Pareto Analysis - Onlinemeonline.engin.umich.edu/.../Blackbelt/08t-pareto.pdf · 2010-01-09 ·...
Transcript of Pareto Analysis - Onlinemeonline.engin.umich.edu/.../Blackbelt/08t-pareto.pdf · 2010-01-09 ·...
1
Pareto Analysis
2
Seven Basic Quality Tools
1. Process Mapping / Flow Charts* 2. Check Sheets
3. Pareto Analysis4. Cause & Effect Diagrams5. Histograms6. Scatter Diagrams (XY Graph)7. Control Charts
3
Topics
I. Pareto Principle
II. Performing a Pareto AnalysisConstructing a Pareto Chart
III. Pareto: Frequency Vs. Cost Analysis
IV. Pareto Analysis Exercise Using QETools
V. Pareto Drill Down
4
I. Pareto Principle
Pareto* Principle provides the foundation for the concept of the “vital few” and a “trivial many”.
Examples:Quality – a small percentage of defect categories (causes) will constitute a high % of the total # defects.Cost – a small percentage of components will constitute a high % of total product cost.Others: Inventory, absenteeism, downtime.
*Note: Wilfredo Pareto – 19th Century Italian economist studying wealth who observed that a large proportion of wealth is owned by a small percentage of the people. Pareto principle was later applied to quality by J.M. Juran.
5
80/20 Rule
Pareto principle is sometimes referred to as the 80/20 rule.
In quality, this rule suggests that ~20% of defect categories will account for ~80% of the total number of defects.
“Vital Few – Trivial Many”
6
II. Pareto AnalysisRanking of data by importance in descending frequency (highlights most significant concern)
Example: Reasons for Delays in Preparing New Product Bids
Pareto - New Bid Delays
0102030405060
Insu
ffici
ent c
usto
mer
spe
c's
Req
uire
men
t cha
nge
by c
ust
Unk
now
n te
st re
quire
men
ts
Wai
t for
app
licat
ion
revi
ew
Pric
ing
info
not
ava
ilabl
e
Res
earc
h si
mila
r pro
duct
pric
ing
Wai
t for
eng
inee
ring
reso
urce
s
Wai
t for
sal
es re
sour
ces
Quo
te p
acka
ge in
corre
ct
Dat
a ba
se s
earc
h er
rors
Freq
uenc
y
7
First, Obtain Frequency Sum Data for Each Category
From check sheet, create a table of categories and occurrences (i.e., frequency).Example: Reasons for Delays in Preparing New Bids
Reasons for Delays FrequencyInsufficient customer specifications 56Internal pricing information not available 18Wait for application review kickoff 5Requirement change by customer 30Quote package filled out incorrectly 45Wait for engineering resources 8Wait for sales processing resources 10Research for similar product pricing 10Unknown test requirements 11Data base search errors 3
Total 196
8
Second, Create a Pareto TableSort in Descending Order (by Freq or Relative Freq)Compute Relative and Cumulative Frequencies
Relative Frequency ~ Frequency / Total (56/196=29%)Cumulative Freq % ~ Running total of % (29% + 23% = 52%)
Reasons for Delays Freq Rel Freq, % Cum Freq Cum Rel Freq, %Insufficient customer spec's 56 29% 56 29%Requirement change by cust 45 23% 101 52%Unknown test requirements 30 15% 131 67%Wait for application review 18 9% 149 76%Pricing info not available 11 6% 160 82%Research similar product pricing 10 5% 170 87%Wait for engineering resources 10 5% 180 92%Wait for sales resources 8 4% 188 96%Quote package incorrect 5 3% 193 98%Data base search errors 3 2% 196 100%
Total 196 100%
9
Pareto Chart
Left Y-axis – Frequency or Relative FrequencyRight Y-axis – Nothing or cumulative percentage line.
0%
5%
10%
15%
20%
25%
30%
Insuffic
ient c
ustomer
spec
's
Require
ment ch
ange b
y cust
Unknown t
est re
quire
ments
Wait for a
pplica
tion re
view
Pricing i
nfo no
t ava
ilable
Research
simila
r prod
uct p
ricing
Wait for e
ngine
ering
reso
urces
Wait for s
ales r
esou
rces
Quote pa
ckag
e inc
orrec
t
Data base
searc
h erro
rs
Rel
Fre
q %
0%
20%
40%
60%
80%
100%
120%
Cum
ulat
ive
Freq
%
10
III. Pareto Analysis: Frequency Versus Cost (or Severity)
Pareto Analysis may be performed using:Frequency of occurrence (expressed as a frequency count or relative frequency %), OrTotal cost, Or Severity, adverse outcome, or avoidability
Note: the most frequently occurring item may not be the most important item to address first.
11
Assessing Cost Impact
Suppose a hospice has the following Pareto Frequency Analysis for Medicare denials.
If the cost for an occurrence varies by category, one may weigh the categories by multiplying the frequency by estimated cost per occurrence (e.g., average cost).
Category FrequencyCost per
OccurTotal Cost
Inc supervisory visit 113 362 40906Not recipient 46 536 24656Unsigned election 12 650 7800Non-terminal disease 11 882 9702Unsigned Certification 8 13790 110320Unmet Level of Care 6 31851 191106Unmet Plan of Care 4 1289 5156
12
Pareto: Cost Vs. Frequency
Would the priorities be different based on a cost analysis?
