ANAL

YZE

Lean Six SigmaImproving FTX/STX2 Tank

Draw QualitySFC Henry, Don H. II

Project Initiation Date: 31/03/08

Analyze Tollgate Date: 03/07/08

AnalyzeAnalyze

v 2.0

Agenda Project Charter and Measure Phase Review

Critical X’s

Potential Root Causes Affecting Critical X’s

Reducing the List of Potential Root Causes

Root Cause Analysis (Qualitative)

Impact of Root Causes on Key Outputs (Y)

Prioritized Root Causes

Analyze Summary

Lessons Learned

Barriers/Issues

Next Steps

Storyboard

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v 2.03

Analyze – Executive Summary

Improve tank maintenance quality by giving the 1/16 soldiers more time to perform maintenance during the draw.

The project starts at the FTX/STX2 T-6 IPR and ends when the tanks are ready for HETT transport. This project is contained within the Fort Knox Garrison and can transfer to other training support missions on Fort Knox.

We are feeling the pain in training and tank maintenance.

Soldiers fail to do a quality PMCS for the lack of time, training, and command emphasis.

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Project Charter Review

Scope: this process begins with the T-6 IPR and ends when 1/16 loads the tanks on HETT’s.

Goal: Improve tank draw quality

Problem/Goal Statement

Tollgate Review Schedule

Business Impact

Core Team

State financial impact of project Expenses-none Investments-none Revenues-potential savings in time 819 hours per year

Non-Quantifiable Benefits are increased tank maintenance quality, soldiers morale, maintenance fault tracking, and less training time lost.

PES Name MAJ Mackey, Andre PS Name MAJ Mackey, Andre DD Name LTC Naething, Robert GB/BB Name SFC Henry, Don MBB Name Nathan SpragueCore Team Role % Contrib. LSS

Training CW2 Warren SME 20% none MAJ Aydelott SME 20% none MAJ Mackey SME 20% none SSG Jones SME 10% none CW4 Lucy SME 10% none SFC Henry BB 100% BB

Tollgate Scheduled Revised Complete

Define: 04/30/08 - 04/29/08

Measure: 05/14/08 04/06/08 04/06/08

Analyze: 06/13/08 07/13/08 XX/XX/08

Improve: 07/18/08 08/13/08 XX/XX/08

Control: 08/23/08 09/13/08 XX/XX/08

Reduce rework during tank draw from 90% to 45%, per FTX/STX2 by 1 October 2008.

Improve 5988-E fault tracking during tank draw from 10% to 85%, per FTX/STX2 by 1 October 2008.

Improve tank bumper number accuracy from 10% to 90%, during the T-2 preparation week by 1 October 2008.

Problem Statement Soldiers of 1/16 express dissatisfaction with the Unit Maintenance Activities M1 series tank quality prior to mission support. Currently, 90% of the tanks drawn require maintenance for mission readiness. Approximately 10% of faults listed on the 5988-E ‘s completed by soldiers are tracked by UMA. Lastly, tank bumper number accuracy during T-2 is currently at 10% which causes excess work in the last days of the mission support draw.

AnalyzeAnalyze

v 2.05

Baseline Data The current tank draw

process has a non-normal distribution

The mean time to draw one tank is .56 or 34 minutes

The tank draw range is .25 hours (15 minutes) to 2 hours (120 minutes) and the standard deviation is .4 (24 minutes)

The mean number of 5988-E’s updated by UMA is .1 or 10%

The average Tank Draw Time is 34 minutes +/- 24 minutes.

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Baseline Data Cont.

33% of tanks presented to draw are not ready for issue.

10% of 5988-E faults annotated by soldiers during tank draw is updated by UMA clerks.

67% of tank bumper numbers presented to 1/16 at T-2 by UMA is actually drawn for mission support.

6

These numbers take into account vehicles presented to draw but never actually drawn or PMCSed.

These numbers represent what was actually given, PMCS’ed, and drawn.

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Critical X’s: Cause and Effect Matrix

Cause and Effect Matrix

Key Process Output Variables

Customer Importance

10

9 2 6 8

Customer Rank

1 2 3 4 5

1 2 3 4 5 6 7 8 910

11

12

13

14

15

Process Step KPIV

accurat

e t

ank li

st

5988-

E

QA/

QC

DA For

m

2062

Dispatch

Rank

Rating Total

Process Steps & Key Process Input

Variables

1 T-1 RATSS 1 9 9 9 1 2 7.533 171

2 PMCSTechnical Manual

9 1 1 9 9 1 10 227

3 QA/QCUMA inspector

9 1 1 3 3 3 6.3 143

4tank sign over

DA 2062 9 1 1 1 3 4 5.771 131

5tank dispatch

5987-E 9 1 1 1 1 5 5.066 115

###

#####

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v 2.08

Potential Root Causes: C & E Diagram

Effect:

The tank draw takes too long.

