Tag Archives: ASQ

Quality 4.0 in Basic Terms (Interview)

On October 12th I dialed in to Quality Digest Live to chat with Dirk Dusharne, Editor-in-Chief of Quality Digest, about Quality 4.0 and my webinar on the topic which was held yesterday (October 16).

Check out my 13-minute interview here, starting at 14:05! It answers two questions:

  • What is Quality 4.0 – in really basic terms that are easy to remember?
  • How can we use these emerging technologies to support engagement and collaboration?

You can also read more about the topic here on the Intelex Community, or come to ASQ’s Quality 4.0 Summit in Dallas next month where I’ll be sharing more information along with other Quality 4.0 leaders like Jim Duarte of LJDUARTE and Associates and Dan Jacob of LNS Research.

Quality 4.0: Reveal Hidden Insights with Data Sci & Machine Learning (Webinar)

Quality Digest

What’s Quality 4.0, why is it important, and how can you use it to gain competitive advantage? Did you know you can benefit from Quality 4.0 even if you’re not a manufacturing organization? That’s right. I’ll tell you more next week.

Sign up for my 50-minute webinar at 2pm ET on Tuesday, October 16, 2018 — hosted by Dirk Dusharme and Mike Richman at Quality Digest. This won’t be your traditional “futures” talk to let you know about all of the exciting technology on the horizon… I’ve actually been doing and teaching data science, and applying machine learning to practical problems in quality improvement, for over a decade.

Come to this webinar if:

  1. You have a LOT of data and you don’t know where to begin
  2. You’re kind of behind… you still use paper and Excel and you’re hoping you don’t miss the opportunities here
  3. You’re a data scientist and you want to find out about quality and process improvement
  4. You’re a quality professional and you want to find out more about data science
  5. You’re a quality engineer and you want some professional preparation for what’s on the horizon
  6. You want to be sure you get on our Quality 4.0 mailing list to receive valuable information assets for the next couple years to help you identify and capture opportunities

Register Here! See you on Tuesday. If you can’t make it, we’ll also be at the ASQ Quality 4.0 Summit in Dallas next month sharing more information about the convergence of quality and Big Data.

Happy 10th Birthday!

10 years ago today, this blog published its first post: “How Do I Do a Lean Six Sigma (LSS) Project?” Looking back, it seems like a pretty simple place to have started. I didn’t know whether it would even be useful to anyone, but I was committed to making my personal PDSA cycles high-impact: I was going to export things I learned, or things I found valuable. (As it turns out, many people did appreciate the early posts even though it would take a few years for that to become evident!)

Since then, hundreds more have followed to help people understand more about quality and process improvement in theory and in practice. I started writing because I was in the middle of my PhD dissertation in the Quality Systems program at Indiana State, and I was discovering so many interesting nuggets of information that I wanted to share those with the world – particularly practitioners, who might not have lots of time (or even interest) in sifting through the research. In addition, I was using data science (and some machine learning, although at the time, it was much more difficult to implement) to explore quality-related problems, and could see the earliest signs that this new paradigm for problem solving might help fuel data-driven decision making in the workplace… if only we could make the advanced techniques easy for people in busy jobs to use and apply.

We’re not there yet, but as ASQ and other organizations recognize Quality 4.0 as a focus area, we’re much closer. As a result, I’ve made it my mission to help bring insights from research to practitioners, to make these new innovations real. If you are developing or demonstrating any new innovative techniques that relate to making people, processes, or products better, easier, faster, or less expensive — or reducing risks and building individual and organizational capabilities — let me know!

