Lean Manufacturing Case 01

Published on July 2016 | Categories: Documents | Downloads: 51 | Comments: 0 | Views: 341
of 13
Download PDF   Embed   Report

Lean Manufacturing Case 01

Comments

Content

Journal of Manufacturing Technology Management
Lean manufacturing performance in Indian manufacturing plants
Manimay Ghosh

Article information:

Downloaded by Institut Teknologi Sepuluh Nopember At 07:30 08 May 2016 (PT)

To cite this document:
Manimay Ghosh, (2012),"Lean manufacturing performance in Indian manufacturing plants", Journal of
Manufacturing Technology Management, Vol. 24 Iss 1 pp. 113 - 122
Permanent link to this document:
http://dx.doi.org/10.1108/17410381311287517
Downloaded on: 08 May 2016, At: 07:30 (PT)
References: this document contains references to 34 other documents.
To copy this document: [email protected]
The fulltext of this document has been downloaded 3662 times since 2012*

Users who downloaded this article also downloaded:
(2013),"A methodology for effective implementation of lean strategies and its performance evaluation in
manufacturing organizations", Business Process Management Journal, Vol. 19 Iss 1 pp. 169-196 http://
dx.doi.org/10.1108/14637151311294912
(2011),"The impact of lean operations on the Chinese manufacturing performance",
Journal of Manufacturing Technology Management, Vol. 22 Iss 2 pp. 223-240 http://
dx.doi.org/10.1108/17410381111102234
(2008),"Lean manufacturing performance in China: assessment of 65 manufacturing
plants", Journal of Manufacturing Technology Management, Vol. 19 Iss 2 pp. 217-234 http://
dx.doi.org/10.1108/17410380810847927

Access to this document was granted through an Emerald subscription provided by emerald-srm:593785 []

For Authors
If you would like to write for this, or any other Emerald publication, then please use our Emerald for
Authors service information about how to choose which publication to write for and submission guidelines
are available for all. Please visit www.emeraldinsight.com/authors for more information.

About Emerald www.emeraldinsight.com
Emerald is a global publisher linking research and practice to the benefit of society. The company
manages a portfolio of more than 290 journals and over 2,350 books and book series volumes, as well as
providing an extensive range of online products and additional customer resources and services.
Emerald is both COUNTER 4 and TRANSFER compliant. The organization is a partner of the Committee
on Publication Ethics (COPE) and also works with Portico and the LOCKSS initiative for digital archive
preservation.
*Related content and download information correct at time of download.

The current issue and full text archive of this journal is available at
www.emeraldinsight.com/1741-038X.htm

Lean manufacturing performance
in Indian manufacturing plants

Lean
manufacturing
performance

Manimay Ghosh
Institute of Management Technology (IMT), Nagpur, India

Downloaded by Institut Teknologi Sepuluh Nopember At 07:30 08 May 2016 (PT)

Abstract

113
Received 11 April 2011

Purpose – The purpose of this paper is to examine the current state of lean adoption in Indian Revised 22 September 2011
2 December 2011
manufacturing plants and its impact on operational performance.
Accepted 3 January 2012
Design/methodology/approach – A survey questionnaire was developed and adapted based on
work done in US industries. The survey questionnaire was sent to 400 firms in four geographic regions
in India. In total, 79 usable responses were received for the study.
Findings – Since lean manufacturing is a multi-dimensional construct, the results demonstrate that
approximately 80 percent of the respondents have implemented many dimensions of lean – focus on
customer needs, pull system, setup time reduction, total productive maintenance, supplier
performance, statistical process control, and cross-departmental problem solving. The operational
metrics have improved on all accounts: high productivity, reduced lead time, improved first-pass
correct output, reduced inventory and space requirement. Interestingly, respondents have indicated
that first-pass correct output, reduced manufacturing lead time, and increased productivity are the
three main drivers of lean implementation.
Research limitations/implications – Since the sample size is not very large, the results need to be
considered with caution.
Originality/value – Lean manufacturing is a very popular concept in the developed world and in
some countries in the developing world as well. Yet, little is known about its current status in India,
except for a few case studies. The study investigates the degree of lean production implementation in
Indian manufacturing plants and its impact on operational metrics. The study also indicates the
relationship between lean dimensions/practices and operational outcomes.
Keywords India, Manufacturing industries, Lean production, Lean manufacturing, Manufacturing,
Manufacturing performance, Manufacturing operations
Paper type Research paper

1. Introduction
Lean manufacturing, often coined as Toyota production system (TPS) in academic
literature, started in Toyota Motor Manufacturing Company after the Second World
War when most Japanese organizations including Toyota were confronted with the
challenge of managing production facilities with limited resources (Liker, 1998;
Pavnaskar et al., 2003). This challenge motivated Toyota managers to develop various
elements of TPS aimed at reducing waste. Thus, lean is about producing the same
output with lesser resources (men, material, space, and machinery). Today, it has helped
Toyota achieve the distinction of being the best car manufacturing company in the
world (Stewart and Raman, 2007). Application of lean is not limited to the automotive
sector only, but, it has also found acceptance in a wide range of manufacturing
industries operating under a unionized or a non-unionized environment in the US
(Shah and Ward, 2003) or elsewhere (Cua et al., 2001; Anand and Kodali, 2008), and
is being applied in big as well as small organizations (White et al., 1999).
This paper is organized as follows: Section 2 provides an overview of the
relevant literature and the motivation behind this research. Section 3 describes

