To effectively analyze issue root causes, it is crucial that evidence is properly collected, coded, filtered and analyzed. This presentation will outline proper data gathering and organizing techniques. It will examine various analysis methods, comparing their relative strengths and weaknesses. Lastly, it will introduce Root Cause Pattern Diagrams, a breakthrough analysis technique that displays data in colored graphical patterns that are compared against a library of known root cause patterns.

1. Field Failure Analysis
Field Failure Analysis
Using Root Cause
Using Root Cause
g
Pattern Diagrams
Robert D. Lloyd
©2012 ASQ & Presentation Lloyd
Presented live on Mar 08th, 2012
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3. LCG
Lloyd Consulting Group, Inc.
Lloyd Consulting Group Inc
Product & Transactional Excellence
Six Sigma, Lean, & Shainin Tools
Course Design and Deployment
Background:
Director Design Excellence
Johnson & Johnson
Field Failure Analysis
Director Six Sigma / MBB
Ocwen Financial
Director 5‐NINES Performance
Di t 5 NINES P f
Using Root Cause
Pattern Diagrams
Motorola PCS
Director Product Reliability
Motorola PPG
Led team that broke Japanese
trade barrier in electronics to
become #1 seller pagers in Japan
Six Sigma Instructor / Mentor
& Shainin Tools Instructor at
Motorola (1987 deployment)
Certifications:
Certified Mgr Quality / Org Excel
Certified Six Sigma Black Belt
Certified Lean Sensei
Licensed US Patent Agent
Robert D. Lloyd
President, Lloyd Consulting Group, Inc.
Recipient:
Motorola CEO Quality Award
Motorola CEO 10X Award
SFMA Achievement Award
CES Innovation Award
14 Issued US Patents
Inductee:
Motorola Science Advisory Board

4. Product Handling
Do not lose evidence by improper handling of
defective product returns
How products are handled
Do Not • ESD protective procedures
Induce Other • Protective trays
Issues
How products are shipped
• Static protective bags
Preserve • Boxing or egg-crating
Evidence
How products are stored
• Organized for retrieval
• Protected from harm

5. Product Analysis
Do not lose evidence by service centers “shot-gunning”
problems by repairing products “at-will”
Would you want your car serviced that way?
• New products should have first 200
Do Not returns sent directly back to Product
Induce Other Engineering for a detailed analysis
Issues
• Service centers should have a INTERNAL OPERATING PROCEDURE
FOR ADDRESSING FIELD RETURNS
structured process for the handling,
p g, 1.0 HANDLING OF RETURNED GOODS
2.0 ANALYSIS OF RETURNED GOODS
Preserve analysis, storage of returned goods 3.0 DOCUMENTATION OF ANALYSIS
4.0 ARCHIVING OF RETURNED GOODS
Evidence
• A id service center fi
Avoid i t financial
i l
incentives that might inadvertently $ per Repair
promote unnecessary repairs

6. Product Analysis
Investigation & repairs should follow fault tree maps
• Call Center “Over-the-Phone” Analysis
• Analysis Lab Progressive Investigation
To Avoid
Unnecessary Complaint: Loss of Prime
Can
C the complaint be
Returns No Works Yes
enter
after rebooting
device? investigated & resolved
& Yes Covered by
No over the phone?
Call Ce
g
Troubleshooting
(A-F)?
To Avoid No Covered by
Resolved Yes
Troubleshooting
“Shot-gun” (G-J)?
Record Cartridge
Repairs Return Device
& Cartridge Lot # & Monitor
Utilize Fault
ab
Return all pertinent items
lysis La
Tree Maps Cartridge Issue Device Issue
and conduct progressive
Profile Examine Test Examine Examine non-destructive analysis
Break
“Break” Cartridge Force Force PCB
Anal
& “Flow”
Forces
for Defects Sensor Sensor
Flex
Sockets before making repairs
b f ki i

7. Complaint & Failure Coding
Fail re
For consistent reporting, standardized coding schemes must be implemented
Inconsistent /Insufficient Failure Statements
(when no codes exist and technicians write their own observations)
Codes Must “Broken display”
p
Be Specific
and “Fractured LCD”
“F d
Consistent “Display cracked”
“Blackened LCD”
Best to
Establish
E t bli h How do you search a data base
Codes As when terms are inconsistent?
Early As Which statements refer to
Possible which of these failures?

