Six-Sigma: It’s a Dirty Job
Andrew Gonce, McKinsey
Bob Landel and Jitendra Gupta MBA ‘08, Darden
Six-sigma approach
Practical
Problem
Traditional
Approach
Statistical
Problem
Six-sigma
Approach
Statistical
Solution
Practical
Solution
Six-sigma is a systematic data-driven approach, which leads to a
sustainable solution for any problem
2
Narrowing the Project Scope: F(x)
3
DMAIC Example – It’s a Dirty Job
Define
What are the customer expectations of the process?
Purpose and scope of the project
Reduce the Incidence of Dirt in the
Primer Coat that occurs on the Hood of
the vehicles at the Lexington Assembly
Plant between the E-Coat Scuff Booth
and the Prime Scuff after Oven station
The outcomes with defects are identified as red in the
population
Six-sigma leaders have a mind-set for meeting customer needs
5
DMAIC Example – It’s a Dirty Job
Key Deliverables of Define Phase
Define
• Project Scope and estimation of benefit based on
customer requirements and bottom-line performance
Y
Dirt in Paint
y
Dirt in Primer spray area; Dirt in Ovens
x
Critical X’s to be determined in Analyze phase
• A team charter with defined roles and responsibilities
• A high-level process map
Few of the applicable tools
Baseline Performance for Y, Customer Survey Methods
(focus groups, interviews, etc.) Project Risk Assessment,
Stakeholder Analysis, High Level Project Plan
6
Narrowing the Project Scope: F(x)
• Y = Dirt in Paint (F20)= f(x) {Prime, E-Coat,
Base Coat, Clear Coat}
o X = Dirt in Prime (37%) = f(w) {Agglomerates,
Sealer, Fibers, Rust, Condensate, Pollen}
 W = Agglomerates in Prime (33%)= f(v) {Primer Spray
Booth, Ovens}
Critical “X” Contribution = 8-10% of F20 Calls
7
DMAIC Example – It’s a Dirty Job
Measure
• Perform Gauge R&R on Primary Measurement System
• Evaluate Critical “X” Process capability
• Determine controls in place for Critical “X”
If you can’t measure it accurately, you can’t improve it!
8
DMAIC Example – It’s a Dirty Job
Measure
What is a defect? Is the measurement system capable
of separating acceptable from defective parts?
• For a continuous metric (such as distance, time etc)
capture the specification limits (LSL, USL) and the target
to determine the tolerance band (for acceptable parts).
For discrete metric, identify the characteristics of a part
that result in it being acceptable/defective
• The total observed variation in the data is a sum of
variation in the process and variation in the measurement
system. If the latter is higher than a limit, we will not be
able to differentiate between good and bad parts. A good
measurement system has to be both repeatable and
reproducible
If you can’t measure it accurately, you can’t improve it!
9
Gauge R&R – Dirt Count at Spill Out
• The Gauge R&R was
conducted on the
Hoods alone.
– The Hood area is the
easiest to see Dirt in
Prime.
– 15% of Warranty
Verbatims call out the
Hood as the location
of Dirt.
10
DMAIC Example – It’s a Dirty Job
Key Deliverables of Measure Phase
Measure
• Defined Performance standards (Spec limits and target)
• Gauge R&R analysis of measurement system
Few of the applicable tools
GR&R, FMEA, Pareto analysis, Data collection plan
11
Baseline Data: Prime Dirt Count
Attribute Control Chart
Dirt in Prime Count Prior to Prime Scuff
Average DPUs
5
4
DPU
3
UCL: 3.1
2
MEAN: 2.3
LCL: 1.1
1
4/
10
/0
4/ 2
24
/0
2
5/
8/
0
5/ 2
22
/0
2
6/
5/
0
6/ 2
19
/0
2
7/
3/
0
7/ 2
17
/0
7/ 2
31
/0
8/ 2
14
/0
8/ 2
28
/0
9/ 2
11
/0
2
0
Time
• The Dirt Analysts report on 20 unit samples before
each scuff station in daily inspections
12
DMAIC Example – It’s a Dirty Job
Measure
Is a the process in statistical control?
What is the current process capability?
• The practical problem is converted to a statistical one.
Capability is measured in terms of Z score and Cpk, which
captures the mean and variation relative to specifications.
3.1 DPU Upper Spec. Limit
0 DPU Lower Spec. Limit
Current Sigma Level: 1.33
Objective of six-sigma is to reduce variation and to center process
13
DMAIC Example – It’s a Dirty Job
Measure
Is a the process in statistical control?
What is the current process capability?
• The practical problem is converted to a statistical one.
Capability is measured in terms of Z score and Cpk, which
captures the mean and variation relative to specifications.
3.1 DPU Upper Spec. Limit
0 DPU Lower Spec. Limit
Current Sigma Level: 1.33
Objective of six-sigma is to reduce variation and to center process
15
DMAIC Example – It’s a Dirty Job
Analyze
What is the current and desired process capability?
Why, when and where do the defects occur?
The fundamental objective of
analyze phase is to identify those
key process inputs (critical X’s)
that are different for the good
and the defective ones (or are
statistically significant).
Def
Acceptable
Critical X’s for Dirty Job example
Factor 1: Temperature and Humidity
Factor 2: Weekday Variability
Factor 3: Prime Automation Equipment
Factor 4: Prime Ovens
Factor 5: Area Conditioning
Tools
Fish bone, normality
test, Hypothesis testing
(for mean, median and
variation), Regression,
chi-square
16
Factor 4: Prime Ovens
 A
Dirt
Count
was
conducted for 28 vehicles,
immediately before and
after the Prime Ovens
 The average increase in
counted dirt was 10
defects per vehicle hood
18
Factor 4: Prime Ovens
 The 28 units that were
counted
were
also
tracked by which Prime
Oven
they
passed
through
 There was a significant
difference between the
Oven Dirt Contribution,
with Oven 2 adding the
most defects
19
Factor 4: Prime Ovens
The ANOVA Analysis for Smoke Primed Vehicles only shows
that there is a greater than 95% significance between the
change in dirt counts for each oven.
