Donald Berwick

Donald Berwick, a Harvard University professor and leading advocate for improving health-care quality and efficiency, has been named by President Obama as his choice to head the Centers for Medicare and Medicaid Services (CMS.) Berwick is well-known in Quality circles for aggressively advocating quality improvement in healthcare. Berwick, who specializes in health-care policy and pediatrics, has never led such a large organization. As head of the Boston-based Institute for Healthcare Improvement, however, he is known for persuading doctors and hospitals to adopt innovative methods for reducing medical errors. Dr. Berwick is author of numerous articles and books, including the classic work demonstrating the application of quality technology to health care issues, Curing Health Care. He is one of the nation’s leading authorities on health care quality and improvement. He is also Clinical Professor of Pediatrics and Health Care Policy at the Harvard Medical School, and Professor in the Department of Health Policy and Management at the Harvard School of Public Health.

If confirmed by the Senate, Berwick will face a number of daunting challenges. One is the sheer size of the CMS, which is about to become even larger. The agency, which is part of the Department of Health and Human Services, must oversee a massive expansion of Medicaid, the federal-state insurance program for the poor, with an estimated 16 million people expected to join its rolls by 2020. At the same time, Medicare, the insurance program for the elderly, will need to reduce payments to health-care providers by about $400 billion over 10 years without impacting the quality of coverage. Lean Six Sigma and Quality technologies provide an approach for doing this while minimizing the impact on value-added health care processes, operations and activities. Berwick’s familiarity with these areas provides reason for optimism or, at least, hope. This blog has frequently posted examples of poor quality in health care. Let’s hope that Dr. Berwick will have a positive impact at CMS.

To many quality engineers and managers, process capability is a jumbled confusion of ideas expressed in jargon that only the anointed can understand.

Imagine the following scene. The boss rushes into the quality director’s office. He’s obviously distraught.

(Boss enters, walking quickly from stage right.)

Boss: “Jane, we’ve got a serious problem. Our biggest customer just called. Their assembly line is shut down because the last batch of XYZ-50’s that we shipped won’t fit into their assembly fixtures. What happened?”

(Jane, sitting at her desk, puts down her pen and looks up at her boss. She shakes her head in dismay.)

Jane: “I knew this would happen sooner or later, boss. The problem is that our customer requires us to provide a Cpk of 1.33 or higher. But the formula they make us use assumes normality, and the XYZ-50 has a skewed distribution. If we center the process to maximize Cpk, then the tail area extends beyond the specification limit .”

(Boss exits, stage right, shaking his head and wearing a puzzled expression.)

I fear that when the quality profession talks about process capability, this is how we sound to others. To many quality engineers and managers, process capability is a jumbled confusion of ideas expressed in jargon that only the anointed can understand. Let me try to clear the air on the subject.

Process capability is about one thing, and one thing only: quality. It answers the simple question, “Can you meet my requirements?” Ideally the customer would like a simple answer, yes or no. Unfortunately, this is not possible due to one or more of the following:

Inspection is not perfect; even 100-percent inspection won’t guarantee 100-percent quality. Explaining this becomes complicated.

All processes vary, and the variation must be analyzed using statistical methods that always predict at least an occasional failure. The statistics virtually always get complicated.

Measurement isn’t perfect, so even if a process did have zero variation, our measurements would still vary. This means that we might accidentally ship a defective item even if we measure it carefully. Not only that, our measurements of a particular item might be somewhat different from our customer’s measurements. Explaining how two trained people using the same type of instruments can check the same item and get different results can get complicated.

We or our customer might not properly understand the requirements. Human communication is always complicated.

Yet it’s really not complicated at all. In fact, the customer’s question can be answered easily, and the answer is: no. For all of the reasons listed, and many more, we cannot guarantee that we will always deliver a product or service that meets the customer’s requirements as understood by the customer.

So, now what? The best approach is also the most radical: Be honest. Tell customers about how many items they are likely to receive, on average, that will not meet the requirements. This cuts right to the heart of the matter. It tells customers what they want to know. It works for variables data and attributes data. If control charts are being used, the estimate can be obtained directly from the process average (for attributes data) or the process average and standard deviation (for variables data). The count can be adjusted to include sorting operations, inspection error, measurement error and all of the other factors that influence what the customer receives.

If our process is extremely good, we can tell the customer that, while we can’t guarantee perfection, we can provide quality in the near-perfect range. One good way of quantifying this is to use parts-per-million quality statements. For example: “Our return rate on this item is three returns per million items in service per year.” Most people can easily understand this statement. A customer ordering up to several thousand items will probably, and accurately, interpret this to mean “zero defects.”