8. Complaint & Failure Coding
Fail re
For consistent reporting, standardized coding schemes should be implemented
Need for Detailed Failure Coding
Display Edge Fracture DEF
Codes Must
p
Be Specific
and Display Shelf Fracture DSF
Consistent
Display Center Fracture DCF
Best to
Establish
E t bli h Display Black Spotting
Di l Bl k S i DBS
Codes As
Early As •S t
Set-up a robust library of codes i advance b
b t lib f d in d based on
d
Possible past complaint/failure history and FMEA’s
• Adding codes later disrupts historical data tracking

9. Complaint & Failure Coding
Fail re
Complaint “Product does not turn on”
• Report of an issue from a Customer’s
Effectively perspective (symptomatic view)
Document:
Doc ment
Failure “Reason why product does not turn on”
Customer s
Customer’s
Issue • Report of an issue from an Analysis
Lab’s perspective (causation view)
and
d
Reason Why If an issue was due to customer not knowing how to
It Occurred
O d operate the product, it is still indicative of a f il
t th d t i till i di ti f failure of
f
the instruction manual or of the product’s ergonomic
design. Do not write off such issues as “User Errors”.
g

10. Complaint & Failure Coding
Fail re
Separate Complaint and Failure coding schemes are recommended
Standardized Coding Requirements
Effectively • For consistency in reporting, standardized
Document:
Doc ment
cod g schemes should
coding sc e es s ou d be implemented
p e e ted
• Separate Complaint and Failure coding
Customer s
Customer’s schemes are recommended
h d d
Issue
– Multiple complaints may be associated
p p y
and
d with the same failure mechanism
Reason Why
– Advisable to use:
It Occurred
O d
• Numeric for complaints
• Alpha for failures
p

11. Cross Matrix Analysis
Matri Anal sis
Separate Complaint and Failure coding schemes are recommended
TOP 16 FAILURE CODES 11/1/06 – 10/31/08
Product A DKP BFH DSS CON NDF UIS DSF BLC DCB DEF CSW SLD PTK DFD RPN ULF
15 17.22% 0.05% 0.08% 5.26% 0.56% 0.03% 0.67% 0.03% 0.07% 0.03% 0.30% 1.55% 0.02% 0.01% 0.01%
03 0.11% 12.98% 0.10% 0.07% 0.32% 0.05% 0.67% 0.58% 2.74% 0.10% 0.04% 0.05% 0.03% 0.07% 0.03%
10
NT CODES
0.10% 0.03% 10.81% 0.03% 0.35% 0.02% 0.46% 0.01% 0.02% 0.01% 0.01% 1.58% 0.47% 0.02%
14 0.05% 0.58% 0.07% 0.52% 0.24% 0.16% 3.30% 0.26% 0.01%
07 0.28% 1.29% 0.49% 0.01% 0.01% 2.31% 0.02% 0.04% 1.11%
02 0.01% 0.01% 0.01%
0 01% 0 01% 0 01% 0.01% 1.85% 0.21%
0 01% 1 85% 0 21% 0.01%
0 01% 1.66%
1 66% 0.02% 0.01%
0 02% 0 01%
5 COMPLAIN
11 0.03% 0.12% 0.04% 0.08% 0.55% 0.60% 0.32% 0.04% 0.34% 0.02% 0.79% 0.01% 0.04% 0.04%
Done to 06 0.01% 0.01% 0.63% 0.12% 0.49% 0.17% 0.01% 0.01% 0.12% 0.07% 1.02% 0.04%
12 0.02% 0.22% 0.01% 1.13% 0.05% 0.77% 0.08% 0.08% 0.01% 0.72% 0.02%
Identify 05 0.01% 0.02% 0.10% 1.33% 0.24% 0.01% 0.04% 0.06% 0.02%
TOP 15
01 0.01% 0.01% 0.27% 0.62% 0.24% 0.05% 0.03% 0.02%
Relations 19 0.32% 0.12% 0.10% 0.77% 0.01%
Between 08
04 0.08%
0.01% 0.01% 0.24% 0.14% 0.22% 0.03% 0.03%
0.27% 0.10% 0.01% 0.04% 0.03% 0.01% 0.01%
Complaints 13 0.01% 0.02% 0.01%
0 01% 0 02% 0 01% 0.49% 0.02% 0.05%
0 49% 0 02% 0 05% 0.03% 0.01%
0 03% 0 01% 0.05% 0.01%
0 05% 0 01%
and Failures
• Which failure codes are linked to which complaints?
• Are certain failures linked with multiple complaints?
• Are certain complaints linked with multiple failures?
• Create Pareto charts to prioritize analysis efforts

