Directory UMM :Data Elmu:jurnal:I:International Journal of Production Economics:Vol70.Issue1.Mar2001:
Int. J. Production Economics 70 (2001) 89}97
An international comparison of the e!ect of manufacturing
strategy-implementation gap on business performanceq
Boo-Ho Rho!, Kwangtae Park",*, Yung-Mok Yu#
!Sogang University, Seoul, South Korea
"Department of Management, Korea University, 1, 5Ka, Anam-dong, Sungbuk-ku, Seoul, South Korea
#Dankook University, Seoul, South Korea
Received 28 January 1999; accepted 12 April 2000
Abstract
This study has the purpose of empirically testing the importance of consistency between manufacturing strategies and
practices in achieving better business performances. An empirical test has been conducted and compared on the data sets
from three di!erent nations, each of which seems to have quite di!erent manufacturing capabilities and competitive
environments. The empirical test result implies that the gap variable indicating inconsistency between manufacturing
strategy and implementation practices plays a more important role than the strategy or implementation variable in
discriminating the superior from the inferior performance groups. For those data sets from the US and Korea, the gap
variables of #exibility, quality and/or cost show more signi"cant contribution in discriminating business performance
groups. But none of the gap variables outperform other strategy or implementation variables in discriminating
performance groups in Japan. ( 2001 Published by Elsevier Science B.V. All rights reserved.
Keywords: Manufacturing strategy; Empirical study; Comparative study; Manufacturing practices; Business performance; Gap analysis;
Discriminant analysis
1. Introduction
What will be the ultimate goal of manufacturing
strategies or practices? Why are we compelled to
adopt so many up-to-date three-letter manufacturing innovation approaches such as BPR (Business
Process Reengineering), ERP (Enterprise Resource
Planning), TQM (Total Quality Management), JIT
q
This paper was supported by NONDIRECTED RESEARCH FUND, Korea Research Foundation, 1996.
* Corresponding author. Tel.: #82-2-3290-1944; fax: #822-922-7220.
E-mail address: ktpark@kuccnx.korea.ac.kr (K. Park).
(Just-In-Time), TPM (Total Productive Maintenance), CIM (Computer Integrated Manufacturing),
QFD (Quality Function Deployment), DFM
(Design For Manufacturer), FMS (Flexible Manufacturing System), CAD (Computer-Aided Manufacturing) and CAE (Computer-Aided Engineering)?
The answer is simply: in order to be more competitive and pro"table. But how we can be so is not that
simply answered. A "rm usually can use only limited resources for implementing its strategies
and/or practices. Because of this limitation, it has
to seek more cost-e!ective as well as goal-achievable ways of allocating its resources. A commonly
suggested approach to prioritize resource allocations among manufacturing activities is to take into
0925-5273/01/$ - see front matter ( 2001 Published by Elsevier Science B.V. All rights reserved.
PII: S 0 9 2 5 - 5 2 7 3 ( 0 0 ) 0 0 0 4 9 - 9
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B.-H. Rho et al. / Int. J. Production Economics 70 (2001) 89}97
account their relative contributions toward achieving the "rm's strategic goals pursued in its manufacturing strategy. In this context, manufacturing
strategy consists of two core elements, as Miller
and Roth [1] pointed out: the manufacturing task
and the pattern of manufacturing choices. The "rst
states what the manufacturing function must accomplish [2], the so-called competitive priorities
such as quality, cost, delivery or #exibility, while
the second is concerned with the major decisions on
manufacturing structure and infrastructure that
a company makes to achieve its addressed manufacturing tasks [3].
There have been a large number of theoretical
and empirical research studies designed to investigate the relationship between manufacturing strategies, practices and performances. Even though
some of the most recent will be brie#y discussed
later, most of these research studies are focused on
a one-to-one relationship between strategic orientation and performance or between manufacturing practices and performances. Very few empirical
studies directly address the appropriateness of
manufacturing practices for a certain strategic orientation. And most of them also fail to extend their
"ndings to show how this appropriateness can affect business performance. As Dixon et al. [4] point
out, however, strategically important manufacturing activities should be given more attention and
resources than those that are not important. Overor under-allocation of manufacturing resources
should be avoided to achieve a more balanced and
cost-e!ective use of them.
