© 2003 Six Sigma Academy0 Impact and Depth Projects General Case Studies Champion Workshop

1© 2003 Six Sigma Academy

Impact and Depth ProjectsGeneral Case Studies

Champion Workshop

2© 2002-2003 Six Sigma Academy

Project Case Studies

The purpose of this section is demonstrate to varying detail the breadth and depth of projects tackled using the Breakthrough Strategy. Included are projects that apply to a variety of industries and processes. The tools used range from simple lean applications to more complex business process Design for Six Sigma. These 8 project examples are not specific to the financial services field, but are intended to broaden your thoughts about the application of this methodology.

Section One – Impact Projects

Focus on Projects and business cases that had a profound effect of evolving the concept of Six Sigma from a manufacturing/back office process improvement tool suite to a business improvement problem solving approach.

Section Two – Depth Projects

Focus on Projects and business cases that show a depth and variation of the tools used to solve business problems through a wide range of processes and industries.


Impact ProjectsShort Case Studies


Impact Projects

Focus on Projects and business cases that had a profound effect of evolving the concept of Six Sigma from a manufacturing/back office process improvement tool suite to a business improvement problem solving approach.

The examples chosen:

Pricing

Sales Force Effectiveness

Financial Closing Process

Reduction of Teller Transactions

Adhesive

Delivery Logistics


Client: GE

Challenge: Gain a 2 –2.5% increase in price of goods and services.

Goal: Negotiate price increases with large clients while maintaining positive relationships

Results: Initially, identified as a huge success… 2.50% price increase year over year. (Equivalent to $4.4MM in margin)… actual results in terms of landed/pocket price a different story.

Pricing


Pricing

Pricing Waterfall Before

0.39%

0.54%

0.21%0.45%

0.63%

0.47%

0.52%2.50%

0.39%

-1.10%

List Price T erms Extension Addit ional T ermsT aken

Red Arrow Pricing T ransportat ion Field Support Volume Discount Special ContractRates

Concession Landed Price

Initially, the 2.5% price increase was hailed as a big success.However, once the idea of landed price was understood and Calculated, a different story emerged.


PricingPricing Waterfall After

1.75%

0.05%0.03%0.08% 0.18% 0.40%

0.21%

3.00%

0.30%

List Price T erms Extension Red Arrow Pricing T ransportat ion Field Support Volume Discount Special Contract Rates Concession Landed Price

Six Sigma was a natural way for the organization to deal with the gaps identified by the waterfall… Once the data was collected and verified, each of the waterfall elements were addressed either by policy (pricing changes by sales force) or via green/black belt projects.

The 1999 price increase process netted the business $3.7MM in Contribution Margin.


Client: Multiple

Challenge: Understand the amount of time spent by the sales force with the decision makers – clients… and increase this time. (In one example the time spent with decision makers was 5% and the other firm found their first quartile sales force spent 14% of their time with decision makers).

Goal: Increase the time spent with the client and specifically increase the time spent with the decision makers at the client.

Results: The first organization increased the time spent with decision makers from 5% to 22%. The second organization is working a current project – current projections show the increasing the time spent from 14% to 30% in trials.



The sales force effectiveness approach was started in both companies well after the organizations started Six Sigma.

What was typified as the “art of selling,” was not an acceptable reason for why some salespeople met their goals while others did not.

The first example brought the business estimated additional revenue of $39MM (20% increase) with a reduction of sales staff of 15%. The second business identified is still implementing the changes, but expect to drive $500MM in increased sales the first year without adding sales professionals.



Client: Large conglomerate

Challenge: Reduce the quarterly closing cycle time from 30 days to 5 days. (this included affiliate closing cycles which were never included previously.)

Goal: Reduce the amount of time and associated headcount to accurately close the quarterly books

Results: Multiple projects (typically green belt, though the process was led by a Black Belt)… the results were a 3 day close in 9 months.



This mega-project dubbed, “Free nights and weekends,” was one of the first finance organizational projects.

The genesis of this project was a CFO who had as her background a MBB certification, asking why the processes of finance took so much time, were fraught with rework and caused as much stress to the finance organization.

The P&L impact of these projects is not readily available, but it can be estimated that over $1MM in cost was removed.



