We’re teaching a class on blockchain and cryptocurrencies this semester, and since the field is so new and changing rapidly, we’ve asked our students to make finding and reviewing articles part of their learning practice this semester. This is a particularly challenging topic for this task because there’s so much hype, marketing, and fluff around these topics. We want to slice through that, and improve the signal-to-noise for people new to learning about blockchain and cryptocurrencies. Here are some tips I just prepared for our students — they may be helpful to anyone writing article reviews, especially for technology-related areas.
0 – Type of Source. Reviews or articles from from arXiv, Google Scholar were strong; reviews from Coindesk, CNN were weak; reviews from WSJ and Hacker Noon went both ways. Here are two submissions that were publishable with only minor edits:
1 – Spelling & Grammar. Most of you are college seniors, and the few who aren’t… are juniors. Please use complete sentences that make sense, with words that are spelled correctly! If this is hard for you, remember that every one of you has spell check. One way to remember this is to draft your posts in Word, and then perform spell check before you copy and paste what you wrote into WordPress.
1 – Your job is to create the TL;DR. What’s the essential substance of the source you’re reviewing? What are the main lessons or findings? If you were taking notes for an exam, what elements would you capture? (Using this perspective, commentary about how good or bad you think the article was, or what it didn’t cover well, would not help you on an exam.)
2 – Choose solid source material — primary sources, e.g. research papers, if possible. If the article is less than ~400-500 words, it’s probably not detailed enough to write a 250-300 word summary/analysis. Your job in this class is to break down complex topics & help people understand them. If your article is short and already very easy to understand, there’s nothing for you to do.
3 – Avoid “weasel words” (phrases or sentences that sound like marketing or clickbait but actually say nothing) and words/sentences that sound like you’re writing a Yelp or Amazon review rather than a critical academic review. Here are a couple weaselly examples drawn from this week’s draft posts (see if you can spot what’s wrong):
It is clear how beneficial blockchain can be to smaller businesses.
Blockchain has the potential to change the world.
Each other the topics covered in the article deserve their own piece and could be augmented upon greatly.
There is a degree of uncertainty that comes with an emerging technology.
Blockchain can bring them into the 21st century to compete with larger corporations.
Many people are scared of the changes, and governments will seek to regulate it.
4 – Answer the “so what” question. Why is this topic interesting or compelling?
5 – Choose information-rich tags. For example, in our class, don’t include blockchain as a tag… pretty much everything we do will be related to blockchain, and everyone will tend to use it, so there won’t be much information contained in the tag.
You may wonder why I’m reviewing a book written by the creator of the Occupy movement for an audience of academics and practitioners who care about quality and continuous improvement in organizations, many of which are trying to not only sustain themselves but also (in many cases) to make a profit. The answer is simple: by understanding how modern social movements are catalyzed by decentralized (and often autonomous) interactive media, we will be better able to achieve some goals we are very familiar with. These include 1) capturing the rapidly changing “Voice of the Customer” and, in particular, gaining access to its silent or hidden aspects, 2) promoting deep engagement, not just in work but in the human spirit, and 3) gaining insights into how innovation can be catalyzed and sustained in a truly democratic organization.
This book is packed with meticulously researched cases, and deeply reflective analysis. As a result, is not an easy read, but experiencing its modern insights in terms of the historical context it presents is highly rewarding. Organized into three sections, it starts by describing the events leading up to the Occupy movement, the experience of being a part of it, and why the author feels Occupy fell short of its objectives. The second section covers several examples of protests, from ancient history to modern times, and extracts the most important strategic insight from each event. Next, a unified theory of revolution is presented that reconciles the unexpected, the emotional, and the systematic aspects of large-scale change.
The third section speaks directly to innovation. Some of the book’s most powerful messages, the principles of revolution, are presented in Chapter 14. “Understanding the principles behind revolution,” this chapter begins, “allows for unending tactical innovation that shifts the paradigms of activism, creates new forms of protest, and gives the people a sudden power over their rulers.” If we consider that we are often “ruled” by the status quo, then these principles provide insight into how we can break free: short sprints, breaking patterns, emphasizing spirit, presenting constraints, breaking scripts, transposing known tactics to new environmental contexts, and proposing ideas from the edge. The end result is a masterful work that describes how to hear, and mobilize, the collective will.