This study aims to empirically test the importance of consistency between manufacturing strategies and practices in achieving better business
performances. An empirical test has been conducted on the data sets from three di!erent nations,
each of which seems to have quite di!erent manufacturing capabilities and competitive environments. This international comparison is to see
whether a consistency}performance relationship
can be generalized regardless of the nation's speci"c
characteristics of manufacturing systems. A consistency measure is de"ned in terms of the gap between
the perceived importance of a competitive priority
and thought-to-be-important manufacturing practices to achieve this priority. Consistency measures
along with other strategy and implementation
measures are then compared to investigate which
measures are more e!ective to di!erentiate high
performance business units from low performance
ones.
2. Literature review
2.1. Manufacturing strategies and performances
The notion of manufacturing strategy as an
important functional component of business strategy was initiated by Skinner [5,6] Skinner emphasized that manufacturing has the potential to
strengthen or weaken a company's competitive
ability. Wheelwright [7] articulated how manufacturing can support a "rm's competitiveness by de"ning four basic competitive priorities of manufacturing: cost e$ciency, quality, #exibility and dependability. This framework has greatly in#uenced
the terminology and direction of manufacturing
strategy research. Hayes and Wheelwright [3] provided more speci"c descriptions about how manufacturing capabilities can help a business attain
a desired competitive advantage. Since then many
researchers, for example Swamidass and Newell
[8], Hill [9], McDougall et al. [10] and Kim and
Lee [11], have explored the role of manufacturing
strategy in the strategy formulation and the strategy implementation process of a business unit.
Many empirical studies have also reported that
well-formulated and e!ective manufacturing strategies, aligned with business strategies and goals,
can produce better performances. For example,
there is a study that the business units with a formulated manufacturing strategy outperformed the
business units without one in terms of business
performance such as return on sales [12]. Another
study states that high-productive "rms are more
likely to have more clearly de"ned competitive
strategies [13]. This study helps to highlight the
importance of manufacturing strategy to productivity and other measures of performance.
Several studies deal with the orientations or
types of business strategies and/or manufacturing
strategies and relevant performance levels. The relationship between business strategic orientation
B.-H. Rho et al. / Int. J. Production Economics 70 (2001) 89}97
and manufacturing strategic orientation as well as
the relationship between manufacturing strategic
orientation and business performance has been
analyzed [14]. The manufacturing practices and
performances of the "rms in the four strategic
groups that were formed based on manufacturing
scope and production engineering resource commitment have also been discussed and showed statistically signi"cant di!erences in the performance of
each of the strategic groups formed [15]. From the
results of the empirical investigation, one study
reports that internally as well as externally oriented
businesses have achieved the competitive advantage with higher returns on investment and lower
business risk components [16]. Based on eleven
competitive priorities, three distinct clusters of
manufacturing strategy groups have also been formed and compared in terms of the business context,
manufacturing activities, and manufacturing
performance measures [1].
2.2. Manufacturing practices and performances
Recently, the Industry Week Census of Manufacturers has reported the practices currently most in
favor among US manufacturers, and the practices
most likely to produce the best performances [17].
This Census also shows that facilities deemed at or
nearest to world-class level typically do attain the
best manufacturing metrics, and they are doing it
by voraciously pursuing the best practices [18].
