About This P roject

About This Project

• This project is a simulation of actual occurrences

• Covers key six sigma concepts including

• Seeks to accomplish key outlined objectives

– Applying the DMAIC approach to process improvement– Identification and selection of process improvement opportunities– Utilizing Statistical Analysis and Tests– Addressing/Improving Customer Satisfaction– Cost Savings & Ongoing Financial Benefits

– Provide Detailed Explanations Throughout– Illustrative Analysis – Comprehensive Use of Recommended Tools– Effective Resolution/ Final State– Presenters Knowledge of Six Sigma Methodology

INSTALLATIO

N DEPARTM

ENT

Gh o s t I ns t a l l a

t i on R

e d u c t i on P

r o j ec t

P r e p a r e d B y :

J o e B a n k s & C e c i l y n C a y e t a n o

DSL EASTERN DIVISION

Definition:Ghost Installation (GI’s): Installation attempt in which the installer found no one available on-site once he/she arrived to perform an installation; resulting in a defective installation job.

Resource Requirements General InformationProject No: Location:

Project Name: Business Green Belt or Black Belt Segment:

Master Black Belt Business Finance Partner: Objective:

Champion:Customer

CTQ(s):

Team Members: Name Function % Time InitialsCecilyn Cayetano 100 CC StakeholdersJoe Banks 100 JB Current

ProcessSupporting Members: Name As of Date:

Installers N/A N/A Expected Benefits: Higher Customer Satisfaction, More Customers ServedDavid Morrow (Unit Supervisor) N/A N/A Financial Benefits $:Jennifer Bell Financial Analysis N/A N/A Organization Benefits: Increased Billing Revenues, Less Employee Rework

Atlanta,GADSL-Eastern Division

ConsultantsData & Stats $99,700 (Revenues and Cost Savings)

216Ghost Installation (GI's) Reduction ProjectBlack BeltNADSL-East Unit

Installation Department

Internal: Completed Installations, Daily Install Routes Completed, Low-Reinstallation Rates External: On-Time Installs, Communication, Correct Installation, Pleasant Expirience

Resources

Primary: To reduce the amount of Ghost Installations by 33% within 6mo. Secondary: To increase rate of completed jobs by 5% or more above current levels.

Installation Z score = 1.0803, GI's DPMO= 150,000,

6/16/2010

Installers, DSL-East, DSL Division

Robert Price, DSL EVP

Project AnalystProject Analyst

Project Description DMAIC

Problem:

During a review of year over year comparisons of DSL-East installation reports it was discovered that the GI rate across the DSL-Eastern Division’s territory is trending an all time high of 15%, causing repeat installs and lost customers.

In conjunction with the rise in GI’s there has also been a 10% increase in customer complaints due to the missed installation appointments.

Objective:

To reduce the rate of GI’s (Big Y) below the upper specification limit of 10%, which will in turn increase the rate of completed jobs back to normal levels of 90% or more.

It is our goal to reduce the rate of Ghost Installations from 15% of total installs to below 10%, a 33% reduction resulting in DPMO < 100,000 and a yield of 90%.

Measurements:

• Completed installations >90% (5% improvement).

• Installations achieve a

long term process sigma of > 2.7.

• Eliminate the 10% increase in customer complaints.

• Achieve cost savings of $99,700 within 12 months.

Scope:

Metrics (unit of measure):The rate of successfully completed Installations, non-defective.

Defect Definition: Installation attempt in which the installer found no one to be available on-site once he/she arrived to perform an installation resulting in a defective installation job.

DSL-East Total Install Process DMAIC

Project Selection:Several departments within the unit have improvement areas and possible projects. We selected this project by using a Project Prioritization Matrix.

Prioritization Scores: scores are weighted

Unit Project ScoreSales A 3.1Sales B 3.6Warehouse C 2.9Warehouse D 2.3Installation E 4.1

Key Point: Our Project’s Focus will be in the DSL Installation Department

Sales person receives call with new install order

Sales forwards the new order to RLD for 48 hrs confirmation

RLD receives order and begins processing

RLD confirms equipment inventory, installers schedule, availability of

date & time

RLD Sends YES or No confirmation back to sales within 48hrs.