TOTAL 389688
Category Total Cost Relative Frequency
Cumulative Frequency
Unmet Level of Care 191103 49.0% 49.0%Unsigned Certification 110313 28.3% 77.3%Inc supervisory visit 40924 10.5% 87.8%Not recipient 24678 6.3% 94.2%Non-terminal disease 9711 2.5% 96.7%Unsigned election 7802 2.0% 98.7%Unmet Plan of Care 5157 1.3% 100.0%
TOTAL 200
Category Frequency Relative Frequency
Cumulative Frequency
Inc supervisory visit 113 56.5% 56.5%Not recipient 46 23.0% 79.5%Unsigned election 12 6.0% 85.5%Non-terminal disease 11 5.5% 91.0%Unsigned Certification 8 4.0% 95.0%Unmet Level of Care 6 3.0% 98.0%Unmet Plan of Care 4 2.0% 100.0%
By denial
By cost ofdenial
Medicare billed forHigher cost thanexpected based on criteria.
13
IV. Lecture Exercise: Pareto Analysis for Loan Turndowns
Defect Categories for Loan Turndowns Closing costs too high, selling home, change in marital status, change in job status, not saving enough, lost interest, interest rate is too high, miscellaneous.
Using the data file, pareto.xls create a pareto table of frequency, relative frequency (%), and cumulative frequency, and then a Pareto Chart.
14
Exercise: Pareto Analysis
Step 1: Sum by Defect CategoryUsing QE Tools – perform Binary Cross Tabulation to obtain frequency counts for each category for loan check sheet data.
Step 2: Run a Pareto Analysis Using the Sum Data from Step 1, create a paretotable and chart.
15
Step 1: Binary Cross Tabulation
Using check sheet data (see sample of data below) for the different loan turndown categories, select:QETools >> Tabulation >> Binary Cross Tabulation
Note: only first 9 rows are shown from file pareto.xls
16
Binary Cross Tabulation Example
From binary cross tabulation, QETools automatically creates new data columns for categories and frequency counts in “Datasheet”
Auto Save CategoriesSum to “Datasheet”
17
Step 2: Pareto Analysis
Select: QETools >> Graphical Tools >> Pareto
18
Pareto Table: Results
TOTAL 132
Category Frequency Relative Frequency
Cumulative Frequency
HighClosingCosts 63 47.7% 47.7%Selling_home 37 28.0% 75.8%Change_marital 16 12.1% 87.9%Change_job 5 3.8% 91.7%Insuff_Saving 4 3.0% 94.7%Lost_interest 3 2.3% 97.0%rate-too-high 3 2.3% 99.2%miscellaneous 1 0.8% 100.0%
19
Pareto Chart by Relative Frequency (with Cumulative Frequency Line)
Pareto Chart
0.0%
10.0%
20.0%
30.0%
40.0%
50.0%
60.0%
HighClos
ingCos
tsSell
ing_h
ome
Chang
e_mari
talCha
nge_
jobIns
uff_S
aving
Lost_
intere
strat
e-too
-high
miscell
aneo
us
Category
Rel
ativ
e Fr
eque
nc
0.0%
10.0%
20.0%
30.0%
40.0%
50.0%
60.0%
70.0%
80.0%
90.0%
100.0%
Cum
ulat
ive
Freq
uenc
Or, useFrequency
Based on the following chart, what is the most common loan turndown reason?
20
V. Pareto Drill Down
If data set includes stratification or grouping variables, one may perform a Pareto Drill Down.
Drill Down ApproachSubset data for a single item (or items) such as high closing costs.Subset data by some value of grouping variables (e.g., Branch=C, or Branch = C and Closing Costs)Here is the loan turndown data stratified by the worst branch (C) and by Loan Officer.
Loan-Off-C Closing-Costs-C
C-1 2
C-2 10
C-3 0
C-4 2
C-5 8
C-6 0
C-7 0
C-8 6
C-9 2
C-10 2
Occurrences by Loan Officer
21
Pareto Drill Down by Branch
Decomposition of data.
0
10
20
30
40
50
60
70
# Tu
rn d
owns Closing Costs Too High
0
5
10
15
20
25
30
35
Branch C Branch B Branch D Branch A
# Tu
rn D
owns
for H
igh
Clo
sing
Cos
ts
Stratify High ClosingCosts by 4 Branches (C Worst)
22
Pareto Drill Down by Loan Officer
Decomposition from a system level down.
0
10
20
30
40
50
60
70
# Tu
rn d
owns Closing Costs Too High
0
5
10
15
20
25
30
35
Branch C Branch B Branch D Branch A
# Tu
rn D
owns
for H
igh
Clo
sing
Cos
tsStratify High ClosingCosts by 4 Branches (C Worst)
Stratify Branch “C”Closing Costsby Loan Officer
0
2
4
6
8
10
12
C-2 C-5 C-8 C-1 C-10 C-4 C-9 C-3 C-6 C-7
Freq
uenc
y
23
Summary
Pareto Analysis provides a visual tool to highlight most critical issues.
Pareto analysis often involves a drill down to find root causes.
5 Whys? Keep asking why? In the loan turndown example, have we found the root cause yet?
Note: 3 Loan Officers have most closing cost turndowns.
24
Pareto Analysis – Define Phase
Pareto Charts also may be used for project scoping using numerical data during the Define phase.
Common usage Pareto Cost Analysis
Example: if the potential scope encompasses several bars on the Pareto chart, the project may be over-scoped.