Lean Article

Jesse Allred’s picture

By: Jesse Allred

Imagine a manufacturing facility prioritizing cleanliness and organization—aisles are kept clear, equipment is well maintained, the plant floor is regularly cleaned, operators can easily locate tools, and materials are always stored in the right place. All employees contribute to managing work spaces, creating a culture of efficiency and quality.

Quality Digest’s picture

By: Quality Digest

This is supposed to be trade-show season. The time when companies send their employees to industry tech shows and user-group meetings to see and experience the latest offerings in their field. A time when companies expend a good portion of their budget on booth space, shipping costs, and hotel and travel expenses to get their products and employees in front of thousands of people.

This year, however, due to concerns about the Covid-19, conferences are being cancelled left and right. From fashion to food to finance, show websites are plastering “cancelled” notices across their home pages. Design News  lists dozens of tech shows around the world that have shuttered or postponed. These include shows from Apple, Facebook, Google, Gartner, and both the China and Korea Semicon shows.

Ben Aston’s picture

By: Ben Aston

A large portion of a digital project manager’s job is making sure the right parts of the project are being worked on. Projects need to be prioritized. Tasks within projects need to be prioritized, too.

Plan View’s Project and Portfolio Management Landscape Report found that prioritization was consistently the second biggest challenge that organizations face. Also, McKinsley surveyed 1,500 professionals and found that only 9 percent were happy with their time allocation.

Many famous writers, businesspeople, and global influencers have stressed the importance of getting your priorities in order.

Mark Twain famously said, “To change your life, you need to change your priorities.”

The same applies to project management.

Nico Thomas’s picture

By: Nico Thomas

Each new year brings about a period of reflection, where one can think back on the path that the previous year took us on. 2020 represents an even larger opportunity for reflection as the world enters a new decade. Reflection provides an opportunity to learn and improve, and extends beyond just an individual to include industries and businesses. As a U.S. manufacturing enthusiast, I’m looking back over the past 10 years at how manufacturing has changed, evolved, and innovated so that I can continue to support that evolution.

The U.S. manufacturing industry is entering this new decade in a much different state than when it entered the last. The industry is no longer shaking off the aftereffects of the Great Recession, but it is still grappling with the economic uncertainty that comes with new trade deals, tariffs, and other global uncertainties. There is also the need to keep pace with the ever-increasing speed of technological change. Industry 4.0 and its adoption by U.S. manufacturers has begun to pick up steam, and manufacturing’s digitization is only going to increase. Things like 3D printing, advanced robotics, artificial intelligence, and smart factories are becoming more commonplace in the U.S. manufacturing industry, emphasizing a deepening need for stronger cybersecurity.

William A. Levinson’s picture

By: William A. Levinson

Almost half of Americans work in low-wage jobs despite the nation’s low unemployment rate. Aimee Picchi, writing for CBS News, cites a Brookings study that says “44 percent of U.S. workers are employed in low-wage jobs that pay median annual wages of $18,000.”1 A Bloomberg story adds, “An estimated 53 million Americans are earning low wages, according to the study. Their median wage is $10.22 an hour and their annual pay is $17,950.”2

These wage levels are not consistent with the United States’ industrial and technological development or its standard of living, but this is far from the only issue. Executives with profit-and-loss responsibility should realize that low wages are also often symptomatic of low profits. Purchasing managers should recognize that a supplier’s low wages are often symptomatic of excessively high prices, even though this seems counterintuitive. The reason is that low wages, low profits, and high prices all have the same root causes: waste (muda) and opportunity costs. Recognizing this simple fact, for which there are proven, off-the-shelf, and simple remedies, opens the door to almost limitless wealth for all stakeholders.

Casandra Robinson’s picture

By: Casandra Robinson

Perhaps for as many as 40,000 years, people have been protecting their feet with some type of covering, initially using animal hides and fur. Today, footwear has become high-tech, sophisticated, and in some cases smart, incorporating sensors that communicate with apps on your phone. Much of the advancement in footwear is possible because of standards that address the basic performance and functionality, allowing manufacturers to go beyond the basics.

There are hundreds of standards for all types of shoes, from industrial work boots to high-heeled dress shoes and everything in between, and for the shoe materials and components. Most of these are published by private-sector standards-developing organizations, such as SATRA, ISO, and ASTM International. But what do I care if my shoes meet any standards? I just want them to look good, feel good, and be fit for my activity—running shoes for jogging, boots for hiking, high heels for dancing, safety shoes for work—that’s all there is to it, right? Not quite. In terms of construction, fit, comfort, functionality, and protection, footwear is probably the most complex of all the clothing that we wear.

Phanish Puranam’s picture

By: Phanish Puranam

Machine learning, the latest incarnation of artificial intelligence (AI), works by detecting complex patterns in past data and using them to predict future data. Since almost all business decisions ultimately rely on predictions (about profits, employee performance, costs, regulation, etc.), it would seem obvious that machine learning (ML) could be useful whenever “big” data are available to support business decisions. But that isn’t quite right.

The reality in most organizations is that data may be captured but they are stored haphazardly. Their quality is uneven, and integrating them is problematic because they sit in disparate locations and jurisdictions. But even when data are cleaned up and stored properly, they’re not always appropriate for the questions or decisions that management has in mind. So, how do you know whether applying predictive analytics through AI techniques to a particular business problem is worthwhile? Although every organization and context is different, here are five general principles that should be useful in answering that question.

Jim Benson’s picture

By: Jim Benson

‘It’s the shoes!” Spike Lee yelled into the camera on the Air Jordan ads.

But it was never the shoes. Michael, Magic, and LeBron would have outplayed their leagues in golf cleats.

It was never the shoes.

But it was us, the salespeople. In our case, the intelligencia that “trains” people to be lean, agile, or whatever.

In companies all over the world, we are convincing people that success lies in the shoes. In huddles or iterations or A3s or DMAIC or story points. All these tools are not even hard tools, like hammers; they are conceptual tools, like voting. We don’t seem to get this.

When someone who has experience looks at these tools, we don’t see the tools; we see the results we’ve enjoyed in the past. We are strangely blind to the failures of deploying them or the near misses (which our brains will inevitably turn into wins). So, when we describe these tools in classes, we describe them with a high degree of certainty that whoever touches them will be successful.

Our classes are so convincing that we, strangely, even convince ourselves.

Even though we always struggle with clients to get them to “just do it.”

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.

Ryan E. Day’s picture

By: Ryan E. Day

Lean: an employee-championed method of waste reduction. Six Sigma: a robust method of defect reduction. Embracing both methods provides organizations with multiple tools for continuous improvement. Developed for manufacturing, lean Six Sigma has now been recognized by government agencies as a practical way to realize their outcome goals.

Improving response time for client services

Expediency is always crucial to the well-being of government services clients. California’s Office of Emergency Services (Cal OES) and Washington state’s King County Treasury Operations are two organizations that were motivated to explore more efficient processes to reduce response times for client services.

The improvements these teams sought to bring about would require changes in the way things were done, but change is not always easy, and the way forward can be elusive. New ways of doing things require new methods. For organizations as large and complex as these government agencies to effect positive change, robust tools are needed.

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