Are you an instructor with a Spring 2016 intro to statistics course coming up… and yet you haven’t started preparing? If so, I have a potential solution for you to consider. The materials (lecture slides, in-class practice problems in R, exams, syllabus) go with my book and are about 85% compiled, but good enough to get a course started this week (I will be finishing the collection by mid-January).
There is also a 36MB .zip file if you would like to download the materials.
Whether you will be using them or just considering them, please fill in the Google Form athttps://docs.google.com/forms/d/1Z7djuKHg1L4k7bTtktHXI7Juduad3fUW9G69bxN6jRA/viewformso I can keep track of everyone and provide you with updates. Also, I want to make sure that I’m providing the materials to INSTRUCTORS (and not students), so please use the email account from your institution when you sign up. Once I get your contact information, I will email you the link to the materials.
If you would like permission to edit the materials, I can do that as well — I know a couple of you have expressed that you would like to add to the collection (e.g translate them to another language). If you see any issues or errors, please either fix and/or email to tell me to fix!
Thanks for your interest and participation! Also, Happy New Year!
Image Credit: Doug Buckley of http://hyperactive.to
Hey everyone! I just wanted to give you the heads up on a book project that I’ve been working on (which should be available by Spring 2016). It’s all about using the R programming language to do simulation — which I think is one of the most powerful (and overlooked) tools in data science. Please feel free to email or write comments below if you have any suggestions for material you’d like to have included in it!
Originally, this project was supposed to be a secret… I’ve been working on it for about two years now, along with two other writing projects, and was approached in June by a traditional publishing company (who I won’t mention by name) who wanted to brainstorm with me about possibly publishing and distributing my next book. After we discussed the intersection of their wants and my needs, I prepared a full outline for them, and they came up with a work schedule and sent me a contract. While I was reading the contract, I got cold feet. It was the part about giving up “all moral rights” to my work, which sounds really frightening (and is not something I have to do under creative commons licensing, which I prefer). I shared the contract with a few colleagues and a lawyer, hoping that they’d say don’t worry… it sounds a lot worse than it really is. But the response I got was it sounds pretty much like it is.
While deliberating the past two weeks, I’ve been moving around a lot and haven’t been in touch with the publisher. I got an email this morning asking for my immediate decision on the matter (paraphrased, because there’s a legal disclaimer at the bottom of their emails that says “this information may be privileged” and I don’t want to violate any laws):
If we don’t hear from you, unfortunately we’ll be moving forward with this project. Do you still want to be on board?
The answer is YEAH – of COURSE I’m “on board” with my own project. But this really made me question the value of a traditional publisher over an indie publisher, or even self-publishing. And if they’re moving forward anyway, does that mean they take my outline (and supporting information about what I’m planning for each chapter) and just have someone else write to it? That doesn’t sound very nice. Since all the content on my blog is copyrighted by ME, I’m sharing the entire contents of what I sent to them on July 6th to establish the copyright on my outline in a public forum.
So if you see this chapter structure in someone ELSE’S book… you know what happened. The publisher came up with the idea for the main title (“Simulation for Data Science With R”) so I might publish under a different title that still has the words Simulation and R in them.
I may still publish with them, but I’ll make that decision after I have the full manuscript in place in a couple months. And after I have the chance to reflect more on what’s best for everyone. What do you think is the best route forward?
Simulation for Data Science With R
Effective Data-Driven Decision Making for Business Analysis by Nicole M. Radziwill
Audience
Simulation is an essential (yet often overlooked) tool in data science – an interdisciplinary approach to problem-solving that leverages computer science, statistics, and domain expertise. This easy-to-understand introductory text for new and intermediate-level programmers, data scientists, and business analysts surveys five different simulation techniques (Monte Carlo, Discrete Event Simulation, System Dynamics, Agent-Based Modeling, and Resampling). The book focuses on practical and illustrative examples using the R Statistical Software, presented within the context of structured methodologies for problem solving (such as DMAIC and DMADV) that will enable you to more easily use simulation to make effective data-driven decisions. Readers should have exposure to basic concepts in programming but can be new to the R Statistical Software.
Mission
This book helps its readers 1) formulate research questions that simulation can help solve, 2) choose an appropriate problem-solving methodology, 3) choose one or more simulation techniques to help solve that problem, 4) perform basic simulations using the R Statistical Software, and 5) present results and conclusions clearly and effectively.