I’ve also learned a lot in the past decade, most of which I’ve spent helping undergraduate students develop and refine their data-driven decision making skills, and more recently at Intelex (provider of integrated environment, health & safety, and quality management EHSQ software to enterprises and smaller organizations). Here are some of the big lessons:

  1. People are complex. They have multidimensional lives, and work should support and enrich those lives. Any organization that cares about performance — internally and in the market — should examine how it can create complete and meaningful experiences. This applies not only to customers, but to employees and partners and suppliers. It also applies to anyone an organization has the power and potential to impact, no matter how small.
  2. Everybody wants to do a good job (and be recognized for it). How can we create environments where each person is empowered to contribute in all the areas where they have talent and interest? How can these same environments be designed with empathy as a core capability?
  3. Your data are your most valuable assets. It sounds trite, but data is becoming as valuable as warehouses, inventory, and equipment. I was involved in a project a few years ago where we digitized data that had been collected for three years — and by analyzing it, we uncovered improvement opportunities that when implemented, saved thousands of dollars a week. We would not have been able to do that if the data had remained scratched in pencil on thousands of sheets of well-worn legal paper.
  4. Nothing beats domain expertise (especially where data science is concerned). I’ve analyzed terabytes of data over the past decade, and in many cases, the secrets are subtle. Any time you’re using data to make decisions, be sure to engage the people with practical, on-the-ground experience in the area you’re studying.
  5. Self-awareness must be cultivated. The older you get, and the more experience you gain, the more you know what you don’t know. Many of my junior colleagues (and yours) haven’t reached this point yet, and will need some help from senior colleagues to gain this awareness. At the same time, those of you who are senior have valuable lessons to learn from your junior colleagues, too! Quality improvement is grounded in personal and organizational learning, and processes should help people help each other uncover blind spots and work through them — without fear.

 

Most of all, I discovered that what really matters is learning. We can spend time supporting human and organizational performance, developing and refining processes that have quality baked in, and making sure that products meet all their specifications. But what’s going on under the surface is more profound: people are learning about themselves, they are learning about how to transform inputs into outputs in a way that adds value, and they are learning about each other and their environment. Our processes just encapsulate that organizational knowledge that we develop as we learn.

Quality 4.0: Let’s Get Digital

Want to find out what Quality 4.0 really is — and start realizing the benefits for your organization? Check out this month’s issue of ASQ’s Quality Progress, where my new article (“Let’s Get Digital“) does just that. Quality 4.0 — which we’re working to bring to the practice of quality management and quality engineering at Intelex — asks how we can leverage connected, intelligent, automated (C-I-A) technologies to increase efficiency, effectiveness, and satisfaction: “As connected, intelligent and automated systems are more widely adopted, we can once again expect a renaissance in quality tools and methods. The progression can be summarized through four themes:

  • Quality as inspection: In the early days, quality assurance relied on inspecting bad quality out of the total items produced. Walter A. Shewhart’s methods for statistical process control helped operators determine whether variation was due to random or special causes.
  • Quality as design: Inspired by W. Edwards Deming’s recommendation to cease dependence on inspection, more holistic methods emerged for designing quality into processes to prevent quality problems before they occurred.
  • Quality as empowerment: TQM and Six Sigma advocate a holistic approach to quality, making it everyone’s responsibility and empowering individuals to contribute to continuous improvement.
  • Quality as discovery: In an adaptive, intelligent environment, quality depends on how quickly we can discover and aggregate new data sources, how effectively we can discover root causes and how well we can discover new insights about ourselves, our products and our organizations.”

Read more at http://asq.org/quality-progress/2018/10/basic-quality/lets-get-digital.html  or download the PDF (http://asq.org/quality-progress/2018/10/basic-quality/lets-get-digital.pdf)

Where is Quality Management Headed?

Image Credit: Doug Buckley of http://hyperactive.to

Image Credit: Doug Buckley of http://hyperactive.to

[This post is in response to ASQ’s February topic for the Influential Voices group, which asks: Where do you plan to take your career in 2016? What’s your view of careers in quality today—what challenges is this field facing? How can someone starting out in quality succeed?]