12. Determining Time In Field
Date of Manufacture versus Time in Service
Product Tracking
• Serial Number • Manufacture Date Code
Product • Model Number • Lot Number
Tracking
Strategies Product History After Manufacture
y
Manuf Active Failure
Date Date Date
Product ID, • Manufacturer’s stock room
Time In Field
Ti I Fi ld Storage
S Active Life
f
and User • Distributor's stock room
Warranty
Date
Demographic • On store shelves
Lean activities work to reduce all inventories

13. Determining Time In Field
Date of Manufacture versus Time in Service
Manuf Active Failure
Date Date Date
SKU
Storage Active Life
Product Warranty
Date
D t
Tracking
Strategies When was product placed into active use?
• Indirect sale to end user (database of S/N & sale date)
Product ID,
Time In Field
Ti I Fi ld • Service provider activation date
and User • Direct sale to end user
Demographic Can also obtain
• Registration (“Bingo”) cards important user
– Historically low response rates demographics
– Need incentives to increase returns

14. Virgin vs Repaired Units
s
Virgin Units
• Representative of the designs, materials,
p
process settings, and operators at the time
g p
of manufacture
The
Challenges
Of Trying Repaired Units
To Obtain • A mixture of designs, materials, operators,
Clean Data process settings from date of manufacture
and from various dates of repair
• Effects of being repaired (heat, stress, etc)

15. Virgin vs Repaired Units
s
Virgin Product Segregation
• Best to analyze virgin units and repaired
units separately
• To identify virgin units: subtract Initial Ship
Strategies Month from Last Ship Month for each S/N
p
To Obtain Last Month Shipped Apr 2007
Clean Data Initial Manuf Ship Month Aug 2006
Manufacturing Ship Delta 8 months
Virgin Products Have Mfg Ship Delta = 0
(because it was never returned to ship again)

16. List of Data Filters
• Month of Manufacture
• Month of Complaint
• Time in Field
Importance of • Warranty Date
Filtering the • Activation Date
Data • Virgin vs Repaired vs Reconditioned
• Product Model
• P d t Revision
Product R i i
Cleaner, • Geographic Region (Country, State)
Relevant • Complaint Codes
Data More
• Failure Codes
Important
• User Demographics
Than Sample
Size
• Gender
• Age
• Location (State/Pro ince)
(State/Province)

17. Month of Complaint
Hinge Complaints by Month of Complaint
30%
(Product Introduced in Fall 2006)
o)
Complaint Rat (%/mo
25%
te 20%
Looks at All 15%
Complaints
for a Given 10%
Month 5%
g
Regardless
of When It 0%
Apr-07
May-07
Aug-07
Apr-08
May-08
Aug-08
Nov-06
Nov-07
Dec-06
Jan-07
Feb-07
Jun-07
Jul-07
Sep-07
Dec-07
Jan-08
Feb-08
Jun-08
Jul-08
Sep-08
Oct-06
Mar-07
Oct-07
Mar-08
Was Made
MONTH OF COMPLAINTS
# of Complaints for Specific Month
Complaint Rate =
Install B
I t ll Base for Specific Month
f S ifi M th

18. Pareto Diagrams
June 2008 Failure Code Pareto - Product A
Although It
g
Identifies the
Important
Few,
It is only a
“Snap-Shot”
In Time
But what have been the trends
over th prior months?
the i th ?