Objectives and achievements
The reader will:
Learn about essential and foundational concepts in modeling and simulation
Determine whether a simulation project is also a data science project
Choose an appropriate problem-solving methodology for effective data-driven decision making
Select suitable simulation techniques to provide insights about a given problem
Build and interpret the results from basic simulations using the R Statistical Software
SECTION I: BASIC CONCEPTS
Introduction to Simulation for Data Science
Foundations for Decision-Making
SECRET NEW CHAPTER THAT YOU WILL BE REALLY EXCITED ABOUT
SECTION II: STOCHASTIC PROCESSES
Variation and Random Variable Generation
Distribution Fitting
Data Generating Processes
SECTION III: SIMULATION TECHNIQUES
Monte Carlo Simulation
Discrete Event Simulation
System Dynamics
Agent-Based Modeling
Resampling Methods
SECRET NEW CHAPTER THAT YOU WILL BE REALLY EXCITED ABOUT
SECTION IV: CASE STUDIES
Case Study 1: Possibly modeling opinion dynamics… specific example still TBD
Case Study 2: A Really Practical Application of Simulation (especially for women)
Chapter 1: Introduction to Simulation for Data Science – 35 pages
Description
This chapter explains the role of simulation in data science, and provides the context for understanding the differences between simulation techniques and their philosophical underpinnings.
Level
BASIC
Topics covered
Variation and Data-Driven Decision Making
What are Complex Systems?
What are Complex Dynamical Systems? What is systems thinking? Why is a systems perspective critical for data-driven decision making? Where do we encounter complex systems in business or day-to-day life?
What is Data Science?
A Taxonomy of Data Science. The Data Science Venn Diagram. What are the roles of modeling and simulation in data science projects? “Is it a Data Science Project?” — a Litmus Test. How modeling and simulation align with data science.
Types of Simulation: Static vs. Dynamic, Stochastic vs. Deterministic, Discrete vs. Continuous, Terminating and Non-Terminating (Steady State). Philosophical Principles: Holistic vs. Reductionist, Kadanoff’s Universality, Parsimony, Sensitivity to Initial Conditions
Why Use Simulation?
Simulation and Big Data
Choosing the Right Simulation Technique
Skills learned
The reader will be able to:
Distinguish a model from a simulation
Explain how simulation can provide a valuable perspective in data-driven decision making
Understand how simulation fits into the taxonomy of data science
Determine whether a simulation project is also a data science project
Determine which simulation technique to apply to various kinds of real-world problems
Chapter 2: Foundations for Decision Making – 25 pages
Description
In this chapter, the reader will learn how to plan and structure a simulation project to aid in the decision-making process as well as the presentation of results. The social context of data science will be explained, emphasizing the growing importance of collaborative data and information sharing.
Level
BASIC
Topics covered
The Social Context of Data Science
Ethics and Provenance. Data Curation. Replicability, Reproducibility, and Open Science. Open, interoperable frameworks for collaborative data and information sharing. Problem-Centric Habits of Mind.
Selecting Key Performance Indicators (KPIs)
Determining the Number of Replications
Methodologies for Simulation Projects
A General Problem-Solving Approach
DMAIC
DMADV
Root Cause Analysis (RCA)
PDSA
Verification and Validation Techniques
Output Analysis
Skills learned
The reader will be able to:
Plan a simulation study that is supported by effective and meaningful metadata
Select an appropriate methodology to guide the simulation project
Choose activities to ensure that verification and validation requirements are met
Construct confidence intervals for reporting simulation output
Chapter 3: Variability and Random Variate Generation – 25 pages
Description
Simulation is powerful because it provides a way to closely examine the random behavior in systems that arises due to interdependencies and variability. This requires being able to generate random numbers and random variates that come from populations with known statistical characteristics. This chapter describes how random numbers and random variates are generated, and shows how they are applied to perform simple simulations.
Level
MEDIUM
Topics covered
Variability in Stochastic Processes
Why Generate Random Variables?
