Your Reliability Engineering Professional Development Site
Quality during Design is the place for product designers to use quality thinking throughout the design process to create products others love, for less.
What is design of experiments, or DOE? What do we use it for and what is it all about? We talk about when we might want to use it during the design cycle, and we do this without getting into all of the how-to and mathematical equations.
We’ve collected all sorts of preliminary information about our users that we’re using for a new product design. We may be faced with so much data we’re not sure where to turn first, or what design feature is a priority. There’s a simple, 2-way matrix we can use to help us sort it all out: an urgent/important matrix. We may have used it to prioritize tasks for ourselves or as a management strategy for our team. But, we can also use it to evaluate the tasks our users take when using our product.
We talk more about this matrix in the podcast, and we talk through an example of how to use it to evaluate user tasks.
When we’re looking at results (like measures of a characteristic), we need to take care not to get too hung-up on what the statistics is trying to tell us. Yes, statistical tools are a good way for us to make decisions and the results can act as proof for us. But, there’s a practical, engineering side to results, too. We need to evaluate the statistical significance along with the practical significance.
We review an example and how to document it.
In this episode we review sampling for design tests. We talk through a generic thought process for choosing a statistically relevant sample size and propose some basics that we can all learn about to better understand sampling.
Our goal is for us to be able to better talk through a sampling scenario with our quality and reliability engineering friends, and to better prepare for the information that they’re going to want to know when asked, “How many do we need to test?”
It costs our customers to own our designed products. It costs them initially to purchase it, but it also costs them to use it, maintain it, repair it, and eventually dispose of it. And it costs our company in ways too, like processing, training requirements for customers, and warranty repair costs.
Life cycle costing can help our team choose between design alternatives, like alternate design options, features, manufacturing methods, or suppliers.
Good reliability requirements are going to drive our design decisions relating to the concept, the components, the materials, and other stuff. So, the moment to start defining reliability requirements is early in the design process. But, what makes a well-defined reliability requirement? There are five aspects it should cover: do you know what they are?
We’ll describe what makes a good reliability requirement and examples of common (but not good) requirements.
We have good requirements for the reliability of our design. We also have a preliminary design with ideas of how we’re going to manufacture it. Is our design idea good enough? Are there things we should do to improve its performance and reliability?
For a physical product, there are three general stages in its life cycle. In many cases the failure rates of physical products can be represented by a reliability bathtub curve. This curve is really a plot of a hazard rate function, also known as a failure rate function. We talk about data collection in the early design phase, what types of failures are typical for each phase, and design decisions we may make to improve our reliability, based on the failure rate function we plot for our system.
Flowcharting isn’t just useful for manufacturing processes. We can use them in lots of ways to help us with design of products and to identify quality characteristics. After all, products are used by people, and the way in which they use them is a process.
We also talk about specific flowchart analyses and how they can be used to analyze the user process for design.
Defining tolerances for our quality characteristics is sometimes not an easy task. If we set tight tolerances, it could be costly to make with a lot of rejects and rework in the future. If we’re too sloppy with our tolerances, then it can affect the functionality of our product, leading to unhappy customers, and also possible rework and scrap. There’s economics involved in setting-up tolerances.
Genichi Taguchi related his measure of quality (variation from the target spec) with economics, called the Taguchi Loss Function. It’s used to calculate the cost (in money) of a certain deviation from a target value. It assumes that the farther our quality characteristic is from our target value, the more costly it is to us.
We talk about how we can use the Taguchi Loss Functions as a way for us to set tolerances for our designs. Scroll lower to get an interactive tool and the equations.
When we’ve identified a risk to our design or user process – and that risk can pose a potential harm – how many controls do we need to add?
We discuss prevention vs. detection controls, ALARP, as low as possible, and some scenarios where we could (and maybe couldn’t) justify a risk as acceptable without adding additional controls.
How we describe and approach the issues we need to solve can affect how we react to them. We know we’re supposed to be solving for root causes. But are we, instead, really just addressing symptoms? Are we celebrating the quick fix and then moving on without addressing the root of our issue?
“If you define the problem correctly, you almost have the solution.” – Steve Jobs
Recognize the common mishaps of us solving symptoms instead of really getting to the causes. And, clearly describe our problem in two parts (statement and description of facts) to be confident that we’re moving toward solving the causes.
Objectives are goalposts of what it is we’re trying to accomplish. Though they’re only a part of planning a project, if we don’t have well-defined and clear objectives, it could lead to us not having the stellar project results we want. Our objective should be a SMART ACORN. We review these two, complementing acronyms as ways to check that we’re setting up our project for success.
Standards are everywhere, and lots of organizations are sourcing and creating them. The U.S. Federal Trade Commission held a workshop 2019 that recently made the news, again, in July 2021. It was called “Nixing the Fix: a Workshop on Repair Restrictions”. Here’s what it’s about: manufacturers are not creating products with standard parts, or their design is created so its difficult for consumers to repair.
Are standards part of the answer? How can we proactively use them for design? Why should we?
Before we can link customer needs to design, production, and service of our products, we need to listen to and understand the voice of the customer. We review 6 common VOC collection strategies, and talk about their pros and cons. We also discuss an affinity diagram process, which may help our team organize all of the data to get to customer needs.
We can look at test methods like a product in and of itself. Because tests also have requirements and need to produce usable results, we validate against those requirements. Validating our test method also ensures that they are precise and accurate.
We talk about things to consider when looking at product tests: tying test methods to the product’s use environment, standards, and cumulative effects of testing.
