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.

James J. Kline’s picture

By: James J. Kline

In today’s coronavirus environment, governments at all levels are under greater fiscal pressure. For instance, Oregon’s governor has told state departments to prepare for a 12-percent reduction in their budgets. Given this environment, perhaps it is time to reexamine an established approach to improving operational performance. That approach is quality management.

From 1992 to 2002, I researched and wrote about quality award-winning governments in the United States.1 With extra time on my hands, I started cleaning out old files. In the process, I found a few of the documents backing up that work.

The documents included information about 32 local governments that were using total quality management (TQM). While reviewing the current websites of these local governments, I discovered that at least seven are using or mentioning quality management. It might seem disappointing that only seven of 32 are using some form of lean management, Six Sigma, continuous improvement, or Baldrige Criteria. However, that several of these local governments have been implementing quality management for 20 years shows there is a sound quality management foundation in local government. This is a foundation that can be built upon.

Ryan Ayers’s picture

By: Ryan Ayers

Data are valuable assets, so much so that they are the world’s most valuable resource. That makes understanding the different types of data—and the role of a data scientist—more important than ever. In the business world, more companies are trying to understand big numbers and what they can do with them. Expertise in data is in high demand. Determining the right data and measurement scales enables companies to organize, identify, analyze, and ultimately use data to inform strategies that will allow them to make a genuine impact.

Data at the highest level: qualitative and quantitative

What are data? In short, they are a collection of measurements or observations, divided into two different types: qualitative and quantitative.

Taran March @ Quality Digest’s picture

By: Taran March @ Quality Digest

What is quality intelligence, exactly? It’s more than marketing spin. More, even, than the sum of its many control charts. It’s not collecting data simply to further go/no-go actions. And it doesn’t mean turning the cognitive wheel entirely over to artificial intelligence, either—far from it.

We might think of quality intelligence as a natural progression of quality control. It’s both granular, in that core quality tools underpin it, and forward-looking because quality data are used to improve not only products and processes but also operational performance. It’s very deliberate in that its goal is to wring the maximum value possible from reliable data.

To do this, quality intelligence employs four key tools: ensuring compliance, grading collected data, exploiting software, and implementing data strategically.

Ensuring compliance

People often assume that compliance applies solely to government or industry standards, but the term surfaces in many shop-floor conversations and processes. For instance, there is compliance to limits: Are data in specification? Are the appropriate statistical rules being met? There’s also compliance to procedures: Are people collecting data in the right way, and on time?

Ryan E. Day’s picture

By: Ryan E. Day

An organization can achieve great results when everyone is working together, looking at the same information generated from the same data, and using the same rules. Changes can be made that affect a company’s bottom line through operational improvements, product quality, and process optimization. There are quality intelligence (QI) solutions that can help reveal hidden opportunities.

Companies can save money and improve operational efficiency by effectively focusing resources on the problems that matter most from both a strategic and tactical perspective. A proper QI system makes this practical in several ways.

The QI advantage

With a QI system, data are captured and analyzed consistently in a central repository across the organization. This means there aren’t different interpretations of the truth, and there is alignment among those on the shop floor, site management, and corporate quality.

Alignment is possible because of a positive cascade of events:
• Notifications are sent to the appropriate people, and workflows trigger the required actions. This means people are appropriately accountable for addressing issues. Those issues can then be analyzed to understand recurring problems and how to avoid them.

Gleb Tsipursky’s picture

By: Gleb Tsipursky

So many companies are shifting their employees to working from home to address the Covid-19 coronavirus pandemic. Yet they’re not considering the potential quality disasters that can occur as a result of this transition.

An example of this is what one of my coaching clients experienced more than a year before the pandemic hit. Myron is the risk and quality management executive in a medical services company with about 600 employees. He was one of the leaders tasked by his company’s senior management team with shifting the company’s employees to a work-from-home setup, due to rising rents on their office building.

Specifically, Myron led the team that managed risk and quality issues associated with the transition for all 600 employees to telework, due to his previous experience in helping small teams of three to six people in the company transition to working from home in the past. The much larger number of people who had many more diverse roles they had to assist now was proving to be a challenge. So was the short amount of time available to this project, which was only four weeks, and resulted from a failure in negotiation with the landlord of the office building.

Celia Paulsen’s picture

By: Celia Paulsen

Nobody likes business to be slow. If you’re in a fast-paced world like manufacturing, seeing your machines or employees idle can drive a person insane. If you’re used to your production line working to capacity and suddenly business slows down, it can be a frustrating time.

When I was in the U.S. Army, we used our downtime to train and clean. On one occasion, we spent nearly two weeks waiting for a change of orders. By the end of the first week, every weapon, every desk, and every blade of grass was spotless. There was nothing left to clean, so we cleaned it all over again!

Over time, I learned that downtime can actually provide a good opportunity to refocus before driving forward again. It offers time to take inventory, get a little creative, and do some renovation, literally and figuratively. My personal downtime to-do list includes organizing my papers, redesigning my closet, playing with my 3D printer, replacing my stair treads, fixing that one light switch, learning something I’ll soon forget, and though you may laugh, improving my cybersecurity posture.

It’s true; I’m a cybersecurity geek. I’ve been a cybersecurity researcher at NIST since 2011 and am now detailed to NIST MEP as the cybersecurity services specialist.

Multiple Authors
By: Donald J. Wheeler, Al Pfadt

Each day we receive data that seek to quantify the Covid-19 pandemic. These daily values tell us how things have changed from yesterday, and give us the current totals, but they are difficult to understand simply because they are only a small piece of the puzzle. And like pieces of a puzzle, data only begin to make sense when they are placed in context. And the best way to place data in context is with an appropriate graph.

When using epidemiological models to evaluate different scenarios it is common to see graphs that portray the number of new cases, or the demand for services, each day.1 Typically, these graphs look something like the curves in figure 1.


Figure 1: Epidemiological models produce curves of new cases under different scenarios in order to compare peak demands over time. (Click image for larger view.)

Ken Maynard’s picture

By: Ken Maynard

When educational and public sectors consider applying a proven method like lean Six Sigma, the perception persists that this “manufacturing program” will not work in a nonmanufacturing environment. Along with that limiting assumption, there is an underlying expectation within the service industry that it requires a substantially customized approach.

This makes perfect sense. It’s logical. If I’m not making the proverbial “widget”—if I’m processing transactions, delivering services, or providing a learning environment—then how do I use lean Six Sigma? It’s got a proven track record of success in manufacturing, but this success can morph into a hindrance when considering spreading the gains in nonmanufacturing.

If a different approach is necessary to successful lean Six Sigma implementation in the public sector, then how do we adapt the approach without compromising the fundamentals that lead to success?

Dirk Dusharme @ Quality Digest’s picture

By: Dirk Dusharme @ Quality Digest

Government bureaucracies are inefficient. They waste taxpayer dollars, and they have no incentive to improve. We’ve all heard and probably repeated these axioms about wasteful government spending.

And it’s often true; you don’t have to look far to find examples of government overpaying for products or services, contracts going to companies ill-equipped to handle the job, or just outright wasted money. According to the Government Accountability Office (GAO), we waste tens of billions of dollars each year because of what amounts to process inefficiencies. Take a quick look at the GAO’s “2019 Annual Report: Additional Opportunities to Reduce Fragmentation, Overlap, and Duplication and Achieve Billions in Financial Benefits” to get an idea. But, conventional wisdom aside, at its roots, the issues pointed out by the GAO are really no different than those found in the private sector. Just more visible.

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