Client: Large Financial Service Corporation

Challenge: Bank teller transactions are the highest cost and seemingly low value to the client relationship. Nearly 2/3’s of all the teller transactions could be accomplished using an ATM…yet only about 35% of the total are accomplished using the ATM.

Goal: Shift 80% of all transfer-eligible transactions to ATMs and manage the teller workforce to maximize service while minimizing cost.

Results: Through the adoption of waste elimination, variation reduction tools and implementing a queuing model, the results were a 65% transfer of transactions and a reduction of labor costs by 50%.




This mega-project included the use of sophisticated analysis tounderstand time-based forecasting and queuing theory. Determiningthis load and the reasons for the load, specific information technology projects, lean projects, and variability reduction projects were launched.

The P&L impact of these changes to this bank were in the tens of millionsof dollars


Depth ProjectsLonger Case Studies


Depth Projects

Focus on Projects and business cases that show a depth and variation of the tools used to solve business problems through a wide range of processes and industries.

The examples chosen:

Branch Office Rework

Increase Market Share


Branch Office Rework


Define

Branch Office redundancy and rework result in $7 million of excess expense over a budget of $200 million as well as client dissatisfaction.

Problem Statement

ObjectiveIdentify and reduce the cause for work redundancy and rework by 70% for a savings of > $4.9 million.

Critical to Quality - CTQ• Percentage of redundant and rework expense.• Client satisfaction measured by errors generated

per 100 clients.• Cycle time measured by marketing policy as 14

calendar days.

Current/Goal/Stretch Goal

35,000 DPMO - Current 10,500 DPMO – Goal 10,500 DPMO – Final Actual

Benefits Achieved

$5 million annualized savings80% reduction in branch redundancy and rework expenseNo decrease in cycle time


High Level Process Map

Start

Stop

No

Yes

Branch i

Branch i+1

Rework

Rework Analysis

• The network consisted of over 200 branches.

• The average branch revenue was ~ $125,000,000.

• The average rework expense per branch was ~ $125,000.

The Focus Was on Representative Branches With the Eventual Goal to Understand Common Operations and

Sources of Redundancy to Create System Wide Mistake Proofing Strategies

The Focus Was on Representative Branches With the Eventual Goal to Understand Common Operations and

Sources of Redundancy to Create System Wide Mistake Proofing Strategies


Data Collection

Data Collection Focused On The Following Inputs:

(1) Annual Sales (2) Geographical Region (3) Client Affluence Level

Under $10 million $10-25 Million >$25 millionNE SE NW SW Central NE SE NW SW Central NE SE NW SW Central

>$5 MM

$1-5MM

<$500K -$1MM

$100K-$499K

<$100K

Outputs Were Redundant Operations,rework Types, Frequency and Expense As Well As Cycle Time


Defect Analysis By Branch

Branch Six (Sales Revenue Less Than $10 MM and Located in the Northeast) Had the Most Redundant Operations

89 45 43 34 24 23 21 11

30.7 15.5 14.8 11.7 8.3 7.9 7.2 3.8

30.7 46.2 61.0 72.8 81.0 89.0 96.2 100.0

0

100

200

300

0

20

40

60

80

100

Branch

CountPercentCum %

Per

cen

t

Cou

nt

Redundant Operations By Branch1st Level Pareto Analysis

89 45 43 34 24 23 21 11

30.7 15.5 14.8 11.7 8.3 7.9 7.2 3.8

30.7 46.2 61.0 72.8 81.0 89.0 96.2 100.0

0

100

200

300

0

20

40

60

80

100

Branch

CountPercentCum %

Per

cen

t

Cou

nt

Redundant Operations By Branch1st Level Pareto Analysis


Defect Analysis By Redundancy For Branch Six

Duplicated Reports Had the Highest Incident Rate

98 45 31 1252.7 24.2 16.7 6.5

52.7 76.9 93.5 100.0

0

50

100

150

0

20

40

60

80

100

Category

CountPercentCum %

Per

cen

t

Cou

nt

Branch Six Work Redundancy2nd Level Pareto For Branch Six


Analysis Of Redundant Operation By Sales

Several Analyses Were Conducted To Determine The Key Process Input Variables (KPIVs)…Finally, It Was Found Branch Sales Level and Region

Were Significant (East and West Coast As Well As Central United States) …These Were Incorporated Into an Optimization Experiment