Journal of Manufacturing Technology
Management
Vol. 24 No. 1, 2013
pp. 113-122
q Emerald Group Publishing Limited
1741-038X
DOI 10.1108/17410381311287517

JMTM
24,1

Downloaded by Institut Teknologi Sepuluh Nopember At 07:30 08 May 2016 (PT)

114

the research methodology. Section 4 deals with the results of the survey. Section 5
presents a discussion on the research findings and ends with a conclusion.
2. Literature review
Lean manufacturing is viewed by the scholarly community primarily at three levels.
At the first level, which is the philosophical level, lean is about eliminating “waste”
from the production system (Ohno, 1988; Shingo, 1989; Womack et al., 1990; Womack
and Jones, 1996) and yet be able to produce products of the highest quality that satisfies
the ultimate customers. As Shingo (1989) aptly remarked, 80 percent of lean is about
waste elimination and the balance about system. Waste, often called muda, in Japanese,
comprises seven types of common waste: over production, unnecessary motion, excess
inventory, excess transportation, rejections/rework, waiting, and over processing
(Cachon and Terwiesch, 2009). Apparently, elimination of these wastes looks simple
and straightforward, yet their identification is often difficult in most organizations.
At the second level, some scholars construed lean as a rule driven system (Spear and
Bowen, 1999). Spear and Bowen studied 40 plants over a four year period in the USA,
Europe, and Japan – some plants were operating according to lean and some not. They
concluded that Toyota uses three rules for designing production system and another
rule for systematic problem solving. Rule 1 states that all activities need to be specified
in terms of content, sequence, timing, and outcome. Rule 2 suggests that every supplier
and customer connection need to be direct and unambiguous. Rule 3 supports direct and
simple pathways for every product and service. Rule 4 advocates small improvements
done scientifically under the guidance of a teacher at the lowest possible level.
At the third level, lean is viewed as congregation of tools and techniques (Shah and
Ward, 2003; Pavnaskar et al., 2003; Li et al., 2005; Seth and Gupta, 2005; Hines et al., 1999;
Lasa et al., 2008; Basu, 2009) aimed at eliminating waste. Shah and Ward (2003)
investigated the application of 22 lean practices and categorized them into four
“bundles”, i.e. just-in-time (JIT), total quality management (TQM), total productive
management (TPM), and human resource management (HRM), in a variety of industries
in the US, and observed that 23 percent variation in operational performance attributed
to the use of lean bundles. They also observed a strong influence of plant size on lean
implementation and a less influence on plant age and unionization. Shah and Ward
(2007) in a subsequent paper argued that lean is a multi-dimensional construct
and developed ten distinct factors/dimensions to characterize lean production system.
Thus, the philosophical perspective of lean provides the highest level of abstraction;
the guiding rules providing the second level of abstraction, and, the tools and
techniques providing the least level of abstraction. The tools and techniques residing at
the lowest abstraction level are widely used for lean implementation in the industry
(Shah et al., 2008).
Investigation of the effect of lean was not just limited to the US. Scholars have tested
this popular concept in the developing world as well. Lawrence and Hottenstein (1995)
investigated the impact of JIT manufacturing on plant performance (quality, lead time,
productivity, and customer service) in 124 plants in Mexico. Cua et al. (2001) studied
163 plants in Germany, Italy, Japan, UK, and USA and observed higher level of
manufacturing performances (cost, quality, delivery, and flexibility) when TQM, JIT
and TPM were jointly implemented.

Downloaded by Institut Teknologi Sepuluh Nopember At 07:30 08 May 2016 (PT)

