Lean Article

Alison Hawke’s picture

By: Alison Hawke

Historically, quality in a process was something that was done at the end of the line. You inspected your widget once it was made, and if it had flaws, you fixed it or threw it out.

As in many modern manufacturing environments, quality in software has become a process you do from start to finish. Moving from that end-of-line quality control mindset to a more user-focused, whole-team quality advocacy helps deliver a better product where quality is baked in from the start, and part of the job of all roles on a software team.

Quality advocates start on the first day of a software development project and are now part of the development process from idea to final deployment. With the strengthened emphasis on quality, the title for quality professionals has rightly been designated as “advocacy” to display that someone is working hard, from conception to shipment, and is determined that a software development team will deliver a superior quality product or solution for the customer.

In other words, quality advocacy is now more of a broad-ranging activity.

Laurel Thoennes @ Quality Digest’s picture

By: Laurel Thoennes @ Quality Digest

In the foreword of Mark Graban’s book, Measures of Success: React Less, Lead Better, Improve More (Constancy Inc., 2018), renowned statistician, Donald J. Wheeler, writes about Graban: “He has created a guide for using and understanding the data that surround us every day.

“These numbers are constantly changing,” explains Wheeler. “Some of the changes in the data will represent real changes in the underlying system or process that generated the data. Other changes will simply represent the routine variation in the underlying system when nothing has changed.”

The problem is in deciding whether data changes are “noise” or signals of real changes in the system.

“Mark presents the antidote to this disease of interpreting noise as signals,” adds Wheeler. “And that is why everyone who is exposed to business data of any type needs to read this book.”

Mike Richman’s picture

By: Mike Richman

With more than 110,000 expected attendees, IMTS is Chicago’s hottest suburb this week. (I like to refer to it as “Manufactureville.”) Here’s what we covered during our second show of the week, from the booth of today’s sponsor, Q-Mark Manufacturing:

“Tapping Your Employee’s Knowledge”

It’s no secret that the employees closest to a process know best how to improve it. But how do you tap that knowledge without ruffling feathers?

“What Business Are You Really In?”

Author and consultant Jesse Lyn Stoner offers this trenchant look at the true reasons why a business exists: To better serve customers.

Tech Corner: Q-Mark Styli

Mark Rosenthal’s picture

By: Mark Rosenthal

During a TED talk, Amy Edmondson, the Novartis Professor of Leadership and Management at Harvard Business School, talks about “How to turn a group of strangers into a team.” Although long-standing teams are able to perform, our workplaces today require ad-hoc collaboration between diverse groups. The question is: What kind of leadership, and what kind of structure, contribute to working together on the problem?

Edmondson studies people and teams seeking to make a positive difference through the work they do. For those of you unfamiliar with her work, I’ll add that I have found anything that she writes or speaks about is worth reading or listening to.

The key message in her Ted talk starts around the 10-minute point:

“When teaming works, you can be sure that leaders, leaders at all levels, have been crystal clear that they don’t have the answers. Let’s call this ‘situational humility.’ It’s appropriate humility. We don’t know how to do it.”

Jared Evans’s picture

By: Jared Evans

Implementing 6S, the lean strategy for reducing waste and optimizing efficiency in a manufacturing environment, is more than just creating work protocols that people must follow. Because that’s the thing about people: If they don’t know the “why,” they are less likely to buy in to any initiative, especially one that requires significant changes in the way they work and think, simply because they “must.”

Building a culture around 6S is the key to preventing it from becoming the latest flavor-of-the-month workplace initiative. But the question is, how? It turns out, the answer is right in front of you.

What is 6S?

Don’t be fooled by the new name. Like 5S, 6S is a system of workplace standardization and organization that originated in Japan. Its purpose is to create a clean and well-ordered workplace in order to minimize error and reduce waste. But, of course, there’s more to it than that.

Hélène Horent’s picture

By: Hélène Horent

Founded in 1947, in Veles, Macedonia, BRAKO produces parts and components used in medical devices, road sweeper trucks, airport ground equipment, forklift accessories, metal-welded constructions, small hydro plants, telecommunications shelters, and antenna towers.

The company also makes various wire products, including nails, mesh, and welding wire. Its components range from simple shafts and bushes to hydro-turbine houses and covers. Most of the work at BRAKO requires three-axis milling, primarily using mild steel but also some stainless steel and aluminum.