19. Pareto Timelines
A Better Perspective on the Larger Picture
Pareto Timeline of Top 6 Failure Codes for Product A
(Specific failures reported for each month divided by respective month install base)
A
Pareto B
Rankings
R ki
Change D
Over Time
C
Seasonality,
Data
Designs, from
Prior
Materials, Page
Processes
A - Hinge failures are steadily rising at a rapid rate
B - Water intrusion failures are seasonally cyclical
C-C Crystal f il
t l failures have been steadily decreasing
h b t dil d i
D - Keypad failures falling, but check for seasonality

20. Weaknesses Regarding
Month of Complaint (MoC)
# of Complaints Received in Specific Month
MoC Complaint Rate =
Install Base for That Specific Month
Month of Complaints Received June 2006
Complaint Different dates of manufacture
Reflects A • Different design revisions Mfr’d Mfr’d
Mixture of • Different materials Mfr’d
Sep ‘05
Mfr’d
Aug ‘04
Mfr’d
Apr ‘04 Oct ‘04 May ‘05
Products • Different process settings Mfr d
Mfr’d
Jan ‘05
Mfr d
Mfr’d
Nov ‘04
• Different operators Install Base June 2006
Mainly “The Chowder
The Chowder”
Reflects
External MoC mainly reflects the impact of external
Factors
environmental f t
i t l factors. It does not clearly
d t l l
represent internal design/process impact.
Might not even reflect current production.

21. An Improper Application
of Month of Manufacture
Man fact re
Hinge Failures by Month of Manufacture
12%
10%
Month of
Failu Rate (% / mo) 8%
Manufacture
ARTIFICIAL IMPROVEMENT CURVE
Alone Is 6%
DUE TO RECENT MONTHS HAVING
Misleading
ure
LESS TIME IN THE FIELD
4%
2%
Must Also
Factor In 0%
Aug-07
Aug-08
Apr-07
Jan-07
Jul-07
Jul-08
Nov-06
Apr-08
Dec-06
May-07
Sep-07
Nov-07
Dec-07
May-08
Sep-08
Feb-07
Jun-07
Oct-07
Jun-08
Mar-07
Jan-08
Feb-08
Mar-08
Oct-06
Time in
A
A
Month of Manufacture
the Field
# of Complaints for Specific Month of Manuf
p p
MoM Complaint R t =
M MC l i t Rate
Install Base for Specific Month of Manuf

22. Month of Manufacture
Man fact re
Cumulative Field Failure Rate
4.5%
4.0% May-07
y
Jun-07
3.5% Jul-07
Aug-07
TURE
Cumulative mo)
3.0%
LIKELY MATERIAL OR PROCESS ISSUES IN
JUN-AUG 2007 CAUSING RISE IN FAILURES
Sep-07
Oct 07
Oct-07
MONTH OF MANUFACT
FAILURE RATE (%/m
Failure R t
F il Rates Nov-07
2.5% Dec-07
Tracked By Jan-08
2.0% Feb-08
Time-in-Field
F
Mar-08
Mar 08
Apr-08
for Multiple 1.5%
May-08
Jun-08
Months of 1.0% Jul-08
Aug-08
Manufacture 0.5% Sep-08
for Specific 0.0%
Product
Prod ct 1 2 3 4 5 6 7 8 9 10
TIME IN FIELD
11 12 13 14 15 16 17 18 19
Those months that deviate garner attention, but
this is not the most effective way to communicate.
ff

23. Preparing MoM Chart
Time to Failure
(months)
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
May-07
Jun-07
ses
Jul-07
re
Aug-07
g
stall Bas
Month of Manufactur
Sep-07
Oct-07
Nov-07
Dec-07
Jan-08
Feb-08
MoM Initial Ins Mar-08
Mar 08
Apr-08
May-08
Data Table Jun-08
Jul-08
Aug-08
Used for p
Sep-08
Oct-08
Nov-08
Creating Dec-08
Jan-09
Feb-09
Chart on Mar-09
Apr-09
Apr 09
Prior Slide
Cumulative Failure Rates:
Failures (for specific MoM and Time-in-Field)
Install Base (specific MoM minus all prior failures)
Rates for each MoM are plotted as shown on prior page

