Richard Harpster’s picture

By: Richard Harpster

On Dec. 7, 2021, Ford Motor Co. updated its IATF 16949—“Customer specific requirements” (CSR), which require the use of reverse FMEAs (RFMEA) on new equipment (“tooling”). The first sentence of the reverse FMEA requirement reads: “Organizations are required to have a process in place that ensures all new launches complete an RFMEA event once the equipment is installed and running.”

As one might expect, multiple webinars are offered by RFMEA training providers, as well as two-day RFMEA classes ranging in price from $795 to $995. My guess is that it won’t be long before RFMEA software is available for purchase.

Having spent six years as a Ford plant and equipment design engineer, and an additional 32 years afterward helping companies manage new tooling risk, I see significant problems with the RFMEA processes being proposed by RFMEA training providers. Ford’s objective in requiring companies to use the RFMEA is to ensure that their suppliers effectively manage tooling risk.

Brittney McIver’s picture

By: Brittney McIver

At some point, every medical device company will encounter an issue that requires an internal investigation. Whether it’s due to a nonconformance, complaint, CAPA, or an audit issue, you’ll have to conduct a failure or root cause investigation to pinpoint why the issue occurred in order to resolve it.

For many companies, the news that they need to conduct an investigation may be accompanied by a sense of dread. It’s never pleasant to think about the implications of anything going wrong with a medical device. But don’t let that put you off the heart of the matter.

An investigation can be an excellent learning mechanism—an opportunity for your business to gain valuable insights that spark process and product improvements that will only serve you better moving forward.

That said, investigations can be confusing for many medical device companies. So, let’s dive deeper into understanding root cause analysis as a high-level concept, what kinds of steps are involved, and the primary methodologies that are used.

V R Vijay Anand’s picture

By: V R Vijay Anand

As the world moves toward a new, post-pandemic normal, industries must leverage digital transformation at an accelerated pace. This is already happening. According to IBM, 67 percent of manufacturers have accelerated digital projects since Covid-19.

Although improved operational efficiency is typically the reason for these changes, manufacturers should capitalize on the convergence of Industry 4.0 and environmental, social, and governance (ESG) goals to improve their sustainability credentials. Data hold the key to reducing manufacturing’s waste problem.

Donald J. Wheeler’s picture

By: Donald J. Wheeler

Most of the world’s data are obtained as byproducts of operations. These observational data track what happens over time and have a structure that requires a different approach to analysis than that used for experimental data. An understanding of this approach will reveal how Shewhart’s generic, three-sigma limits are sufficient to define economic operation for all types of observational data.

Management requires prediction, yet all data are historical. To use historical data to make predictions, we will have to use some sort of extrapolation. We might extrapolate from the product we have measured to a product not measured, or we might even extrapolate from the product measured to a product not yet made. Either way, the problem of prediction requires that we know when these extrapolations are reasonable and when they are not.

The structure of observational data

Before we talk about prediction, we need to consider the structure of observational data. For any one product characteristic we can usually list dozens, or even hundreds, of cause-and-effect relationships which affect that characteristic. Some of these causes will have larger effects than the others. So, if we had perfect knowledge, we could arrange the causes in order according to their effects to obtain a Pareto like Figure 1.

Cameron Shaheen’s picture

By: Cameron Shaheen

With the holidays fast approaching, manufacturers, distribution centers, and e-commerce providers are working to meet growing customer demand, while also navigating severe supply-chain disruptions and mounting labor shortages. At this point, we all had hoped to have the devastating effects of the pandemic behind us. Yet the transportation delays, rising prices, component shortages, and labor challenges facing suppliers and retailers are even worse than last year. And that’s saying a lot.

This year, with retail stores reopened and online shopping in full swing, holiday sales are projected to hit a record high. According to Deloitte’s annual forecast, e-commerce holiday sales are projected to grow 11–15 percent, and retail sales are predicted to increase 7–9 percent this year. To meet this demand, manufacturers of everything from electronics to bicycles and dolls have been ramping up production to accelerate output and prepare for the inevitable onslaught of returns after the holidays.