RLD sends order to equipment warehouse for processing

Equipment warehouse receives order and begins physical

processing

Equipment Warehouse sends YES or No instock confirmation back to

RLD within 72hrs

If equipment is in stock the warehouse packages it and moves it to the

Installation dock 72hrs prior to install

Installation dock places equipment in proper bay and organizes by

date of delivery order

When install date arrives it is placed on the proper truck

Installer takes truck and goes to perform insatall

Series 1

Series 2

Series 3

Series 4

Overview of DSL Series of Events

I’d like to order DSL Service

* RLD = Regional Logistics Department

Our Focus

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 20100%

5%

10%

15%

20%

25%

30%DSL-East GI Trend

InstallsGI's

DSL-East’s GI Defectsvs. Other Unit

DMAIC

Project Validation:From the historical data we can see that the amount of DSL-East GI’s is at an all time high. The DSL-West Division is performing normally.

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 20100%

5%

10%

15%

20%

25%

30%

DSL-West GI Trend

InstallsGI's

Key Point: The DSL East’s GI’s are Higher than Normal

8% Historical Baseline

6.5% Historical Baseline

Scale zoomed in for impact.

Scale zoomed in for impact.

Our Focus

Fact:Eastern U.S. Cities Experience Explosive Population Growth During the Recent Housing Boom: • Jacksonville, FL 10.6%

• Orlando, FL 21.5%• Charlotte, NC 24.9%• Nashville, TN 11.0%, • Atlanta, GA 29.2%• Miami, FL19.5%• Raleigh, NC 40.7% *Source: USA Today

With the recent housing expansion in the United States we have seen new neighborhoods and rural expansion surrounding many previously smaller eastern US cities. This is in contrast to the West having greater population than geographical growth in major cities with less rural territory expansion, this evidenced by higher home prices.

http://www.usatoday.com/news/nation/census/2010-06-22-census_N.htm

Recent Changes for DSL-East Specifically

Key Point: Geographical Expansion Has Expanded Service Areas for Cities Serviced by DSL-East Units

DMAIC

http://www.usatoday.com/news/nation/census/2010-06-22-census_N.htm

CustomersStart Point:

End Point:

Confirm Location with Customer

Confirm Approx. Travel Time with CustomerTravel to Customer SiteAttempt Installation

Enter Location into GPS

Confirm Travel Distance and Time Estimates

Choose Traffic Route

Check for Customer Communication DetailsCall Customer (if requested by customer)

Signed Work Order

Email Detailed Installation

Data

New DSL Customers

Data Collection Hub

Customer/Business Partners/Others

Department

Regional Logistics Department

Completed Installation

New Customer Sales Data

Sales Agents

Retrieve Installation Orders via Daily Installation Order System

(DIOS)

Output

Enter Data Into Completion System and email.

1

2

Suppliers Input Process (High Level)

3 DispatchManually Relays Installion Orders

and Changes

Internet Order

System

Operation or ActivityInstallation Orders

Installation Orders

Review Order Details

DMAICScope, VOC & VOB

SIPOCVoice of the Customer:We used the call center database to retrieve details on missed installations. The data contains customer comments about why the install was missed, the order info that was provided to the installer originally, as well as the installers reference code for the Ghost Installation.

Voice of the Business:There are several key factors that accurate, timely, and courteous installations affect. All of which add to the success of the business, the business wants...• High Customer Satisfaction • Potential Referrals W.O.M• To Secure New Billings• Fewer Re-Installs (Rework)• Reduce Equipment Restocks• Reduce Customer Complaints

I don’t care i f you 're stuck in

t raffic. I have to leave in 30mins !! !

I h a d my p h o n e w i t h m e … T h e

j e r k n e ve r c a l l e d ! ! !

S o y o u ’re go i n g t o

b e 3 0 m i n s l a te …

Key Point: Customers are Complaining; There’s a Problem…

Find Location

On Time

Familiar w/Area

Scores Available 1,3,9

Customer Importance

10 8 6

Process Steps Process Input

Call Customer Operator 9 3 0 114 Est. Traffi c Times Operator 6 9 9 186Measure Distance GPS 6 6 6 144

Correlation of Input to Output

Top 3 Arrival Requirements

Total Scores

Process Outputs

0 = no possible effect, 3 = possible effect, 6 = known moderate effect, 9 =

known large effect

DMAICKPIV,KPOV, & Data Collection

Cause & Effects MatrixFrom the results of our cause and effects matrix we can see that the key inputs (x’s) to the process are estimating traffic delays and effectively measuring the distance from location to location ahead of leaving for the installation.