24. Preparing MoM Chart
Pump
Product 1250
A32
Country US
RGA Y/N?
Manuf Ship Delta
Y
0
Filtering
Complaint Code 07
12
207 Criteria
Failure Code NDF
(All)
Sum of Count TTF
Manuf Ship Month O 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 Total
Oc 06
Oct-06 1 6 2 3 3 1 2 4 1 2 2 2 1 1 3
31
Pivot T bl
Pi Table Nov-06
Dec-06
Jan-07
4
4
4
5
6
4
5
3
1
2
4
5
2
1
3
1
4
2
6
3
1
4
2
2
4
1
2
2
4
1
3
2
1
3
1
2
1
2
3
1
1
1
2
2
1
1
1
2
1
1
33
43
48
for Filtering Feb-07
Mar-07
Apr-07
8
4
5
3
2
4
4
3
3
2
1
1
1
1
3
3
3
4
6
1
3
4
4
2
2
2
3
1 1 1
2 1
1
2
1 1
2
1
1
1
36
32
30
May-07 1 1 1 1 2 1 1 1 9
Failure Data Jun 07
Jun-07
Jul-07
1 2
4
3
1
5
1 1
1
1 3
2 1
4
1
2
2 1
1
1 20
18
Aug-07 6 3 3 2 1 1 1 3 1 1 22
for Use in Sep-07
Oct-07
Nov-07
3
3
5
1
4
2
5
3
1
3
1
2
1
2
1
2
2 1
3 2
1
1
1
2
1
1 23
16
15
Failure Rate Dec-07
Jan-08
Feb-08
7
1
1
2
1
2
2
1
1
2
2
2
1
1
2
1
15
9
5
Mar-08 3 1 5 2 2 13
Calculations Apr-08
May-08
3
2 1
2
3 2
1 6
8
Jun-08 2 1 3
on Prior Slide Jul-08
Aug-08
Sep-08
4 3 7
Total 71 53 48 32 29 22 35 29 24 24 14 14 8 7 7 6 7 6 2 2 1 1 442
Failures reported by their Month of Manufacture
and by their Time To Failure (in months)