ManMachine

Material Method

Spread thinly across multiple tasks

Shortage of UMA maintenance personnel

Deadlines, AOAP, Service Schedule, affect # of tanks available

Tanks already in use by other units/missions

BII draw uses excessive people and excess time

RATTS request is not referenced by UMA to assign accurate bumper number list

Tanks are PMCS’d

Tanks are QA/QC’d

Tanks are dispatched

Excessive delays from lack of UMA personnel

5988-E not updated by UMA

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Potential Root Causes: FMEAProcess

Step / Input

Potential Failure Mode

Potential Failure Effects

SEVERITY

Potential CausesOCCURRENCE

Current Controls

DETECTION

RPN

What is the

process step and

Input under

investiga-tion?

In what ways does the Key

Input go wrong?

What is the impact on the Key Output Variables (Customer

Requirements)?

What causes the Key Input to go

wrong?

What are the existing controls and procedures

(inspection and test) that prevent either the cause or the Failure Mode?

T-1 bumper

number listnot accurate excessive delays 7

lack of organization

7 none 7 343

PMCS not updated rework 7 lack of personnel 6 Army Policy 5 210

QA/QC not timely rework 4lack of

maintenance4 EXSOP/Army policy 4 64

tank sign over

already issued rework 7lack of

organization2 Army Policy/EXSOP 2 28

tank dispatch

does not go wrong

no problems 1 no problems 1 EXSOP 5 5

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Reducing List of Root Causes: Pareto Analysis

Track able causes contained over 90% of the Defects. Our project will focus on tracking vehicle maintenance status.

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4.5

Deadlined Services Due QA/QC Needed Already Issued

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Root Cause Analysis: Non-Value Add Analysis

1111

QAQC

Maintenance leader

Dispatch

Soldier

Issues bumper number list to soldier

Maintenance leader checks 5988-E and verifies

faults/makes repairs if needed

Hand receipt

Vehicle signed over to soldier

Avg.Delay

2 hoursAvg.Delay

15 min

Avg. Delay

90 min

Soldier conducts PMCS and completes 5988-E, turns it in to maintenance leader

Passes

QAQC

Receives signed QAQC sheet

Vehicle dispatched to

soldier

YESNO

NVA time is in dark blue

Total delay time is 3.75 hours

Retrieves info from

RATSS system

Notify UMA of the # of tanks

needed

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v 2.012

Root Cause Analysis: HistogramThe outlier was a vehicle issued that was actually NMC and required 90 minutes to repair.

The vehicle that required 60 minutes was actually dispatched to another unit.

Two of the five that required 45 minutes of work were deadline with a third needing a QAQC from UMA

5.25 hours were spent doing rework that is non value added

AnalyzeAnalyze

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One-Way ANOVA of Time and Defects

The data is distributed non-normally with an outlier shown here

The variance in the data is also constant but there are no systematic effects due to collection order or time.

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One-Way ANOVA of Time and Defects Data

14

Source DF SS MS F P

DEFECT 3 4915 1638 5.04 0.012

Error 16 5199 325

Total 19 10114

S = 18.03 R-Sq = 48.59% R-Sq(adj) = 38.95%

Individual 95% CIs For Mean Based on Pooled StDev

Level N Mean StDev -------+---------+---------+---------+--

D 4 63.75 37.50 (-------*------)

I 1 60.00 * (--------------*--------------)

N 14 26.79 8.68 (---*---)

Q 1 45.00 * (--------------*--------------)

-------+---------+---------+---------+--

25 50 75 100

Pooled StDev = 18.03

The R-squared value of 48.59% is statistically significant meaning the model predicts nearly half of the variation causing increased tank draw times as being caused by defects.

Therefore we reject the null hypothesis.

AnalyzeAnalyze

v 2.0

Mood’s Median Test The medians

may tell a more complete story. The outlier falsely inflates the averages, this test omits outliers.