Causes for Ghost Installations Based on the coded data retrieved from the data entry system it appears that the most common cause for missed appointments as stated by installers is traffic (construction, detours, accidents), followed by distance (location to location distance), communication (cannot reach customer), etc...

020406080

100120140160180200 186

144

114

C&E Matrix Results

Est. Traffic Times Measure Distance Call Customer

Key Point: KPIV’s: Traffic & Distance, KPOV: Completed Jobs

Other

Location Distance

Reaching Customers Traffic

Weather

Measure System Analysis DMAICKey Point: The Overall Process is Normally Distributed

1.00.90.80.7

9

8

7

6

5

4

3

2

1

0

% of Total Completed Installs

Freq

uenc

y

0.8548 0.09309 310.9135 0.06892 31

Mean StDev N

East % of Completed InstallsWest % of Completed Installs

Variable

Normal East vs. West Completed Installs

The frequency histograms below helped us determine that our data is normal. On the left we can see that the combined % of completed installations across both divisions is normally distributed at a rate of about 88%. To the right is the completion rate for both divisions shown independently; DSL-East’s mean is below the LL specification of 90%.

0.940.920.900.880.860.84

5

4

3

2

1

0

% of Total Installs Completed

Freq

uenc

y

Mean 0.8852StDev 0.02686N 30

Histogram of Total InstallsNormal

West’s Benchmark

1.00.90.80.7

9

8

7

6

5

4

3

2

1

0

% of Total Completed Installs

Freq

uenc

y

0.8548 0.09309 310.9135 0.06892 31

Mean StDev N

East % of Completed InstallsWest % of Completed Installs

Variable

Normal East vs. West Completed Installs

0.940.920.900.880.860.84

5

4

3

2

1

0

% of Total Installs Completed

Freq

uenc

y

Mean 0.8852StDev 0.02686N 30

Histogram of Total InstallsNormal

MSA Continued DMAICKey Point: X’s & Y’s are in Control, Yet Not Meeting Process Specs

The P Chart corresponds with the histograms that about 15% of the installations are actually defective.

The sample data used for the I-MR charts of traffic and distance (KPIV’s) shows us that the data is in control, although we know by the rate of defective installations (15%) that the process isn’t meeting specifications (<10%).

464136312621161161

100

50

0

Observation

Indi

vidu

al V

alue

_X=35.3

UCL=93.7

LCL=-23.1

464136312621161161

80

60

40

20

0

Observation

Mov

ing

Rang

e

__MR=21.96

UCL=71.75

LCL=0

I-MR Chart of DSL-East Traffic Data

51464136312621161161

0.30

0.25

0.20

0.15

0.10

0.05

0.00

Sample

Prop

ortio

n

_P=0.1481

LCL=0

UCL=0.2988

P Chart of Defectives

Control Charts Analysis

Defectives Baseline

Baseline for logged traffic

times

464136312621161161

40

30

20

10

0

Observation

Indi

vidu

al V

alue

_X=21.11

UCL=46.35

LCL=-4.13

464136312621161161

30

20

10

0

Observation

Mov

ing

Rang

e

__MR=9.49

UCL=31.01

LCL=0

I-MR Chart of DSL-East Distance Before_

Baseline for logged distance

traveled

MSA Continued

DMAICKey Point: GPS’s are Performing their Desired Function; Installer Can Trust the Route Information Given to Them by the GPS System

Testing The System:We evaluated the measurement system (GPS’s) used to determine the distance from the dispatch location to a fueling station with a known distance of 2mi. We’ve imposed a tolerance level of .1 mi, and performed 50 observations.

The Result: Accept HoThe P Value in the measurement system is .477 suggesting that no bias is present in the measurement system. This result preserves the H0; there is no difference in the results the GPS provides over multiple uses /users. Also, we noticed that many of the observations plotted on the run chart appear evenly distributed both above and below the reference of 2mi.

The difference of the largest and smallest values = .04 which is less than our tolerance level of .1 signaling the gage (GPS) and its user(s) may be considered accurate and repeatable and therefore shouldn’t be improved.

This conclusion leaves us with the unanswered question of why is distance the #2 reason for GI’s?