In the last decade or so, China has become the manufacturing or sourcing hub for
many global organizations primarily due to its low manufacturing costs. China’s
resurgence as a global economic power started in the late twentieth century and has
established itself as a fast growing economy in the world (Das, 2006). The focus on
efficient production to compete in the global market has triggered many Chinese firms to
implement lean. In fact, Taj (2007) reported that lean was introduced in first automotive
works (FAW), the first Chinese automotive plant, in 1977, even before its adoption in USA
and Europe. Taj observed the application of lean manufacturing in 65 manufacturing
plants in China, encompassing a variety of industries (electronics, telecommunication,
wireless, food/beverage, garment, pharmaceutical, etc.) and found significant benefits
achieved in material flow, on-time delivery of finished goods, defect rate, and scheduling.
In a subsequent paper, Taj and Morosan (2011) reported that lean operations practice
(human resources and supply chains) and product system design had a significant
and positive impact on three performance dimensions – flow, flexibility, and quality.
In comparison to China’s stellar economic performance, India’s post 1991 economic
performance showed discernible improvement. The average GDP growth rate between
1992 and 2002 was approximately 6 percent, which put India among the fast growing
developing countries in the 1990s (Ahluwalia, 2002). India’s GDP in 2010 stood at
$1.3 trillion with 8.5 percent growth rate. Eichengreen and Gupta (2010) reported that
share of industry in GDP grew rapidly between 1950 and 1965, modestly from the mid
1960s until early 1990s. Since post liberalization in 1991, the industry’s share stagnated
at 27 percent. On the contrary, service sector’s share in GDP grew from 30 percent in 1950
to 55 percent in 2007-2008. After liberalization, many foreign players in the automotive,
electronics and other sectors opened manufacturing facilities in India with better
operating systems, which exposed Indian manufacturing firms to fierce competition.
Witnessing intense competition, Indian firms did start implementing lean in the early
1990s, but the adoption process gained momentum very slowly due to various reasons.
Thus, the academic literature, reports application of lean manufacturing in many
countries across the world, yet little is known about its degree of adoption in India.
In fact, many organizations in India have started implementing it recently (Vinodh et al.,
2010). As a consequence, little is known about the current status of lean implementation
in India except some case studies in select companies (Dhandapani et al., 2004; Seth and
Gupta, 2005; Kumar et al., 2006; Singh and Sharma, 2009; Vinodh et al., 2010).
Upadhye et al. (2009) empirical work is a notable exception. Upadhye and others sought
the opinion of executives from 71 different organizations in India on different attributes
of lean manufacturing system, quality, inventory and maintenance. Their research
indicated that Indian executives are cognizant of the importance of lean on a firm’s
performance and big organizations are predominantly implementing lean. In a
subsequent paper, Upadhye et al. (2010) presented a model for the implementation of
lean. This paper, therefore, makes an attempt to report the degree of lean implementation
in Indian manufacturing plants primarily from the manufacturing sector. It also
investigates what set of lean management practices lead to improvement in operational
practices – productivity, lead time, first-pass correct output and the like.
3. Research methodology
Since lean manufacturing is a fairly new concept in India, the intent behind the survey
was to measure the current state of lean implementation in Indian manufacturing

Lean
manufacturing
performance
115

JMTM
24,1

116

plants located in the four regions (North, South, East, and West). Stratified sampling
was used for the survey, as automotive companies (automobiles and auto ancillaries),
the biggest users of lean system, are clustered in the Western, Southern, and Northern
regions of India. Additionally, simple random sampling was used for manufacturing
companies spanning a wide variety of sectors (e.g. metal products, metal processing
and power equipment). The survey was carried out between May and September 2010.
The total sample size used for the study was 79. Table I provides the distribution in
percent of the 79 plants classified by industry.

Downloaded by Institut Teknologi Sepuluh Nopember At 07:30 08 May 2016 (PT)

3.1 Measurement scale
The items used in the questionnaire were initially developed from the two empirical
studies of Shah and Ward (2007, 2003) on lean manufacturing in US. Seven questions
pertaining to seven distinct dimensions of a lean system were used – supplier feedback,
customer needs, pull system, setup times, total productive maintenance, statistical
process control (SPC), and cross-departmental problem solving. Some alterations were
made in the questionnaire design to suit the Indian context. A five-point Likert scale
was developed for each item for the lean practices using the following criteria:
(1) no implementation (0 percent);
(2) little implementation (around 25 percent);
(3) some implementation (around 50 percent);
(4) extensive implementation (around 75 percent); and
(5) complete implementation (100 percent).
Six questions were asked related to operational performance metrics – employee
productivity, first-pass correct output, cost of conversion, inventory, manufacturing
lead time, and space requirement (Shah and Ward, 2003). A five-point Likert scale was
developed for each of those six operational dimensions. The respondents were also
asked to rank the six operational dimensions that motivated them for lean
implementation. Additionally, questions related to size of the plant, age of the plant,
product type, year of implementation of lean were also asked.
3.2 Validation
The validation of the questionnaire was done by two very senior executives from major
corporations – one from steel and the other from an automobile sector with extensive
experience on lean implementation in India. The questionnaire was also pre-tested
by three academicians with an extensive experience in academia and industry.

Table I.
Responding industries
in the survey

Industry

%

Automobiles and automobile components
Machine tools and metal products
Electrical goods
Others (food, tobacco, textiles, printing)
Total

22
30
30
18
100

Note: n ¼ 79

Downloaded by Institut Teknologi Sepuluh Nopember At 07:30 08 May 2016 (PT)

3.3 Data collection and review
400 firms (small, medium, and big) were contacted by e-mail, and phone from various
industries and geographic locations over a five month period. The Kompass online
business directory (in.kompass.com) was used to source contact details of the firms
for this study. Additionally, government managed companies were contacted. Initially,
the author and his graduate students contacted the respondents by phone or e-mail.
The questionnaire (Microsoft document) along with an introductory letter indicating
the intent of the study was then e-mailed to every possible respondent. A web
link to the same questionnaire was also mailed to the respondents for convenience.
Follow-up phone calls were made after two-weeks to expedite the response. A total of
80 companies responded representing a response rate of 20 percent. Out of 80 responses
received, one was deleted from the data analysis as substantial data were missing.
Thus, 79 useful responses were used for data analysis.