If our process is less capable, stating the expected number of defective items that the customer will receive might result in a shock to both the employees and the customer. This may provide the incentive needed to improve quality.

High-volume production is another area where stating process capability as expected defectives can provide insights. A defect rate of 1/10 percent sounds pretty good. But a can line may produce in excess of 1,000 cans per minute, so a reject rate of 1/10 percent would result in the production of 1,440 defective cans per day. If the defect is major, say a leaking can that could damage many cases of product in a warehouse or truck, even a defective rate of one in a million might not be acceptable; it would result in several serious problems each week. For such processes, parts per billion quality may be required.

If a process is not in statistical control for unknown reasons, there is no way to state the process capability with any degree of precision. The best option is to tell the customer what the expected defectives will be (based on the historical data) and hope for the best.

The key to good customer relations is clear communication. The easiest way to get the point across is to tell the customer what level of product or service quality to expect, using plain language.

I was at Douglas Aircraft in the early 80’s when sevral Ford executives, including Larry Sullivan came out to understand how the aerospace industry managed quality. We had a lengthy discussion regarding supplier quality. Ford was just starting to train their suppliers and since they were facing significant economic challeges the suppliers had to pay to attend these sessions. The mesage was very direct. The suppliers had to take responsibility for the quality of the product. This was all happening during the launch of the Taurus/ sable, one of the most successful cars ever introduced.

Study: Ford Quality Rivaling Japanese Counterparts – Industry Headlines – Quality.

Ford Motor Co. surpassed Honda in initial vehicle quality for the first time and reached new levels of customer satisfaction with vehicle quality, according to a 2009 U.S. Global Quality Research System (GQRS) survey conducted for Ford by RDA Group of Bloomfield Hills, MI. Ford also is statistically tied with Toyota at the top of the industry when it comes to initial vehicle quality, according the survey.

This is great news. It gives all of us hope that American industry can turn things around. Of course, there’s more to winning in the business world than quality, but quality is an excellent place to start. In fact, it’s your ticket to the game. If you can’t get quality right, then you can’t play. Your customers will look elsewhere.

In the early 1980s Ford turned their business around. They began by loosing more money than any company had ever lost, and within five years they made more profit than any company had ever made. The transition was remarkable, and I had a ringside seat. I was able to work with Ford’s suppliers in a consultant and trainer role. I was also able to visit Ford for Dr. W. Edwards Deming’s seminars. Those meetings featured panels of Ford executives and Dr. Deming discussing their transformation activities. Without a doubt it was the best training a young consultant could get. I learned that when the top people take over a transformation with a sense of urgency that most of the barriers evaporate. All of us consultants were challenged to explain our approach to an audience that was intensely interested. We shared our success and failure stories with Ford’s leadership, and they shared theirs with us. It was a serious business, and it was an exciting time.

It looks as if Ford is having exciting times once again. Hopefully this time it will stick and they’ll have permanent prosperity. I think that the fact that Ford has refused the government’s offer of “help” is a significant factor. It was a risky thing to do, but by doing so Ford was faced with an urgency similar to that which existed when they made their last transformation. I believe the sense of urgency is a vital ingredient in success.

Did Rush Limbaugh really say that he wants Obama to fail? Does it matter? What’s my opinion? (Okay, so nobody asked. But it’s my blog after all!)

Personally, I sincerely hope the world returns to prosperity soon. I think there is overwhelming agreement on the fact that there are few downsides to prosperity. But the great debate is how to achieve prosperity. I won’t argue the relative merits of the different economic systems because I doubt that you care. However, I have a rather strong opinion that spending more money will not, by itself, lead to improvements. Deming used to say (I’m paraphrasing here) that doubling the pay of every worker in the auto industry wouldn’t make any difference in the quality or productivity of the auto companies. Why? Because the systems were the same. Unless systems (root causes) are changed, the results (effects) won’t change. When I look at the plans proposed by the government they largely consist of spending more on, for example, roads and schools without changing the way roads are built or education is delivered. Medical records are to be digitized, while the healthcare systems being automated are not substantially improved beforehand.

In short, I think a good deal of the money being spent will fail to provide any fundamentally different results because the underlying systems won’t be improved by the spending. A penny spent on six sigma, lean, or quality improvement would go a lot further than a dollar spent on the current systems.