We are about to experience a paradigm shift in production, operations, and service: a shift that will have direct consequences on the principles and practice of design, development, and quality management. This “fourth industrial revolution” of cyber-physical systems will require more people in the workforce to understand quality principles associated with co-creation of value, and to develop novel business models. New technical skills will become critical for a greater segment of workers, including embedded software, artificial intelligence, data science, analytics, Big Data (and data quality), and even systems integration. 

Over the past 20 years, we moved many aspects of our work and our lives online. And in the next 20 years, the boundaries between the physical world and the online world will blur — to a point where the distinction may become unnecessary.

Here is a vignette to illustrate the kinds of changes we can anticipate. Imagine the next generation FitBit, the personalized exercise assistant that keeps track of the number of steps you walk each day. As early as 2020, this device will not only automatically track your exercise patterns, but will also automatically integrate that information with your personal health records. Because diet strategies have recently been shown to be predominantly unfounded, and now researchers like Kevin Hall, Eran Elinav, and Eran Siegal know that the only truly effective diets are the ones that are customized to your body’s nutritional preferences [1], your FitBit and your health records will be able to talk to your food manager application to design the perfect diet for you (given your targets and objectives). Furthermore, to make it easy for you, your applications will also autonomously communicate with your refrigerator and pantry (to monitor how much food you have available), your local grocery store, and your calendar app so that food deliveries will show up when and only when you need to be restocked. You’re amazed that you’re spending less on food, less of it is going to waste, and you never have to wonder what you’re going to make for dinner. Your local grocery store is also greatly rewarded, not only for your loyalty, but because it can anticipate the demand from you and everyone else in your community – and create specials, promotions, and service strategies that are targeted to your needs (rather than just what the store guesses you need).

Although parts of this example may seem futuristic, the technologies are already in place. What is missing is our ability to link the technologies together using development processes that are effective and efficient – and in particular, coordinating and engaging the people  who will help make it happen. This is a job for quality managers and others who study production and operations management

As the Internet of Things (IoT) and pervasive information become commonplace, the fundamental nature and character of how quality management principles are applied in practice will be forced to change. As Eric Schmidt, former Chairman of Google, explains:  “the new age of artificial intelligence is beginning, and it’s a big deal.” [2] Here are some ways that this shift will impact researchers and practitioners interested in quality:

  • Strategic deployment of IoT technologies will help us simultaneously improve our use of enterprise assets, reduce waste, promote sustainability, and coordinate people and machines to more effectively meet strategic goals and operational targets.
  • Smart materials, embedded in our production and service ecosystems, will change our views of objects from inert and passive to embedded and engaged. For example, MIT has developed a “smart band-aid” that communicates with a wound, provides visual indicators of the healing process, and delivers medication as needed. [3] Software developers will need to know how to make this communication seamless and reliable in a variety of operations contexts.
  • Our technologies will be able to proactively anticipate the Voice of the Customer, enabling us to meet not only their stated and implied needs, but also their emergent needs and hard-to-express desires. Similarly, will the nature of customer satisfaction change as IoT becomes more pervasive?
  • Cloud and IoT-driven Analytics will make more information available for powerful decision-making (e.g. real-time weather analytics), but comes with its own set of challenges: how to find the data, how to assess data quality, and how to select and store data with likely future value to decision makers. This will be particularly challenging since analytics has not been a historical focus among quality managers. [4]
  • Smart, demand-driven supply chains (and supply networks) will leverage Big Data, and engage in automated planning, automatic adjustment to changing conditions or supply chain disruptions like war or extreme weather events, and self-regulation.
  • Smart manufacturing systems will implement real time communication between people, machines, materials, factories and warehouses, supply chain partners, and logistics partners using cloud computing. Production systems will adapt to demand as well as environmental factors, like the availability of resources and components. Sustainability will be a required core capability of all organizations that produce goods.
  • Cognitive manufacturing will implement manufacturing and service systems capable of perception, judgment, and improving quality autonomously – without the delays associated with human decision-making or the detection of issues.
  • Cybersecurity will be recognized as a critical component of all of the above. For most (if not all) of these next generation products and production systems, quality will not be possible without addressing information security.
  • The nature of quality assurance will also change, since products will continue to learn (and not necessarily meet their own quality requirements) after purchase or acquisition, until the consumer has used them for a while. In a December 2015 article I wrote for Software Quality Professional, I ask “How long is the learning process for this technology, and have [product engineers] designed test cases to accommodate that process after the product has been released? The testing process cannot find closure until the end of the ‘burn-in’ period when systems have fully learned about their surroundings.” [5]
  • We will need new theories for software quality practice in an era where embedded artificial intelligence and technological panpsychism (autonomous objects with awareness, perception, and judgment) are the norm.