0 5 10 15

95% Confidence Intervals for Sigmas

Bartlett's Test

Test Statistic: 0.144

P-Value : 0.931

Levene's Test

Test Statistic: 0.158

P-Value : 0.856

Factor Levels

-1

0

1

Test for Equal Variances for % Redundancy By Branch Sales

Low Sales $<100 K

Intermediate Sales $500K- $1 MM

Sales >$ 5 MM

-1 0 1

1

2

3

4

5

Sales

% R

ed

un

da

ncy

Boxplots of % Redundency by Branch Sales(means are indicated by solid circles)

< $100 K $500K - $1MM >$5 MM


Building The Optimization Model

Region Is the Most Important Variable

Estimated Regression Coefficients for % Redundancy

Term Coef SE Coef T P

Constant 1.8983 0.1315 14.433 0.000

Sales 0.0167 0.1293 0.129 0.901

Region 1.5833 0.1293 12.244 0.000

Sales*Sales 0.0710 0.1906 0.373 0.720

Region*Region 0.7710 0.1906 4.045 0.005

Sales*Region -0.1000 0.1584 -0.631 0.548

S = 0.3168 R-Sq = 96.1% R-Sq(adj) = 93.3%


Optimization To Reduce Redundancy

Redundancy Should Be Eliminated By Branch On a Regional Basis Across All Branches

1

0-1

1.0Region

1.5

2.0

2.5

3.0

3.5

4.0

4.5

0

% Redundancy

-11Sales

Percent Redundency Versus Branch Sales and Region


0 5 10 15 20 25

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

Month

Exp

ense

($

Mill

ion

)

Redundant and Rework Expenses Before Versus After Improvements

Mean=0.1775

2.0SL=0.2628

-2.0SL=0.09220

1 2Before

After

Business Benefits

Redundant Work Was Consolidated by Geographical Region … 40 Branches Were Consolidated Leaving Just 160 in the Network.

• $5 Million Annualized Savings• 80% Reduction in Branch

Redundancy and Rewok• No Decrease in Cycle Time


Adhesive Project Example


Problem Statement

The amount of adhesive consumed in the assembly operation is higher than specified by engineering, resulting in significantly higher production expenses, which impact profitability of the product.

Target:

Identify the causes of excessive adhesive consumption and reduce the usage of adhesive in the assembly line operation by 400%.

Critical To Quality - CTQ

Applying the specified amount of adhesive is essential to bond strength.

Applying excessive adhesive causes costs to exceed targets.

Benefits Achieved

$500,000 savings in material costs for adhesive.


1st Qtr Usage Projected Usage For the Year

Brought in house

14,983 Gal.$174,738

56,479 Gal.$658,689

Usage Per Projected Specification

3,542 Gal.$41,305

13,353 Gal.$155,731

Adhesive Volume/Cost Total Plant

MetricsAdhesive usage and cost


Purging process Purging process --

--Leaking during Leaking during applicationapplication

-Limit switch set up (too high causesLimit switch set up (too high causes excessive adhesive remaining in drums)excessive adhesive remaining in drums)

Over use Over use of of adhesiveadhesiveduring during assemblyassemblyprocessesprocesses

Cause-Effect DiagramCause-Effect Diagram

MachinesMachines

MethodsMethods

ManpowerManpower MeasuresMeasures

- - Drum’s change over processDrum’s change over process

Nozzles Height Nozzles Height --vs. Panel nestvs. Panel nest -- Needed trainingNeeded training

on spec’s.on spec’s.

Pressure set upPressure set up --

-- In- ProcessIn- ProcessMeasurement Measurement systemsystem

- - Nozzles timingNozzles timing


Current dimensions data: Base line

Date/Spot 257-1 257-2 257-3 257-4 257-5 257-6 352-1 352-2 352-3 352-4 352-5 352-6 381-1 381-2 381-3 381-4 381-5 381-65/21, 12:30 37 42 38 34 41 38 36 33 33 32 33 36 32 35 38 37 35 375/25, 1:16 35 35 31 31 31 32 41 43 36 45 42 40 40 39 41 40 40 425/29, 10:10 32 34 28 27 34 35 41 43 37 47 42 41 41 40 41 39 39 425/30, 3:10 33 35 33 32 34 27 40 38 35 46 36 42 41 42 42 43 41 425/31, 3:00 39 37.5 33.4 31.6 35.1 33.5 42 41 37 48 38 43 45 44 44 44 43 436/01, 5:00 35 38 34 35 37 36 40 36 33 42 36 41 38 36 39 38 36 39

