Lean Six Sigma Project on Reduction ofr Analyze

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Road Map : Analyze

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C ount 185 157 140 124 109 95 70 62 41 155407 354 347 295 288 264 218 188

Percent 5 4 4 4 3 3 2 2 1 412 10 10 8 8 8 6 5

C um % 73 77 81 85 88 91 93 94 96 10012 22 32 40 48 56 62 67

Co

un

t

Pe

rce

nt

Parameters

Oth

er

Inco

rrect A

code

prov id

ed

S urvey o

ffice

r wrong A

code

Cust

ord

ers diff

prod

ltr

Inco

rrect addr

ess

Ser

vice

not c

ompat

ible

Cnld b

y cus

t via open

reac

h

Cnld

by agen

t req-

nt re iss

BT - Open

reac

h Issue

No lo

nger re

quire p

rodu

ct

AX Q

uery

Dup

lica te

ord

er is

sued

Er ro

r on D

FD S

creen

SQ in

corre

ctly a

ssigned

CRD a

mm

ended

-oth

er dept

Ceas

e ord

er not a

uto p

rogr

A dd cla rif

icatio

n post

LPE

Ass O

rder n

ot close

d

4000

3000

2000

1000

0

100

80

60

40

20

0

Pareto Chart of Parameters

Pareto Chart on different parameters

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Control & Impact Matrix

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Controllable and Un controllable Factors

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%age

High OFR

O thers

Advisors

Not Sale s Q uery

Back O ffice

Sales

Sale s C ance l lation

Customer

openreachCancelled by customer via

product laterCustomer orders different

productNo Longer require the

request and reissuedCancelled at sales agent's

request and not reissuedCancelled at sales agent's

code providedIncorrect A

requestDuplicate sales

addressIncorrect

Error on DFD Screen

Issued on incorrect account

exchangeIssued on incompatible

ISDN number not compatible

Service not compatible

Duplicate order issued

ammended by otherCRD/appointment

LPEAddress clarification post

A codeSurvey office using wrong

assignedSales query incorrectly

workingOrder closed line not

New advisors

High productivity target

Backlog

Will issues

closedAssociated Order not

progressedCease order not auto

not alignAX Query - Charges do

BT - Openreach Issue

WLR/CPS Cancellations

CSS Error

Probable causes leading to high OFR %age in BTLB simpl & complex

Cause & Effect Diagram

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Data Collection Plan

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Analysis Potential X1 (Error On DFD Screen)

Question : Does "Error on DFD Screen" has any impact on OFR?

Tool : One ProportionEstablish Hypothesis :Ho - "Error on DFD Screen" doesn't have any impact on OFRHa - "Error on DFD Screen" has impact on OFR

How You will analysis your data : Error on DFD Screen is marked if, HNA or DIDs did not get allocated because the advisor did not add feature line information. We have performed the testing to check the error is greater than 5 % margin with the 95 % confidence interval. As our data is discrete, we have taken One Proportion test.

Test and CI for One ProportionTest of p = 0.05 vs p > 0.05

Lower ExactSample X N Sample p Bound P-Value1 107 628 0.170382 0.146097 0.000

Conclusion : P value is lesser than 0.05, hence, we will reject null hypothesis and accept alternate hypothesis, i.e. “Error on DFD

Testing has been done on 10 weeks data starting from ( 11th March 07 to 11th May 07). Total errors are 628 in Back office category and the errors related to “Error on DFD Screen” are 107.

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Analysis Potential X2 (Issued on incompatible exchange)

Question: Does "Issued on incompatible exchange" has any impact on OFR?

Tool : One ProportionEstablish Hypothesis :Ho - "Issued on incompatible exchange“ doesn't have any impact on OFRHa - "Issued on incompatible exchange“ has impact on OFR

How You will analysis your data : Error on Issued on incompatible exchange is marked, if the advisor did not check using DNN. We have performed the testing to check the error is greater than 5 % margin with the 95 % confidence interval. As our data is discrete, we have taken One Proportion test.

Conclusion : P value is greater than 0.05, hence, we will reject alternate hypothesis and accept null hypothesis, i.e.


Lower ExactSample X N Sample p Bound P-Value

1 7 628 0.011146 0.005243 1.000

Testing has been done on 10 weeks data starting from ( 11th March 07 to 11th May 07). Total errors are 628 in Back office category and the errors related to “Issued on incompatible exchange” are 7.

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Analysis Potential X3 (Cancelled at sales agent's request and not reissued)

Question: Does “Cancelled at sales agent's request and not reissued" has any impact on

OFR?

Tool : One ProportionEstablish Hypothesis :Ho - “Cancelled at sales agent's request and not reissued" doesn't have any impact on OFRHa - “Cancelled at sales agent's request and not reissued" has impact on OFR

Conclusion : P value is lesser than 0.05, hence, we will reject null hypothesis and accept alternate hypothesis, i.e. “Cancelled at sales


Lower ExactSample X N Sample p Bound P-Value1 140 173 0.809249 0.753360 0.000

How You will analysis your data : Cancelled at sales agent’s request and not reissued is marked if, order is cancelled at sales agent’s request but not reissued hence delayed. We have performed the testing to check the error is greater than 5 % margin with the 95 % confidence interval. As our data is discrete, we have taken One Proportion test.