464136312621161161

2.02

2.01

2.00

1.99

1.98

Observation

Dis

tanc

e

Ref

Ref + 0.10 * Tol

Ref - 0.10 * Tol

Reference 2Mean 1.999StDev 0.01206 * StDev (SV) 0.0717Tolerance (Tol) 0.1

Basic StatisticsBias -0.001T 0.7159PValue 0.477(Test Bias = 0)

BiasCg 0.28Cgk 0.25

Capability

%Var(Repeatability) 71.74%%Var(Repeatability and Bias) 81.62%

Gage name: DistanceDate of study:

Reported by: Tolerance: 0.1Misc:

Run Chart of Distance

GPS Distance Repeatability

MSA Continued

DMAICKey Point: GPS’s are Performing their Desired Function, Estimating the Area Traffic Isn’t Proving to be a Consistent Method Across Installers

Understanding The Results:In the Components of Variation graph (located in the upper left corner), the percent contribution from Total Gage R&R (97.97) is larger than that of Part-To-Part (2.03). Thus, most of the variation arises from the measuring system (estimating traffic times) not the locations themselves.

In the Xbar Chart by Operator most of the points in the X and R chart are inside the control limits, indicating the observed variation is mainly due to the measurement system. In the By Part graph (located in upper right corner), there is little difference between parts, as shown by the nearly level line.

Testing The Operators vs. The System:Three locations were selected that represent the expected range of the process variation. Three operators measured the expected traffic times for the three locations (assuming no special circumstances), three different days per location, in a random order.

The Total Gage R&R accounts for 98.98% of the study variation. The measurement system of individual drivers estimating traffic times/conditions is unacceptable and should be improved.

Part-to-PartReprodRepeatGage R&R

100

50

0

Per

cent

% Contribution% Study Var

321321321

40

20

0

Loc

Sam

ple

Ran

ge

_R=18.67

UCL=48.05

LCL=0

1 2 3

321321321

40

20

0

Loc

Sam

ple

Mea

n

__X=22.30

UCL=41.39

LCL=3.20

1 2 3

321

50

25

0

Loc

321

50

25

0

Operator

321

45

30

15

Loc

Ave

rage

123

Operator

Gage name: Date of study:

Reported by: Tolerance: Misc:

Components of Variation

R Chart by Operator

Xbar Chart by Operator

Traffic Time Est. by Loc

Traffic Time Est. by Operator

Loc * Operator Interaction

Gage R&R (ANOVA) for Traffic Time Est.

MSA Continued DMAICKey Point: The Process is 5% below the Lower Specs, We Now Have Clues as to Why

The Result:Here we’ve displayed the Current State of DSL-East Completed Installs, we can see that the DSL-East division is currently completing only 85% of their installations on average, we can expect performance below our specified (LSL) completion rate of 90%, 96% of the time. This process is incapable of meeting the specs and must be corrected!0.990.960.930.900.870.840.81

LSL USL

LSL 0.9Target *USL 1Sample Mean 0.859Sample N 30StDev(Within) 0.0255495StDev(Overall) 0.0224914

Process Data

Z.Bench -1.60Z.LSL -1.60Z.USL 5.52Cpk -0.53

Z.Bench -1.82Z.LSL -1.82Z.USL 6.27Ppk -0.61Cpm *

Overall Capability

Potential (Within) Capability

PPM < LSL 933333.33PPM > USL 0.00PPM Total 933333.33

Observed PerformancePPM < LSL 945723.14PPM > USL 0.02PPM Total 945723.16

Exp. Within PerformancePPM < LSL 965842.31PPM > USL 0.00PPM Total 965842.31

Exp. Overall Performance

WithinOverall

Process Capability of DSL-East Completed Installs

Process Capability DMAICKey Point: The Process is Incapable of Meeting Specification

Capability Analysis:Running a capability analysis we confirmed that the DSL-East division is yielding 85% of their installations on average, with 15% of all installations being defective, producing 150,000 defectives per million opportunities. With a yield of less than 6%, and a dismal long term process sigma of .1, we must reduce process variability and move into the spec range.

* Defects = Defectives : There are no defects for GI’s, the job is simply defective if the installer found no one present or no location to perform the install .

* Capability Analysis Courtesy of Thomas A. Little Consulting

We c u r r e n t l y r u n t h e r i s k o f

b e i n g o u t o f t h e s p e c r a n g e 9 5 %

o f t h e ti m e .