Pseudorandom Number Generation
Linear Congruential Generators
Inverse Transformation Method
Using sample for Discrete Distributions
Is this Sequence Random? Tests for Randomness
Autocorrelation, Frequency, Runs Tests. Using the randtests package
Tests for homogeneity
Simple Simulations with Random Numbers
Skills learned
The reader will be able to:
Generate pseudorandom numbers that are uniformly distributed
Use random numbers to generate random variates from a target distribution
Perform simple simulations using streams of random numbers
Chapter 4: Data Generating Processes – 30 pages
Description
To execute a simulation, you must be able to generate random variates that represent the physical process you are trying to emulate. In this chapter, we cover several common statistical distributions that can be used to represent real physical processes, and explain which physical processes are often modeled using those distributions.
Level
MEDIUM
Topics covered
What is a Data Generating Process?
Continuous, Discrete, and Multivariate Distributions
Discrete Distributions
Binomial Distribution
Geometric Distribution
Hypergeometric Distribution
Poisson Distribution
Continuous Distributions
Exponential Distribution
F Distribution
Lognormal Distribution
Normal Distribution
Student’s t Distribution
Uniform Distribution
Weibull Distribution
Chi2 Distribution
Stochastic Processes
Markov. Poisson. Gaussian, Bernoulli. Brownian Motion. Random Walk.
Stationary and Autoregressive Processes.
Skills learned
The reader will be able to:
Understand the characteristics of several common discrete and continuous data generating processes
Use those distributions to generate streams of random variates
Describe several common types of stochastic processes
Chapter 5: Distribution Fitting – 30 pages
Description
An effective simulation is driven by data generating processes that accurately reflect real physical populations. This chapter shows how to use a sample of data to determine which statistical distribution best represents the real population. The resulting distribution is used to generate random variates for the simulation.
Level
MEDIUM
Topics covered
Why is Distribution Fitting Essential?
Techniques for Distribution Fitting
Shapiro-Wilk Test for Normality
Anderson-Darling Test
Lillefors Test
Kolmogorov-Smirnov Test
Chi2 Goodness of Fit Test
Other Goodness Of Fit Tests
Transforming Your Data
When There’s No Data, Use Interviews
Skills learned
The reader will be able to:
Use a sample of real data to determine which data generating process is required in a simulation
Transform data to find a more effective data generating process
Estimate appropriate distributions when samples of real data are not available
Chapter 6: Monte Carlo Simulation – 30 pages
Description
This chapter explains how to set up and execute simple Monte Carlo simulations, using data generating processes to represent random inputs.
Level
ADVANCED
Topics covered
Anatomy of a Monte Carlo Project
The Many Flavors of Monte Carlo
The Hit-or-Miss Method
Example: Estimating Pi
Monte Carlo Integration
Example: Numerical Integration of y = x2
Estimating Variables
Monte Carlo Confidence Intervals
Example: Projecting Profits
Sensitivity Analysis
Example: Projecting Variability of Profits
Example: Projecting Yield of a Process
Markov Chain Monte Carlo
Skills learned
The reader will be able to:
Plan and execute a Monte Carlo simulation in R
Construct confidence intervals using the Monte Carlo method
Determine the sensitivity of process outputs and interpret the results
Chapter 7: Discrete Event Simulation – 30 pages
Description
What is this chapter about?
Level
ADVANCED
Topics covered
Anatomy of a DES Project
Entities, Locations, Resources and Events
System Performance Metrics
Queuing Models and Kendall’s Notation
The Event Calendar
Manual Event Calendar Generation
Example: An M/M/1 system in R
Using the queueing package
Using the simmer package
Arrival-Counting Processes with the NHPoisson Package
Survival Analysis with the survival Package
Example: When Will the Bagels Run Out?
Skills learned
The reader will be able to:
Plan and execute discrete event simulation in R
Choose an appropriate model for a queueing problem
Manually generate an event calendar to verify simulation results
Use arrival counting processes for practical problem-solving
Execute a survival analysis in R and interpret the results
Chapter 8: System Dynamics – 30 pages
Description
This chapter presents system dynamics, a powerful technique for characterizing the effects of multiple nested feedback loops in a dynamical system. This technique helps uncover the large-scale patterns in a complex system where interdependencies and variation are critical.
