Why Six Sigma Can Help Improve Customer Quotes

Why Should I learn Six Sigma?
(A Hypothetical Office Simulation to Demonstrate the
Merits of Statistical Analysis in Continuous Improvement)
Jim McCarthy - 4/28/15
©Product Ventures, Inc., March, 2015 1

Agenda
1. CEO wants customer quoting issues resolved fast
2. I am selected to get it done
3. Initial known facts & data
4. Conventional thinking & methods
5. Capability Study
6. Analysis of Variance (ANOVA) & Std. Dev. Test
7. Charter: Deliverables, Team, Gantt Chart, Scope,
Dates, Report-Out/s, Process Analyses, etc.
8. Design of Experiments (DOE)
9. Possible solutions

How It Started
CEO receives phone calls from customers:
“You are not providing quotes in a timely manner!”
Customers threaten to go to a competitor
Global
Customer Base
(All Time Zones)

I Get The Job to “Fix Quoting”
All I know is:
• Customers unhappy with quoting lead time
• CEO wants this fixed “Fast”
• No other direction is given

Initial Investigation
(Discussion with Department Supervisor)
1. Quoting Department composed of six stations
2. Stations serve all customers at random (not
region specific)
3. Supervisor compiled data - last 1,200 quotes (200
from each of six stations) to check lead time
4. Company Lead Time policy expectation has been
72 hours – (24/7 basis)
5. Supervisor is adamant that policy is being met!

How Did Supervisor Know?
1. Compiled a bar chart depicting the
performance over the last 1,200 quotes
2. Average of all six stations = 68.1185 Hrs.
3. “See I told you we were meeting the company
policy!” (Said the supervisor)
4. We are 5.4% better than the 72 hr. policy!
5. Supervisor carried out to four decimal places
just to show how precise the data was!

Station FStation EStation DStation CStation BStation A
70
60
50
40
30
20
10
0
LeadTimeQuotingHours(24/7basis)
68.5326
66.0853
68.5282 69.5961 68.8705
67.0983
Average Quoting Lead Times, Jan.- March, 2015 - 200 Quotes/Station

Where Do I Go From Here?
1. Customer complaints (Why?–averages look good)
2. How to respectfully challenge data?
3. Supervisor is taking it personal
4. Need specific data on quotes associated with
customer complaints, but data is not available.
I am Very Nervous!
&
the Clock is Ticking!

Can Six Sigma Help?
1. Averages are only one way to measure
2. What are the variations around average/s?
3. How stable/predictable is the process?
4. Are all quoting stations statistically similar?
5. Does system “Capability” = 72 hours or less?
A Typical “Normal
Distribution”
Looks Like This

Data Shown = 24/7 Lead Times (in
hours) for (Jan. – March, 2015)
1,200 quotes (200 per Station)
Data are “Time Ordered” per
individual Station (42.0 Hrs. is first
completed quote for Station A)

How Bad could It be from Customer’s View?
• Capability study - compare to 72 hr. max.
• What is the % of late quotes
• Analyze all six stations as “one system” (1st)

Choose
“Continuous”

Natural Process width (NPW) = 6
x Std. Dev = 6 x 15.197= 91.98
39.9%
Ouch!
Ppk = Difference between mean & closest
spec. ÷ 1/2 NPW (Long Term)
Z Bench = metric of normal distribution with
same PPM level as that shown (close to
sigma level) - Closest spec. ÷ Std. dev. (ST)
Why?

1
0
5
01
51
02
52
03
53
02 04 06 08 001 021 04
68.53 12.84 200
66.09 14.02 200
68.53 12.92 200
69.60 13.28 200
68.87 12.41 200
67.10 22.90 200
Mean StDev N
D
tnecreP
ata
S
elbairaV
FnoitatS
EnoitatS
DnoitatS
CnoitatS
BnoitatS
Anoitat
H
lamroN
...,DnoitatS,CnoitatS,BnoitatS,AnoitatSfomargotsi

What Else Do We Know?
 Quoting is a 1st shift, Monday-Friday operation
 1,200 completed quotes (Jan.– March, 2015)
 90 days & 68 hours (2.833 calendar days - lead Time)