Six Sigma Capability Analysis (Before)Defective Variables, (Normal)Number of units 20,000 Average 0.86

Number defective observed 3,000.0 Stdev. 0.0255 USL 1.00 6.65%

Proportion defective 0.1500000 LSL 0.90 6.65%

% Yield 85.00% Cp 0.652

Cpk (0.535)

DPU 0.15 DPU 0.9457232 DPPM 150,000.0 DPPM 945,723.2 Yield 85.00% Yield 5.43%

Cpk Ppk 0.35 Cpk Ppk (0.53) Process Sigma 1.04 Process Sigma (0.1)

Capability Measure Short Term Capability Measure Long Term (+1.5)

Process MapOrganization Operation Sequence or TimeOrganization 1

No Yes

No

No

Yes

Yes

No

Retrieve Installation Orders via Daily Installation Order

System (DIOS)


Start or End Operation

Wait or Delay

Decision

Data

Data Stored

Confirm Travel Distance/With

Time Est.

Check for Customer Communication

Details

Confirm Location w ith Customer

Travel to Customer Site

Confirm Approx. Travel Time w ith

Customer

Is Customer Ready for

Install

Wait for customer or

Wait for Next Job

Data Travel to Customer Site

Data Customer Available

Perfom Installation

Attempt Installation

Log the GI into Sytem

Data


Data

Customer Available

?

Perfom Installation

Log the GI into Sytem

Data

Choose Traff ic Route

Current State Process Map

VOC Opportunity

VOC Opportunity

VOC Opportunity

Starts the Process

Ends the process

Ends the process

Ends the process

Causes Rework

Ends the process

Causes Rework

Causes Rework

Cost of Poor QualityPoor execution of this process leads to…

• Costly Rework• Worker Inefficiency

(overstaffing)• Equipment Restocking• Lost Business Opportunities• Low Customer Satisfaction• Rise in Customer Complaints• Low Return on Investments

Key Point: Missed Installs Causes Rework & Increased CostsDMAIC

Ishikawa (Fishbone) Diagrams

No way to gauge changes

Installation Times (am,pm)Training

Unfamiliar with Area

Road Blocks

Poor Weather

Fuel Needs

Source for Traffic Info

Missing Equipment

Environment

Waiting in Traffic

Machinery Materials

Methods ManMeasurement

No Standard Policies

GPS has no traffic function

Why is traffic causing Ghost Installations?

GPS Use Not Mandatory

Training

No Local Familiarity

Poor Time Estimates

Severe Weather

Slow Responses from GPS

Address not visible

GPS Cannot Find Loc.

Environment

Determining Distance

Machinery Materials

Methods ManMeasurement

Are the GPS Systems Out of Date?

Wide Service Area

Doesn’t Display Traffic

Use Personal Experience

Too Much Traffic

Construction

Route Errors

Radius too wide

Key Point: Brainstorming on Possible Causes of KPIV’sDMAIC

FMEA

Failure Modes & Effects Analysis:Walking through the FMEA process has allowed us to assign values to critical process inputs so that we can prioritize our corrective efforts.

DMAIC

Action Results

Process Steps / Input

Potential Failure Mode(s)

Potential Effect(s) of Failure

Potential Cause(s)/

Mechanism(s) of Failure

Current Design/Process

Controls

Recommended Action(s) Responsibility

What is the process step and input under investigation?

In w hat w ays does the Key Input go w rong?

What is the impact on the Key Output Variable (Customer Requirements) ?

What causes the Key Input to go w rong?

What are the existing controls and procedures (inspection and test) that prevent the cause of the Failure Mode?

What are the actions for reducing the occurance of the cause or improving detection?

Who is responcible for implementing reccommended actions?

Estimating Traffic Conditions

Under estimates traffic conditions

Installer arrives late and misses appointment

8 No SOP for getting updated traffic info

10 None 10 800 Update equipment to provide real time traffic updates

Jennifer

Estimating Traffic Conditions

Over estimates traffic conditions

Installer arrives too early and must wait to begin work

2 No SOP for getting updated traffic info

6 None 6 72 Update equipment to provide real time traffic updates

Jennifer

Estimating Time Under estimates time to arrive at location


8 Equipment arrival times are not accurate

6 Use travel times given by GPS

8 384 Test multiple mfg's for the most accurate equipment

Joe & Cecilyn

Estimating Time Over estimates time to arrive at location


2 Equipment arrival times are not accurate

6 Use travel times given by GPS

4 48 Test multiple mfg's for the most accurate equipment

Joe & Cecilyn

Estimating Distance

Under estimates distance to arrive at location


10 Operators choose alternate routes

8 Installers descretion as to use GPS Route, no SOP

8 640 Create SOP to use GPS routes/directions

David

Estimating Distance

Over estimates distance to arrive at location


2 Operators choose alternate routes

6 Installers descretion as to use GPS Route, no SOP

8 96 Create SOP to use GPS routes/directions

David

Customer Communication Requirements

Installer cannot reach customer

Installer cannot get needed info

8 Customer contact is by request only

6 Customers can request or decline to be contacted by installer prior to arrival

10 480 Create policy that customers must be contacted before installer proceeds to their location