Lean
manufacturing
performance
117

4. Results
4.1 Descriptive statistics
Prior to any analysis, the responses were checked for any missing data. Very few items
were found missing. The missing data were imputed using the mean of that variable
(Meyers et al., 2006). Descriptive statistics (mean and standard deviation) were then
calculated for the lean dimensions and the operational metrics used in the study. The
statistics were calculated based on five-point Likert scale (“1” being no implementation
and “5” being full implementation as mentioned before). For each variable, the
individual score of all firms were added up and the total score was divided by
the number of companies to arrive at the mean score. The standard deviation was also
calculated for each dimension. Table II presents the descriptive statistics.
The individual scores on each of the seven lean dimensions were summed up to derive
the total score for each firm. Since the maximum allowable points for each dimension
was five, the total score computed initially was out of 35. It was then extrapolated out
of 100. The highest score was 94, the lowest score recorded was 46, and the mean score

Lean dimensions
1. Supply performance
2. Focus on customer needs
3. Implementing pull system
4. Setup time reduction
5. Total productive maintenance
6. SPC
7. Cross-departmental problem solving
Operational metrics
Productivity
First-pass correct output
Conversion cost reduction
Inventory reduction
Lead time reduction
Space reduction
Note: n ¼ 79

Mean

SD

4.10
4.33
3.73
3.73
3.59
3.62
3.84

0.69
0.76
0.96
0.67
0.78
1.11
0.97

4.01
4.11
3.91
3.94
4.01
4.00

0.57
0.57
0.62
0.61
0.54
0.57

Table II.
Descriptive statistics:
lean dimensions and
operational metrics

JMTM
24,1

Downloaded by Institut Teknologi Sepuluh Nopember At 07:30 08 May 2016 (PT)

118

noted was 77. The results indicated that most companies surveyed had made significant
progress in terms of implementing lean. Table III summarizes the degree of lean
implementation. It is evident from the data that more than 80 percent of the firms have
implemented lean to a significant extent.
The six operational metrics were studied to check for improvement. A marked
improvement was observed (Table IV) in employee productivity, first-pass correct
output and manufacturing lead time reduction.
4.2 Ranking
The respondents were asked to rank (Table V) the operational performance parameters
that motivated them for lean implementation. It is important to note that first-pass
correct output was found to be the biggest motivator for implementing lean. After all,
defective output leads to unnecessary waste and delays in deliveries. Reduction of the
Points earned

Table III.
Degree of lean
implementation

90-100
80-89
70-79
60-69
50-59
40-49

8. Employee productivity
9. First-pass correct output
10. Costs of conversion
11. Inventory
12. Manufacturing lead time
13. Space requirement

8
28
27
12
2
2

10.12
35.44
34.18
15.19
2.53
2.53

Improvement (%)
19.14
13.10
11.29
10.51
11.31
10.67

"
"
#
#
#
#

Note: n ¼ 79

Operational performance parameters

Table V.
Ranking in order of
importance

%

Note: n ¼ 79

Operational performance

Table IV.
Operational
improvements achieved

No. of firms

First-pass correct output
Manufacturing lead time reduction
Productivity increase
Inventory reduction
Reduction in cost of conversion
Reduction in space requirement
Note: n ¼ 79

Rank
1
2
3
4
5
6

Downloaded by Institut Teknologi Sepuluh Nopember At 07:30 08 May 2016 (PT)

manufacturing lead time was ranked two. It makes perfect sense because organizations
need to be very responsive with the right quality product to satisfy the customer.
4.3 Plant size, plant age, and number of years of lean existence
In terms of plant size, 91 percent of the respondents were big (investment in plant and
machinery exceeded INR 100 Million) in size and the balance 9 percent comprised small
and medium enterprises.
With respect to plant age, it was noted that 39 percent of the plants that had
implemented lean were 31 years old or above; 41 percent were between the ages of 16 and
30 years, and 20 percent of the plants were between one and 15 years of age. So age of the
plant did not appear to be a major deterrent to lean implementation.
In terms of years of lean system existence, the study indicated that the oldest adopter
adopted lean 18 years back (implemented in 1993), the newest adopter implemented
lean a year back (2010). The average number of years lean has been adopted in the
sample studied was found to be 7.6 years.