Macedonian’s population is just over 2 million. With such a small market, the majority of BRAKO’s products (96%) are exported. The annual revenue for the 550-employee company was about €28 million in 2016. Its customers include:
• Handicare Group in Sweden (healthcare)
• Invacare in the United Kingdom (healthcare)
• S-P-S (Dutch airport equipment manufacturer)
• Green Machines in Austria (street sweeper manufacturer)
• Biostrada in Italy (street sweeper manufacturer)
• Global Hydro Energy (Austrian turbine producer)

Ryan E. Day’s picture

By: Ryan E. Day

As manufacturing finds its way through the 21st century, there’s a groundswell change emerging. Organizations are jockeying for competitive position as they endeavor to describe this phenomenon. Industry 4.0, the fourth industrial revolution, and the industrial internet of things (IIoT) are a few terms being tried on for size. Although the current transition is enabled by technology, there is a timeless underlying impetus: productivity.

The first industrial revolution involved physically mechanized production—machines powered by water, steam, or internal combustion engines. The second used electric power to create mass production. The third used electronics and information technology to automate production. Now a fourth industrial revolution is building, characterized by a fusion of technologies enabling real-time data to be collated, analyzed, and actionized right in the production environment.

Early industrial revolution mainly focused on increased output. For a while, those brute-force methods were advantages, and productivity flourished. But as improved transportation and communication shrunk our world, global competition revealed the bloated underbelly of inefficient mass-production methods.

Ryan E. Day’s picture

By: Ryan E. Day

‘In God we trust; all others bring data.” “Follow the data.” “Let the data talk.” Nice clichés, but there’s one problem... data can’t talk. In fact, data don’t say a darn thing. Data are bits of raw information. If you want to reduce product variation, improve your manufacturing processes, and increase profits, you need to interrogate and analyze your data.

It is the data analysis that provides actionable insights, and the success of statistical process control (SPC) depends on data analytics. SPC is the cornerstone of quality control and quality assurance in manufacturing processes.

SPC and Six Sigma are nearly synonymous with improving quality consistency and reducing process variation and waste. Naturally, these methods begin with data monitoring and collection—it’s the analysis stage where things can get dicey. But that’s also where data can turn into increased profits.

Listen to Doug C. Fair of InfinityQS, as he reveals the way companies are using data analysis tools to take advantage of the data they are already collecting with their quality control programs.

Glenn S. Wolfgang’s picture

By: Glenn S. Wolfgang

Flow quality management (Flow QM) is a logistical alternative to handling product in lots for the purpose of assessing and mitigating defects. It features a streamlined, automated acceptance sampling methodology, is built on empirical metrics, and facilitates timely, meaningful performance monitoring and management.

Flow QM also expedites output. It offers more precise and immediate, intentional control over the balance of inspection costs and quality improvement (essentially producer and consumer risks). Further, it is enabled by technological advances in computing speed, data storage, and software applications.

This methodology is an adaptation of traditional acceptance sampling with rectification. Rather than deciding acceptance of lots or subsets of product, it identifies and rectifies defects among inspected units individually and adjusts the selection of future inspections as needed to satisfy quality goals. Each product unit flows directly through production and inspection, if designated, to distribution. Its progress is not delayed or complicated by awaiting other within-lot inspection outcomes. In this processing flow, quality acceptance is implemented by a simple automated calculation for each product unit based on a few tallies accumulated over prior units.

William A. Levinson’s picture

By: William A. Levinson

Quality and manufacturing practitioners are most familiar with the effect of variation on product quality, and this is still the focus of the quality management and Six Sigma bodies of knowledge. Other forms of variation are, however, equally important—in terms of their ability to cause what Prussian general Carl von Clausewitz called friction, a form of muda, or waste—in production control and also many service-related activities. This article shows how variation affects the latter as well as the former applications.

W. Edwards Deming’s Red Bead experiment was an innovative, hands-on exercise that demonstrated conclusively the drawbacks of blaming or rewarding workers for unfavorable and favorable variation, respectively, in a manufacturing process. The exercise consisted of using a sampling paddle to withdraw a certain number of beads from a container. White beads represented good parts, and red beads nonconforming parts. Results for five workers might, for example, be as follows for 200 parts, of which 3 percent are nonconforming. (You can simulate this yourself with Excel by means of the Data Analysis menu and Random Number Generation. Use a binomial distribution with 200 as the number of trials, and nonconforming fraction p = 0.03.)

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