other parameters that might be relevant to design and manufacturing. The more accurately this data is gathered, the more beneficial it’s going to be in changing the design of the product in the right direction.
This also means that Weibull analysis can take some time to “kick in” properly, and you may need to spend some more time gathering data than you initially anticipated before you can do anything significant with it.
Once you’ve been able to identify the critical life data points and have gathered enough to analyze, you should draw up some estimations about how the product is going to behave over its lifetime. This is the main purpose of Weibull analysis, and how exactly you’re going to approach this task depends mostly on the type of product you’re dealing with, and subsequently, the kinds of parameters that you’re interested in studying.
Keep in mind that you may notice some strange patterns in the initial estimates that you draw up, but this should not discourage you from utilizing Weibull analysis further. Just like any other form of statistical analysis, it can take a while for results to stabilize and start making more sense, and this doesn’t mean that the system isn’t working properly in the meantime. As long as you know what kinds of results you’re expecting in the first place, you should be able to navigate the results of your analysis just fine, and figure out if they’re in line with what you expected to see.
There are some points you should specifically consider in your analysis, especially when it comes to product that are critical in your portfolio. For example, the expected lifetime of a product is something that will most likely be at the top of your list of points for consideration, and you could even split it up further into several categories according to the criteria that causes your product to fail in the end. Companies operating in markets with a high failure rate should be able to especially benefit from this approach in their analysis, as this can allow them to improve their designs over time and ensure that their products don’t fail as much.
In the end though, it’s up to you to determine what points really matter in your analysis, as this will be highly specific to the product you’re developing in your own organization. Just keep in mind that the suggestions you’ll get from the results of your Weibull analysis should not be disregarded, even if they don’t seem very intuitive at first. The whole point of statistical analysis is that it can often show us connections that we might miss in a manual inspection, and it’s important to pay attention to what your results might be trying to tell you.