Lean
manufacturing
performance
119

4.4 Lean dimensions affecting lean operational metrics
Multiple regression analysis (Table VI) was performed to examine the impact of the
seven lean manufacturing practices (independent variables) on the three most important
performance measures (dependent variables) productivity, manufacturing lead time,
and first-pass correct output, as perceived by the respondents. Before running the
regression analysis, linearity of each independent variable with respect to output
variable was checked using bivariate scatter plot. The plot showed linearity. The
normality of the data was also ascertained using Jarque-Bera test ( p , 0.000). To check
homoscedasticity, the residuals plot was drawn, which showed no obvious pattern.
The data represents the regression coefficients for each lean manufacturing
practice. In the first model for productivity, use of pull system was found to be positive
predictor ( p , 0.10) and TPM was found to be a negative predictor ( p , 0.10). The
negative impact of TPM on productivity was found surprising. TPM is a maintenance
program to maintain machines and equipment in good working condition. Ideally,
implementation of TPM leads to higher plant productivity because of higher
availability of machines for production. A closer investigation of the data set revealed

Constant
Supplier feedback
Customer need
Pull system
Setup time
TPM
SPC
Cross dept problem solving
R2
Adj. R 2
F-value

Productivity

Mfg. lead time

First-pass correct output

2.42 * * *
0.10
0.12
0.13 *
0.10
20.17 *
0.03
0.06
0.20
0.13
2.61 * *

2.30 * *
0.13
0.16 *
0.09 *
0.25 * * *
2 0.15 *
2 0.02
2 0.04
0.24
0.17
3.23 * * *

2.08 * * *
0.23 * *
0.17 * *
20.05
20.09
0.12
0.12 *
0.00
0.32
0.25
4.67 * * *

Notes: Significant at: *p , 0.1, * *p , 0.05 and * * *p , 0.01; n ¼ 79

Table VI.
Multiple regression
models for the three key
drivers of lean system

JMTM
24,1

Downloaded by Institut Teknologi Sepuluh Nopember At 07:30 08 May 2016 (PT)

120

that 39 percent of the firms that participated in the study were 30 years or older,
41 percent of the firms were 16 years or older. Additionally, the survey data (Table II)
revealed that the average mean score recorded for TPM was 3.59 (lowest among all
lean dimensions studied) indicating approximately 50 percent or slightly higher levels
of TPM implementation. When probed further, senior employees from different firms
indicated inadequate understanding of some TPM concepts and its deployment. Thus,
moderate levels of TPM implementation coupled with low reliability of old machines
possibly contributed little to improve productivity. The first model explained
20 percent (R 2) variance in the outcome with an associated significance at p , 0.05. In
the second model for manufacturing lead time, focus on customer needs ( p , 0.10) and
implementation of pull system ( p , 0.10) were found to be positive predictors while
setup time reduction ( p , 0.01) was found to be significant positive predictor of
manufacturing lead time reduction. The coefficient of TPM was found to be negative
which was again surprising; the possible reasons could be same as those explained for
the effect of TPM on productivity. The overall model was significant and explained
24 percent (R 2) of the variation in outcome at p , 0.01. In the third model for first-pass
correct output, supplier feedback ( p , 0.05) and customer needs ( p , 0.05) were
statistically significant positive predictors of first-pass correct output while SPC
( p , 0.10) was found to be moderately significant positive predictor. The model
explained 32 percent (R 2) of the variation in outcome at p , 0.01.
In sum, Table VI seems to indicate that different lean practices may affect specific
lean performance measures. Thus, if a plant desires high first-pass correct output,
focusing on suppliers, understanding customer needs and stressing on SPC might be
the approach. In a similar vein, reduction in manufacturing lead time necessitates
concentrating on understanding customer needs, introducing pull system, focusing on
setup time reduction. Contrary to the popular belief, the coefficient of TPM was found
to be negative with respect to productivity and manufacturing lead time. This result is
a bit surprising given the fact that regular maintenance practices prevent machines
from preemptive outages thus reducing throughput time. The benefits of TPM in
improving productivity and manufacturing lead time could have been achieved had the
responding firms implemented TPM in its entirety. In fact, many firms that
participated in the study fell short on this account. Similarly, a firm interested in
improving productivity should be focusing on introducing pull system.
5. Discussion and conclusion
The academic and the industrial community in the Western world have long
acknowledged lean manufacturing or TPS as the gold standard for operational
management. Its success has been so stunning that its adoption has not remained
restricted to manufacturing, but, has propagated to the non-manufacturing arena as well.
Yet, its adoption in India in the industrial sector has begun very recently. This study
makes an attempt to find the degree of lean implementation in India and the different
lean manufacturing practices that have been embraced in automotive and other
manufacturing plants in India. The study suggests that many Indian plants are at
an advanced level of lean implementation and have achieved superior operational
performance by implementing lean. The findings of this study in terms of improvement
in operational metrics seem to be in agreement with the findings of other scholars
(White et al., 1999; Detty and Yingling, 2000; Cua et al., 2001; Shah and Ward, 2003).