There seems to be an epidemic in American Management of copying the form of things, without bringing the substance along. I was reminded of this when touring a factory with a team evaluating a potential new supplier. The company had put much of their equipment on wheeled dollies. Such things as jigs, drill presses, etc. were mounted in this way. I’d seen a lot of this during my visits to factories overseas. The objective was to make the factory easy to reconfigure. When orders were received for a new product family the factory could be quickly changed and production would hardly miss a beat. The problem was, the American company didn’t do this. The equipment was movable alright, but it didn’t move. Production was pure batch-and-queue with all of the inventory, quality problems, and waste that this approach entails.

Copyright © 2003
by Thomas Pyzdek, all rights reserved

1. In general, a Six Sigma Black Belt should be quantitatively oriented.
2. With minimal guidance, the Six Sigma Black Belt should be able to use data to convert broad generalizations
   into actionable goals.
3. The Six Sigma Black Belt should be able to make the business case for attempting to accomplish
   these goals.
4. The Six Sigma Black Belt should be able to develop detailed plans for achieving these goals.
5. The Six Sigma Black Belt should be able to measure progress towards the goals in terms meaningful
   to customers and leaders.
6. The Six Sigma Black Belt should know how to establish control systems for maintaining the gains
   achieved through Six Sigma.
7. The Six Sigma Black Belt should understand and be able to communicate the rationale for continuous
   improvement, even after initial goals have been accomplished.
8. The Six Sigma Black Belt should be familiar with research that quantifies the benefits firms
   have obtained from Six Sigma.
9. The Six Sigma Black Belt should know or be able to find the PPM rates associated with different
   sigma levels (e.g., Six Sigma = 3.4 PPM)
10. The Six Sigma Black Belt should know the approximate relative cost of poor quality associated
    with various sigma levels (e.g., three sigma firms report 25% COPQ).
11. The Six Sigma Black Belt should understand the roles of the various people involved in change
    (senior leader, champion, mentor, change agent, technical leader,
    team leader, facilitator).
12. The Six Sigma Black Belt should be able to design, test, and analyze customer surveys.
13. The Six Sigma Black Belt should know how to quantitatively analyze data from employee and customer
    surveys. This includes evaluating survey reliability and validity
    as well as the differences between surveys.
14. Given two or more sets of survey data, the Six Sigma Black Belt should be able to determine
    if there are statistically significant differences between them.
15. The Six Sigma Black Belt should be able to quantify the value of customer retention.
16. Given a partly completed QFD matrix, the Six Sigma Black Belt should be able to complete it.
17. The Six Sigma Black Belt should be able to compute the value of money held or invested over
    time, including present value and future value of a fixed sum.
18. The Six Sigma Black Belt should be able to compute present value and future value for various
    compounding periods.
19. The Six Sigma Black Belt should be able to compute the break even point for a project.
20. The Six Sigma Black Belt should be able to compute the net present value of cash flow streams,
    and to use the results to choose among competing projects.
21. The Six Sigma Black Belt should be able to compute the internal rate of return for cash flow
    streams and to use the results to choose among competing projects.
22. The Six Sigma Black Belt should know the COPQ rationale for Six Sigma, i.e., he should be able
    to explain what to do if COPQ analysis indicates that the optimum for a given process is less than Six Sigma.
23. The Six Sigma Black Belt should know the basic COPQ categories and be able to allocate a list
    of costs to the correct category.
24. Given a table of COPQ data over time, the Six Sigma Black Belt should be able to perform a statistical
    analysis of the trend.
25. Given a table of COPQ data over time, the Six Sigma Black Belt should be able to perform a statistical
    analysis of the distribution of costs among the various categories.
26. Given a list of tasks for a project, their times to complete, and their precedence relationships,
    the Six Sigma Black Belt should be able to compute the time to completion
    for the project, the earliest completion times, the latest completion
    times and the slack times. He should also be able to identify which
    tasks are on the critical path.
27. Give cost and time data for project tasks, the Six Sigma Black Belt should be able to compute
    the cost of normal and crash schedules and the minimum total cost
    schedule.
28. The Six Sigma Black Belt should be familiar with the basic principles of benchmarking.
29. The Six Sigma Black Belt should be familiar with the limitations of benchmarking.
30. Given an organization chart and a listing of team members, process owners, and sponsors, the Six
    Sigma Black Belt should be able to identify projects with a low probability
    of success.
