Quality Progress - September 2017 - 23

through algorithms and compare predictive accuracy.
Taguchi methods,2 for example, were in vogue in
the 1990s to maximize information about a process
when analyzing only a few variables from a controlled
sample of data. The technology of the time limited us to
isolated analytics.
Taguchi methods provide information about a process from a sample of data with a restricted number
of controlled variables. This minimizes the resources
necessary for a study and usually provides more
information than was previously available. But Taguchi
methods also have their drawbacks-so little data are
collected with so few variables that large numbers of
variables can't be placed in the model, nor can interactions be estimated.
Quality professionals lacked the technology to support
a big data study using machine learning. It also was
assumed that the key variables identified by process
experts were the best variables to analyze and put into
the DoE. Today, building predictive models from data
warehouses (data at rest)-in which the restrictions on
the number of observations and the number of variables
to be evaluated is an analytical luxury.
When a quality problem arose early in my career, my
team used all the traditional tools to solve it. When the
problem persisted, analysts resorted to taking all the
variables associated with the process and putting them
into a large correlation matrix on a mainframe computer-an infant machine learning example. A lurking
variable with high correlation rose to the surface. It
was one that none of the process experts thought was
related to the quality problem, and it took a lot of coaxing to convince them otherwise.
Eventually, a control chart was placed on the lurking
variable and the quality problem was solved. This started
me down the road of evangelizing merging advanced
analytics tools with process improvement activities.
The team found that end-to-end data mining was
beneficial over isolated analytics in many instances
for high-volume, velocity data issues-especially for
complex processes. Advanced analytics may not be
disruptive so much as they are progressive.

Text analysis

Software exists that uses natural language processing
(NLP) to look at text (unstructured data) in two ways:
1. To examine a body of text and determine groupings
of words that are related. Because computers read all
information as binary code, typically zeros and ones, a
computer converts text into strings of zeros and ones
so it can analyze the information. These strings are

grouped to find common terms and related phrases by
using cluster analysis algorithms. The software reverses
the process and converts the zeros and ones back into
text to show the clusters as related words, phrases and
ideas. It provides a good view of the themes in a maintenance log, for example.
2. To take known word themes, such as those found by a
cluster analysis, and place them in a lexicon that is used to
search documents for those themes. This typically is called
classification.
Searching documents for themes significantly accelerates
analyses. When talking about VOC, for example, text from
social media postings can be analyzed using both methods.
An organization can scour the web to identify what themes
are trending on social media about their products or services, but that is time consuming.
Instead, an organization can create a lexicon of relevant
terms and search the web to stay ahead of issues, which
could help it improve or maintain customer satisfaction. The
lexicon can classify text into sub-themes to perform sentiment analysis, which an organization can use to evaluate in
real time the positive and negative
tweets, for example, its customers are posting.
In some cases, this type
of data gathering and
text analysis is more
timely and complete,
which can be disruptive
to traditional survey
design, distribution and
retrieval techniques.
Here is an example
from a pizza restaurant: Through surveys,
the restaurant found
its customers were
significantly dissatisfied
with its delivered pizza. But the information gathered wasn't
specific enough to determine the root cause of the dissatisfaction. After realizing a reduction in delivered pizza sales,
the restaurant began monitoring social media. Not only was
it able to obtain specifics about the problem, it also saw
pictures that were posted by its customers.
This restaurant made deliveries on a motor bike. When
the delivery driver leaned into a turn, the pizzas would slide
around in their boxes, causing the toppings to slide off. With
more specific and timely information, the restaurant devised
a system to keep the pizzas horizontal. By deploying this
technology to the motor bikes, the pizza box stayed level to
the horizon and eliminated the problem.

qualityprogress.com ❘ September 2017

QP 23


http://www.qualityprogress.com

Table of Contents for the Digital Edition of Quality Progress - September 2017

Seen and Heard
Progress Report
Mr. Pareto Head
Career Coach
Expert Answers
Field Notes
Data Disruption
The Deal With Big Data
Better Intelligence
A Study in Measurement
Innovation Imperative
Statistics Spotlight
Standard Issues
ASQ's 2017 Quality Resource Guide
Marketplace
Footnotes
Back to Basics
Quality Progress - September 2017 - Intro
Quality Progress - September 2017 - cover1
Quality Progress - September 2017 - cover2
Quality Progress - September 2017 - 1
Quality Progress - September 2017 - 2
Quality Progress - September 2017 - 3
Quality Progress - September 2017 - 4
Quality Progress - September 2017 - 5
Quality Progress - September 2017 - Seen and Heard
Quality Progress - September 2017 - 7
Quality Progress - September 2017 - Progress Report
Quality Progress - September 2017 - 9
Quality Progress - September 2017 - Mr. Pareto Head
Quality Progress - September 2017 - 11
Quality Progress - September 2017 - Career Coach
Quality Progress - September 2017 - 13
Quality Progress - September 2017 - 14
Quality Progress - September 2017 - Expert Answers
Quality Progress - September 2017 - Field Notes
Quality Progress - September 2017 - 17
Quality Progress - September 2017 - 18
Quality Progress - September 2017 - 19
Quality Progress - September 2017 - Data Disruption
Quality Progress - September 2017 - 21
Quality Progress - September 2017 - 22
Quality Progress - September 2017 - 23
Quality Progress - September 2017 - 24
Quality Progress - September 2017 - 25
Quality Progress - September 2017 - The Deal With Big Data
Quality Progress - September 2017 - 27
Quality Progress - September 2017 - 28
Quality Progress - September 2017 - 29
Quality Progress - September 2017 - 30
Quality Progress - September 2017 - 31
Quality Progress - September 2017 - 32
Quality Progress - September 2017 - 33
Quality Progress - September 2017 - Better Intelligence
Quality Progress - September 2017 - 35
Quality Progress - September 2017 - 36
Quality Progress - September 2017 - 37
Quality Progress - September 2017 - 38
Quality Progress - September 2017 - 39
Quality Progress - September 2017 - 40
Quality Progress - September 2017 - 41
Quality Progress - September 2017 - A Study in Measurement
Quality Progress - September 2017 - 43
Quality Progress - September 2017 - 44
Quality Progress - September 2017 - 45
Quality Progress - September 2017 - 46
Quality Progress - September 2017 - 47
Quality Progress - September 2017 - Innovation Imperative
Quality Progress - September 2017 - 49
Quality Progress - September 2017 - 50
Quality Progress - September 2017 - Statistics Spotlight
Quality Progress - September 2017 - 52
Quality Progress - September 2017 - 53
Quality Progress - September 2017 - Standard Issues
Quality Progress - September 2017 - 55
Quality Progress - September 2017 - 56
Quality Progress - September 2017 - 57
Quality Progress - September 2017 - ASQ's 2017 Quality Resource Guide
Quality Progress - September 2017 - 59
Quality Progress - September 2017 - 60
Quality Progress - September 2017 - 61
Quality Progress - September 2017 - 62
Quality Progress - September 2017 - 63
Quality Progress - September 2017 - 64
Quality Progress - September 2017 - 65
Quality Progress - September 2017 - 66
Quality Progress - September 2017 - 67
Quality Progress - September 2017 - Marketplace
Quality Progress - September 2017 - 69
Quality Progress - September 2017 - Footnotes
Quality Progress - September 2017 - 71
Quality Progress - September 2017 - Back to Basics
Quality Progress - September 2017 - cover3
Quality Progress - September 2017 - cover4
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