Diameter (mm)


50454035302520

USLLSL

Process Capability Analysis for Diameter

PPM Total

PPM > USL

PPM < LSL

PPM Total

PPM > USL

PPM < LSL

Cpm

Ppk

Z.LSL

Z.USL

Z.Bench

StDev (Overall)

Sample N

Mean

LSL

Target

USL

993932.96

993605.73

327.23

981481.48

981481.48

0.00

*

-0.83

3.41

-2.49

-2.51

4.35552

108

37.8435

23.0000

*

27.0000

Expected PerformanceObserved Performance

Overall Capability

Process Data

Current Process not capable- Negative Sigma Level


"USAGE OPTIMIZATION OF ADHESIVE"TWO LEVEL - TWO FACTORS EXPERIMENT

LINE: 257 / 352 / 381 SHIFT: 2 / 3 DATE: / / Time: ___:___

LEVELS DEFINITION:LEVEL / FACTOR PRESSURE TIMING

HILOW

EXPERIMENT:

RUNS PRESSURE(psi)

TIMING(seconds)

SPOTS DIAMETER(millimeters)

#1 /#2 /#3 /#4 /#5 /#61 HI HI / / / / /2 HI HI / / / / /3 HI HI / / / / /4 HI HI / / / / /1 HI LOW / / / / /2 HI LOW / / / / /3 HI LOW / / / / /4 HI LOW / / / / /1 LOW HI / / / / /2 LOW HI / / / / /3 LOW HI / / / / /4 LOW HI / / / / /1 LOW LOW / / / / /2 LOW LOW / / / / /3 LOW LOW / / / / /4 LOW LOW / / / / /

OBSERVATIONS:______________________________________________________________________________________________________________________________________________________________________________________________________________________

Material: Adhesive (from same drum). Temperature: N/AViscosity: Constant Humidity: Constant

Top of panel

#1 Spots #6

DOE - DOE - Optimize Pressure & Time SettingsOptimize Pressure & Time Settings for the dispensing nozzlesfor the dispensing nozzles


Regression Analysis: Diameter versus Pressure, Time

The regression equation isDiameter = - 39.0 + 0.740 Pressure + 1.61 Time

Predictor Coef SE Coef T PConstant -38.985 8.038 -4.85 0.000Pressure 0.73956 0.09967 7.42 0.000Time 1.6121 0.2100 7.68 0.000

S = 1.596 R-Sq = 94.0% R-Sq(adj) = 93.5%

Time Pressure

25

29

33

37

41

Dia

met

er

Main Effects Plot - Data Means for Diameter

Results Main Effects Plot Regression Model

Results used to optimize settings for nozzles


27 mm27 mmUPPER SPEC..UPPER SPEC..

25 mm25 mmTARGETTARGET

23 mm23 mmLOWER LOWER SPEC.SPEC.

WRONGWRONGTOO BIGTOO BIG

WATCH FOR THIS DEFECT

WRONGWRONGTOO SMALLTOO SMALL

Visual Aid


BEFORE CHANGING ADHESIVE BEFORE CHANGING ADHESIVE DRUMS CHECK FOR:DRUMS CHECK FOR:

1. The drum to be removed from the line is empty. 2. The piston is all the way down to the bottom of the drum.

3. Air pressure closed for drum to be changed4. T-valve closed for the drum to be changed.5. T-valve open for the remaining drum.

AFTER CHANGING ADHESIVE DRUMSAFTER CHANGING ADHESIVE DRUMSCHECK FOR:CHECK FOR:

1. T-valve is open for both drums.2. Air lines open for both drums; if not, if not, one drum will pump Adhesive to the other.one drum will pump Adhesive to the other.