Jack

SEV

PROB

DET

RPN

FMEA Objective, scope and goal(s): To identify critical improvement needs and to understand the improvement implementation risks

Key Point: Critical Effects: 1) Est. Traffic 2) Est. Distance 3) Customer Communication

Ah Ha… Installers Discretion Causes Errors in Distance

Measurements!!!

Root Cause & DOE Analysis

DMAICKey Point: Traffic & Distance Have the Most Significant Effect on Travel Times; Also GI’s vs. Customer Complaints p-value = .000

Defect Inputs: Pareto

The Pareto chart illustrates that over 80% of GI’s are due to the top 3 causes (x’s).

Traffic - 40.2%Dist. - 26.6%Comm. - 16.6%

DOE: Pareto Effects

This chart indicates that all the main effects are significant although weather (temp.) much less than the others. We can also see the interactions that are significant are Traffic and Distance or all 3.

In the analysis of variance table Traffic * Distance (p = 0.021), and main effects are significant.

Interaction Plot: Time

The non parallel lines found across all the interactions indicate that at high levels of any 2 of the factors (traffic, distance, temp.) the response (travel time) will increase.

Scale: 3= High, 2= Med, 1= Low

321 321

32

24

16

32

24

16

Traffic

Distance

Temperature

123

Traffic

123

Distance

Interaction Plot for TimeData Means

0

0.1

0.2

0.3

0.4

0.5

Traffic Distance Comm.Weather Other

1206

498 312

186

798

2009 Common Causes of GI’s

BC

AC

AB

ABC

C

B

A

121086420

Term

Standardized Effect

2.36

A TrafficB DistanceC Temperature

Factor Name

Pareto Chart of Effects(response is Time, Alpha = 0.05)

Regression: Reject H0

The p-value in the Analysis of Variance table (0.000), indicates that the relationship between defects (x) and customer complaints (y) is statistically significant at an alpha level of .05.

Because there is significance in the rate of complaints versus GI’s we must reject H0: That there is no significance between the two occurrences, and accept the alternative.

100500-50-100

99

90

50

10

1

Residual

Perc

ent

240180120600

100

50

0

-50

Fitted Value

Resi

dual

80400-40-80

10.0

7.5

5.0

2.5

0.0

Residual

Fre

quency

35302520151051

100

50

0

-50

Observation OrderR

esi

dual

Normal Probability Plot Versus Fits

Histogram Versus Order

Complaints vs. Defects

Future StateBrainstorming

DMAICKey Point: 3 Main Areas Identified for Improvement Opportunities

Technology:

• Upgrade to GPS w/ Live Traffic Conditions

• Upgrade to GPS that provides alternative routing

• Text Weather Alerts• Automated Calling -

Confirmation System Policy & Procedures:

• No Discretionary Routes • Require Customer Confirmation

Before Traveling to Site• Post New Obstructions• Assign Drivers as Locally as

Possible to Their Neighborhood • Hand-Off Routing (Flexible Ad-

HOC Dispatching)

Training:

• GPS Features• Route Selection• Time Management• Quarterly Service Area

Briefings

Potential Solutions* solutions in green text can be implemented immediately

Construction Starts Downtown October 2nd

Qui

ckly

Im

plem

ente

d

Cost

Less

than

60

K

Mos

t Eff

ect

on D

efec

t Re

ducti

ons

Crea

tes

High

er

Cust

omer

Sa

tisfa

ction

0.2 0.1 0.4 0.33 5 9 7 6.8 28 8 8 3 6.5 37 8 6 9 7.3 14 8 8 5 6.3 410 10 5 1 5.3 57 8 2 1 3.3 6

Weighted Benefits

Solutions Fina

l Wei

ghte

d Sc

ore

Ove

rall

Rank

Automated Calling ConfirmationNew GPS w/ Live Traffi c RoutingHand-Off DispatchingRequire Cust. ConfirmationsAssign Local Drivers & RoutesTime Management Training

Future StateBrainstorming

DMAICKey Point: Top Three Solutions Identified at Kaizen Event

Prioritization of SolutionsWhen possible…

Why not move up waiting customers by dispatching close-by waiting installers?