How do we design quality into a broad, adaptive, dynamically evolving ecosystem of people, materials, objects, and processes? This is the extraordinarily complex and multifaceted question that we, as a community of academics and practitioners, must together address.

Just starting out in quality? My advice is to get a technical degree (science, math, or engineering) which will provide you with a solid foundation for understanding the new modes of production that are on the horizon. Industrial engineering, operations research, industrial design, and mechanical engineering are great fits for someone who wants a career in quality, as are statistics, data science, manufacturing engineering, and telecommunications. Cybersecurity and intelligence will become increasingly more central to quality management, so these are also good directions to take. Or, consider applying for an interdisciplinary program like JMU’s Integrated Science and Technology where I teach. We’re developing a new 21-credit sector right now where you can study EVERYTHING in the list above! Also, certifications are a plus, but in addition to completing training programs be sure to get formally certified by a professional organization to make sure that your credentials are widely recognized (e.g. through ASQ and ATMAE).

 

References

[1] http://www.huffingtonpost.com/entry/no-one-size-fits-all-diet-plan_564d605de4b00b7997f94272
[2] https://www.washingtonpost.com/news/innovations/wp/2015/09/15/what-eric-schmidt-gets-right-and-wrong-about-the-future-of-artificial-intelligence/
[3] http://news.mit.edu/2015/stretchable-hydrogel-electronics-1207
[4] Evans, J. R. (2015). Modern Analytics and the Future of Quality and Performance Excellence. The Quality Management Journal22(4), 6.
[5] Radziwill, N. M., Benton, M. C., Boadu, K., & Perdomo, W., 2015: A Case-Based Look at Integrating Social Context into Software Quality. Software Quality Professional, December.

Analytic Hierarchy Process (AHP) using preferenceFunction in ahp

Yesterday, I wrote about how to use gluc‘s new ahp package on a simple Tom-Dick-Harry one level decision making problem using Analytic Hierarchy Process (AHP). One of the cool things about that package is that in addition to specifying the pairwise comparisons directly using Saaty’s scale (below, from https://kristalaace2014.wordpress.com/2014/05/14/w12_al_vendor-evaluation/)…

saaty-scale

…you can also describe each of the Alternatives in terms of descriptive variables which you can use inside a function to make the pairwise comparisons automatically. This is VERY helpful if you have lots of criteria, subcriteria, or alternatives to evaluate!! For example, I used preferenceFunction to compare 55 alternatives using 6 criteria and 4 subcriteria, and was very easily able to create functions to represent my judgments. This was much easier than manually entering all the comparisons.

This post shows HOW I replaced some of my manual comparisons with automated comparisons using preferenceFunction. (The full YAML file is included at the bottom of this post for you to use if you want to run this example yourself.) First, recall that the YAML file starts with specifying the alternatives that you are trying to choose from (at the bottom level of the decision hierarchy) and some variables that characterize those alternatives. I used the descriptions in the problem statement to come up with some assessments between 1=not great and 10=great:

#########################
# Alternatives Section
# THIS IS FOR The Tom, Dick, & Harry problem at
# https://en.wikipedia.org/wiki/Analytic_hierarchy_process_%E2%80%93_leader_example
#
Alternatives: &alternatives
# 1= not well; 10 = best possible
# Your assessment based on the paragraph descriptions may be different.
  Tom:
    age: 50
    experience: 7
    education: 4
    leadership: 10
  Dick:
    age: 60
    experience: 10
    education: 6
    leadership: 6
  Harry:
    age: 30
    experience: 5
    education: 8
    leadership: 6
#
# End of Alternatives Section
#####################################

Here is a snippet from my original YAML file specifying my AHP problem manually ():

  children: 
    Experience:
      preferences:
        - [Tom, Dick, 1/4]
        - [Tom, Harry, 4]
        - [Dick, Harry, 9]
      children: *alternatives
    Education:
      preferences:
        - [Tom, Dick, 3]
        - [Tom, Harry, 1/5]
        - [Dick, Harry, 1/7]
      children: *alternatives

And here is what I changed that snippet to, so that it would do my pairwise comparisons automatically. The functions are written in standard R (fortunately), and each function has access to a1 and a2 (the two alternatives). Recursion is supported which makes this capability particularly useful. I tried to write a function using two of the characteristics in the decision (a1$age and a1$experience) but this didn’t seen to work. I’m not sure whether the package supports it or not. Here are my comparisons rewritten as functions:

  children: 
    Experience:
          preferenceFunction: >
            ExperiencePreference <- function(a1, a2) {
              if (a1$experience < a2$experience) return (1/ExperiencePreference(a2, a1))
              ratio <- a1$experience / a2$experience
              if (ratio < 1.05) return (1)
              if (ratio < 1.2) return (2)
              if (ratio < 1.5) return (3)
              if (ratio < 1.8) return (4)
              if (ratio < 2.1) return (5) return (6) } children: *alternatives Education: preferenceFunction: >
            EducPreference <- function(a1, a2) {
              if (a1$education < a2$education) return (1/EducPreference(a2, a1))
              ratio <- a1$education / a2$education
              if (ratio < 1.05) return (1)
              if (ratio < 1.15) return (2)
              if (ratio < 1.25) return (3)
              if (ratio < 1.35) return (4)
              if (ratio < 1.55) return (5)
              return (5)
            }
          children: *alternatives

To run the AHP with functions in R, I used this code (I am including the part that gets the ahp package, in case you have not done that yet). BE CAREFUL and make sure, like in FORTRAN, that you line things up so that the words START in the appropriate columns. For example, the “p” in preferenceFunction MUST be immediately below the 7th character of your criterion’s variable name.

devtools::install_github("gluc/ahp", build_vignettes = TRUE)
install.packages("data.tree")

library(ahp)
library(data.tree)

setwd("C:/AHP/artifacts")
nofxnAhp <- LoadFile("tomdickharry.txt")
Calculate(nofxnAhp)
fxnAhp <- LoadFile("tomdickharry-fxns.txt")
Calculate(fxnAhp)

print(nofxnAhp, "weight")
print(fxnAhp, "weight")

You can see that the weights are approximately the same, indicating that I did a good job at developing functions that represent the reality of how I used the variables attached to the Alternatives to make my pairwise comparisons. The results show that Dick is now the best choice, although there is some inconsistency in our judgments for Experience that we should examine further. (I have not examined this case to see whether rank reversal could be happening).

> print(nofxnAhp, "weight")
                         levelName     weight
1  Choose the Most Suitable Leader 1.00000000
2   ¦--Experience                  0.54756924
3   ¦   ¦--Tom                     0.21716561
4   ¦   ¦--Dick                    0.71706504
5   ¦   °--Harry                   0.06576935
6   ¦--Education                   0.12655528
7   ¦   ¦--Tom                     0.18839410
8   ¦   ¦--Dick                    0.08096123
9   ¦   °--Harry                   0.73064467
10  ¦--Charisma                    0.26994992
11  ¦   ¦--Tom                     0.74286662
12  ¦   ¦--Dick                    0.19388163
13  ¦   °--Harry                   0.06325174
14  °--Age                         0.05592555
15      ¦--Tom                     0.26543334
16      ¦--Dick                    0.67162545
17      °--Harry                   0.06294121