T-valve

To avoid Adhesive spillage…To avoid Adhesive spillage…

LOSSLOSS

$11.7/gallon$11.7/gallon

There is 25% of adhesive remainingin this drum

Piston is herePiston is at bottom

When plate is here

Visual AidsVisual Aids


Material: PlexiglassMaterial: Plexiglass

23mm23mm 27mm27mm

1000mm1000mm

Measurement tool for Adhesive spotsMeasurement tool for Adhesive spotsPrototype IIPrototype II

Gage type: Go / no goGage type: Go / no go

Higher specification limitHigher specification limit Lower specification limitLower specification limit

60mm60mm

Measurement SystemMeasurement System


11stst Qtr Cost Improved Process Cost Difference Yearly Savings Qtr Cost Improved Process Cost Difference Yearly Savings

$ 0.58/panel $ 0.58/panel $0.24 /panel $0.34/panel $0.24 /panel $0.34/panel $503,000$503,000

Project Results

Actions:Actions:Material handling ChangesMaterial handling ChangesOptimized Application SettingsOptimized Application Settings

Process Capability for Diameter Improved Ppk= 3.1


Delivery - Logistics


Define

• Point one• Point two• Point three• Trailers are not being unloaded in their scheduled window times causing extra inventory to be carried by the plant and costing the plant in switching services. Also impacts ability of plant to build vehicle in station.

Problem Statement

Objective• Improve the live unloading of

scheduled window trailers by 70% in the main plant

• $250 K cost savings to the plant and $250 K to company freight budget

Critical to Quality - CTQ• Inventory and carrying costs

• On-time delivery to line

Metrics (Baseline/Final)

814,000 DPMO - Baseline 18,000 DPMO – Final Actual

Benefits Achieved

$1.27 M in inventory, freight, switching and late line feeds

97% defect reductionTimeframe = 6 months


Measure

• Process fully mapped• Subjective problem solving tools

used• Operational Definition established• Data collected manually• Process capability measured

