Quality Progress - September 2017 - 24

F E AT U R E

BIG DATA
DES software

After a process improvement project is chosen, a VSM
should be used to better understand the process. Historically, this has been an arduous task involving sticky notes
and a big wall. DES software now simplifies this process.
A process is merely a series of things moving through
queues like lines at the grocery store, appliances moving
down an assembly line or mortgages moving through
the approval process. The queues exist between process
steps (or nodes) where something happens to the things.
In his book, The Goal: Excellence in Manufacturing,
Eliyahu M. Goldratt3 discusses constraints at each node.
The question becomes, "Which node creates the biggest
bottleneck?" For simple processes, finding the bottleneck
might be obvious. The problem arises for long, complicated processes.
The process of creating electrical equipment, for example, is quite complex-from circuit boards and assembly
to testing each component individually and collectively. A
VSM of this process can take up a ream of sticky notes and
several walls. DES software, on the other hand, can hold
not only the flow, but also all the data related to each step.
The complexity of such a process yields a different
statistical distribution of processing times at each stage.
Because of this complexity, organizations rarely even
attempt to optimize the flow. Lean techniques help significantly, but organizations usually depend on isolated
analytics to improve the flow in a piecewise fashion. This
is analogous to the Taguchi methods example of isolated
analytics in which DES software allows for an end-to-end
view of the process with all the interactions and the work
in process that is backing up.
There are two major benefits to DES software. First,
theory of constraints analysis can be done rapidly and
quantitatively. A kaizen event, for example, can create the
basic VSM and identify the nodes. The team can collect
sample data at each node to estimate the statistical distribution of the respective service times. The most accurate
distribution can be loaded into the DES software to understand the flow through the queues. Putting VSMs into DES
software is merely using technology and statistics to show
how VSMs can be analyzed as queues-which they are.
Second, changes in the process can be made via the software to evaluate alternative paths. Making physical changes
to the flow in a factory, for example, can be expensive and
time-consuming. Using the software to create alternative
flows requires no physical movement of equipment or
personnel to evaluate improvements in the flow.
For example, imagine that a team member suggested
an adjustment to the flow, but everyone thought it was a
terrible idea. Because testing the idea using the software

24 QP

September 2017 ❘ qualityprogress.com

is quick and easy, the adjustment was tried. The result was a
major improvement because the change integrated a piece of
information that was held by one individual and ignored by the
collective.
Again, this tool is not so much disruptive as it is progressive. It can eliminate, or at least minimize, the arduous sticky
note activity for complex processes (and free up a wall).
Optimizing flow by finding and removing bottlenecks benefits the entire organization. It also assists in make-or-buy
decision making because DES software can create a process
to evaluate an organization's ability to save money by keeping certain activities in-house.

Keep an open mind

Advanced analytics helped me solve several quality problems in my career when traditional tools failed. Now, they are
available to more people to accomplish similar results.
I am not suggesting that traditional analytical tools should
be discarded. Contrary to the definition, there is always a
time and place for things that have been deemed widely
obsolete. Classic car enthusiasts still work on carburetors,
some subsets of society still commute by horse and buggy,
and landlines still exist in some homes.
What I am suggesting is that people open their minds to
the possibilities of technology-how it can help us better
understand processes, customer needs and expectations,
and how it can assist in the selection of the best technology
and software to accomplish these objectives.
REFERENCES
1. Taiichi Ohno and Norman Bodek, Toyota Production System: Beyond
Large-Scale Production, first edition, CRC Press, 1988.
2. Genichi Taguchi, Taguchi on Robust Technology Development:
Bringing Quality Engineering Upstream, ASME Press, 1992.
3. Eliyahu M. Goldratt, The Goal: Excellence in Manufacturing, North
River Press, 1984.
BIBLIOGRAPHY
Bekti, Antonius, Stephanie Cipressi, Emma Clark, Ariane Gragnani, Lewis
Jones, Dongho DK Kang, Kaci Karlsson and Roland Salita, "Station
Simulation," undergraduate study at the University of Queensland
School of Engineering, Australia, May 2015.
"Difference Between Classification and Clustering in Data Mining,"
stackoverflow.com.
Konstantinova, Natalia, "Machine Learning Explained in Simple Words,"
nkonst.com.

Jim Duarte is the principal at LJ Duarte
& Associates LLC in Clermont, FL. He
earned a master's degree in statistics
from Brigham Young University in Provo,
UT. Duarte is an ASQ fellow and an ASQcertified quality engineer and Six Sigma
Black Belt. He also holds two domestic and
two international patents for analytics.


http://www.ow.com http://www.nkonst.com 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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