1,200
90
= 𝟏𝟑. 𝟑𝟑 𝑸𝒖𝒐𝒕𝒆𝒔 (𝒄𝒐𝒎𝒑𝒍𝒆𝒕𝒆𝒅) /𝑐𝑎𝑙𝑒𝑛𝑑𝑎𝑟 𝑑𝑎𝑦 (TH)
From Little’s Law:
 Cycle Time (24/7)=
𝑊𝐼𝑃 (𝑊𝑜𝑟𝑘 𝑖𝑛 𝑝𝑟𝑜𝑐𝑒𝑠𝑠)
𝑇𝐻 (𝑇ℎ𝑟𝑜𝑢𝑔ℎ𝑝𝑢𝑡)
 2.833 Days/Quote x 13.33 Quotes/Day = 𝟑𝟖 𝑾𝑰𝑷
 90 Calendar days = 65 work days:
1,200
65
=
18.5
𝑞𝑢𝑜𝑡𝑒𝑠
𝑑𝑎𝑦
𝑜𝑟 𝑎 𝑙𝑖𝑡𝑡𝑙𝑒 𝑜𝑣𝑒𝑟
3
𝑠𝑡𝑎𝑡𝑖𝑜𝑛
/𝑤𝑜𝑟𝑘𝑑𝑎𝑦

Need to Be “Data Driven”!
1. What is present incoming rate from customer?
2. Visual daily metrics & “stand-up” meetings (10
minutes) for: Cycle Time, Throughput, & WIP
3. Can’t improve (or manage) what we don’t measure!

Let’s Dig Deeper
Analysis of Variance (ANOVA)
• 6 Sigma tool (comparison-multiple data sets)
• ANOVA tries to determine if all data sets are from the
same population
• Looks at both the mean (average) & variation of all data
sets
• Develops a ratio of the “between” data groups to “within”
each data group.
• Calculates an “F” ratio & compares it to a critical value to
determine if the null hypothesis (data is statistically the
same) can be rejected
• 1st – ANOVA assumes “Normal” data – need to check

ANOVA
• Between-Groups Variance (BGV)
• Within Groups Variance (WGV)
• "𝐹" 𝑅𝑎𝑡𝑖𝑜 =
𝐵𝐺𝑉
𝑊𝐺𝑉
• If the “F” ratio is larger than “F critical” then
we reject the null hypothesis because we
cannot be sure that all data sets are from the
same population.
• ANOVA requires significant calculations and is
best done using software.

We have 6
samples to
compare so
use “One
Way Anova”
A Hypothesis
test is testing to
see if all data
are of the same
population.
Null hypothesis
assumes all data
are from the
same population

Red dashed lines = α÷2 = 0.05÷2 significance level (95% probability)
α = .05 significance level
(critical value) 95%
probability- 2 tailed
(.025 per each tail)

β = 1- Power (Power is probability of being able to detect a 7 hour difference
β = Probability of making “Type 2 error”(not being able to detect 7 hr. difference
β =17.79% (Worst) to 0.42% (Best) – Power goes up with increase in sample size

Station F
Station E
Station D
Station C
Station B
Station A
28262422201816141210
P-Value 0.000
P-Value 0.000
Multiple Comparisons
Levene’s Test
Test for Equal Variances: Stations A-F - Customer Service Lead Time
Multiple comparison intervals for the standard deviation, α = 0.05
If intervals do not overlap, the corresponding stdevs are significantly different.
If “P” value is
low, the null
Hypothesis is
rejected
(We can’t say
that variances
are the same)

Observations, Questions, & Next Steps
1. Something is different about Station “F”
2. Although Station F has a mean of 67.098 hrs.,
it has large variability
3. What do the individual station outputs look
like with respect to 72 hours ( What is the
yield?)

What Next?
1. Station “F” has high variability & is not capable (41.53%
failure rate for 72 hr. Spec.)
2. “A-F” have a 39.92% (collective) failure rate (not capable)
3. “A-E” have 38.97 (collective) failure rate (not capable)
4. This requires a systemic change to reduce common cause
variability & reduce “mean” across all stations.
5. Write a charter, form a team, get understanding &
agreement from management , & write project plan
6. Spend some time investigating Station “F” variance in order
to better understand process. (DOE)
7. Do “Data Tagging” & value stream map (I.D. waste)
8. I.D. working hours, shifts, RFQ input rate, departments, etc.
9. Design & review daily metrics charts: WIP, Throughput, 24/7
Cycle Time, & Quality Issues

Touch Time
White Space
Working on Quotes 2.9% of time! :
𝟐 𝑯𝒓𝒔.
𝟔𝟖 𝑯𝒓𝒔.
= .029 x 100=2.9%
0.15 hrs. 1.0 hrs. 0.75 hrs. 0.10 hrs.