Process severely incapable

Revised Process Map

Trailer is loaded at supplier

Carrierdeliverstrailer

Trailer arrives at the plant

Trailer dropped

in yard

Supervisor callstrailer in to dock

Switcher brings

trailer todock

Receiving checker unloads truck

Stock staged onDock

Stocktaken

to mkt. place

Stocktakento line

Stock placedline side

Supv. unloadsor drops

Stock/dunnagewarehousedin mkt. place

Dunnagetaken

to warehouse

Dunnage taken

to dock

Dunnage reloaded/bills cut

Start

Trailer arrivesat supplier

End

Drop

CarrierdeliverstrailerCTQ

CTQVA

NVA

NVANVA

NVA

Bottle necks reduced

VA

VAVA

Stock loadedon dollies/AGV

NVA

X

X

XX

X

Revised Process Map




Trailer dropped

in yard


Switcher brings

trailer todock


Stock staged onDock

Stocktaken

to mkt. place

Stocktakento line




Dunnagetaken

to warehouse

Dunnage taken

to dock


Start


End

Drop


CTQVA

NVA

NVANVA

NVA


VA

VAVA


NVA

X

X

XX

X

Revised Process Map




Trailer dropped

in yard


Switcher brings

trailer todock


Stock staged onDock

Stocktaken

to mkt. place

Stocktakento line




Dunnagetaken

to warehouse

Dunnage taken

to dock


Start


End

Drop


CTQVA

NVA

NVANVA

NVA


VA

VAVA


NVA

Revised Process Map




Trailer dropped

in yard


Switcher brings

trailer todock


Stock staged onDock

Stocktaken

to mkt. place

Stocktakento line




Dunnagetaken

to warehouse

Dunnage taken

to dock


Start


End

Drop


CTQVA

NVA

NVANVA

NVA


VA

VAVA


NVA

X

X

XX

X

Revised Process Map




Trailer dropped

in yard


Switcher brings

trailer todock


Stock staged onDock

Stocktaken

to mkt. place

Stocktakento line




Dunnagetaken

to warehouse

Dunnage taken

to dock


Start


End

Drop


CTQVA

NVA

NVANVA

NVA


VA

VAVA


NVA

X

X

XX

X

Revised Process Map




Trailer dropped

in yard


Switcher brings

trailer todock


Stock staged onDock

Stocktaken

to mkt. place

Stocktakento line




Dunnagetaken

to warehouse

Dunnage taken

to dock


Start


End

Drop


CTQVA

NVA

NVANVA

NVA


VA

VAVA


NVA

X

X

XX

X

Revised Process Map




Trailer dropped

in yard


Switcher brings

trailer todock


Stock staged onDock

Stocktaken

to mkt. place

Stocktakento line




Dunnagetaken

to warehouse

Dunnage taken

to dock


Start


End

Drop


CTQVA

NVA

NVANVA

NVA


VA

VAVA


NVA

Revised Process Map




Trailer dropped

in yard


Switcher brings

trailer todock


Stock staged onDock

Stocktaken

to mkt. place

Stocktakento line




Dunnagetaken

to warehouse

Dunnage taken

to dock


Start


End

Drop


CTQVA

NVA

NVANVA

NVA


VA

VAVA


NVA

X

X

XX

X

Revised Process Map




Trailer dropped

in yard


Switcher brings

trailer todock


Stock staged onDock

Stocktaken

to mkt. place

Stocktakento line




Dunnagetaken

to warehouse

Dunnage taken

to dock


Start


End

Drop


CTQVA

NVA

NVANVA

NVA


VA

VAVA


NVA

Cause and Effect Diagram

YTrailer LiveUnloaded

Supplier Carrier

Dock Warehouse

Trailer arrives on time

Trailer loaded on time

Trailer has correct stock

Departs on time

TrafficConstruction

Equipment

Door open

Driver

Supervisor/OperatorsPoints on trailer

Condition of trailerUnload time

Set up timeEquipment function

Trailer arrival timeReload time

Bills Cut

Dwell time on dock

Transit time to warehouse

Warehouse inventory levels

Storage space allocation

Storage space dunnage

Dunnage return processSupervisor/Operator

Cause and Effect Diagram

YTrailer LiveUnloaded

Supplier Carrier

Dock Warehouse

Trailer arrives on time

Trailer loaded on time

Trailer has correct stock

Departs on time

TrafficConstruction

Equipment

Door open

Driver

Supervisor/OperatorsPoints on trailer

Condition of trailerUnload time

Set up timeEquipment function

Trailer arrival timeReload time

Bills Cut

Dwell time on dock

Transit time to warehouse

Warehouse inventory levels

Storage space allocation

Storage space dunnage

Dunnage return processSupervisor/Operator

Operational DefinitionLive Unload: Trailer is scheduled on window deliveries managed by the LLP. Trailer is brought to the dock at the scheduled window time on the dock schedule (+/- 15 minutes). Trailer is spotted by the carrier and unloaded/reloaded (as required) within 120 minutes of the window time. Bills are cut and the driver leaves with a reloaded trailer. Drop and swap trailers count as live unload as long as the carrier does the switch and no trailers are added to the yard inventory. Late trailers that are unloaded and taken away by the carrier (without dropping in the yard) will be counted as live unload.

Data Collection Sheet for LCL Dock

Route Commodity TrailerPlan Date Act Date

Plan Time Act Time

Planned Dock Door

Actual Dock Door In Door

Start Unload

Finish Unload

Start Reload

Finish Reload

Time Bills Cut Code

Instructions.

1. Stay out of the way of the receiving checkers unloading (Safety)

2. Fill in each box accurately, Remember to change the date after midnight.

3. Fill out all times using a 24 hour clock.

4. List the most prevent part on the trailer or ODC for commodity. ( ex. Romulus ODC, Bumpers,..)

5. Add comments about specific trailers to clarify reasons for information gathered. (Dropped, Late, Dropped by Carrier, Trailer Damaged,…)

6. Turn in completed document to LLP in Parts Control at the end of the shift.

Arrive Dock Door Process Time

-1.0 -0.5 0.0 0.5 1.0 1.5

LSL USL

Target

Capability Analysis for In-Transit

USL

Target

LSL

Mean

Sample N

StDev (Within)

StDev (Overall)

Cp

CPU

CPL

Cpk

Cpm

Pp

PPU

PPL

Ppk

PPM < LSL

PPM > USL

PPM Total

PPM < LSL

PPM > USL

PPM Total

PPM < LSL

PPM > USL

PPM Total

0.100000

0.000000

-0.100000

0.235433

127

0.166048

0.345609

0.20

-0.27

0.67

-0.27

0.08

0.10

-0.13

0.32

-0.13

23622.05

448818.90

472440.94

21686.35

792643.66

814330.01

165884.68

652422.03

818306.71

Process Data

Potential (Within) Capability

Overall Capability Observed Performance Exp. "Within" Performance Exp. "Overall" Performance

