Prevention Controls vs. Detection Controls [transcript]

You’re listening to an installment of the Quality during Design “Versus Series”. In this series, we’re comparing concepts within quality and reliability to better understand them and how they can affect product design engineering. We have eight episodes in this series, which means we’ll be reviewing at least 16 topics. Let’s get started.

Hello and welcome to Quality during Design the place to use quality thinking to create products others love for less. My name is Dianna Deeney. I’m a senior level quality professional and engineer with over 20 years of experience in manufacturing and design. Listen in and then join the conversation. Visit QualityduringDesign.com and subscribe.

Hey, thanks for joining me today. Today, we’re going to be talking about controls, controls as in product development and design engineering. Now, we think about these as part of the engineering design and the ways that we monitor it and help others use it. But controls can also include engineering drawings and specifications, manufacturing, equipment controls, and quality inspections. Controls can also include broader systems like maintenance of rooms for manufacturing or online availability of instructions to our customers. The decisions that design engineers make about a product at the onset of design has a trickle-down effect on subsequent areas of business. And there are a couple of buckets we consider controls to be within. And we’re going to talk about that today: those two different kind of buckets, specifically detection and prevention controls. What’s the difference? Why do we have these different classifications of controls? And what does it mean as far as design and the effects of the design and control choices, that it has on our end user and the performance of our product. So let’s get into it.

Making rounds on social media was a graphic about different parts of an organization, and which one has the biggest influence on cost. It was titled “Who Casts the Biggest Shadow”. I thought it was interesting, so I want to share it with you. On the x-axis was the cost and on the y-axis was influence. The parts of the organization that were identified were overhead, labor, material, and design. And those are the kind of areas of the business that we think about when we are thinking about manufacturing production. By far, design had the greatest influence with the least amount of cost, at least 50% more influence than any other part of the organization. And at least one-third less the cost of the least costly option. The control set in motion at design have an influence on not just the cost of a business, but in the success of the design’s performance, safety, and usability.

If we have a potential design failure, we want to think about what design controls we have in place to prevent the failure like it’s designed to a standard or it’s designed in a way that prevents the failure. Or we think about what we have in place to detect the failure. Like we’re planning to run a test for this, or have a built-in warning system. Think about the dashboard of a car. It tells us when there could be an issue or when there’s a problem that we need to stop and check.

If we have a potential usability failure, where there’s an issue with how our user interacts with our product, we wanna think about what user process controls we have in place to prevent the failure. Like the user can only orient the part this one way because of the design. And we think about how our user will detect the failure. Like an alarm that’s designed to go off if a condition is met. Or, the part is labeled for the correct orientation.

Why do we need to classify controls into these buckets of prevention and detection? Isn’t it enough to have at least one control? Depending on what design function we’re controlling, it may not be enough to have one control and the control we have may not be sufficient. Prevention and detection controls affect our design risks in different ways. And that’s why we have different buckets.

Prevention controls are a part of the design. They require someone to do it right or something to function properly. They’re a feature of a design that could fail, or they’re the lack of a feature. Prevention controls, design out the issue. We can rate the effectiveness of our prevention controls on the likelihood that they’ll prevent an issue and its subsequent failure from ever happening.

Detection controls affect our ability to notice or pick out when there’s been an error or issue. Detection controls requires someone or something to monitor. It could be inspection performed by quality personnel or by an automated vision system. It could be an internal system check that monitors and reports on inputs or outputs. It can be an anomaly detection using machine learning. We can rate the effectiveness of our detection controls on the likelihood that they’ll help us (or some other monitoring system) notice an issue.

It’s better to prevent an issue than to detect it after-the-fact. Implementing prevention controls has other side benefits, too. Taking the approach that we want to implement prevention controls, wherever possible, gives us a different mindset toward design. We’re looking for ways to eliminate and reduce failures and mistakes. Prevention controls are usually linked to better quality products and a user process that’s easier. Prevention controls may also have an effect of reducing waste. Recalls and scrap may be reduced if our design is built to avoid failures.

We probably can’t prevent all failures. We can focus our efforts on preventing the failures that do the most damage create the most harm or frustrate the user of the most. We can use an FMEA (or failure mode and effects analysis) as a source for those measures using its ratings to rank and prioritize our decisions. When we’re evaluating design risk in an FMEA , in general, prevention affects the occurrence rating of the failure itself relating to how often the cause will happen and lead to a failure. Detection has its own rating relating to how well the monitoring activity will work at detecting a failure or its cause, but before the customer is affected.

A change in prevention controls affects many different FMEA measures. Prevention controls are going to reduce our occurence rating, our criticality measure (or the product of severity and occurrence), our RPN (or risk prioritization number, which is the product of severity, occurrence and detection) and our levels in our risk index table (which is a matrix of severity and occurrence). We could also design-out a potential failure mode altogether. A change in detection controls affects the detection rating itself and the RPN. We don’t see as many effects to our risk measures by implementing more detection controls. That makes sense for an FMEA and for the real world.

If we feel we need to rely on the user to detect a failure or a problem, and then do something about it, we can use poka-yoke quality principles to design ways to quickly correct the problem. Now we’re getting into corrective controls. We talk out poka-yoke for a product design and the user’s process in a previous episode of the Quality during Design podcast, which I’ll link to.

What can you do today with what we’ve been talking about?

With your own designs early in the concept phase of your design, take some time to analyze the potential ways your user can interact with your concept product. You’ll likely immediately see some features of your design that you’ll want to add as prevention controls.

Use your FMEA as a way to assess your design for failures. By studying the FMEA measures and the current controls you already have in place, you’ll notice gaps in places where prevention controls may be needed.

If you’re working toward detection controls, know they’re not as good as prevention controls, but they still have a great place in design and reducing risks. Take the next step with the detection control and figure out who notices it and when, the method of inspection, and any alerts you want to add. I’ll link to a one-page mistake proofing download that you may find useful.

If this topic affects your work or interest, then subscribe to quality during design and visit the website of the same name for more content that’s related to this episode. Many of these “Versus” episodes are sticking with comparisons, not competitions, but in this episode, I think we can compare prevention controls versus detection controls in a competitive nature. Hands down, prevention controls win.

If you like the content in this episode, visit quality during design.com, where you can subscribe to the weekly newsletter to keep in touch. This has been a production of Deeney Enterprises. Thanks for listening!