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.

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.

Taran March @ Quality Digest’s picture

By: Taran March @ Quality Digest

At the University of California at San Diego, lean concepts have taken hold. Along with its process improvement curriculum, the university applies what it teaches through initiatives around campus. Projects both complex and simple tackle the snags, waste, and bottlenecks of academic life. Students, as both customers and process output, learn about lean Six Sigma (LSS) tools and use them to improve their college experience. UC San Diego has become, in effect, its own moonshine shop.

Unfortunately, the same can’t be said for most public schools and colleges elsewhere in the country.

Ryan E. Day’s picture

By: Ryan E. Day

Lean looks at ways to reduce waste and improve flow. The principles are relevant to virtually every organizational sector and vertical. It’s no surprise, then, that so many organizations tout lean and devote resources to lean initiatives. But, too often, there is a tendency for a company to promote lean initiatives before it has really developed a lean culture. How about yours? Is it truly striving for a lean culture, or just paying lip service?

A lean culture is born when progress is made within four separate dimensions: cultural enablers, enterprise alignment, customer-focused results, and continuous improvement. If you’re not sure where your company stands on the lean continuum, walk through the following exercise and see what you discover.

Read the statements below each category and assess how frequently your organization exhibits these characteristics and behaviors. Respond to the statements with something along the lines of: almost always; sometimes; rarely; and almost never.

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William A. Levinson’s picture

By: William A. Levinson

The Automotive Industry Action Group’s (AIAG’s) and German Association of the Automotive Industry’s (VDA’s) new Failure Mode and Effects Analysis Handbook (AIAG, 2019) offers significant advances over FMEA as practiced 15 or 20 years ago.The publication is definitely worth buying because the new approach includes valuable methodology; this article will cover the most important points and highlights.

New features

The new process is qualitative rather than quantitative, which overcomes a major drawback of the previous approach. The older occurrence ratings were based on the probability of a failure, and the older AIAG manuals even tabulated recommended nonconforming fraction ranges. If, for example, the failure was 50 percent or more likely, the occurrence rating was 10 (worst possible on a 1 to 10 scale), while one or fewer per 1.5 million opportunities earned a rating of 1. These probabilities can be estimated from a process capability study, assuming that one is available; otherwise, one might easily have to guess.

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Quality Digest’s default image

By: Quality Digest

As usual with Quality Digest’s diverse audience, this year’s top stories covered a wide range of topics applicable to quality professionals. From hardware to software, from standards to risk management, from China trade to FDA regulations. It’s always fun to see what readers gravitate to, and this year was no different.

Below are five articles that garnered a lot of interest from our readers. As you can see, the topics are quite diverse.

Improve Risk Management and Quality Across the Value Chain by Increasing Visibility
by Kelly Kuchinski

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Donald J. Wheeler’s picture

By: Donald J. Wheeler

In the past two months we have looked at how three-sigma limits work with skewed data. This column finds the power functions for the probability limits of phase two charts with skewed probability models, and compares the trade-offs made by three-sigma limits with the trade-offs made by the probability limits.

Phase two charts

Ever since 1935, there have been two approaches to finding limits for process behavior charts. There is Walter Shewhart’s approach using fixed-width limits, and there is Egon Pearson’s fixed-coverage approach based on probability models. (For more on these two schools of thought, see “The Normality Myth,” Quality Digest, Sept. 19, 2019.) About the year 2000, some of my fellow statisticians tried to reconcile these two approaches by talking about “phase one and phase two control charts.”

Phase one charts use Shewhart’s fixed-width, three-sigma limits. These charts are used to help identify assignable causes of exceptional variation so that the process can be adjusted or fixed as needed. Then, under the assumption that once a process is fixed it will stay fixed, it is time for phase two.

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Jody Muelaner’s picture

By: Jody Muelaner

In a general sense, capability is the ability to do something. Within manufacturing, capability is given a much more specific definition. It is an expression of the accuracy of a process or equipment, in proportion to the required accuracy.

This can be applied to production processes, in which case any random variation and bias in the process must be significantly smaller than the product tolerance. It can also be applied to measurements, where any uncertainties in the measurement must be significantly smaller than the product tolerance or process variation that is being measured.

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Rohit Mathur’s picture

By: Rohit Mathur

Whatever the process or type of data collected, all data display variation. This is also true in software development. Any measure or parameter of interest to our business will vary from time period to time period, e.g., number of incidents per week or month, time taken in resolving incidents, number of tickets encountered in a production support environment per month, and defect density in code.

Understanding variation is about being able to describe the behavior of processes or systems over time. This variation can be stable, predictable, and routine, or unstable, unpredictable, and exceptional. Being able to distinguish between stable or common-cause variation, and unstable or special-cause variation, helps us to decide the type of action needed to improve the process. The control chart, developed by Walter Shewhart, is the tool that enables us to do so.

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