Based on the P value, two or more medians are significantly different and we reject the null hypothesis

Mood Median Test: TIME versus DEFECT

Mood median test for TIMEChi-Square = 13.37 DF = 1 P = 0.000

Individual 95.0% CIsDEFECT N<= N> Median Q3-Q1 -----+---------+---------+---------+-D 0 4 45 56 *------------------------)I 0 1 60 Not UsedN 13 1 30 15 (----*Q 0 1 45 Not Used -----+---------+---------+---------+- 30 60 90 120

Overall median = 30* NOTE * Levels with < 6 observations have confidence < 95.0%

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Tukey’s Pairwise Comparison

16

One-way ANOVA: TIME versus DEFECT

Tukey 95% Simultaneous Confidence IntervalsAll Pairwise Comparisons among Levels of DEFECT

Individual confidence level = 98.87%

DEFECT = D subtracted from:

DEFECT Lower Center Upper --------+---------+---------+---------+-I -61.47 -3.75 53.97 (----------*-----------)N -66.23 -36.96 -7.70 (-----*----)Q -76.47 -18.75 38.97 (----------*-----------) --------+---------+---------+---------+- -50 0 50 100

DEFECT = I subtracted from:

DEFECT Lower Center Upper --------+---------+---------+---------+-N -86.65 -33.21 20.22 (---------*----------)Q -88.01 -15.00 58.01 (--------------*--------------) --------+---------+---------+---------+- -50 0 50 100

DEFECT = N subtracted from:

DEFECT Lower Center Upper --------+---------+---------+---------+-Q -35.22 18.21 71.65 (----------*---------) --------+---------+---------+---------+- -50 0 50 100

Statistically significant factors are in RED

legendI= issued alreadyN= no defectsQ= need QAQCD=deadlined

Deadlined tanks are statistically significantly different in terms of time and defects

Tanks with no defects are statistically significantly different in terms of time and defects

AnalyzeAnalyze

v 2.0

Current Process Capability

The average issue time was 36 minutes

The target issue time was 30 minutes

The variability of the process is greater than the specification limits (Cpk<1.33)

The process is not meeting customer expectations

AnalyzeAnalyze

v 2.0

One-Way ANOVA Boxplot Deadlines

were the largest time wasters with a mean time of 63 min.

Deadlines also represented the largest range of values

The mean time for the tank that was already issued had a mean of 60 min.

AnalyzeAnalyze

v 2.0

A Different View The same

data classified as tanks that are ready “R” and not ready “NR”

The total NR time is 340 minutes

The total ready time is 480 minutes

The defects make up 40% of the time spent on the tank draw!

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DELETE ME after tollgate review

N= no defect D= deadlined Q=need qa/qc I=issued already

DEFECT TIME

N 15

N 15

N 15

N 15

N 30

N 30

N 30

N 30

N 30

N 30

N 30

N 30

N 30

D 45

D 45

Q 45

D 45

N 45

I 60

D 120

Data used in minitab to get calculations, I later changed the D,Q, & I variables into Defects to create different comparisons of defects vs no defects

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v 2.021

Impact of Root Causes on Y

AnalyzeAnalyze

v 2.0

Prioritized Root CausesEffect (Y) Root Cause

(X)Hypothesis for Relationship

In/Out of Team’s Control1

Impact2 Score (Control x Impact)

Priority of Effort

rework In-accurate bumper numbers

Accurate bumper numbers will increase throughput

3 9 27 2

Poor tank maintenance

PMCS not completed correctly

Correctly performed PMCS will improve tank draw quality

9 9 81 1

Poor tank maintenance

5988-E’s are not regularly updated

Regularly update 5988-E’s will improve tank draw quality

3 9 27 3

rework Tanks are issued that are not ready for issue

Rework will be reduced if the tanks are ready for issue at the time they are to be issued to units

3 3 9 4

1 In team’s Control = 9; In team’s sphere of influence = 3; Out of team’s control = 12 High impact = 9; Medium impact = 3; Low impact = 1

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v 2.023

Analyze SummaryImpact of Root Causes:

Hypothesis Tests

Tools Used

Reducing List of Root Causes

Prioritized Root Causes / Effects Root cause #1: No visual tracking method

Effect-in-accurate bumper numbers

Root cause #2: 5988-E’s not completed correctly

Effect-poor tank maintenance

Root cause #3: 5988-E’s are not updated regularly

Effect-poor tank maintenance

Root cause #4: Tanks issued that are not ready for issue

Effect-rework

Value Add Analysis Pareto Plot Histogram One-Way ANOVA

C&E Matrix Cause & Effect

Diagram FMEA Process Capability

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Deadlined Services Due QA/QC Needed Already Issued

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v 2.024

Lessons Learned Application of Lean Six Sigma Tools

Communications

Team building

Organizational activities

Other

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v 2.025

Barriers/Issues/Project Action Log Resources

Unexpected delays

Team or organizational issues

Updated risk analysis and mitigation plan

Revised project scopeLean Six Sigma Project Action Log

       Last

Revised: 10/15/2007  

No Description/ Recommendation

Status Open/Closed/

Hold

Due Date Revised Due Date

Resp. Comments / Resolution

1 Leave of critical team member    14 May  4 May    

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Next Steps Outline activities for Improve Phase