31. The Six Sigma Black Belt should be able to identify measurement scales of various metrics (nominal,
    ordinal, etc).
32. Given a metric on a particular scale, the Six Sigma Black Belt should be able to determine if a particular
    statistical method should be used for analysis.
33. Given a properly collected set of data, the Six Sigma Black Belt should be able to perform a
    complete measurement system analysis, including the calculation of
    bias, repeatability, reproducibility, stability, discrimination (resolution)
    and linearity.
34. Given the measurement system metrics, the Six Sigma Black Belt should know whether or not a given
    measurement system should be used on a given part or process.
35. The Six Sigma Black Belt should know the difference between computing sigma from a data set
    whose production sequence is known and from a data set whose production
    sequence is not known.
36. Given the results of an AIAG Gage R&R study, the Six Sigma Black Belt should be able to
    answer a variety of questions about the measurement system.
37. Given a narrative description of “as-is” and “should-be” processes, the Six
    Sigma Black Belt should be able to prepare process maps.
38. Given a table of raw data, the Six Sigma Black Belt should be able to prepare a frequency tally
    sheet of the data, and to use the tally sheet data to construct a
    histogram.
39. The Six Sigma Black Belt should be able to compute the mean and standard deviation from a grouped
    frequency distribution.
40. Given a list of problems, the Six Sigma Black Belt should be able to construct a Pareto Diagram
    of the problem frequencies.
41. Given a list which describes problems by department, the Six Sigma Black Belt should be able to
    construct a Crosstabulation and use the information to perform a Chi-square
    analysis.
42. Given a table of x and y data pairs, the Six Sigma Black Belt should be able to determine if
    the relationship is linear or non-linear.
43. The Six Sigma Black Belt should know how to use non-linearity’s to make products or processes
    more robust.
44. The Six Sigma Black Belt should be able to construct and interpret a run chart when given a
    table of data in time-ordered sequence. This includes calculating
    run length, number of runs and quantitative trend evaluation.
45. When told the data are from an exponential or Erlang distribution the Six Sigma Black Belt should
    know that the run chart is preferred over the standard X control chart.
46. Given a set of raw data the Six Sigma Black Belt should be able to identify and compute two
    statistical measures each for central tendency, dispersion, and shape.
47. Given a set of raw data, the Six Sigma Black Belt should be able to construct a histogram.
48. Given a stem & leaf plot, the Six Sigma Black Belt should be able to reproduce a sample
    of numbers to the accuracy allowed by the plot.
49. Given a box plot with numbers on the key box points, the Six Sigma Black Belt should be able to
    identify the 25th and 75th percentile and the median.
50. The Six Sigma Black Belt should know when to apply enumerative statistical methods, and when
    not to.
51. The Six Sigma Black Belt should know when to apply analytic statistical methods, and when not
    to.
52. The Six Sigma Black Belt should demonstrate a grasp of basic probability concepts, such as
    the probability of mutually exclusive events, of dependent and independent
    events, of events that can occur simultaneously, etc.
53. The Six Sigma Black Belt should know factorials, permutations and combinations, and how to
    use these in commonly used probability distributions.
54. The Six Sigma Black Belt should be able to compute expected values for continuous and discrete
    random variables.
55. The Six Sigma Black Belt should be able to compute univariate statistics for samples.
56. The Six Sigma Black Belt should be able to compute confidence intervals for various statistics.
57. The Six Sigma Black Belt should be able to read values from a cumulative frequency ogive.
58. The Six Sigma Black Belt should be familiar with the commonly used probability distributions,
    including: hypergeometric, binomial, Poisson, normal, exponential,
    chi-square, Student’s t, and F.
59. Given a set of data the Six Sigma Black Belt should be able to correctly identify which distribution
    should be used to perform a given analysis, and to use the distribution
    to perform the analysis.
60. The Six Sigma Black Belt should know that different techniques are required for analysis depending
    on whether a given measure (e.g., the mean) is assumed known or estimated
    from a sample. The Six Sigma Black Belt should choose and properly
    use the correct technique when provided with data and sufficient information
    about the data.
61. Given a set of subgrouped data, the Six Sigma Black Belt should be able to select and prepare
    the correct control charts and to determine if a given process is
    in a state of statistical control.
62. The above should be demonstrated for data representing all of the most common control charts.
63. The Six Sigma Black Belt should understand the assumptions that underlie ANOVA, and be able
    to select and apply a transformation to the data.
64. The Six Sigma Black Belt should be able to identify which cause on a list of possible causes