Downloaded by Institut Teknologi Sepuluh Nopember At 07:30 08 May 2016 (PT)

Out of the six operations metrics considered for the study, first-pass correct output,
reduction in manufacturing lead time and productivity increase are found to be the three
key drivers for lean implementation. However, as the sample size for this study was not
large enough, generalizations from this study to the population need to be made with
caution.
References
Ahluwalia, M.S. (2002), “Economic reforms in India since 1991: has gradualism worked?”, Journal
of Economic Perspectives, Vol. 16 No. 3, pp. 67-88.
Anand, G. and Kodali, R. (2008), “Selection of lean manufacturing systems using the
PROMETHEE”, Journal of Modelling in Management, Vol. 3 No. 1, pp. 40-70.
Basu, R. (2009), Implementing Six Sigma and Lean: A Practical Guide to Tools and Techniques,
Butterworth-Heinemann, Oxford.
Cachon, G. and Terwiesch, C. (2009), Matching Supply and Demand, International Edition,
McGraw-Hill, Singapore.
Cua, K.O., McKone, K.E. and Schroeder, R.G. (2001), “Relationship between implementation
of TQM, JIT, and TPM and manufacturing performance”, Journal of Operations
Management, Vol. 19, pp. 675-94.
Das, D.K. (2006), “The Chinese and Indian economies: comparing the comparables”, Journal of
Chinese Economic and Business Studies, Vol. 4 No. 1, pp. 77-89.
Detty, R.B. and Yingling, J.C. (2000), “Quantifying benefits of conversion to lean manufacturing
with discrete event simulation: a case study”, International Journal of Production Research,
Vol. 38 No. 2, pp. 429-45.
Dhandapani, V., Potter, A. and Naim, M. (2004), “Applying lean thinking: a case study of an Indian steel
plant”, International Journal of Logistics: Research and Applications, Vol. 7 No. 3, pp. 239-50.
Eichengreen, B. and Gupta, P. (2010), “The service sector as India’s road to economic growth”,
Working Paper No. 249, Indian Council for Research on International Economic Relations,
New Delhi, April.
Hines, P., Rich, N. and Esain, A. (1999), “Value stream mapping – a distribution industry
application”, Benchmarking International Journal, Vol. 6 No. 1, pp. 60-77.
Kumar, M., Antony, J., Singh, R.K., Tiwari, M.K. and Perry, D. (2006), “Implementing the lean
sigma framework in an Indian SME: a case study”, Production Planning and Control,
Vol. 17, No. 4, pp. 407-23.
Lasa, I.S., Laburu, C.O. and Vila, R.C. (2008), “An evaluation of the value stream mapping tool”,
Business Process Management, Vol. 14 No. 1, pp. 39-52.
Lawrence, J.J. and Hottenstein, M.P. (1995), “The relationship between JIT manufacturing and
performance in Mexican plants affiliated with US companies”, Journal of Operations
Management, Vol. 13, pp. 3-18.
Li, S., Subba Rao, S., Ragu-Nathan, T.S. and Ragu-Nathan, B. (2005), “Development and
validation of a measurement instrument for studying supply chain management
practices”, Journal of Operations Management, Vol. 23 No. 6, pp. 618-41.
Liker, J.K. (1998), Becoming Lean: Inside Stories of US Manufacturers, Productivity Press,
Portland, OR.
Meyers, L.S., Gamst, G. and Guarino, A.J. (2006), Applied Multivariate Research, Sage,
Thousand Oaks, CA.
Ohno, T. (1988), Toyota Production System, Productivity Press, Cambridge, MA.

Lean
manufacturing
performance
121

JMTM
24,1

Downloaded by Institut Teknologi Sepuluh Nopember At 07:30 08 May 2016 (PT)