Planned Lean Six Sigma Tool use

Barrier/risk mitigation activities

AnalyzeAnalyze

v 2.0

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Deadlined Services Due QA/QC Needed Already Issued

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Analyze StoryboardDefine

Problem: Poor tank quality at issue

Goal: Improve tank quality at issue, Reduce rework, improve fault tracking

Non-quantifiable BenefitsMorale, tank maintenance, fault tracking

Project CharterT-6 IPRT2T

RATSS

T-5T2T

T-4T2T

T-3T2T

T-2IPR vehicleBumper #s

Given to unit

T-1Tank draw,

HETT,5988-E update

Measure

BII draw measured Tank draw measured 5988-E updates measured

RIE Baselines collected

Critical X’s Identified Potential Root Causes Identified Root Causes Prioritized

Analyze

AnalyzeAnalyze

v 2.0

Sign Off

• I concur that the Measure phase was successfully completed on 03/07/08.

• I concur the project is ready to proceed to next phase: Improve

CW4 David LucyResource Manager/Finance

COL Leopoldo Quintas Deployment Director

SFC Don H. Henry II Black Belt

Nathan SpragueMaster Black Belt

MAJ Andre L. Mackey Sponsor / Process Owner

AnalyzeAnalyze

v 2.029

Analyze Tollgate Checklist

Has the team examined the process and identified potential bottlenecks, disconnects, and redundancies that could contribute to the problem statement?

Has the team analyzed data about the process and its performance to help stratify the problem, understand reasons for variation in the process, and generate hypothesis as to the root causes of the current process performance?

Has an evaluation been done to determine whether the problem can be solved without a fundamental ‘white paper’ recreation of the process? Has the decision been confirmed with the Project Sponsor?

Has the team investigated and validated (or devalidated) the root cause hypotheses generated earlier, to gain confidence that the “vital few” root causes have been uncovered?

Does the team understand why the problem (the Quality, Cycle Time, or Cost Efficiency issue identified in the Problem Statement) is being seen?

Has the team been able to identify any additional ‘Quick Wins’?

Have ‘learnings’ to-date required modification of the Project Charter? If so, have these changes been approved by the Project Sponsor and the Key Stakeholders?

Have any new risks to project success been identified, added to the Risk Mitigation Plan, and a mitigation strategy put in place?

Tollgate ReviewTollgate Review

StopStop Has the team identified the key factors (critical X’s) that have the

biggest impact on process performance? Have they validated

the root causes?

Deliverables: List of Potential Root

causes

Prioritized List of Validated Root Causes

Additional “Quick Wins”, if applicable

Refined Charter, as necessary

Updated Risk Mitigation Plan

Green Belt/Black Belt Actions:

Deliverables Uploaded in PowerSteering

Deliverables Inserted into the Project “Notebook” (see Deployment Director)

AnalyzeAnalyze

v 2.0

DMAIC Methodology - Analyze

Identify PotentialRoot Causes

Reduce List ofPotential Root

Causes

PrioritizeConfirmed Root

Causes

Estimate Impactof Root Causeson Key Output

Metrics

ConfirmRelationship

between PotentialRoot Cause andOutput Metrics

Refine RiskManagement

Plan

List ofConfirmed

Root Causes

Risk Mitigation Plan

Conduct 'Analyze'Tollgate Review

Quantitative Data Analysis Cause & Effect Matrix/FMEA Qualitative Process Analysis

Brainstorming Cause & Effect (Fishbone)

Diagrams 5 Whys Value Stream Map

Analysis Spaghetti Diagram

Statistical Analysis Comparison of Means - t-test - Paired t-test - ANOVA Comparison of

Proportions - Chi Square - 1-Proportion & 2-

Proportion Test Simple & Multiple Linear

Regression Components of Variation DOE Taguchi Methods

Other Analyses Time Study Visual Inspection Process Complexity Queuing Theory Process Knowledge Process Cycle Time/

Efficiency Operation Load Analysis Labor Skills Flexibility

Risk MitigationSpreadsheet

Simulation Benchmarking Industry Stds Estimates Based

on ExtensiveProcessKnowledge

Limited Pilot DOE Taguchi Methods Regression

Analysis

Root CauseConfirmation

PlanReduced

List of PotentialRoot Causes

List ofPotential Root

Causes

Prioritized Listof ValidatedRoot Causes

Estimated IndividualReductions

in Key Y MetricsThrough Elimination

of IndividualRoot Causes

Additional‘QuickWins’

Root Cause Analysis Pareto Analysis Statistical Analysis

Refine ProjectScope, asNecessary

Project Charter Project Tracking

System (PTS)

RefinedCharter in PTS

Revised Charter Project Presentation Storyboard Risk Mitigation Plan