Your Reliability Engineering Professional Development Site
How do we go about mistake-proofing our product design? As we’re looking at our user process, we can use a quality method that’s well-used in manufacturing production: poka-yoke (mistake-proof).
Download the guidelines and checklist, and then follow-along with the podcast.
We want to engage a reliability engineer in an analysis for our product design. They can help us produce some great information from which we can make decisions. You might be feeling uncomfortable about our team making a design decision based on those results. You don’t quite understand how the reliability engineer came up with the answer. You want to know where that information comes from so you can gauge the level of project risk of our decision.
We peel-back the curtain on reliability engineering methods. We explore reliability engineering’s roots and development, from the 1950’s through today, to better understand the results of an analysis. Having a general understanding of reliability methods can help us get comfortable with using the results.
There are many stories of design successes attributed to the right level of understanding of the customer. Product designers make decisions, daily, about how a product is going to look and perform. So, we need to really understand the customer. And, to really get the customer, engineers need to spend time with them.
Sometimes, the business doesn’t want us to interact with the customer or doesn’t think it would be valuable. Objections include that we’re not prepared for the user’s environment, that we’re too blunt or honest, or that we just overgeneralize what we learn, anyway. Or, there’s a reluctance because of costs. Besides seeing these objections first hand, someone also listed them out in a published book! This shows that this is common across industries.
Is that fair to design engineers? No matter if it’s fair or not. We can prepare ourselves to address those objections. We talk about how we can prepare ourselves to self-advocate for more customer face time.
Within our quality toolbox, there are a lot of graphical organizers. Some are better at fulfilling different goals than others. If we have a goal in mind, then we may choose a certain tool. However, we don’t want analysis paralysis about which tool is best to stop us from using any tool at all.
I share my 3 general guidelines about choosing a graphical quality tool, how to draw them, and when to use them.
Plus, we talk about 3 tools in particular, prompted by a social media ask: mind map, process flowchart, and spaghetti diagram. We get into what they are, when we’re most likely to use them, and how they can be used for design.
Design for Excellence (DFX) is a concept that includes many initiatives, like design for usability, design for manufacturability and assembly, and design for environment and disassembly. It’s a focus on doing things right, exceeding customer expectations, optimizing what’s needed while minimizing costs, and continuous improvement.
Today we take a deep dive on one of those aspects: design for environment (DFE), including design for disassembly. What are reasons that a business would consider this important, and how does a design engineer fit in?
We want to ensure our designs perform reliably, as expected and intended. With today’s high-reliability products and quick release to market, we probably don’t have enough time to just test our parts at normal use rates. It would take too long, because our products ARE so reliable.  Or, we’ll miss our window of opportunity to get our product to market.
There are several ways to get reliability data to make design decisions. One of the ways is reliability life testing of our product.
This episode explores how accelerated stress testing is one subset of other reliability life testing methods, when it’s a good idea, how we can approach doing it, and what we can do with the results.
What is design of experiments, or DOE? What do we use it for and what is it all about? Â 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.Â
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?Â
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.