Station A-F
12 hrs.
Engineering
24 hrs.
Purchasing
18 hrs
Station A-F
14 hrs.
Total Lead Time = 68 Hours (24/7- Average)
Work on 97% or 3% to Reduce Overall Time?
Total Touch Time = 2.0 Hrs. (Ave.)
4 Operations in Our Quoting Example
[Initial guesses (no confirmed data) shown below]

Station “F” Design of Experiments
Observations About Station “F”:
1. Two, operators, not one, work in station “F”- usually
separately, but sometimes simultaneously- one
experienced & one new
2. They both use two separate processes “at will” –
manual or computerized (no standardized pattern)
3. The station gets incoming requests for quotes via
email (local) or Fax (remote)

Design of Experiments (DoE)
Controlled Experiments for Improvement
35©Product Ventures, Inc., March, 2015

What is “Design of Experiments”
• A controlled experiment in which the
experimenter is interested in both quantifying
and optimizing the effect of a number of
variables on one or more variables
• Input variables causing the variation = “factors”
• Output variables being measured = “responses”

When Should it Be Used?
1. To identify factors which most affect responses
2. To identify those factors which act together on a
particular response - these are called interactions
3. To determine the optimum settings of factors to
satisfy a number of required responses

DOE Basics
• Follows the 6 sigma transfer function:
𝑌 = 𝑓(𝑋)
Which means that “Y” (output or response) is equal
to a function of the “X” input/s (factors)

A Typical DoE Application
• Determine how to get the best gas mileage
(response) based on factors (input variables)
of tire pressure & speed
39
Y = 𝑮𝒂𝒔 𝑴𝒊𝒍𝒆𝒂𝒈𝒆 𝑜𝑢𝑡𝑝𝑢𝑡 𝑜𝑟 𝑟𝑒𝑠𝑝𝑜𝑛𝑠𝑒
𝑋1 = 𝑻𝒊𝒓𝒆 𝑷𝒓𝒆𝒔𝒔𝒖𝒓𝒆 (𝑖𝑛𝑝𝑢𝑡 𝑜𝑟 𝑓𝑎𝑐𝑡𝑜𝑟)
𝑋2 = 𝑺𝒑𝒆𝒆𝒅 (𝑖𝑛𝑝𝑢𝑡 𝑜𝑟 𝑓𝑎𝑐𝑡𝑜𝑟)
©Product Ventures, Inc., March, 2015

What are Factor levels?
• Factors have agreed upon settings based on experience
& /or observation
• Settings typically “high” & “low”
• For gas mileage (MPG) example:
Tire pressure ; low = 25 Psi
Tire pressure ; high = 35 Psi
Speed ; low = 45 MPH
Speed ; High = 65 MPH
Output (Response) = Miles Per Gallon (MPG)

One Factor at a Time (OFAT)
• OFAT experiments change one thing at a time
• This seems reasonable, but it is costly and does
not allow us to see interactions between inputs
DOE Enables:
1. logical Experiments
2. Minimal trials
3. Visible Interactions

Common Types of DOEs
• Fractional Factorials = 4 to 15 variables
Screening designs when product knowledge is
low – does not test all combinations
• Full Factorials = 2 to 5 Input variables
Used when necessary to fully understand effects
of interactions – not one factor at a time (OFAT)
• Response Surface Methods (RSM) = 2 to 4
input variables
Typical when response surface has significant
curvature

DOE Notation
• General notation used to designate a full
factorial design is shown as:
2 𝑘
• Where k is the number of input variables or
factors
• 2 = the number of “levels” that will be used
for each factor

Levels & Coding
• Typically uses coding: -1, +1 for low & high
respectively
44
Level
Factor 1
Pressure
(PSI)
Factor 1
Coding
Factor 2
Speed
(MPH)
Factor 2
Coding
Low 25 -1 45 -1
High 35 +1 65 +1

