Improve Customer Complaint Resolution Process Using Six
Sigma
Sanjit Ray
SQC and OR Division
Indian Statistical Institute
8th Mile, Mysore Road,
Bangalore 560 059, Karnataka, India.
Email: sanjitisi@yahoo.co.in
Prasun Das
SQC and OR Division
Indian Statistical Institute
203 B.T. Road, Kolkata 700 108, India
Email: dasprasun@rediffmail.com
Bidyut Kr. Bhattacharya*
Department of Mechanical Engineering
Bengal Engineering and Science University
Shibpur, Howrah 711103, India
E-mail: bidyut53@yahoo.co.in
*Corresponding author
Abstract: Six-Sigma is a strategy for improving customer satisfaction by reducing
variation and thus producing products and services better, faster and cheaper. Usually, an
organization uses the DMAIC methodology to improve current process performance.
Initially, Six-Sigma methodologies were implemented to improve manufacturing
processes and now its use is rapidly expanded to other functional areas. Customer
complaint investigation and analysis are critical for customer satisfaction as complaints
produce customer dissatisfaction and probable business loss. The structured methodology
of DMAIC was applied to reduce the cycle time for customer complaint resolution
process. This project has substantially benefitted the organization by reducing the followup time for customer complaint resolution, lowering the closure time and decreasing the
ratio of pending complaints. This paper will be of interest to academic researchers and
practical managers. It describes the justification and selection of the project, how the
tools and techniques of DMAIC methodology were employed in the different phases and
how the improvement actions were implemented. This project methodology can be used
generally to reduce cycle time for any other transactional processes as well, which will
help in reduce response time to customer issue, improve customer-supplier relation and
finally improve customer satisfaction.
Keywords: Six Sigma; DMAIC (Define – Measure – Analyse – Improve – Control),
SIPOC; Critical to Quality (CTQ); Causes; Classification Analysis, Regression Tree;
Sigma Rating.
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1. Introduction
Six-Sigma is considered as a business strategy that focuses on improving the
understanding of customer requirements, business systems, productivity and financial
performance (Kwak and Anbari, 2006). It focuses on improving quality by reducing
variation and thus helping an organization to produce products and services better, faster
and cheaper (Mahanti and Antony, 2005). Normally, an organization uses the Six Sigma
methods to achieve bottom line benefits or customer satisfaction. Six-Sigma is a well
structured methodology that can help a company achieve expected goal through a
continuous improvement methodology (Chun-Chin Wei et al., 2010; Chao-Ton Su et al.,
2008), which is project-by-project. It is a project-driven scheme that employs a wellstructured methodology, called DMAIC, comprising of the five phases: Define, Measure,
Analyze, Improve and Control. This approach reduces process variation, waste and cycle
time, thus enhances profitability and customer satisfaction via effective application of
statistical techniques (Coronado and Antony, 2002; Douglas et al., 2008). Six-Sigma is a
scientific and statistical quality assessment for all processes in the organization through
measurement of quality level, which provides the opportunity and discipline to eliminate
mistakes, improve moral and thus reduce cost (Park, 2002). Six Sigma methodologies
were typically implemented first to improve manufacturing processes; however,
organizations realised the benefits of Six Sigma, and its use is rapidly expanded to
different functional areas such as marketing, engineering, purchasing, servicing, and
administrative support (Henderson and Evans, 2000; Rajagopalan, Francis and Suarez,
2004; Roberts, 2004; Das 2005; Das et al., 2006, 2007; Ray and Das, 2009). Thus the
fundamental goal of Six-Sigma methodology is process improvement for better
performance (Antony et al., 2010).
Overall operational excellence is the key requirement of any business to have competitive
advantage over others and sustained growth (Desai et al., 2009). In a large manufacturing
industry, high cycle time for customer complaints resolution is critical element of
business management as it produces customer dissatisfaction due to reduced product
efficiency and loss to customer, and possible loss of business. This paper integrates SixSigma objectives and concepts (Bunce et al., 2008) and industrial processes by using the
DMAIC roadmap, to be followed for the purpose of reducing cycle time for customer
complaint resolution. The main objective of this analysis is to learn about the causes of
high cycle time so that the process can be improved. The reasons, causes, variables of
customer complaint resolution process are very generic in nature and may be found
available in most of the industries. The only thing that varies from industry to industry is
the list of significant variables or root causes. The details of the five phases of Six Sigma
DMAIC methodology, as used for this study, are discussed in the subsequent sections.
This particular application offers practical insight for improving transactional processes
for cycle time reduction and creates new knowledge among managers and seeks to
advance theoretical understanding of process improvement (Anand et al., 2010).
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2. Define Phase
This phase defines the goals and boundaries of an improvement study in terms of
customer requirements or business requirements and the process delivering these
requirements (Porter 2001). The main activities of define phase are to
a) Define the project boundary by SIPOC (Supplier, Input, Process, Output,
Customer) analysis
b) Prepare Critical to Quality (CTQ) specification table.
The problem faced by an organization was that the average time taken for closure of
customer complaints was 42 days during 2009 against set target of 7 days. This was
resulting in Customer dissatisfaction, extra efforts for follow up and discussion for
closure and piling up of customer complaints. It was decided to reduce the customer
complaint resolution time (Y) from 42 days to less than 7 days.
60
53
50
43
42
Average no of
Days
40
30
Target Closure
Days
20
10
7
0
2007
2008
2009
Figure 1: Bar graph of number of customer complaints
The reduction of customer complaint resolution time will result in the following benefits
1. Enhanced Customer satisfaction
2. Reduction in follow-up time from 2 days per week to one day per week for
resolution of complaints.
The customer complaint analysis process is defined by using process mapping (SIPOC)
(ref. Table 1). This high-level process map helps to define the project boundary, can
identify the data collection system and identifies the resources required to carry out the
improvement activity.
Table 1: Process Mapping (SIPOC) of Customer Complaint Analysis
SUPPLIER
(S)
Customer
INPUT (I)
PROCESS
(P)
Complaint – Letter, e-mail
Resolution of
Customer
Complaints
Customer Care
Discussion
with
Customer,
Action Plan, Repair
Design
Drawing, Specification of Spares
Manufacturing
Manufacturing of Spares
Supplier
Drawing, Specification of Spares
Manufacturing of Spares
QA
Compilation & Follow up
Meeting
Analysis of
Identification of
Complaint
Complaint

 Root Causes and
Reporting
Data
Corrective Action
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OUTPUT
(O)
CUSTOME
R (C)
Closed
Complaints
Customer
Confirmation
from Customer

Implement
Corrective
Actions

Weekly
Meeting &
Follow up
Based on the objective of the study and definition of the process, the critical measures of
the process output CTQ is defined next along with their specifications, by measuring and
monitoring of which, the process quality and level of control can easily be found out (ref.
Table 2). With these definitions and interpretation of CTQ, the study moves to the
Measure phase.
Table 2: CTQ Specification Table
NEED
CTQ
Reduce time taken
for closure of B
type complaint
Closure
time (days)
OPERATIONAL
DEFINITION OF
MEASURE
Number of days =
closure datecomplaint date
DEFECT
DEFINITION
>7 days
KANO
STATUS
Must be
3. Measure Phase
The Measure phase of a Six Sigma study deals with the following
 Data collection plan and identification of stratification factors.
 Collection of information on cycle time.
 Analysis of cycle time data for Sigma level estimation.
 Initial Process analysis based on the stratification factors.
Data on cycle time for closure of customer complaints were collected for 97 reportable
complaints are documented with different defects. The cycle time data thus subjected to
normality test to find out whether the data comes from a normally distributed process or
not. As observed from Figure 2, since the p value is < 0.05, the data does not follow
normal distribution (Montgomery D.C., 2002). The cycle time data are plotted on run
chart to observe any special cause(s) of variation. As observed from Figure 3 the plotted
points do not display any out of control situation but some customer complaints have
taken a very high time to close.
N o r m a lity T e st fo r C o m p la in t C lo su r e T im e
A n d e r s o n - D a r li n g N o r m a li ty T e s t
A -S quared:
P - V a lu e :
: Normality Test for Cycle Time data
0
40
80
120
160
200
9 5 % C o n f i d e n c e I n te r v a l f o r M u
6 .7 7 3
0 .0 0 0
Mean
S tD e v
V a r ia n c e
S kewness
K u r to s i s
N
4 1 .1 7 5 3
4 2 .4 8 1 6
1 8 0 4 .6 9
1 .8 0 6 1 6
3 .0 4 7 7 8
97
M in im u m
1 s t Q u a r ti le
M e d ia n
3 r d Q u a r ti le
M a xim u m
1 .0 0 0
1 1 .0 0 0
2 8 .0 0 0
5 2 .0 0 0
2 0 6 .0 0 0
9 5 % C o n f i d e n c e I n te r v a l f o r M u
3 2 .6 1 3
20
30
40
50
4 9 .7 3 7
9 5 % C o n f i d e n c e I n te r v a l f o r S i g m a
3 7 .2 2 9
4 9 .4 7 4
9 5 % C o n f i d e n c e I n te r v a l f o r M e d i a n
9 5 % C o n f i d e n c e I n te r v a l f o r M e d i a n
1 9 .7 8 9
Figure 2: Normality Test for Cycle Time data
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3 4 .6 0 5
R u n C h a r t fo r C lo s u r e T im e t a k e n ( D a y s )
a n d N o . o f C o m p la in t s
Closure time( Days)
200
100
0
20
45
70
95
O b s e r v a tio n
N u m b e r o f ru n s a b o u t m e d ia n :
E x p e c te d n u m b e r o f r u n s :
L o n g e s t ru n a b o u t m e d ia n :
A p p r o x P - V a lu e f o r C lu s t e r i n g :
A p p r o x P - V a lu e f o r M i x t u r e s :
5 1 .0 0 0 0
4 9 .4 5 3 6
5 .0 0 0 0
0 .6 2 4 0
0 .3 7 6 0
N u m b e r o f ru ns u p o r d o w n :
E x p e c te d n u m b e r o f ru n s :
L o n g e s t ru n u p o r d o w n :
A p p r o x P - V a lu e f o r T r e n d s :
A p p r o x P - V a lu e f o r O s c i lla ti o n :
6 0 .0 0 0 0
6 4 .3 3 3 3
4 .0 0 0 0
0 .1 4 6 1
0 .8 5 3 9
Figure 3: Run Chart for Cycle Time data
P r o c e s s C a p a b ility A n a ly s is fo r C o m p la in t C lo s u r e T im e ( D a y s )
USL
P r o c e s s D a ta
7 .0 0 0 0
USL
*
T a rg e t
LSL
*
4 1 .1 7 5 3
Mean
S a m p le N
S tD e v ( O v e r a ll)
97
4 2 .5 9 2 4
O v e r a ll C a p a b ility
*
Pp
PPU
- 0 .2 7
PPL
*
P pk
- 0 .2 7
C pm
*
-1 0 0
-5 0
0
50
O b s e r v e d P e r fo r m a n c e
S ig m a L e v e l = 0 .4 0
100
150
200
250
E x p e c te d P e r fo r m a n c e
P PM < LSL
*
P P M < LS L
*
PPM > USL
8 6 5 9 7 9 .3 8
8 6 5 9 7 9 .3 8
P P M > US L
7 8 8 8 3 3 .2 6
P P M T o ta l
7 8 8 8 3 3 .2 6
P P M T o ta l
Figure 4: Sigma Level Calculation for Cycle Time
The initial data of customer complaint resolution time for 97 complaints does not follow
Normal distribution and the initial Sigma Level is 0.40.
4. Analysis Phase
At this phase of the study, it is essential to find out the main causes responsible for the
high cycle time for closure of complaints in the industry. A brainstorming session was
conducted by involving a cross functional team, which resulted causes for the high cycle
time for closures at different levels (L0, L1 and L2) as identified in the tree diagram. The
figure 5 will present the tree diagram of causes.
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Figure 5: Causes at Different Level for High Cycle Time
A level 1 analysis was conducted to identify which function is responsible for high cycle
time. A one-way ANOVA was conducted between Y (cycle time) and X (Company,
Supplier and Customer). The result of the analysis is presented in Table 3 and 4 below.
Table- 3: One-way ANOVA: Closure Time versus Responsible Function
Source of
Variation
Functions
Error
Total
d.f.
2
94
96
Sum of
Squares
33199
140051
173250
Mean Sum
of Squares
16599
1490
F Ratio
P Value
11.14
0.000
Table – 4: Closure Time Summary versus Functions
Function
Customer
Supplier
Company
N
17
44
36
Mean
77.94
40.75
24.33
s.d.
43.17
39.38
35.27
Time taken to close a customer complaint is significantly different for responsible
Functions. Whenever customer is responsible for action (T3), to provide shut down for
corrective action, those complaints have taken more time. The break-up of total cycle
time (T5) with respect to T1, T2, T3 & T4 was compiled to find out variation of which of
these sub time elements significantly affect T5. This analysis was conducted with the
help of regression analysis (Draper and Smith, 1998). The no of days taken to rectify (T4)
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is 0 for all complaints. From the analysis it is clear that Identification of Action by
Company (T1) & Arranging for Spare (T2) by Company/Supplier is significant. The
results of regression analysis are given below. The regression model as identified:
T5 = 3.33 + 0.918 T1 + 0.926 T2 + 0.924 T3
s = 8.886
R-Sq = 83.3% R-Sq (adj) = 82.1%
(1)
Table – 5 : Table of Coefficients for Regression Analysis
Predictor
Coefficient
Constant
T1
T2
T3
3.328
0.917
0.926
0.924
SE of
Coefficient
1.760
0.149
0.070
0.129
T Statistic
p Value
1.89
6.13
13.20
7.11
0.065
0.000
0.000
0.000
Table- 6: ANOVA of Regression Analysis
Source of
Variation
Regression
Residual
Error
Total
d.f.
3
43
Sum of
Squares
16935.3
3395.5
46
20330.8
Mean Sum
of Squares
5645.1
79
F Ratio
p Value
71.49
0.000
It was decided to find out why Identification of Action by Company (T1) & Arranging
for Spare (T2) by Company/Supplier is high. Since, most of the input factors are nominal
in the data, decision tree-based methods are very useful. A decision tree recursively
partitions the input spaces of a data set into mutually exclusive regions, each of which is
assigned a label. It is a structure consisting of internal and external nodes connected by
branches. An internal node is a decision-making unit that evaluates a decision function to
determine which child node to visit next. In contrast an external node also known as leaf
or terminal node has no child node and is associated with a level or value that
characterizes the given data. The response variable or the CTQ (Y) can be either
continuous or categorical. If it is continuous, then the platform fits means, and creates
splits, which most significantly separate the means by examining the sums of squares due
to the means differences. For continuous responses, the criterion is the Sum of Squares,
that is, the change in the sum of squared errors due to the split. The split is chosen to
maximize the difference in the responses between the two branches of the split. Since, in
this work, the CTQ is continuous and the causes are discrete, the regression tree analysis
is conducted for the CTQ “cycle time” (Brieman et al., 1984). The results of decision tree
analysis are shown in Figure 6.
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ALL ROWS
N = 50
Mean = 20.02
s.d. = 20.54
SPARE
(STD. REQUIRED,
SPARE NOT REQUIRED)
N = 44
MEAN = 16.75
S.D. = 17.43
SHUT SOWN
(REQUIRED)
N = 23
MEAN = 12.74
S.D. = 17.54
SPARE
(NON-STANDARD
REQUIRED)
N=6
MEAN = 44.00
S.D = 27.15
SHUT DOWN
(NOT REQUIRED)
N=21
MEAN=21.2
S.D. = 16.63
SPARE
(STANDARD)
N=9
MEAN = 12.22
S.D. = 12.55
SPARE
(NON-STANDARD)
N = 12
MEAN = 27.83
S.D. = 16.56
Figure 6: Regression Tree for High Cycle Time
The following root causes were finally selected for improvement
1. Actions to improve Closure time taken in Identification of Action to be taken and
2. Supply of Non standard Item (Spares) & Rectification by Company/Supplier
5. Improve and Control Phase
At this stage, the necessary corrective actions needed for the validated root causes are
identified (ref. Table 7). The possible solutions (ref. Table 8) are suggested for
implementation. The proposed actions/solutions are more towards overall system
improvement rather than taking action for the particular complaint. The steps of customer
complaint analysis and improvement process are modified to ensure actions to be
implemented are for system improvement covering the entire plant, processes and people
in a disciplined manner.
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Table 7: Root Cause and Proposed Solution
ROOT CAUSE
WHY
PROPOSED SOLUTION
“Identification of Action
to be taken” taking
more time (Refer
Regression Analysis)
Complaint is incomplete &
ambiguous
Collect information in a comprehensive
manner encompassing 5W1H elements.
Priority not given by internal
Process Owner
1. Provide system for raising alerts to Process
Owners.
2. Provide system for periodical review.
“Supply of non standard
Item (Spare)” is taking
more time (Refer
Regression Tree)
Long Manufacturing Cycle
Time
1. Initiate Failure Analysis and improve
Quality.
2. Establish minimum stock level of critical
items.
Incorporate Design Engineering changes for
bought out items.
Poor Policy of bought out
components
“Rectification of Items”
taking more time at
Company / Supplier
(Refer Regression Tree)
Customer requirement not
properly understood
Delay in dispatch
Review and revise Contracts Review Process.
Priority not given by internal
process owner / Supplier
Lack of accountability to attend the
complaint.
Information for shipping of rectified item is
not clear.
Table 8: Improvement Implemented for Proposed Solutions
Solution
Collect information in a comprehensive manner
encompassing 5W1H elements
1.Provide system for alerts to Process Owners
2.Provide system for periodical review
1.Initiate Failure Analysis and improve Quality
2.Establish minimum stock level of critical items
Incorporate Design Engineering changes for
bought out items.
Review and revise Contracts Review Process
Information for shipping of rectified item is not
clear
Lack of accountability to attend the complaint.
Activity Plan
1. Identify the information required
2. Prepare complaint reporting form
3. Finalize the reporting form in CFT
4. Pilot run the new form
5. Revise if needed and Implement
1. Display of delays in communication. Discuss with HOD.
2. Awareness training program for Process Owners.
1.Establish minimum stock level of critical items to implement
preventive actions
2. Identification of critical items & procures as per that.
3.Stocking of Critical Items
Design Changed note to be released for bought out items based
on customer requirement.
Implemented.
Collect information in a comprehensive manner encompassing
5W1H elements
Initiated mechanism to take into consideration regarding
compliant closure by revising terms and condition in purchase
order.
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The sigma levels for cycle time was calculated by using the data collected for a period of
one year after solution implementation (refer Figure 7 and Table 9). The level of
improvement is reported in Table below.
P roc es s C ap ab ility A n alys is for C los u re T im e (D ays )
LSL
P roc es s D ata
US L
7.0000
US L
*
T arget
0.0000
LS L
15.0833
Mean
60
S am ple N
S tD ev (O verall)
11.2321
O verall C apability
Pp
PPU
0.10
-0.24
PPL
0.45
P pk
-0.24
C pm
*
-2 0
-1 0
0
10
20
O bs erved P erform anc e
S ig m a L e ve l is 0 .8 2
30
40
50
60
E xpec ted P erform anc e
P P M < LS L
0.00
P P M < LS L
89655.10
P P M > US L
750000.00
P P M > US L
764134.68
P P M T otal
750000.00
P P M T otal
853789.77
Figure 7: Sigma Level Calculation for Cycle Time After Improvement
Table-9: Sigma Level – Before and After
CTQ
Sigma Level
Average
s.d.
Before
After
Before
After
Before
After
Complaint
Closure
0.40
0.82
41.51
15.08
42.83
11.23
Time
The following benefits were observed from the improvement, some tangible and some
intangible. The benefits observed are the following
Tangible Benefits
 Savings of Rs.1.070 Lakh per annum.
 Reduction in follow-up time for resolving the complaints from
Man-days / week to 1 man-day / week.
 Average time taken for closure reduced from 42 days to 21 days.
 Ratio of pending complaints reduced from 1.25 to 0.97.
Intangible Benefits
 Improved Customer Satisfaction.
 Quick Response to Customer Complaints.
 Improved Customer-Supplier relation.
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2
6. Discussions
This project has substantially benefitted the organization by reducing the follow-up time
for customer complaint resolution, lowering the closure time and decreasing the ratio of
pending complaints. The DMAIC approach of Six Sigma is a systematic methodology
utilizing training, measurement and data analysis tools to identify the root causes of high
cycle time and also eliminate these causes for improving the current process and thus
achieving better results. The qualitative and quantitative tools helped to identify,
prioritise and validate the possible root causes and their interrelationship to the CTQ’s
(y). Specifically, Six Sigma DMAIC methodology allows effective problem definition,
allows for use of data rather than trials and conjecture during critical decision making,
helps study team to think about the process, and provides an approach for managing real
improvements. This study demonstrates a project in which Six-Sigma is adopted to
improve the customer complaint resolution process of an organization. It describes how
the project was specified, how the tools were employed in the different phases to identify,
improve and control the correct sources of variation resulting into excessive cycle time.
Successful selection and implementation of corrective actions reduced the occurrence and
severity of the root causes and thus improved the process performance. This proves that
the DMAIC methodology of Six Sigma can be applied to any process, to improve the
process performance, where the root causes of process problems are not known. This
paper will be of interest to academic researchers and practical managers. It describes the
justification and selection of the project, how the tools and techniques of DMAIC
methodology were employed in the different phases and how the improvement actions
were implemented. This project methodology can be used generally to reduce cycle time
for any other transactional processes as well, which will help in reduce response time to
customer issue, improve customer-supplier relation and finally improve customer
satisfaction. However, they can modify the detailed contents and tools according to the
organizational condition, selection of variables for analysis & availability of data.
Additional benefits were observed in this work, including the employee participation in
Six Sigma project. The management was impressed with the analysis generated by the
Six Sigma project. To make the Six Sigma principle a culture of the organization, a
guideline was developed to ensure that problem solving of similar nature should be
tackled in the similar fashion.
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