    will most likely explain a non-random pattern in the regression residuals.
65. If shown control chart patterns, the Six Sigma Black Belt should be able to match the control chart
    with the correct situation (e.g., an outlier pattern vs. a gradual
    trend matched to a tool breaking vs. a machine gradually warming up).
66. The Six Sigma Black Belt should understand the mechanics of PRE-Control.
67. The Six Sigma Black Belt should be able to correctly apply EWMA charts to a process with serial
    correlation in the data.
68. Given a stable set of subgrouped data, the Six Sigma Black Belt should be able to perform a complete
    Process Capability Analysis. This includes computing and interpreting
    capability indices, estimating the % failures, control limit calculations,
    etc.
69. The Six Sigma Black Belt should demonstrate an awareness of the assumptions that underlie the
    use of capability indices.
70. Given the results of a replicated full-factorial experiment, the Six Sigma Black Belt should be able
    to complete the entire ANOVA table.
71. The Six Sigma Black Belt should understand the basic principles of planning a statistically
    designed experiment. This can be demonstrated by critiquing various
    experimental plans with or without shortcomings.
72. Given a “clean” experimental plan, the Six Sigma Black Belt should be able to find
    the correct number of replicates to obtain a desired power.
73. The Six Sigma Black Belt should know the difference between the various types of experimental
    models (fixed-effects, random-effects, mixed).
74. The Six Sigma Black Belt should understand the concepts of randomization and blocking.
75. Given a set of data, the Six Sigma Black Belt should be able to perform a Latin Square analysis
    and interpret the results.
76. Ditto for one way ANOVA, two way ANOVA (with and without replicates), full and fractional factorials,
    and response surface designs.
77. Given an appropriate experimental result, the Six Sigma Black Belt should be able to compute the direction
    of steepest ascent.
78. Given a set of variables each at two levels, the Six Sigma Black Belt can determine the correct
    experimental layout for a screening experiment using a saturated design.
79. Given data for such an experiment, the Six Sigma Black Belt can identify which main effects are significant
    and state the effect of these factors.
80. Given two or more sets of responses to categorical items (e.g., customer survey responses categorized
    as poor, fair, good, excellent), the Six Sigma Black Belt will be
    able to perform a Chi-Square test to determine if the samples are
    significantly different.
81. The Six Sigma Black Belt will understand the idea of confounding and be able to identify which
    two factor interactions are confounded with the significant main effects.
82. The Six Sigma Black Beltwill be able to state the direction of steepest ascent from experimental
    data.
83. The Six Sigma Black Belt will understand fold over designs and be able to identify the fold
    over design that will clear a given alias.
84. The Six Sigma Black Belt will know how to augment a factorial design to create a composite
    or central composite design.
85. The Six Sigma Black Belt will be able to evaluate the diagnostics for an experiment.
86. The Six Sigma Black Belt will be able to identify the need for a transformation in y and to
    apply the correct transformation.
87. Given a response surface equation in quadratic form, the Six Sigma Black Belt will be able
    to compute the stationary point.
88. Given data (not graphics), the Six Sigma Black Belt will be able to determine if the stationary
    point is a maximum, minimum or saddle point.
89. The Six Sigma Black Belt will be able to use a quadratic loss function to compute the cost
    of a given process.
90. The Six Sigma Black Belt will be able to conduct simple and multiple linear regression.
91. The Six Sigma Black Belt will be able to identify patterns in residuals from an improper regression
    model and to apply the correct remedy.
92. The Six Sigma Black Belt will understand the difference between regression and correlation
    studies.
93. The Six Sigma Black Belt will be able to perform chi-square analysis of contingency tables.
94. The Six Sigma Black Belt will be able to compute basic reliability statistics (mtbf, availability,
    etc.).
95. Given the failure rates for given subsystems, the Six Sigma Black Belt will be able to use
    reliability apportionment to set mtbf goals.
96. The Six Sigma Black Belt will be able to compute the reliability of series, parallel, and series-parallel
    system configurations.
97. The Six Sigma Black Belt will demonstrate the ability to create and read an FMEA analysis.
98. The Six Sigma Black Belt will demonstrate the ability to create and read a fault tree.
99. Given distributions of strength and stress, the Six Sigma Black Belt will be able to compute the probability
    of failure.
100. The Six Sigma Black Belt will be able to apply statistical tolerancing to set tolerances for
     simple assemblies. He will know how to compare statistical tolerances
     to so-called “worst case” tolerancing.
101. The Six Sigma Black Belt will be aware of the limits of the Six Sigma approach.