Level
ADVANCED
Topics covered
Anatomy of a SD Project
The Law of Unintended Consequences and Policy Resistance
Introduction to Differential Equations
Causal Loop Diagrams (CLDs)
Stock and Flow Diagrams (SFDs)
Using the deSolve Package
Example: Lotka-Volterra Equations
Dynamic Archetypes
Linear Growth
Exponential Growth and Collapse
S-Shaped Growth
S-Shaped Growth with Overshoot
Overshoot and Collapse
Delays and Oscillations
Using the stellaR and simecol Packages
Skills learned
The reader will be able to:
Plan and execute a system dynamics project
Create causal loop diagrams and stock-and-flow diagrams
Set up simple systems of differential equations and solve them with deSolve in R
Predict the evolution of stocks using dynamic archetypes in CLDs
Convert STELLA models to R
Chapter 9: Agent-Based Modeling – 25 pages
Description
Agent-Based Modeling (ABM) provides a unique perspective on simulation, illuminating the emergent behavior of the whole system by simply characterizing the rules by which each participant in the system operates. This chapter provides an overview of ABM, compares and contrasts it with the other simulation techniques, and demonstrates how to set up a simulation using an ABM in R.
Level
ADVANCED
Topics covered
Anatomy of an ABM Project
Emergent Behavior
PAGE (Percepts, Actions, Goals, and Environment)
Turtles and Patches
Using the RNetLogo package
Skills learned
The reader will be able to:
Plan and execute an ABM project in R
Create specifications for the ABM using PAGE
Chapter 10: Resampling – 25 pages
Description
Resampling methods are related to Monte Carlo simulation, but serve a different purpose: to help us characterize a data generating process or make inferences about the population our data came from when all we have is a small sample. In this chapter, resampling methods (and some practical problems that use them) are explained.
Level
MEDIUM
Topics covered
Anatomy of an Resampling Project
Bootstrapping
Jackknifing
Permutation Tests
Skills learned
The reader will be able to:
Plan and execute a resampling project in R
Understand how to select and use a resampling technique for real data
Chapter 11: Comparing the Simulation Techniques – 15 pages
Description
In this chapter, the simulation techniques will be compared and contrasted in terms of their strengths, weaknesses, biases, and computational complexity.
Level
ADVANCED
Topics covered
TBD – at least two simulation approaches will be applied
Skills learned
The reader will learn how to:
Think about a simulation study holistically
Select an appropriate combination of techniques for a real simulation study
As of today, I now have a NEW FAVORITE introductory statistics textbook… the one I’ve always dreamed of having. I’ve been looking for a book to use in my classes for undergraduate sophomores and juniors, but none of the textbooks I considered over the past three years (and I’ve looked at over a hundred!) had all of the things I really, really wanted. So I had to go make it happen myself. These things are:
1) An integrated treatment of theory and practice. All of my stats textbooks have a lot of formulas, and no information about how to do what the formulas do in the R statistical software. All of my R textbooks have a lot of information about how to run the commands, but not really much information about what formulas are being used. I wanted a book that would show how to solve problems analytically (using the equations), and then show how they’re done in R. If there were discrepancies between the stats textbook answers and the R answers, I wanted to know why. A lot of times, the developers of R packages use very sophisticated adjustments and corrections, which I only became aware of because my analytical solutions didn’t match the R output. At first, I thought I was wrong. But later, I realized I was right, and R was right: we were just doing different things. I wanted my students to know what was going on under the hood, and have an awareness of exactly which methods R was using at every moment.
2) An easy way to develop research questions for observational studies and organize the presentation of results. We always do small research projects in my classes, and in my opinion, this is the best way for students to get a strong grasp of the fundamental statistical concepts. But they always have the same questions: Which statistical test should I use? How should I phrase my research question? What should I include in my report? I wanted a book that made developing statistical research questions easy. In fact, I know a lot of people I went to PhD school with that would have loved to have this book while they were proposing, conducting, and defending their dissertations.
3) A confidence interval cookbook. This is probably one of the most important things I want my students to leave my class remembering: that from whatever sample you collect, you can construct a confidence interval that will give you an idea of what the true population parameter should be. You don’t even need to do a hypothesis test! but it can be difficult to remember which formula to use… so I wanted an easy reference where I’d be able to look things up, and find out really easily how to use R to construct those confidence intervals for me. Furthermore, some of the confidence intervals that everyone is taught in an introductory statistics course are wildly inaccurate – and statisticians know this. But they hesitate to scare away novice data analysts with long, scary looking equations, and so students keep learning those inaccurate methods and believing they’re good. Since so many people never get beyond introductory statistics and still turn into researchers in other fields, I thought this was horrible. I want to make sure my students know the best way to do each confidence interval in their first class… even if the equations are not as friendly.