David Isaacson’s picture

By: David Isaacson

Within every organization, problems or incidents arise that can affect the quality of your operations. Take for example, food recalls due to improper food labeling that not only could cause sickness in humans, but also result in a hit to a company’s reputation. Or, automotive product recalls due to defective parts.

Today, given supply-chain disruptions that require broader sourcing of materials, along with a shortage of skilled workers, the chances of problems rising are increasing exponentially.

Whether causing a minor blip in productivity or tragic results, any problem should be evaluated to minimize the chances that it could occur again. And, while we read about manufacturing issues almost daily, we don’t always learn what the root cause of the problem was and how it was corrected.

David Cahn’s picture

By: David Cahn

Lean Six Sigma has improved manufacturing operations and processes for years now. Now the effect of the methodology is extending to supply chain and operations to help eliminate waste and reduce variation. Using lean to eradicate waste and Six Sigma to eliminate defects by reducing process variation creates a powerful tool for continuous process improvement and a resilient supply chain.

Building a resilient supply chain

An organization’s supply chain must be agile and quickly responsive to its customers changing needs. Companies that can deliver this will create a successful supply chain. In fact, there is a tool within Six Sigma known as critical to quality (CTQ) that requires organizations to measure progress in terms that customers consider critical.

Supply chain optimization

Today’s businesses must constantly seek out more efficient methods and processes. This has never been more evident when balancing demand, supply, and price optimization to sustain resiliency in this omni-channel world.

Jay Arthur—The KnowWare Man’s picture

By: Jay Arthur—The KnowWare Man

There are two ways to increase profits: increase sales or reduce costs. Although most data analysis seeks to find more ways to sell more stuff to more people, addressing preventable problems is an often overlooked opportunity. Preventable problems consume a third or more of corporate expenses and profits.

Data analysis can pinpoint problems and eliminate them forever. Problem solving with data is a much more reliable and controllable way to cut costs and increase profits. Sadly, few people know how to do this consistently.

How do you solve operational problems with 100-percent success rate? Take out the guesswork. The vast majority of improvement projects involve reducing or eliminating defects, mistakes, and errors. If you have raw data about when the defect occurred, where it happened, and what type of defect it was, you can create a world-class improvement project that eliminates the guesswork. And you can do it using a tool you most likely already have: Microsoft Excel.

Anthony D. Burns’s picture

By: Anthony D. Burns

Augmented reality (AR) means adding objects, animations, or information, that don’t really exist, to the real world. The idea is that the real world is augmented (or overlaid) with computer-generated material—ideally for some useful purpose.

Augmented reality has been around for about 30 years. But it’s only during the last five years or so that it has been widely used on mobile devices. If you have wondered why your new iPhone 12 has a LiDAR depth sensor, the answer is, in part, for augmented reality. Almost all modern phones now have depth sensors for AR. LiDAR makes depth sensing more accurate.

Unlike virtual reality (VR), AR on mobiles requires no special equipment. There’s no need for headsets or handheld devices. All you need is your mobile phone.

More than fun and games

Although games are probably the most notable use of AR on mobiles (Pokémon Go is a good example), there are business and training applications as well. Perhaps the simplest AR business application is labeling real-world objects. Google Maps, for example, recently launched Live View, adding real-world labeling of objects and directions via the mobile phone’s camera. Real-world objects, when viewed through the mobile phone, can show added text, objects, or 3D animations. Live View has all of these.

Michael Popenas’s picture

By: Michael Popenas

Product development (PD) is the life blood of a company’s success and is the process for innovation. Today, product life cycles are shrinking due to an ever-increasing number of competitive and disruptive products coming to market quicker.

To stay in business, a company’s PD needs to become more effective, more productive, and faster. Product development systems can no longer take years or months to deliver something that the customer will hopefully still want. Planning, design and development, testing, and release can no longer rely on the currently widely practiced sequential phase-gate waterfall methods developed years ago.

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