25. Analysis of Production Issues
Using C diti
U i Conditional Formatting
lF tti
HIGH
IMAGE
Press
Cylinder NS NC FC FS
Application of GRAYBAR LOW
Conditional
LOW PAGES HIGH PAGES
Formatting of Date Page C
IMAGE
M Y C
GRAYBAR
M Y C
IMAGE
M Y C
GRAYBAR
M Y
Data Cells for 3/4/04
3/4/04
3/4/04
NS
NS
NS
0.65
0.64
0.64
0.65
0.63
0.64
0.69
0.66
0.67
0.70
0.73
0.68
0.64
0.64
0.63
0.65
0.64
0.66
0.68
0.65
0.66
0.65
0.61
0.62
0.70
0.65
0.67
0.67
0.67
0.66
0.65
0.64
0.62
0.69
0.67
0.65
Analyzing 3/4/04
3/4/04
3/4/04
3/4/04
NS
NS
NS
NS
0.68
0.67
0.65
0.66
0.68
0.67
0.63
0.65
0.72
0.71
0.67
0.69
0.71
0.71
0.69
0.70
0.66
0.66
0.65
0.65
0.68
0.69
0.68
0.68
0.70
0.69
0.67
0.67
0.68
0.67
0.64
0.64
0.73
0.72
0.71
0.70
0.67
0.67
0.59
0.68
0.65
0.66
0.59
0.67
0.67
0.70
0.62
0.72
Colored Ink 3/4/04
3/4/04
NS
NC
0.66
0 66
0.61
0.65
0 65
0.60
0.66
0 66
0.65
0.70
0 70
0.64
0.66
0 66
0.61
0.70
0 70
0.64
0.69
0 69
0.66
0.66
0 66
0.63
0.68
0 68
0.69
0.63
0 63
0.64
0.63
0 63
0.62
0.65
0 65
0.66
3/4/04 NC 0.62 0.62 0.67 0.68 0.64 0.68 0.66 0.63 0.69 0.66 0.64 0.68
Densities at 3/4/04
3/4/04
3/4/04
NC
NC
NC
0.62
0.65
0.64
0.63
0.65
0.65
0.66
0.68
0.67
0.66
0.68
0.69
0.64
0.64
0.66
0.67
0.67
0.67
0.66
0.69
0.70
0.64
0.66
0.68
0.70
0.70
0.73
0.67
0.52
0.68
0.65
0.52
0.66
0.66
0.54
0.69
NY Times
3/4/04 NC 0.61 0.62 0.66 0.68 0.63 0.66 0.67 0.66 0.71 0.65 0.64 0.67
3/4/04 NC 0.59 0.61 0.65 0.67 0.62 0.64 0.63 0.62 0.68 0.63 0.62 0.65
3/4/04 NC 0.65 0.64 0.69 0.47 0.49 0.55 0.68 0.64 0.69 0.58 0.54 0.59
3/4/04 FC 0.53 0.53 0.58 0.59 0.55 0.58 0.57 0.56 0.62 0.59 0.56 0.60
3/4/04 FC 0.53 0.53 0.57 0.58 0.54 0.58 0.55 0.53 0.57 0.58 0.55 0.57
Communicating 3/4/04
3/4/04
3/4/04
FC
FC
FC
0.53
0.55
0.51
0.53
0.56
0.52
0.58
0.60
0.57
0.56
0.60
0.57
0.53
0.59
0.55
0.57
0.62
0.60
0.56
0.58
0.55
0.54
0.58
0.53
0.59
0.63
0.59
0.58
0.60
0.58
0.55
0.58
0.56
0.58
0.60
0.59
3/4/04 FC 0.51 0.53 0.58 0.58 0.55 0.59 0.55 0.54 0.60 0.59 0.56 0.60
Through Use of 3/4/04
3/4/04
FC
FC
0.54
0.57
0.54
0.58
0.59
0.63
0.60
0.64
0.57
0.60
0.62
0.64
0.57
0.59
0.54
0.58
0.61
0.63
0.63
0.61
0.60
0.59
0.64
0.63
“Visual Data” 3/4/04
3/4/04
3/4/04
FS
FS
FS
0.57
0.55
0.54
0.55
0.54
0.55
0.61
0.60
0.59
0.57
0.56
0.56
0.54
0.53
0.54
0.60
0.58
0.57
0.58
0.59
0.57
0.54
0.56
0.54
0.58
0.59
0.57
0.60
0.58
0.56
0.56
0.54
0.55
0.59
0.58
0.56
3/4/04 FS 0.54 0.55 0.59 0.56 0.55 0.58 0.57 0.56 0.57 0.56 0.57 0.56
3/4/04 FS 0.52 0.54 0.57 0.56 0.54 0.57 0.57 0.55 0.57 0.50 0.51 0.50
3/4/04 FS 0.53 0.53 0.58 0.58 0.54 0.58 0.57 0.54 0.58 0.58 0.55 0.57
3/4/04 FS 0.54
0 54 0.53
0 53 0.58
0 58 0.58
0 58 0.53
0 53 0.59
0 59 0.58
0 58 0.56
0 56 0.60
0 60 0.61
0 61 0.58
0 58 0.60
0 60
3/4/04 FS 0.55 0.55 0.58 0.73 0.68 0.70 0.57 0.55 0.60 0.58 0.56 0.59
LOW DENSITY HIGH DENSITY
Below 0.50 0.50 ‐ 0.59 0.60 ‐ 0.61 0.62 ‐ 0.68 0.69 ‐ 0.70 0.71 ‐ 0.80 Over 0.81

26. Root Cause P tt
R tC Pattern A l i
Analysis
(Lloyd Diagrams)
Reference
Cell
C ll
Time to Failure or Complaint
(months)
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
Jan 08
Jan-08
Feb-08
Init MoM Install Bases
Applying Mar-08
acture
Apr-08
May-08
Conditional Jun-08
Jul-08
Aug-08
Aug 08
Month of Manufa
Formatting
F tti Sep-08
Oct-08
Nov-08
to Failure & Dec-08
Jan-09
Complaint Feb 09
Feb-09
M
Mar-09
Apr-09
Data Tables May-09
Jun-09
Jul-09
tial
Aug-09
Sep-09
Sep 09
M
Oct-09
A New and Nov-09
Dec-09
Better Way
to Identify • Calculate non-cumulative failure rates (without subtraction)
non cumulative
Issues • Instead of plotting data, we conditionally format the data
cells themselves with regard to a master reference cell
• That reference cell is swept to find patterns and rates
Copyrighted © 2010 Robert D Lloyd