20
Factor 4: Oven Cleaning
The Ovens are not covered in the Existing Work Order System – there is a gap
21
DMAIC Example – It’s a Dirty Job
Improve
How can we fix the process?
• Identify the relationship of X’s on Y by developing the
transfer function for Y=F(X), using tools such as DOE
• Determine the optimal settings and tolerance limits for X’s
inputs to achieve the desired Z-score for Y.
• Run a test plan to confirm the causal relationship and to
validate the improvement in Y
Improvement plan for X’s
No
Description
Improvement Plan
1
Temperature and Humidity
Automatic booth balance
2
Weekday Variability
Weekend PM schedule revisions
3
Prime Automation
Equipment
Tracking process initiated, PM
revisions
4
Prime Ovens
Oven cleaning
5
Area Conditioning
Update PM sheets, follow procedures
22
Factor 4: Oven Cleaning
BASF Recommendations
 Reduction or Elimination of Contaminants in all Systems – Lower
Agitation, Overhead Structure, Air Seals and inside Burner Units
 Eliminate Mounds of charred dirt and Paint Chips visible inside of
Conveyor Chain Track
 Eliminate Dirt from rear side of High Temperature Recirculating
Filters
 Eliminate Rust and Dirt lying inside of Air Seals
 Eliminate Dirt blowing out of Lower Convection Hot Air Supply
Ducts
 Eliminate Dirt and Fibers on rear side of Panel Filters
 Eliminate Rust and Dirt Particles falling off Overhead Ceiling and
Hardware onto vehicles traveling through the ovens
23
Factor 4: Oven Cleaning
Oven Cleaning is best conducted in a cycle that allows
the oven to:




be cleaned with dry ice, vacuum, and rags
be heated to operating temperatures for 4-8 hours
be inspected and re-cleaned
and be re-heated for 4-8 hours prior to use
Action Taken
Discussion with Sam Lemay to standardize Oven
Cleaning Procedure and sign-off. A Gap Analysis shows
that the Prime Ovens do not have the level of
standardized cleaning that the Prime Spray Booth,
Sealer Deck and Vestibule have.
24
Factor 4: Oven Cleaning
 The 3rd Pass Oven
Cleaning that occurred in
October ’02 resulted in a
measurable improvement
in dirt count per 20 units
 Oven Cleanliness has a real
effect on overall Dirt
Count!
25
Factor 5: Area Conditioning
 A study was conducted in November 2002 following the
Vehicle View through the entire Paint Process (BASF)
 A number of Maintenance, Cleaning and Repair items
were documented and recommendations were made
26
Factor 5: Area Conditioning
Recommendations
 Prime Spray Booth: Muck cleaning (grates and water)
currently occurs annually. Entering this cleaning into the
PM Work Order system is recommended.
 Develop plan for additional humidity and water flow.
 Trial an adhesive paper on the floor of the vestibule or oven
entrance to trap airborne dirt and sprayed paint.
 Eliminate Cotton Mops, Newspapers and Contaminants
from the Spray Areas, follow the Dress Codes.
27
DMAIC Example – It’s a Dirty Job
How did we improve the process?
Improve
Actions already taken lowered the DPMO from 112,000 to 6000!
Booth Balance, External Environment and a reduction in
environmental variability lowers the problems due to
Prime being out of spec.
Regular cleaning and Maintenance reduces fiber count and
additional dirt in paint from airborne contamination.
Improvement plan for X’s
No
Description
Improvement Plan
1
Temperature and Humidity
Automatic booth balance
2
Weekday Variability
Weekend PM schedule revisions
3
Prime Automation
Equipment
Tracking process initiated, PM
revisions
4
Prime Ovens
Oven cleaning
5
Area Conditioning
Update PM sheets, follow procedures
28
DMAIC Example – It’s a Dirty Job
Control
How can we ensure that process stays fixed?
• Establish post improvement capability and validate that
the pre and post difference is statistically significant
• Run the MSA on X’s and establish control plan for Y and
X’s
Pre-Improvement
Post-Improvement
Few of the applicable Tools
Control charts, Hypothesis testing, Mistake Proofing,, FMEA
29
DMAIC Example – It’s a Dirty Job
How can we ensure that process stays fixed?
Control
Control Chart: Defect Tracking “Y”
30
DMAIC Example – It’s a Dirty Job
How can we ensure that process stays fixed?
Control
Control Chart: Action Plan “X”
31
DMAIC Example – It’s a Dirty Job
How can we ensure that process stays fixed?
Control
Control Chart: Defect Tracking “Y”
Control Chart: Action Plan “X”
32
Lessons Learned
 The walk-through by BASF, the dirt analysts, filter rep’s
et al. was instrumental in discovering a number of
system problems. This should be an annual occurrence
to maintain the systems.
 Improvement efforts need to be quantified with data
(dirt count, operator comments, efficiency etc.) in order
for the results to be weighed.
33
Lessons Learned
 There are many, many factors that effect how clean a
particular vehicle is on any given day.
 There are no easy, cheap or obvious solutions, all will
take some effort to discover and some effort to resolve.
 The Sealer Deck and Prime personnel understand the
issues that they face in producing clean vehicles.
34