4) An inference test cookbook. I wanted a book that stepped me through each of the primary parametric inference tests analytically (using the equations), and then showed me how it was done in R. If there were discrepancies, I wanted to know why. I wanted an easy way to remember the assumptions for each test, and when to use a pooled standard deviation versus an unpooled one. There’s a lot to keep track of! I wanted a reference that it would make it easy to keep track of all of it: assumptions, tests for assumptions, equations, R code, and diagnostic plots.
5) No step left behind. It’s really frustrating to me how so many R books assume you can do a psychic fill-in-the-blank for missing code. Since I’ve been using R for several years now, I’ve gotten to the point where my psychic abilities are pretty good, and at least 60% of the time I can figure out the missing pieces. But wow, what a waste of time! So I wanted a book that had all of the steps for each example. Even if it was a little repetitive. I may have missed this in a few places, but I think beginners will have a much easier time with this book. Also, I put all my data and functions on GitHub for people to run the examples with. I’m growing this slowly, but I don’t want people to be left in the lurch.
6) An easy way to produce any of the charts and graphs in the book. One of my pet peeves about R books is that the authors generate beautiful charts and graphs, and then you’re reading through the book and say “Yes!! Yes!! That’s the chart I need for my report… I want to do that… how did they do that?” and they don’t tell you anywhere how they did it. I did not want there to be any secrets in this book. If I generated a page of interesting looking simulated distributions, I wanted you to know how I did it (just in case you want to do it later).
GRANTED… I am sure it will not be perfect – no book is. (For example, Google Forms changes a lot and there are a couple examples that use it that will probably be outdated when the book gets to press… and I just found out this morning that you don’t need the source_https trick in R 3.2.0 and beyond.) [Note: data access has been fully updated in the 2nd Edition.] However, I will keep updating my blog with posts about useful things as they evolve.
In any case, I hope you enjoy my book as much as I’ve been enjoying using it as a reference for myself… it really is all my most important notes, neatly organized into just over 500 pages of everything I want to remember. And everything I want to make sure my students take with them after they leave my class.
[Note: Any errors and omissions from earlier printings (which have been taken care of in later printings) are being recorded at http://qualityandinnovation.com/errata/.]
Out of the Crisis, by W. Edwards Deming: I’m including this book as a result of my 2013 research, published in ASQ’s Quality Management Journal (QMJ), that examined all of the research articles in the first 15 years of the QMJ to see what resources and references were the most central to the citation network. This classic 1986 book topped the list — it informs the most research articles that have been published by QMJ to date. As a result, everyone should read it! Keep in mind that this was written 30 years ago… and as a result, you have to read it with the zeitgeist of the 1980’s in mind. It’s a unique look into the quality transformation that many organizations were experiencing during the time, and provides fascinating insights into the core philosophy of quality improvement that many of us still honor and promote. (Let me know if you’d like me to send you a copy of my 2013 article, which also provides a research agenda for the future.)
Quality Management for Organizations Using Lean Six Sigma Techniques, by Erick C. Jones: This book is, in my opinion, the best overview of quality management available… integrating basic principles, Lean, and Six Sigma in such an articulate and elegant way that it has encouraged me to design an entire college course around it. Here is the book review I wrote that appeared in the July 2014 QMJ:
This book aims to “establish the concepts and principles by which students… practitioners, and quality managers will learn about Lean Six Sigma and its origins… and how it can be integrated into manufacturing, logistics, and health care operations.” Despite its broad goal, in 29 chapters, this book delivers. Section I provides an overview of quality management, quality awards, and key standards. The highlight is Chapters 4 through 6, which describe Lean and Six Sigma separately, followed by a very nice and concise articulation of the “real difference” that characterizes Lean Six Sigma, and encourages practitioners to find the appropriate balance for each project, given its particular context.