Within

Overall


Analyze

• Communication was leading cause

• Inconsistent or incomplete information being used

• Delivery times affected by many factors

Data analysis determined multiple causes of defects

4002000-200-400-600-800-1000

USL LSL

Min Deviation Arrive VS Schedule

PPM Total

PPM > USL

PPM < LSL

PPM Total

PPM > USL

PPM < LSL

PPM Total

PPM > USL

PPM < LSL

Ppk

PPL

PPU

Pp

Cpm

Cpk

CPL

CPU

Cp

StDev (Overall)

StDev (Within)

Sample N

Mean

LSL

Target

USL

906340.58

341462.00

564878.57

877585.66

290028.38

587557.28

561983.47

165289.26

396694.21

-0.05

-0.05

0.14

0.04

*

-0.07

-0.07

0.18

0.06

122.386

90.352

121

-34.9917

-15.0000

*

15.0000

Exp. "Overall" PerformanceExp. "Within" PerformanceObserved PerformanceOverall Capability

Potential (Within) Capability

Process Data

Within

Overall

0 100 200 300 400

LSLUSL

Process Capability Analysis for start unloadCalculations Based on Weibull Distribution Model

USL

Target

LSL

Mean

Sample N

Shape

Scale

Pp

PPU

PPL

Ppk

PPM < LSL

PPM > USL

PPM Total

PPM < LSL

PPM > USL

PPM Total

10.000

*

0.000

103.678

121

1.771

116.489

0.03

-0.74

2.40

-0.74

0.00

991735.54

991735.54

0.00

0.00

0.00

Process Data

Overall (LT) Capability

Observed LT Performance

Expected LT Performance

0 100 200 300 400

LSL USL

Total Time to Run Once Started

Calculations Based on Weibull Distribution Model

USL

Target

LSL

Mean

Sample N

Shape

Scale

Pp

PPU

PPL

Ppk

PPM < LSL

PPM > USL

PPM Total

PPM < LSL

PPM > USL

PPM Total

120.000

100.000

30.000

102.580

121

1.787

115.311

0.27

0.14

0.45

0.14

0.00

305785.12

305785.12

0.00

0.00

0.00

Process Data

Overall (LT) Capability

Observed LT Performance

Expected LT PerformanceX1 = YA X2 = YB X3 = YC

XA1= Schedule

XA2= Operators

XA3= Carriers

XA4= Suppliers

XB1= Schedule

XB2= Inventory

XB3= Warehouses

XB4= Operators

XC1= ReloadsXC2= OperatorsXC3= Warehouses

XC4= Bills cut

XC5 = Stock moving off the dock

These were the Xs for the new Ys

Arrival Time Unload Start Time Processing time

X1 = YA X2 = YB X3 = YC

XA1= Schedule

XA2= Operators

XA3= Carriers

XA4= Suppliers

XB1= Schedule

XB2= Inventory

XB3= Warehouses

XB4= Operators


XC4= Bills cut




Y=f(X)

X1 = YA X2 = YB X3 = YC

XA1= Schedule

XA2= Operators

XA3= Carriers

XA4= Suppliers

XB1= Schedule

XB2= Inventory

XB3= Warehouses

XB4= Operators


XC4= Bills cut




X1 = YA X2 = YB X3 = YC

XA1= Schedule

XA2= Operators

XA3= Carriers

XA4= Suppliers

XB1= Schedule

XB2= Inventory

XB3= Warehouses

XB4= Operators


XC4= Bills cut




Y=f(X)


Improve

• Eliminated unneeded process steps-used hypothesis testing to verify

• Corrected schedule information supplied by carrier

• Built and tested models to re-calculated formulas used for scheduling

• Asked the people why schedules were not being followed!