122

Pavnaskar, S.J., Gershenson, J.K. and Jambekar, A.B. (2003), “Classification scheme for lean
manufacturing tools”, International Journal of Production Research, Vol. 41 No. 13, pp. 3075-90.
Seth, D. and Gupta, V. (2005), “Application of value stream mapping for lean operations and
cycle time reduction”, Production Planning and Control, Vol. 16 No. 1, pp. 44-59.
Shah, R. and Ward, P.T. (2003), “Lean manufacturing: context, practice bundles, and
performance”, Journal of Operations Management, Vol. 21 No. 2, pp. 129-49.
Shah, R. and Ward, P.T. (2007), “Defining and developing measures of lean production”, Journal
of Operations Management, Vol. 25 No. 4, pp. 785-805.
Shah, R., Goldstein, S.M., Unger, B.T. and Henry, T.D. (2008), “Explaining anomalous high
performance in a healthcare supply chain”, Decision Sciences, Vol. 39 No. 4, pp. 759-89.
Shingo, S. (1989), A Study of the Toyota Production System from an Industrial Engineering
Viewpoint, Productivity Press, Portland, OR.
Singh, B. and Sharma, S.K. (2009), “Value stream mapping as a versatile tool for lean
implementation: an Indian case study of a manufacturing firm”, Measuring Business
Excellence, Vol. 13 No. 3, pp. 58-68.
Spear, S.J. and Bowen, H.K. (1999), “Decoding the DNA of the Toyota production system”,
Harvard Business Review, Vol. 77 No. 5, pp. 97-106.
Stewart, T.A. and Raman, A.P. (2007), “Lessons from Toyota’s long drive”, Harvard Business
Review, Vol. 85 Nos 7/8, pp. 74-83.
Taj, S. (2007), “Lean manufacturing performance in China: assessment of 65 manufacturing
plants”, Journal of Manufacturing Technology Management, Vol. 19 No. 2, pp. 217-34.
Taj, S. and Morosan, C. (2011), “The impact of lean operations on the Chinese manufacturing
performance”, Journal of Manufacturing Technology Management, Vol. 22 No. 2, pp. 223-40.
Upadhye, N., Deshmukh, S.G. and Gard, S. (2009), “Key issues for the implementation of lean
manufacturing system”, Global Business and Management Research: An International
Journal, Vol. 1 Nos 3/4, pp. 57-68.
Upadhye, N., Deshmukh, S.G. and Gard, S. (2010), “Lean manufacturing for sustainable
development”, Global Business and Management Research: An International Journal, Vol. 2
Nos 1/4, pp. 125-37.
Vinodh, S., Arvind, K.R. and Somanaathan, M. (2010), “Application of value stream mapping in
an Indian camshaft manufacturing organisation”, Journal of Manufacturing Technology
Management, Vol. 21 No. 7, pp. 888-900.
White, R.E., Pearson, J.N. and Wilson, J.R. (1999), “JIT manufacturing: a survey of implementations
in small and large US manufacturers”, Management Science, Vol. 45 No. 1, pp. 1-15.
Womack, J. and Jones, D. (1996), Lean Thinking, Simon and Schuster, New York, NY.
Womack, J., Jones, D. and Roos, D. (1990), The Machine that Changed the World, Rawson
Associates, New York, NY.
About the author
Manimay Ghosh is Associate Professor of Operations Management at the Institute of
Management Technology (IMT), Nagpur, India. He teaches Operations Management, Service
Operations Management and Supply Chain Management. He has served large manufacturing
companies in India for 17 years in manufacturing, manufacturing planning, project planning,
and industrial engineering.
To purchase reprints of this article please e-mail: [email protected]
Or visit our web site for further details: www.emeraldinsight.com/reprints

Downloaded by Institut Teknologi Sepuluh Nopember At 07:30 08 May 2016 (PT)

This article has been cited by:
1. Giuliano Marodin University of South Carolina Columbia United States Alejandro Germán Frank
Universidade Federal do Rio Grande do Sul Porto Alegre Brazil Guilherme Tortorella Federal University of
Santa Catarina Florianopolis Brazil Tarcisio Abreu Saurin Federal University of Rio Grande do Sul Porto
Alegre Brazil Beverly Wagner University of Strathclyde Glasgow United Kingdom of Great Britain and
Northern Ireland United Kingdom of Great Britain and Northern Ireland . 2016. Contextual factors and
Lean Production implementation in the Brazilian automotive supply chain. Supply Chain Management:
An International Journal 21:4. . [Abstract] [PDF]
2. K. Srinivasan, S. Muthu, S. R. Devadasan, C. Sugumaran. 2016. Enhancement of sigma level in the
manufacturing of furnace nozzle through DMAIC approach of Six Sigma: a case study. Production
Planning & Control 1-13. [CrossRef]
3. Naga Vamsi Krishna Jasti Department of Mechanical Engineering, Birla Institute of Technology and
Science (BITS), Pilani, Pilani, India Rambabu Kodali Department of Mechanical Engineering, National
Institute of Technology, Jamshedpur, Jamshedpur, India . 2016. An empirical study for implementation of
lean principles in Indian manufacturing industry. Benchmarking: An International Journal 23:1, 183-207.
[Abstract] [Full Text] [PDF]
4. Lara Chaplin Business School, Coventry University, Coventry, UK John Heap Institute of Productivity,
Grimsby, UK Simon T.J. O'Rourke Safran, Gloucester, UK . 2016. Could “Lean Lite” be the cost effective
solution to applying lean manufacturing in developing economies?. International Journal of Productivity
and Performance Management 65:1, 126-136. [Abstract] [Full Text] [PDF]
5. Mashitah Mohamed Esa, Nor Azian Abdul Rahman, Maizurah Jamaludin. 2015. Reducing High Setup
Time in Assembly Line: A Case Study of Automotive Manufacturing Company in Malaysia. Procedia Social and Behavioral Sciences 211, 215-220. [CrossRef]
6. Vikram Sharma Mechanical-Mechatronics Engineering Department, The LNM Institute of Information
Technology, Jaipur, India Amit Rai Dixit Department of Mechanical Engineering, Indian School of
Mines, Dhanbad, India Mohammad Asim Qadri Mechanical Engineering Department, Galgotias College
of Engineering and Technology, Greater Noida, India . 2015. Impact of lean practices on performance
measures in context to Indian machine tool industry. Journal of Manufacturing Technology Management
26:8, 1218-1242. [Abstract] [Full Text] [PDF]
7. Zhixiang Chen Department of Management Science, School of Business, Sun Yat-Sen University,
Guangzhou, China . 2015. The relationships among JIT, TQM and production operations performance.
Business Process Management Journal 21:5, 1015-1039. [Abstract] [Full Text] [PDF]
8. Avinash Panwar, Bimal P. Nepal, Rakesh Jain, Ajay Pal Singh Rathore. 2015. On the adoption of lean
manufacturing principles in process industries. Production Planning & Control 26:7, 564-587. [CrossRef]
9. Jiunn-Chenn Lu, Taho Yang. 2015. Implementing lean standard work to solve a low work-in-process
buffer problem in a highly automated manufacturing environment. International Journal of Production
Research 53:8, 2285-2305. [CrossRef]
10. Jose Arturo Garza-Reyes, Ming K. Lim, Stavros Zisis, Vikas Kumar, Luis Rocha-LonaAdoption of
operations improvement methods in the Greek engineering sector 1-8. [CrossRef]
11. Mohamad Amran Ibrahim, Effendi Mohamad, Muhammad Hazwan Arzmi, Muhamad Arfauz A.
Rahman, Adi Saptari, Abdul Samad Shibghatul, Mohd Amri Sulaiman, Mohd Amran Md Ali. 2015.
Enhancing Efficiency of Die Exchange Process Through Single Minute of Exchanging Die at a Textile
Manufacturing Company in Malaysia. Journal of Applied Sciences 15:3, 456-464. [CrossRef]