Count 1206 798 498 312 186Percent 40.2 26.6 16.6 10.4 6.2Cum % 40.2 66.8 83.4 93.8 100.0

Defect OtherWeatherCommunicationDistanceTraffic

3000

1500

0

100

50

0

Count

Perc

ent

Count_1 502.5 399.0 208.0 190.9 124.0Percent 35.3 28.0 14.6 13.4 8.7Cum % 35.3 63.3 77.9 91.3 100.0

Defect_1 OtherCommunicationWeatherTrafficDistance

1600

800

0

100

50

0

Count_

1

Perc

ent

464136312621161161

100

50

0

Observation

Indiv

idual Valu

e

_X=35.3

UCL=93.7

LCL=-23.1464136312621161161

80

40

0

Observation

Indiv

idual Valu

e

_X=26.8

UCL=71.2

LCL=-17.6

464136312621161161

40

20

0

Observation

Indiv

idual Valu

e

_X=21.11

UCL=46.35

LCL=-4.13464136312621161161

40

20

0

Observation

Indiv

idual Valu

e

_X=18.15

UCL=39.86

LCL=-3.55

GI Defects Before GI Defects After

I Chart of Traffic Before I Chart of Traffic After

I Chart of DSL-East Distance Before_ I Chart of DSL-East Distance After

Pilot Testing of Solutions

DMAICKey Point: Over Our 3 Month Trial Period Major Overall Gains Have Been Made Regarding Installation Attempts

Defects Reduction:• 52% Overall Defective

Reduction Achieved• Traffic, Communication,

and Distance Have the Most Improvement

Traffic Times:• Ave. In-Traffic Times

Reduced by

-24%-8mins

• Logged Traffic Delay Counts Down by 67%

Distance Logged:• Ave. Distance

Reduced by

-14%-3mi

• Logged Traffic Delay Counts Down by 37%

Flexible Dispatching Reduced Ave. Distance

Traveled

Updated GPS Technology w/Alternate Traffic Views Reduced Traffic Counts

Forecasted Reductions Shows We’ll Beat Our

Historical Benchmark!!!

Hypothesis TestingDMAIC

Key Point: The Percentage of Completed Installs Has Risen Into the Specification Area.

After Completed InstallsBefore Completed Installs

1.00

0.95

0.90

0.85

0.80

Perc

ent C

omple

te

Boxplot of Before Completed Installs, After Completed Installs

The Boxplot and Value Plot of before and after completed installations shows the expected average % of completed jobs has risen to meet specs of > 90% , and will slightly surpass the prior historical baseline of 92%.

Two Sample T-Test

After Completed InstallsBefore Completed Installs

1.00

0.95

0.90

0.85

0.80

Perc

ent C

omple

te

Value Plot of Average Completed Installs

In-Spec Area

Non-Spec Area

After Improvements

Before Improvements

Improved Yield Analysis

DMAICKey Point: Process Capability is Higher and Complaints Will Decline by > 30% and Below All Historical Levels by Year End

With the defective installs slashed by 52% we can expect to achieve an acceptable yield of 93% of all jobs completed without GI issues.

We can also see below that customers complaints are decreasing in response to the improvements in service delivery.

Post Improvement Capability

I ’m te l l ing you Homer, the guy

was on time and d id a great job!

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec0

50100150200250300350400

Year Over Year GI Customer Complaints

Customer Complaints Before 2009 Customer Complaints During 2009 Customer Complaints After 2010, 2011 Est.

May 2010 Improvements Implemented!

* Capability Analysis Courtesy of Thomas A. Little Consulting

Six Sigma Capability Analysis (Before)Defective Variables, (Normal)Number of units 20,000 Average 0.94

Number defective observed 1,424.0 Stdev. 0.0278 USL 1.00 6.65%

Proportion defective 0.0712000 LSL 0.90 6.65%

% Yield 92.88% Cp 0.600

Cpk 0.457

DPU 0.0712 DPU 0.0853410 DPPM 71,200.0 DPPM 85,341.0 Yield 92.88% Yield 91.47%

Cpk Ppk 0.49 Cpk Ppk 0.46 Process Sigma 1.47 Process Sigma 2.9

Capability Measure Long Term (+1.5)Capability Measure Short Term

Forecasted Process Capability

DMAICKey Point: Capability Analysis Shows on Average We Can Now Expect to Meet Service Specifications