> print(fxnAhp, "weight")
                         levelName     weight
1  Choose the Most Suitable Leader 1.00000000
2   ¦--Experience                  0.54756924
3   ¦   ¦--Tom                     0.25828499
4   ¦   ¦--Dick                    0.63698557
5   ¦   °--Harry                   0.10472943
6   ¦--Education                   0.12655528
7   ¦   ¦--Tom                     0.08273483
8   ¦   ¦--Dick                    0.26059839
9   ¦   °--Harry                   0.65666678
10  ¦--Charisma                    0.26994992
11  ¦   ¦--Tom                     0.74286662
12  ¦   ¦--Dick                    0.19388163
13  ¦   °--Harry                   0.06325174
14  °--Age                         0.05592555
15      ¦--Tom                     0.26543334
16      ¦--Dick                    0.67162545
17      °--Harry                   0.06294121

> ShowTable(fxnAhp)

tomdick-ahp-fxns

Here is the full YAML file for the “with preferenceFunction” case.

#########################
# Alternatives Section
# THIS IS FOR The Tom, Dick, & Harry problem at
# https://en.wikipedia.org/wiki/Analytic_hierarchy_process_%E2%80%93_leader_example
#
Alternatives: &alternatives
# 1= not well; 10 = best possible
# Your assessment based on the paragraph descriptions may be different.
  Tom:
    age: 50
    experience: 7
    education: 4
    leadership: 10
  Dick:
    age: 60
    experience: 10
    education: 6
    leadership: 6
  Harry:
    age: 30
    experience: 5
    education: 8
    leadership: 6
#
# End of Alternatives Section
#####################################
# Goal Section
#
Goal:
# A Goal HAS preferences (within-level comparison) and HAS Children (items in level)
  name: Choose the Most Suitable Leader
  preferences:
    # preferences are defined pairwise
    # 1 means: A is equal to B
    # 9 means: A is highly preferrable to B
    # 1/9 means: B is highly preferrable to A
    - [Experience, Education, 4]
    - [Experience, Charisma, 3]
    - [Experience, Age, 7]
    - [Education, Charisma, 1/3]
    - [Education, Age, 3]
    - [Age, Charisma, 1/5]
  children: 
    Experience:
          preferenceFunction: >
            ExperiencePreference <- function(a1, a2) {
              if (a1$experience < a2$experience) return (1/ExperiencePreference(a2, a1))
              ratio <- a1$experience / a2$experience
              if (ratio < 1.05) return (1)
              if (ratio < 1.2) return (2)
              if (ratio < 1.5) return (3)
              if (ratio < 1.8) return (4)
              if (ratio < 2.1) return (5) return (6) } children: *alternatives Education: preferenceFunction: >
            EducPreference <- function(a1, a2) {
              if (a1$education < a2$education) return (1/EducPreference(a2, a1))
              ratio <- a1$education / a2$education
              if (ratio < 1.05) return (1)
              if (ratio < 1.15) return (2)
              if (ratio < 1.25) return (3)
              if (ratio < 1.35) return (4)
              if (ratio < 1.55) return (5)
              return (5)
            }
          children: *alternatives
    Charisma:
      preferences:
        - [Tom, Dick, 5]
        - [Tom, Harry, 9]
        - [Dick, Harry, 4]
      children: *alternatives
    Age:
      preferences:
        - [Tom, Dick, 1/3]
        - [Tom, Harry, 5]
        - [Dick, Harry, 9]
      children: *alternatives
#
# End of Goal Section
#####################################

If Japan Can, Why Can’t We? A Retrospective

if-japan-canJune 24, 1980 is kind of like July 4, 1776 for quality management… that’s the pivotal day that NBC News aired its one hour and 16 minute documentary called “If Japan Can, Why Can’t We?” introducing W. Edwards Deming and his methods to the American public. The video has been unavailable for years, but as of just last week, it’s been posted on YouTube. So my sophomore undergrads in Production & Operations Management took a step back in time to get a taste of the environment in the manufacturing industry in the late 1970’s, and watched it during class this week.