Downloaded by Institut Teknologi Sepuluh Nopember At 07:30 08 May 2016 (PT)

12. Avinash Panwar Department of Mechanical Engineering, Government R C Khaitan Polytechnic College,
Jaipur, India Rakesh Jain Department of Mechanical Engineering, Malviya National Institute of
Technology (MNIT), Jaipur, India A.P.S. Rathore Department of Management Studies, Malviya National
Institute of Technology (MNIT), Jaipur, Indi . 2015. Lean implementation in Indian process industries
– some empirical evidence. Journal of Manufacturing Technology Management 26:1, 131-160. [Abstract]
[Full Text] [PDF]
13. Rameshwar Dubey Symbiosis Institute of Operations Management, Constituent of Symbiosis
International University, New Nashik, India Tripti Singh School of Management Studies, Motilal Nehru
National Istitute of Technology (MNNIT), Allahabad, India . 2015. Understanding complex relationship
among JIT, lean behaviour, TQM and their antecedents using interpretive structural modelling and fuzzy
MICMAC analysis. The TQM Journal 27:1, 42-62. [Abstract] [Full Text] [PDF]
14. Sri Hartini, Udisubakti Ciptomulyono. 2015. The Relationship between Lean and Sustainable
Manufacturing on Performance: Literature Review. Procedia Manufacturing 4, 38-45. [CrossRef]
15. Ioannis Belekoukias, Jose Arturo Garza-Reyes, Vikas Kumar. 2014. The impact of lean methods and
tools on the operational performance of manufacturing organisations. International Journal of Production
Research 52:18, 5346-5366. [CrossRef]
16. Jagdish Rajaram Jadhav, S. S. Mantha, Santosh B. Rane. 2014. Development of framework for sustainable
Lean implementation: an ISM approach. Journal of Industrial Engineering International 10:3. . [CrossRef]
17. Anupama Prashar Operations and Management Science, IILM School of Higher Education, Gurgaon,
Haryana, India . 2014. Redesigning an assembly line through Lean-Kaizen: an Indian case. The TQM
Journal 26:5, 475-498. [Abstract] [Full Text] [PDF]
18. Kuldip Singh Sangwan Department of Mechanical Engineering, Birla Institute of Technology
and Science, Pilani, India Jaiprakash Bhamu Department of Mechanical Engineering, Government
Engineering College, Bikaner, India Dhwani Mehta Department of Mechanical Engineering, Birla
Institute of Technology and Science, Pilani, India . 2014. Development of lean manufacturing
implementation drivers for Indian ceramic industry. International Journal of Productivity and Performance
Management 63:5, 569-587. [Abstract] [Full Text] [PDF]
19. S.J. Thanki Department of Mechanical Engineering, S.V.M. Institute of Technology, Bharuch, India
Jitesh Thakkar Department of Industrial Engineering and Management, Indian Institute of Technology
Kharagpur, Kharagpur, India . 2014. Status of lean manufacturing practices in Indian industries and
government initiatives. Journal of Manufacturing Technology Management 25:5, 655-675. [Abstract] [Full
Text] [PDF]

Sponsor Documents

Or use your account on DocShare.tips

Hide

Forgot your password?

Or register your new account on DocShare.tips

Hide

Lost your password? Please enter your email address. You will receive a link to create a new password.

Back to log-in

Close