Improvements implemented to greatly improve communication

Revised Process Map




Trailer dropped

in yard


Switcher brings

trailer todock


Stock staged onDock

Stocktaken

to mkt. place

Stocktakento line




Dunnagetaken

to warehouse

Dunnage taken

to dock


Start


End

Drop


CTQ

CTQVA

NVA

NVANVA

NVA


VA

VAVA


NVA

X

X

XX

X

Revised Process Map




Trailer dropped

in yard


Switcher brings

trailer todock


Stock staged onDock

Stocktaken

to mkt. place

Stocktakento line




Dunnagetaken

to warehouse

Dunnage taken

to dock


Start


End

Drop


CTQ

CTQVA

NVA

NVANVA

NVA


VA

VAVA


NVA

X

X

XX

X

Revised Process Map




Trailer dropped

in yard


Switcher brings

trailer todock


Stock staged onDock

Stocktaken

to mkt. place

Stocktakento line




Dunnagetaken

to warehouse

Dunnage taken

to dock


Start


End

Drop


CTQ

CTQVA

NVA

NVANVA

NVA


VA

VAVA


NVA

X

X

XX

X

SCAC TLR Route IDSupplier

Code PARTSPlant Arrival

DaysArrival Time

Actual Arrival Time SHIFT Dock DR Unload Status

LIVE UNLD (Y/N)

Rack Return Ratio

OJ TP 022211 A439A Bumpers MTWRF 6:00 2 PL 14 DROP YES

CUOT 26002 PC06A Richfield ODC MTWRF 6:00 2 PL 21 LIVE NO

ADXR 29109 PC09A Detroit ODC MTWRF 6:00 2 PL 21 LIVE NO

RSHQ 24404 PC04A Louisville ODC MTWRF 6:00 2 PL 20 LIVE YES

HJ BT 20980 PC20A Charlotte ODC MTWRF 7:00 2 PL 14 LIVE YES

KCCI 022257 C265C 1/4 PANELS LIVE DROP MTWRF 7:00 2 PL 11 LIVE YES

BTZK 27708 PC07A Romulus ODC MTWRF 7:15 2 PL 20 LIVE NO

RSHQ 24405 PC04A Louisville ODC MTWRF 7:15 2 PL 21 LIVE YES

THMB 024513 T73J B Valences TWRF 8:00 2 PL 14 LIVE YES

ADXR 29106 PC09A Detroit ODC MTWRF 8:15 2 PL 21 LIVE YES

MMDT 021934 M363E ACTU ASY-DR LK SW TWRF 9:00 2 PL 20 LIVE YES

GSD3 029182 W411C WEBASTO MOONROOFS MTWRF 9:30 2 PL 21 DROP NO

CUOT 26003 PC06A Richfield ODC MTWRF 10:00 2 PL 18 LIVE YES

OJ TP 022213 A439A Bumpers MTWRF 10:30 2 PL 11 DROP YES

ADXR 22208 PC02A Norton Shores ODC MTWRF 10:30 2 PL 18 LIVE NO

ADXR 21600 PC01A Chicago ODC MTWRF 12:15 2 PL 14 LIVE YES

ADXR 027716 G519S Batteries MTWRF 13:00 2 PL 20 LIVE YES

2 PL

2 PL

2 PL

2 PL

Shipping and Warehouse Formulas:Over Ship = cumulative ship supplier- cumulative required Storage area Max = (container size X numbers of containers truck ) + (container size X numbers of containers OPRES ) Storage area Min = (container size X numbers of containers OPRES )

Two-sample T for TRANSIT vs OLD TRANSIT

N Mean StDev SE MeanTRANSIT 425 0.711 0.998 0.048OLD TRAN 425 0.89 1.28 0.062

Difference = mu TRANSIT - mu OLD TRANSITEstimate for difference: -0.184095% CI for difference: (-0.3387, -0.0293)T-Test of difference = 0 (vs not =): T-Value = -2.33 P-Value = 0.020 DF = 848Both use Pooled StDev = 1.15


Control

• Checklists and Control plans established

• Policies and procedures changed

• Charts verify and sustain improvements

Simple job aids and metrics ensure control

Control Plan

Approved

Approved

Dept. P & P’s

Approved

Approved

Yard History Averages

193 187 171 172153

90 78 93 87 73

129107

38 34 25 30 20

412372

302 293

251223

157

18 24 10 0 1 2 0

100759362

0

50

100

150

200

250

300

350

400

450

November December January February March April May

Month

# of

Tra

ilers Inbound

Reloaded

Empties

Totals

Unbilled

Documents

© 2003 Six Sigma Academy0 Impact and Depth Projects General Case Studies Champion Workshop