Testing has been done on 10 weeks data starting from ( 11th March 07 to 11th May 07). Total errors are 173 in Sales Cancellation category and the errors related to “cancelled at sales agent’s request and not reissued” are 140.

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Analysis Potential X4 (Issued on incorrect Account)

Question: Does “Issued on incorrect A/C" has any impact on OFR?

Tool : One ProportionEstablish Hypothesis :Ho - “Issued on incorrect a/c" doesn't have any impact on OFRHa - " Issued on incorrect a/c " has impact on OFR

How You will analysis your data : Issued on incorrect a/c is marked, when back office’s advisor issued the order on incorrect a/c or cancelled order and didn’t reissue the same. We have performed the testing to check the error is greater than 5 % margin with the 95 % confidence interval. As our data is discrete, we have taken One Proportion test.

Conclusion : P value is greater than 0.05, hence, we will reject alternate hypothesis and accept null hypothesis, i.e.



1 15 628 0.023885 0.014780 1.000

Testing has been done on 10 weeks data starting from ( 11th March 07 to 11th May 07). Total errors are 628 in Back office category and the errors related to “Issued on incorrect A/C” are 15.

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Analysis Potential X5 (Service not compatible)

Question: Does “Service not compatible" has any impact on OFR?

Tool : One ProportionEstablish Hypothesis :Ho - “Service not compatible" doesn't have any impact on OFRHa - “Service not compatible" has impact on OFR

How You will analysis your data : Service not compatible error is marked, when back office’s advisor didn’t check the compatibility chart. We have performed the testing to check the error is greater than 5 % margin with the 95 % confidence interval. As our data is discrete, we have taken One Proportion test.

Conclusion : P value is lesser than 0.05, hence, we will reject null hypothesis and accept alternate hypothesis, i.e. “Service not



1 42 628 0.066879 0.051232 0.037

Testing has been done on 10 weeks data starting from ( 11th March 07 to 11th May 07). Total errors are 628 in Back office category and the errors related to “Service not compatible” are 42.

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Analysis Potential X6 (Incorrect Address)

Question: Does “Incorrect address" has any impact on OFR?

Tool : One ProportionEstablish Hypothesis :Ho - “Incorrect address" doesn't have any impact on OFRHa - " Incorrect address " has impact on OFR

How You will analysis your data : “Incorrect Address” error is marked, if the Sales team has given incorrect address on Performa. We have performed the testing to check the error is greater than 5 % margin with the 95 % confidence interval. As our data is discrete, we have taken One Proportion test.

Conclusion : P value is lesser than 0.05, hence, we will reject null hypothesis and accept alternate hypothesis, i.e. “Incorrect



1 48 154 0.311688 0.250192 0.000

Testing has been done on 10 weeks data starting from ( 11th March 07 to 11th May 07). Total errors are 154 in Sales Error category and the errors related to “Incorrect address” is 48.

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Analysis Potential X7 (Duplicate order issued)

Question: Does “Duplicate order issued" has any impact on OFR?

Tool : One ProportionEstablish Hypothesis :Ho - “Duplicate order issued" doesn't have any impact on OFRHa - “Duplicate order issued" has impact on OFRHow You will analysis your data : “Duplicate order issued” error is marked,

when the back Office advisor did not check that orders have already been issued. We have performed the testing to check the error is greater than 5 % margin with the 95 % confidence interval. As our data is discrete, we have taken One Proportion test.

Conclusion : P value is lesser than 0.05, hence, we will reject null hypothesis and accept alternate hypothesis, i.e. “Service not



1 51 628 0.081210 0.063980 0.001

Testing has been done on 10 weeks data starting from ( 11th March 07 to 11th May 07). Total errors are 628 in Back office category and the errors related to “Duplicate order issued” are 51.

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Analysis Potential X8 (ISDN number not compatible)

Question: Does “ISDN number not compatible" has any impact on OFR?

Tool : One ProportionEstablish Hypothesis :Ho - “ISDN number not compatible" doesn't have any impact on OFRHa - “ISDN number not compatible" has impact on OFR

How You will analysis your data : “ISDN number not compatible” error is marked, when the back Office advisor did not check the compatibility of ISDN number with the customer’s exchange. We have performed the testing to check the error is greater than 5 % margin with the 95 % confidence interval. As our data is discrete, we have taken One Proportion test.

Conclusion : P value is lesser than 0.05, hence, we will reject null hypothesis and accept alternate hypothesis, i.e. “ISDN number



1 12 628 0.019108 0.011062 1.000

Testing has been done on 10 weeks data starting from ( 11th March 07 to 11th May 07). Total errors are 628 in Back office category and the errors related to “ISDN number not compatible” are 12.

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List of Vital Few `X’

HYPOTHESISTESTING

Important Many Xs Vital Few Xs

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List of Vital Few ‘X’ with P value

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ANALYZE Check List

Data & Analytics

Lean Six Sigma Project on Reduction ofr Analyze