Section II examines Lean Six Sigma from the level of the organization as a whole. Chapters within this section explain how to qualitatively and economically justify a Lean Six Sigma project, data-driven approaches for how an organization can decide which projects to resource, how to assess the relationship between LSS efforts and firm performance, benchmarking at the organizational level, and considerations for human resources policies to ensure that the right people are recruited to perform key LSS activities. Section III starts by covering basic concepts of statistics, but then moves on to describe each phase of the Define, Measure, Analyze, Improve, and Control (DMAIC) methodology in detail. There is enough information provided in each of these areas to easily navigate a Six Sigma project in practice.
Section IV is unique and powerful, focused entirely on comprehensive case studies, many of which include using radio frequency identification (RFID). Section V covers roles and responsibilities of Six Sigma professionals, descriptions of certifications and belt levels, and how these individuals typically interact as a project is chartered and executed. Limited case studies are provided throughout the text that effectively supplement the material. Although the case studies do not provide extensive technical detail, they are still instructive and very useful. There are also appendices scattered throughout the book which vary in content and quality. For example, Appendix 3B starts out by stating that its purpose is to compare quality management practices in the U.S. and Mexico. However, even though testable hypotheses are presented along with data, there is no connection made between analysis of the data and what insights it provides regarding the hypotheses. Against the backdrop of the rest of the book, though, such minor issues should not be a concern.
In this reviewer’s opinion, this is the most comprehensive book to date covering Lean Six Sigma in a completely integrated fashion, with material that will be equally valuable to managers, practitioners, and instructors who teach quality management or quality engineering. This is a fantastic guidebook for certification as well, comparable to Kubiak and Benbow’s (2009) book, The Certified Six Sigma Black Belt Handbook. It is sure to have lasting value on many bookshelves.
“The totality of characteristics of an entity that bear upon its ability to satisfy stated and implied needs.” — ISO 8402 (deprecated)
Even though they do not specifically teach about quality, I’d like to share two of my sources of inspiration: philosopher and activist Charles Eisenstein, and psychologist Barbara Fredrickson.
In Sacred Economics and The More Beautiful World Our Hearts Know is Possible, Charles Eisenstein encourages us to look beyond the subtle assumptions and limitations imposed upon us by being embedded in a market economy. What is quality in the absence of a commercial environment to exchange products and services?? How can we more effectively relate to ourselves and to one another, so that we can better satisfy our stated and implied needs? Eisenstein’s work inspires me because it encourages me to reflect on the unspoken assumptions of the quality profession, and how those assumptions might be holding us back from evolving our skill sets to meet the changing needs of society. (Sacred Economics is also available in print from Amazon.)
In Positivity, Barbara Fredrickson provides a simple, data-driven path (the “positivity ratio”) for improving our psychological health; in Love 2.0, she helps uncover ways for us to create substantive, authentic connections with one another. Her work can help us cultivate greater quality consciousness – because we are best able to satisfy others’ stated and implied needs when 1) we understand them, and 2) we are mentally and emotionally equipped to help deliver them! Although aspects of the positivity ratio have been criticized by researchers studying dynamical systems, I still find the concept (and measurement tool) very useful for raising the awareness of individuals and teams.
Postscript: Bill’s post made me think about another related question: “Who ARE the quality gurus?” I mean, everyone in the quality profession can call on Deming, Juran, or Crosby, but I’d toss luminaries like Csikszentmihalyi and Prahalad (plus others) in the mix as well. I searched online and found a nice “List of Gurus” that someone put together that includes my extra picks!
But!! There’s a problem with it.
WHERE ARE THE WOMEN? The one woman in this list is someone I’ve never heard of, which is odd, since I’ve read papers by (or about!) all of the other people referenced in the list. Which brings me back to my original point:WHERE ARE THE WOMEN QUALITY GURUS?It’s time to start celebrating their emerging legacy. If you are a woman who has made significant contributions to our understanding and/or practice of quality and improvement, PLEASE CONTACT ME. I’d like to write an article soon.
We’re teaching a class on blockchain and cryptocurrencies this semester, and since the field is so new and changing rapidly, we’ve asked our students to make finding and reviewing articles part of their learning practice this semester. This is a particularly challenging topic for this task because there’s so much hype, marketing, and fluff around these topics. We want to slice through that, and improve the signal-to-noise for people new to learning about blockchain and cryptocurrencies. Here are some tips I just prepared for our students — they may be helpful to anyone writing article reviews, especially for technology-related areas.
The following book review will appear in an issue of the Quality Management Journal later this year:
