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* Corresponding author.
E-mail address: kapilmittal007@gmail.com (K. Mittal)
© 2016 Growing Science Ltd. All rights reserved.
doi: 10.5267/j.msl.2016.10.001
Management Science Letters 6 (2016) 691–700
Contents lists available at GrowingScience
Management Science Letters
homepage: www.GrowingScience.com/msl
Energy paybacks of six-sigma: A case study of manufacturing industry in India
Prabhakar Kaushika, Kapil Mittalb* and Pardeep Ranac
aAssociate Professor, UIET, Maharshi Dayanand University, Rohtak, Haryana, India, 124001
bAssistant Professor, FET, Gurukul Kangri University, Haridwar, Uttarakhand, India, 249404
cAssistant Professor, Seth Jai Prakash Mukand Lal Institute of Technology, Radaur, Haryana, India, 135133
C H R O N I C L E A B S T R A C T
Article history:
Received September 5, 2016
Received in revised format
October 2, 2016
Accepted October 4, 2016
Available online
October 5, 2016
Industries, nowadays, are concerned about energy consumption and ever narrowing rules of
emissions by the governments. Therefore, a race to clean; green and less energy consuming
manufacturing is going on throughout the world. But in authors’ perspective, the major part of
energy exploitation lies in the production of a rejected product. Therefore alongside the use of
energy saving processes and machinery, industry should primarily look for rejection reduction.
This, apart from energy saving and profitability, will add to the moral responsibility of every
person toward nature. Here in this paper, authors describe a case study in which the increased
rejection rate of a part of cycle chain assembly is controlled by the application of Six Sigma.
Six Sigma, from many years has proved to be an ultimate solution when it comes to the
application part in manufacturing industries. It’s very generic and easily applicable
methodology has drawn tremendous positive results throughout the world. A financial gain of
INR 0.267 million was yielded by implying six-sigma approach. In a move toward energy
saving, the money saved by the project was used for green manufacturing to promote energy
conservation.
© 2016 Growing Science Ltd. All rights reserved.
Keywords:
Six sigma
Energy conservation
Industrial case study
1. Introduction
In today’s world, everybody is talking about energy saving, effective energy utilization, pollution
control, green manufacturing, health problems and its remedies, easy and effective ways of exercise,
traffic control, etc. One stop solution to all above concerns is riding a bicycle and it is therefore, more
and more people nowadays are turning towards bicycle. Cycling results in huge amount of health
benefits including cardiovascular health, improved bone density, muscular fitness, etc. (Oja et al.,
2011). Apart from the health benefits, increasing traffic congestion and air pollution in most of the
cities in world is also growing as a huge problem. That is why; the government of most of the developed
and developing countries are motivating people to adopt the safe, secure and environmental friendly
mode of transport: ‘Bicycle’. As a result, sales of bicycle across the globe are in inclining mode. Many
692
reports suggest that this affordable mode of transportation if properly absorbed in people’s lifestyle can
reduce the total Co2 emission of the world by 11%.
Apart from the direct benefits of cycling, the industries involved in bicycle manufacturing can do their
part in global energy saving by reducing the waste/rejection and implementing green manufacturing
practices. One such case study of an Indian bicycle manufacturer is described in this paper. The industry
was suffering from high rejection rate of one of the components of bicycle. It used the trusted Six-
Sigma DMAIC methodology to reduce the high rejection rate up to acceptable level. The literature
review of six-sigma has been accomplished by various authors in the past. Some have categorised the
papers in order of usage of methodology in different sectors (Srinivasan et al., 2016), type of
manufacturing industries (Biswas & Chowdhury, 2016), journal wise distribution and classification
(Sreedharan & Raju, 2012), classification of tools (Uluskan, 2012) etc. Apart from these there have
been some case studies showing the energy conservation in various forms using six-sigma (Falcon et
al., 2012; Kaushik et al., 2008; Kaushik & Khanduja, 2008). By applying Six-Sigma, not only the
industry was able to save the energy used in production of rejected items by decreasing the rejection
level, but moving forward, the annual amount saved by the project has been used for the work related
to implementing green manufacturing practices and reducing the energy consumption by industry. This
adds to the total energy saving claimed by the industry.
2. Case Study
Bicycle consists of limited number of parts as compared with automobiles. Transmission of power in a
bicycle is performed with the help of a Chain sprocket assembly. Hence chain can be considered as an
important part of bicycle. The main parts of the chain are bush, pin and outer covering. The case study
described in this paper was executed in a bicycle chain industry in India. It produces all parts to be
assembled in a chain. Pin is the key element of chain and is initially in the form of a rod and is cut in
lengths. The tolerance limit of pin length was 9.65±0.5 mm (Fig. 1).
The rejection rate of pin was 8.9 per cent, so there was a huge scope of increasing productivity by
eliminating faults innate in manufacturing process. Six Sigma DMAIC approach was used to resolve
pin rejection problem to attain the acceptable quality level. At first, project was presented to the
management and after their approval official registration was performed. This activity is necessary to
win consent from the higher authorities because unless they approve it can never be possible to devolve
the available resources.
Fig. 1. Main parts of a Bicycle Chain Assembly
1.OuterCover2.Innerattachment3.Pin4.Bush5.Roller
P. Kaushik et al. / Management
Science Letters 6 (2016)
693
The pin manufacturing process was looked over minutely and Six Sigma DMAIC approach has been
effectively applied to improve the standing C
pk
from 0.47 to 1.90. These phases are explained as
follows:
a) Define
In define phase, Process Flow Diagram was drawn for pin manufacturing process as shown in Fig. 2. This
diagram elaborates the different manufacturing steps during the production of pin. By drawing such diagram, it
is easier to focus the attention of the project team on the process that is responsible for the faulty parts.
Fig. 2. Process Map for Pin
b) Measure
In this phase a measure of extent of problem is generally made. There are various tools available for
that. First of all, a measurement system analysis (MSA) was performed which includes the Gauge R&R
(gauge repeatability and reproducibility) study. The experiment can be performed with the help of at
least two people. An operator from the production line and one from the inspection line were chosen.
Ten pieces of known measurement (pin) were given to them and they were asked to make a
measurement using micrometre which was being used to measure the dia of pin during production.
Following are the readings that were recorded (Table 1).
Table 1
Measurement System Analysis
Text
Seq
No.
Op.
Seq.
No.
Operator Trial Part
No.
Measurement
(mm)
Text Seq
No.
Op.
Seq.
No.
Operator Trial Part
No.
Measurement
(mm)
1 1 1 15 9.6 21 1 2 1 3 9.6
2 2 1 1 3 9.61 22 2 2 1 5 9.59
3 3 1 17 9.65 23 3 2 1 6 9.61
4 4 1 1 9 9.6 24 4 2 1 1 9.65
5 5 1 12 9.62 25 5 2 1 8 9.63
6 6 1 1 6 9.62 26 6 2 1 2 9.62
7 7 1 11 9.65 27 7 2 1 9 9.61
8 8 1 1 10 9.63 28 8 2 1 10 9.63
9 9 1 18 9.63 29 9 2 1 7 9.65
10 10 1 1 4 9.62 30 10 2 1 4 9.62
11 1 1 23 9.61 31 1 2 2 8 9.63
12 2 1 2 7 9.65 32 2 2 2 5 9.6
13 3 1 26 9.61 33 3 2 2 2 9.63
14 4 1 2 1 9.65 34 4 2 2 4 9.62
15 5 1 28 9.63 35 5 2 2 9 9.6
16 6 1 2 4 9.62 36 6 2 2 1 9.65
17 7 1 29 9.6 37 7 2 2 6 9.61
18 8 1 2 10 9.63 38 8 2 2 10 9.63
19 9 1 22 9.63 39 9 2 2 7 9.66
20 10 1 2 5 9.6 40 10 2 2 3 9.61
Raw
Material
Receipt
Cutting Hardening Polishing Inspection Assembly
694
Table 2
Result of Gauge R & R (Pin Length) for Micrometer
Study Var % Study Var
Source Std Dev (6* SD) (%SV)
Total Gage 0.0040365 0.024219 21.27
Repeatability 0.0040365 0.024219 21.27
Reproducibility 0 0 0
Part- to- Part 0.0185411 0.111247 97.71
Total Variation 0.0189754 0.113853 100
Data from Table 1 was entered in the Minitab software for performing the gauge r & r study using
ANOVA. Table 2 shows the results of study in which repeatability and reproducibility was noted out
to be 21.27 % and 0.00 %. This value is certainly less than 30 %, showing that Micrometer in use was
accurate.
Histogram
The histogram (Fig. 3) was also drawn for checking the trend of the rejected parts (100 samples size).
It clearly showed that data was not around the mean line and was away from target (9.65mm) value.
Also, most of the parts being produced were undersized.
Process Capability Analysis (Cpk)
Minitab software was again used to draw a Cpk curve for pin length as shown in figure 4. Cpk is a
measure of the capability of the process to produce acceptable parts. Value less than 1 show that there
is great need to rectify the process and increase the Cpk value. Also, Z-bench σ value was found to be
1.35 (Fig. 4) and present PPM was found at 89095.91, which was bizarre.
9.709.689.669.649.629.609.58
LSL Target US L
Process D ata
Sam ple N 100
LSL 9.60000
Target 9.65000
US L 9.70000
Sam ple M ean 9. 63780
Pi n Length (mm)
Fig. 3. Histogram for Pin Length before Implementing DMAIC Methodology
P. Kaushik et al. / Management Science Letters 6 (2016)
695
9.709.689.669.649.629.609.58
LSL Target USL
Process Data
Sample N 100
StD ev (Within) 0.02677
StD ev (O v erall) 0.02599
LSL 9.60000
Target 9.65000
US L 9.70000
Sam ple Mea n 9.63780
Po tentia l (Within) C apa bility
CCpk 0.62
Z.Bench 1. 35
Z.LSL 1.41
Z.U SL 2.32
Cpk 0.47
Exp. Within Performance
PP M < LSL 79008.39
PP M > US L 10087.52
PP M Total 89095.91
Process Capability Analysis for Pin Length
Fig. 4. Cpk Analysis of Pin Rejection before Implementing DMAIC Methodology
c) Analyse
The main contribution of this phase is to find out the root cause of the rejection. In this phase, suspected
causes of rejection were listed by thoughtful study of gathered data. Different statistical tools were tried
for the analysis. Their explanation is as follows-
Fish-bone Diagram
Various brainstorming sessions were performed which includes different members from various
sections of the industry. Thorough study of the possible causes resulted in a list of causes related to
different aspects of 4 M’s. These are depicted in Fig. 5 as fishbone diagram.
Fig. 5. Fishbone Diagram
Hypotheses Testing
After detailed discussions three suspected source of variations were shortlisted for further investigation.
Hypotheses were set and its testing was performed with all three suspected source of variations using
2 sample T test. The sample size was kept at 50 for all assessments. In first case, assessment was
S kill Le ve l o f O p e ra tor
Position of
C entre P unc h
Pin Feeder
Regrinding
of Cutter
Inco n s iste nc y in
selecting the point
of checking
Machine Man
Material Method
Pin
Rejection
Instr um e nt
Calibration
Inconsistent view
of the scale
Grade
696
performed for operator skill (unskilled and skilled). In second case, assessment was performed for re-
grinding of cutter (36 hrs & 24 hrs). In third case, assessment was performed for pin feeder device
(existing & improved).
First Case: Assessment for Operator Skill
Sample 1: Unskilled operator
Sample 2: Skilled operator
Operator skill assessment displayed that p-value for pin length is greater than the 0.05 (confidence
level= 95%). Hence this cannot be a prime cause of rejection.
Second Case: Assessment for Re-Grinding of Cutter
Sample 1: Re-grinding after 24 hrs
Sample 2: Re-grinding after 36 hrs
Re-grinding assessment displayed that p-value for pin length is less than the 0.05 (confidence level=
95%). Hence this cannot be a prime cause of rejection.
Two-Sample T-Test and CI: Pin Length, Operator
Two-sample T for Pin Length
Operator N Mean StDev SE Mean
Operator 1 50 9.62640 0.00802 0.0011
Operator 2 50 9.6276 0.0102 0.0014
Difference = mu (Operator 1) - mu (Operator 2)
Estimate for difference: -0.001200
95% CI for difference: (-0.004845, 0.002445)
T- Test of di ff eren ce = 0 (vs not =) : T -Valu e = -
0
.65
P-Value = 0.515 DF = 98
Bo th use P oo led St De v = 0.00 92
Two-Sample T-Test and CI: Pin Length, Re-Grinding
Two-sample T for Pin Length
Re-Grinding N Mean StDev SE Mean
Aft er 24h r 50 9. 6468 0. 0113 0.0 016
Aft er 36h r 50 9. 6182 0. 0140 0.0 020
Difference = mu (Re-Grinding After 24 hr) - mu (Re-
Grinding After 36 hr)
Estimate for difference: 0.028600
95% CI for difference: (0.023554, 0.033646)
T- Test of di ffere n ce = 0 (vs not =): T-V al ue = 11. 2 5
P-Value = 0.000, DF = 94
Both use Pooled StDev = 0.0161
P. Kaushik et al. / Management Science Letters 6 (2016)
697
Third Case: Assessment for Pin Feeder Mechanism (Existing & Improved)
Sample 1: Improved pin feeder device
Sample 2: Existing pin feeder device
Pin feeder device assessment displayed that p-value for pin length is less than the 0.05 (confidence
level= 95%). Hence this cannot be a prime cause of rejection
d) Improve
After finding out the root causes associated with the process now it is the time to find out the optimum
working parameters. For this, a tool ‘Design of Experiments’ was chosen. This tool helps us to design
the nature and combinations of different parameters during experimentation. There were two factors
and two levels available so 2×2=4 combinations could be tried to optimize the value of the parameters;
regrinding of cutter and pin feed mechanism. Table 3 displays the existing and proposed working
parameters of root causes for pin length variation and table 4 depicts readings of pin length at available
combinations of working parameters.
Table 3
Vital Causes of Pin Length Variation
Factors Low Level High Level
Re-grinding of Cutter 36 hrs 24 hrs
Pin Feed Device Existing Improved
Table 4
Readings (Pin Length) at Different Combinations
S.No. Re-grinding Pin Feeder Pin Length (mm)
1 After 36 hrs Existing 9.61
2 After 24 hrs Improved 9.65
3 After 36 hrs Improved 9.63
4 After 24 hrs Existing 9.63
Two-Sample T-Test and CI: Pin Length, Pin Feed
Two-sample T for Pin Length
Pin Feed N Mean StDev SE Mean
Pin Feed New 50 9.64740 0.00876 0.0012
Pin Feed Old 50 9.6180 0.0114 0.0016
Difference = mu (Pin Feed New) - mu (Pin Feed Old)
Estimate for difference: 0.029400
95% CI for difference: (0.025358, 0.033442)
T-Test of difference = 0 (vs not =): T-
V
alue = 14.44
P-Value = 0.000 DF = 98
Both use Pooled StDev = 0.0102
698
Mean of Pin Length
After 24 hrAfter 36 hr
9.640
9.635
9.630
9.625
9.620
9.615
NewOld
Re-Grinding Pin Feeder
Main Effect Plot for Pin Length
Fig. 6. Main Effect Graph for Pin Length
To concrete our findings, Main effect graph and interaction graph were. The Main Effect graph (figure
6) suggested that both regrinding and pin feeder mechanism were the prime factors for high rejection
rate of pin. Interactions plot (Fig. 7) shows that there is no interaction present between the factors which
mean the value of one factor does not affect the value of another or there is no conflict of interest
between the two.
Pin Feeder
Mea n
NewOld
9.65
9.64
9.63
9.62
9.61
9.60
Re-Grin ding
After 36 hr
After 24 hr
Inter acti on Plot for P in Le ngth
Fig. 7. Interactions Plot for Pin Length
e) Control
In this last phase of six-sigma, feasibility and monitoring of the implemented measures is checked. For
this, X bar/R Control Charts (Figure 8) were plotted (sample size-100) to envisage the occurrence of
different causes of variation and for making sure that the process endures in an established optimized
path.
P. Kaushik et al. / Management Science Letters 6 (2016)
699
3. Results and Discussions
When different similar industries were consulted, Pin length variation rejection is found to be a
prevailing problem. The causes of high rejection rate of pin were found to be pin feed mechanism and
regrinding of cutter. After implementing, documenting and freezing all the proposed measure, a great
amount of improvement in terms of rejection PPM was observed. The PPM which earlier was recorded
to be at 89095.91 now has been improved to 0.01 PPM which is a great achievement. Also, the z bench
sigma level is improved to 5.58 (figure 9), corresponding to a monetary gain of INR 2.67 lakhs
(Appendix – A) and this is a substantial amount for any organization.
Sample
Sample Mea n
2018161412108642
9.660
9.655
9.650
9.645
9.640
_
_
X=9.65
UC L=9.66301
LCL=9.63699
Sample
Sample Range
2018161412108642
0.048
0.036
0.024
0.012
0.000
_
R=0.02256
UC L=0.04771
LC L= 0
Xbar/R Chart for Pin Length
9.709.689.669.649.629.60
LSL T arget USL
Process D ata
Sample N 100
StDev (Within) 0.00878
StDe v (O v erall) 0.00997
LSL 9.60000
Target 9.65000
USL 9.70000
Sample M ean 9.65000
Potential (Within) Capability
CCpk 1.90
Z.Bench 5.58
Z.LSL 5.70
Z.US L 5.70
Cpk 1.90
Exp. Within Performance
PPM < LSL 0.01
PPM > USL 0.01
PPM Total 0.01
Proce ss Capabili ty Analysis for Pin Length Afte r Improvement
Fig. 8. X bar/R Graph for Pin Length Post Measures Fig. 9. Cpk Analysis for Pin Length Data Post Measures
The money saved by the project was used for bringing more energy conservation in the industry. Following
measures were taken some of which have been implemented and some are in process.
Table 5
Description and Status of Various Measures for Energy Conservation
Descri
p
tion of Measure Status
Exclusion of unwanted manufacturin
g
p
rocess Com
p
lete
d
Plant layout reorganization Complete
d
Optimization of refri
g
erator and oven temperature. Complete
d
Old machiner
y
exchan
g
e In Process
C
y
cle time optimization Complete
d
Effluent treatment plant Complete
d
Overtime reduction Complete
d
Central air conditioning In Process
Da
y
li
g
ht savin
g
s in summer months Complete
d
Ener
gy
conservation club to motivate and aware the emplo
y
ees Complete
d
By implementing above measures industry was able to reduce its monthly electricity usage by 11%,
which is a great achievement. It not only adds to the global energy conservation but also the profitability
of the industry increases. This act of the industry also brought the energy awareness among employees
and motivated them to save energy at their homes also. Study demonstrates that organizations using
similar quality management approaches perform better in almost every parameter including yield on
sales, investment, improved organizational culture, personnel development of employee, brand value,
employee satisfaction, effective utilization etc. These can be treated as intangible benefits of Six-Sigma
implementation. This case study also reveals and encourage small industries to implement similar
quality management techniques for productivity improvement because results amply put to rest all the
fears that management techniques like Six Sigma, MRP, ERP, JIT etc. are the domination of only the
large industries that can spend plentifully.
700
4. Conclusion
In this paper a case study of an Indian bicycle chain manufacturing unit is explained with a prime
motive of energy conservation. The case study started with the rejection reduction of one of the part of
cycle chain. Six-Sigma DMAIC methodology was used for finding out the root causes of the rejection
and finally a rejection PPM of 0.1 was achieved after the complete implementation of methodology.
The monetary savings made after the project were used for energy conservation purposes in the
industry. Various measures were proposed by the team many of which are implemented and some are
in process. Successful implementation of the measures brought down the monthly electricity usage of
industry by 11% which is a great achievement. This can be treated as a contribution of industry in
global energy conservation. In author perspective, such projects should be started more frequently in
industries. This not only improves the organizational culture and profitability but also bring the energy
saving awareness among the employees.
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Appendix A
PPM before project initiation : 89095.91
PPM after project completion : 0.01
Cost of one pin : Rs. 0.05
PPM reduction achieved : 89095.90 PPM
Monthly production : 5,000,000 pcs
Saving/month : 89095.90*5,000,000/10
6
= 445479.5 pcs
Cost saving/annum : 445479.5*0.05*12 = INR 267287.7 = 0.267 million
© 2016 by the authors; licensee Growing Science, Canada. This is an open access
article distributed under the terms and conditions of the Creative Commons Attribution
(CC-BY) license (http://creativecommons.org/licenses/by/4.0/).