The last time I watched it was in 1997, in a graduate industrial engineering class. It didn’t feel quite as dated as it does now, nor did I have the extensive experience in industry as a lens to view the interviews through. But what did surprise me is that the core of the challenges they were facing aren’t that much different than the ones we face today — and the groundbreaking good advice from Deming is still good advice today.

  • Before 1980, it was common practice to produce a whole bunch of stuff and then check and see which ones were bad, and throw them out. The video provides a clear and consistent story around the need to design quality in to products and processes, which then reduces (or eliminates) the need to inspect bad quality out.
  • It was also common to tamper with a process that was just exhibiting random variation. As one of the line workers in the documentary said, “We didn’t know. If we felt like there might be a problem with the process, we would just go fix it.” Deming’s applications of Shewhart’s methods made it clear that there is no need to tamper with a process that’s exhibiting only random variation.
  • Both workers and managers seemed frustrated with the sheer volume of regulations they had to address, and noted that it served to increase costs, decrease the rate of innovation, and disproportionately hurt small businesses. They noted that there was a great need for government and industry to partner to resolve these issues, and that Japan was a model for making these interactions successful.
  • Narrator Lloyd Dobyns remarked that “the Japanese operate by consensus… we, by competition.” He made the point that one reason Japanese industrial reforms were so powerful and positive was that their culture naturally supported working together towards shared goals. He cautioned managers that they couldn’t just drop in statistical quality control and expect a rosy outcome: improving quality is a cultural commitment, and the methods are not as useful in the absence of buy-in and engagement.

The video also sheds light on ASQ’s November question to the Influential Voices, which is: “What’s the key to talking quality with the C-Suite?” Typical responses include: think at the strategic level; create compelling arguments using the language of money; learn the art of storytelling and connect your case with what it important to the executives.

But I think the answer is much more subtle. In the 1980 video, workers comment on how amazed their managers were when Deming proclaimed that management was responsible for improving productivity. How could that be??!? Many managers at that time were convinced that if a productivity problem existed, it was because the workers didn’t work fast enough, or with enough skill — or maybe they had attitude problems! Certainly not because the managers were not managing well. Implementing simple techniques like improving training programs and establishing quality circles (which demonstrated values like increased transparency, considering all ideas, putting executives on the factory floor so they could learn and appreciate the work being done, increasing worker participation and engagement, encouraging work/life balance, and treating workers with respect and integrity) were already demonstrating benefits in some U.S. companies. But surprisingly, these simple techniques were not widespread, and not common sense.

Just like Deming advocated, quality belongs to everyone. You can’t go to a CEO and suggest that there are quality issues that he or she does not care about. More likely, the CEO believes that he or she is paying a lot of attention to quality. They won’t like it if you accuse them of not caring, or not having the technical background to improve quality. The C-Suite is in a powerful position where they can, through policies and governance, influence not only the actions and operating procedures of the system, but also its values and core competencies — through business model selection and implementation. 

What you can do, as a quality professional, is acknowledge and affirm their commitment to quality. Communicate quickly, clearly, and concisely when you do. Executives have to find the quickest ways to decompose and understand complex problems in rapidly changing external environments, and then make decisions that affect thousands (and sometimes, millions!) of people. Find examples and stories from other organizations who have created huge ripples of impact using quality tools and technologies, and relate them concretely to your company.

Let the C-Suite know that you can help them leverage their organization’s talent to achieve their goals, then continually build their trust.

The key to talking quality with the C-suite is empathy.

 

You may also be interested in “Are Deming’s 14 Points Still Valid?” from Nov 19, 2012.

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