DOE - Full Factorial With 2 Factors & 2 Levels)
45
Pressure (PSI)
(Factor 1)
25 PSI
35 PSI
Speed (MPH)
(Factor 2)
45 65
(-1,+1) (+1,+1)
(-1,-1) (+1,-1)

DOE Full Factorial Combinations
For our example of 2 factors & 2 levels:
2 𝑘
46
Number of
factors;
In this case we
have 2 factors
“Pressure” &
“Speed”
Number of
Levels;
In this case we
have 2 levels
“high” & “low”
22
= 4 Combinations

Full Factorial Test Combinations
“Standard Order” – (not randomized & no replicates)
47
Test Run
No.
Factor 1
Pressure (PSI)
Factor 2
Speed
(MPH)
Factor 1,
Factor 2
Test
Coding
1 25 45 -1, -1
2 35 45 +1, -1
3 25 65 -1, +1
4 35 65 +1, +1

3 Factors & 2 Levels
Let’s add one more input variable (Fuel Type)
Type A & Type B
48
Level
Factor
1
Pres-
sure
(PSI)
Factor
1
Coding
Factor
2
Speed
(MPH)
Factor
2
Coding
Factor
3
Fuel
Factor
3
Coding
Low 25 -1 45 -1 A -1
High 35 +1 65 +1 B +1

DOE - Full Factorial With 3 Factors & 2 Levels
49
Pressure (PSI)
(Factor 1 = Y)
25 (-)
35 (+)
Speed (MPH)
(Factor 2 = X)
45(-) 65 (+)
(-1,+1,-1)
(+1,+1,+1)
(-1,-1,-1) (+1,-1,-1)
Fuel
(Factor 3 = Z)
A (-)
B (+)
(+1,-1,+1)(-1,-1,+1)
(+1,+1,-1)
(-1,+1,+1)X,Y,Z
(Respectively)

3 Factors & 2 Levels – (8 combinations)
50
Std. Run
Order
(Yates
Order)
Tire Pressure
25 PSI = -1
35 PSI = +1
Speed
45 MPH= -1
65 MPH= +1
Fuel
A=-1
B=+1
Response
(MPG)
1 -1 -1 -1 45
2 +1 -1 -1 55
3 -1 +1 -1 33
4 +1 +1 -1 32
5 -1 -1 +1 48
6 +1 -1 +1 49
7 -1 +1 +1 25
8 +1 +1 +1 33

DOE - Full Factorial - 3 Factors & 2 Levels
(MPG Responses Shown)
51
Pressure (PSI)
(Factor 1 = Y)
25 (-)
35 (+)
Speed (MPH)
(Factor 2 = X)
45(-) 65 (+)
(55 MPG)
(33 MPG)
(45 MPG) (33 MPG)
Fuel
(Factor 3 = Z)
A (-)
B (+)
(25 MPG)(48 MPG)
(32 MPG)
(49 MPG)

Back to Station “F” Quoting Issue
Factors & levels shown below
52
Level
Factor 1
Operator
Factor 2
Process
Type
Factor 3
Input
Method
Low New Computerized Fax
High Experienced Manual Email

Minitab
Populates
Columns C1-C7
After Design is
Completed
Experimenter
Populates the
“Response”
Column (C8)
After Tests are
Done

Then Select “Factorial plots” to get
Main Effects & Interaction Plots.
Select “Cube Plot” to Get Same.

Factors &
Interactions
(A,C,B, & BC) are
Statistically
Significant

Observations & Next Steps
1. Standardize “Computerized” process
2. Train operators to one (computerized) process
3. Do Data tagging - find out issues with the Fax
4. Populate value stream map to show waste
5. Show Value Added vs. Non Value Added time
6. Design Standard work for flow (all stations)
7. Consider “runner” to deliver FAXes or localize
8. Plot WIP, Lead Time, Rework & Throughput daily
9. Design a visual Queue to work FIFO & make it
easy to see “work in temporal jeopardy”

Lessons Learned
SIX Sigma Enabled:
1. Me to “get project started” with little data
2. My frustration level to be lowered significantly
3. Me to logically formulate “next steps” during project
4. Revelation of a “Systemic Problem”- didn’t lead the
team down wrong path chasing “Special Causes”
5. All involved to “rally around” logical process analyses
& not emotional “gut-feel” or “finger-pointing”
6. Us to see our system from the customer’s viewpoint

Why Six Sigma Can Help Improve Customer Quotes

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