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The chi-square (Χ2) test provides the basis for the second case of hypothesis tests for variances. In this case, we want to compare observed and expected frequencies, or counts, of outcomes when there is no defined variance. In other words, we are working with attribute data. [Read more…]
The last post in this series dealt with the design team, and the influence that team has on the product reliability process. This post will turn to product development and design managers, again looking at the role of that position in relation to product reliability. The series of posts on reliability participants will continue after today’s post with the roles of quality and reliability engineers and managers.
Hypothesis testing of data may include two populations that have un-equal standard deviations. The t-test for differences considered in a previous post used the assumption of equal variances to pool the variance value. In this test, we want to consider if one population is different in some way than the other and we use the samples from each population directly even if the population have difference variances. [Read more…]
An effective product reliability process requires a strong team, at every level. The team of employees within an organization that participate and impact product reliability is a vast and widespread group of people. They include members of the design team, design managers, quality and reliability engineers and managers, procurement engineers and managers, warranty managers, failure analysis specialists, members of the marketing and sales staff, members of the finance and manufacturing teams, and field service and call center staffs. [Read more…]
Last week I discussed how the aspects of the structure of an organization relate to product reliability. Related to that, this week the discussion will remain on the level of a reliability organization but will look at something a bit more intangible – how decision-making policy and practice affects product reliability. [Read more…]
Both organizational structure and decision-making policies have an impact on improving product reliability. The former is more quantifiable whereas the latter involves more intangible subtleties. First, in this post, I’ll discuss the connection between organizational structure and reliability, and in a follow-up post, I’ll address reliability and decision focus, still within the structure of an organization. [Read more…]
Hypothesis testing of paired data may include two populations that have the equal standard deviations. The t-test for differences considered in a previous post used the standard deviation of the differences. In this test, we want to consider if one population is different in some way than the other and we use the samples from each population directly. [Read more…]
Hypothesis testing previously discussed (link to past posts) generally considered samples from two populations. Maybe the experiments explored design changes, different component vendors, or two groups of customers. Occasionally you may find data that has some relationship between the samples, or where the samples are from the same population. Paired (or matched) data involves samples that are related in some meaningful way. [Read more…]
Reliability professionals today have a challenge. Engineering and operations staff members are taught to think for themselves, to make decisions, to get things done. The entire staff is highly educated, motivated and willing to lead a team or organization to results. In order to be effective as a reliability profession, we have to engage those independent and fast moving individuals. We have to compel others to listen to and understand reliability predictions, risk assessments and models. If they listen and act on the information we provide, they then may fully consider the impact of decisions on reliability performance. [Read more…]
A product reliability program is a process. Like any process, it has inputs and outputs, generally some form of an objective, and feedback. Furthermore, the process may or may not be controlled, or even a conscious part of the organization. Reliability may just happen, good or bad. Results may or may not be known or understood.
In some organizations, the reliability program may be highly structured with required activities at each stage along the product lifecycle. In other organizations, reliability is considered as a set of tests (e.g. environmental or safety compliance). In some organizations, reliability is effectively a part of everyone’s role. [Read more…]
Statistics is the language of variation. Everything varies, and we use variance (σ2) to describe the spread of the data. For any experimental work aimed at making improvements, whether in the design, manufacturing process or field performance, there are two ways to make improvements. Move the center of the distribution, or reduce the spread of the data. [Read more…]
This is also called the “p test”
When comparing proportions that are from a population with a fixed number of independent trials and each trial has a constant probability of one or another outcome (Bernoulli experiments) then we can use a p test. p is the probability of success, and 1-p is the probability of failure. Caution: stay consistent once you define success otherwise, like me, you’ll have a bit of confusion. n is the number of trials. [Read more…]
The target, objective, mission or goal is the statement that provides a design team with focus and direction. A well-stated goal will establish the business connection to the technical decisions, related to product durability expectations. A well-stated goal provides clarity across the organization and permits a common language for discussing design, supply chain, and manufacturing decisions.
Let’s explore the definition of a ‘well-stated reliability goal.’ First, is it not simple MTBF, “as good as or better than…” or ‘a 5-year product’. These are common ‘goals’ found across many industries, yet none permit a clear technical understanding of the durability expectations for the product.
The common definition for reliability is [Read more…]
The design of a product includes the arrangement of all of the product elements. When considering the reliability of a system, the arrangement matters. Many systems are arranged serially. This means that with the failure of any one element, the system will not work. See the article on Series Systems for more details. [Read more…]
It’s always necessary to estimate the value of specific reliability activities. It is needed to justify the investment required to accomplish the task. Prototypes, diagnostics equipment, and environmental chambers are expensive. The difficulty is an inability to know what will be found, before conducting the experiment.
Not doing the test means the certainty of not finding anything. That is often not enough motivation to invest, to learn something about the reliability performance. The following scenario is just one situation, along with a few ideas to help you estimate the value of investments in reliability work. [Read more…]