0.990.960.930.900.870.840.81

LSL USL


Process Data

Z.Bench -1.60Z.LSL -1.60Z.USL 5.52Cpk -0.53

Z.Bench -1.82Z.LSL -1.82Z.USL 6.27Ppk -0.61Cpm *

Overall Capability






WithinOverall

Process Capability Before

0.990.960.930.90

LSL USL


Process Data

Z.Bench 1.29Z.LSL 1.37Z.USL 2.23Cpk 0.46

Z.Bench 1.46Z.LSL 1.52Z.USL 2.47Ppk 0.51Cpm *

Overall Capability






WithinOverall

Process Capability After

Now We are on Target and Ready to Fully Implement the

Solution!

This Process Simply Missed the Mark Before

Our Analysis.

Updated ProcessMap w/SOPs

DMAICKey Point: Key Process Improvements: 1) Increased Efficiency 2) Increased Customer Contact 3) Key SOP’s are Now in Place

Updated Process MapOrganization Operation Sequence or Time

Yes

No Yes

No

Retrieve Installation Orders via Daily Installation Order

System (DIOS)


Start or End

Call Customer

Wait or Delay Decision

Data Stored

Confirm Travel Distance/With

Time Est. (GPS)

Confirm Customer Communication

Details

Confirm Location w ith Customer

Confirm GPS Travel Time w ith

Customer

Is Customer Ready for

Install?

Data Travel to Customer Site

Customer Available

?


Traff ic Route via

(GPS)

Get Closest Job From Dispatch

Operation

Data

Log the GI Into Sytem

Attempt to Reschedule Customer

Get Closest Job From Dispatch

Data

Log the GI Into Sytem

Attempt to Reschedule Customer

Perform Install

Data

SOP-Process Improvement Steps

SOP-Customer Interaction

Gather VOC Gather VOC

Gather VOC

51464136312621161161

0.3

0.2

0.1

0.0

Sample

Prop

ortio

n

_P=0.1481

UCL=0.2988

LCL=0

51464136312621161161

0.16

0.12

0.08

0.04

0.00

Sample

Prop

ortio

n

_P=0.0642

UCL=0.1682

LCL=0

464136312621161161

0.5

0.4

0.3

0.2

0.1

Sample

Prop

ortio

n

_P=0.298

UCL=0.4920

LCL=0.1040

464136312621161161

0.4

0.3

0.2

0.1

0.0

Sample

Prop

ortio

n

_P=0.19

UCL=0.3564

LCL=0.0236

P Chart of Defectives Before P Chart of Defectives After

% of Customer Complaints Due to GI's Before % of Customer Complaints Due to GI's After

Process Monitoringvia Control Charts

DMAICKey Point: Defectives and Customer Complaints Due to GI’s are Now In Control After the Improvements

Financial Benefits Summary

DMAICKey Point: Through Project Improvement Efforts We Have Created $276,000 in Total Benefits for the next 12mo., and $196,400 in Reoccurring Annual Revenues

Installs LaborNew Billings Completions 167,000 Wages 36,000New Billings from Reffered Customers 29,400 Overtime 9,000

196,400 196,400$ 45,000 45,000$

Equipment Sales due to Installs 11,800 11,800$ Total Revenues 208,200$ Fuel 19,800 19,800$

WarehousingLabor Overtime Reduction 3000 3,000$

Indirect Labor 3300 Total Savings 67,800$ Direct Labor 21,000Overtime 6,500

30,800 30,800$

Equipment 127,089$ GPS Upgrade 32,000Automated Calling System 12,500 196,400$ Misc 3,700 49,100$

48,200 48,200$ 16,367$

Training 276,000$ Travel 1200Print 480Materials 109Food 230Misc. 92

2,111 2,111$ Total Cost (81,111)$

Quarterly Revenue GainsMonthly Revenue Gains

Annual Revenues (based on yr/yr comp. rates) 2011 Savings

12-Mo. Net Project Gains

Financial Summary

Annual Revenue Gains

Total 2011 Benefits

Raw Mat (subject to mrkt prices)

Sales (one time)

Project Cost (one time non-reoccurring)

(Ex-Equipment)

DMAICQUESTIONS or COMMENTS?

Thank You for Your Time

Ghost Installation Reduction Project

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