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
About This P roject. This project is a simulation of actual occurrences C overs key six sigma concepts including S eeks to accomplish key outlined objectives. Applying the DMAIC approach to process improvement Identification and selection of process improvement opportunities - PowerPoint PPT Presentation
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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
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
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.
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.
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?
%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%
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
Installer arrives late and misses appointment
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
Installer arrives too early and must wait to begin work
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
Installer arrives late and misses appointment
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
Installer arrives too early and must wait to begin work
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
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?
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%
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
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 Before
0.990.960.930.90
LSL USL
LSL 0.9Target *USL 1Sample Mean 0.93803Sample N 30StDev(Within) 0.0277588StDev(Overall) 0.0250994
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
Potential (Within) Capability
PPM < LSL 33333.33PPM > USL 0.00PPM Total 33333.33
Observed PerformancePPM < LSL 85340.59PPM > USL 12792.65PPM Total 98133.24
Exp. Within PerformancePPM < LSL 64863.79PPM > USL 6774.83PPM Total 71638.62
Exp. Overall Performance
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)
Review Order Details
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
?
Attempt Installation
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