More empirical studies on the relationship between manufacturing practices and performances
have been reported that restrict their focuses
to more speci"c operational areas such as AMT
(Advanced Manufacturing Technology), quality
improvement, production planning and control,
JIT/lean production, and so on. From the 1994
survey of Australian manufacturers, a statistical
link between the use of AMT, manufacturing
performance and business performance has been
reported [19]. One paper examines the manufacturing performance and management practices of
71 automotive components suppliers located in
Europe, Japan, and North America. This paper
reports on the extent to which these plants have
adopted &lean production' practices and tests the
proposition that such techniques are linked with
91
high manufacturing performance [20]. Using the
Global Manufacturing Research Group survey
data, the e!ects that manufacturing planning and
control practices have on manufacturing competitiveness de"ned in terms of manufacturing
goals have been statistically estimated [21]. Also,
a survey result of US "rms examining the e!ect
of commonly adopted new manufacturing practices such as TQM, JIT, FMS, and CE on various
organizational performance measures has been
reported. This result suggests that companies,
which are using a few appropriate and e!ective
manufacturing approaches, have a higher performance [22].
2.3. Manufacturing strategies, practices and
performances
There have been some e!orts to "gure out how
manufacturing strategies, practices and performances can be related to each other. These e!orts
may be categorized into two directions: analytical
and predictive. The "rst one is interested in investigating under what conditions manufacturing
strategy or practices can be more e!ective in attaining higher performance. A study examines how
combinations of management techniques and
management accounting practices enhance the
performance of organizations, under particular
strategic orientations such as emphasizing
product di!erentiation, low price strategies or a
combination of both [23]. Another empirical
study of a large sample of manufacturing organizations con"rms that the use of integrated manufacturing techniques-particularly total quality
management } in#uences performance, and that
these e!ects are magni"ed or diminished by
both the competitive environment and manufacturing strategy. It also shows that, in some cases,
"rms are missing opportunities to combine
integrated manufacturing techniques and manufacturing strategy in ways that would substantially
a!ect performance [24]. Based on the 1991 survey
of 60 manufacturing companies in Belgium, a
study has compared top priorities of competitive
strategy, performance measures, and improvement
programs. It reveals that in many companies
there is still a lack of consistency between business
92
B.-H. Rho et al. / Int. J. Production Economics 70 (2001) 89}97
strategy, performance measurement systems and
improvement actions [25].
The second category aims to predict business
performance based on strategic importance of competitive priorities and their implementation levels.
That is, it attempts to evaluate the appropriateness
of the relationship between strategic orientation,
implementation levels and business performance
through the construction of manufacturing competence models [26}30]. These studies mainly focus
on a "rm's strategic orientation and its relative
strength to derive manufacturing competence. Even
though they seem to implicitly assume that more
strength in important competitive priorities comes
from better implementation of the appropriate
practices, they do not directly address the consistency between strategic orientation and manufacturing practices to evaluate the manufacturing
competence.
3. Empirical study
3.1. The sample
The questionnaire survey for this research was
conducted as a part of the International Manufacturing Strategy Survey II originally initiated by Per
Lindberg, Chalmers University of Technology in
Sweden and C.A. Voss, London School of Business
in England. For this second round world-wide survey, researchers from more than 20 countries including the US, Japan, Korea, and Western and
Eastern European nations have been involved in
establishing a common manufacturing database in
order to compare di!erent manufacturing strategies among the nations. One of the major objectives of this survey is to suggest a direction for
developing an appropriate country speci"c manufacturing strategy and implementation program.
More detailed explanations on IMSS researches
can be found in a recent publication by participating researchers [31].
This research focuses on how di!erent manufacturing strategies and practices a!ect company
performance. The sample used for this research
consists of "fty Korean, forty-one American and
twenty-nine Japanese manufacturing companies se-
Table 1
IMSS questionnaire used for this study
Section
Contents
Section A
Section C
Section D
Strategies, goals and costs
Past and planned activities in manufacturing
Performance measures
lected from the IMSS (International Manufacturing Strategy Survey) II database. A set of survey
items were also selected from Sections A, C and
D of IMSS II questionnaire. They include items
such as strategic orientation or manufacturing
goals to achieve, manufacturing practices or improvement programs and performance measures as
shown in Table 1. Nine strategic orientation items,
measured in 5-point Likert scale (5"very important), were selected from Section A. Each of
these items refers to the relative perceived importance of the respective manufacturing goal.
Items about manufacturing practices or activities
were selected from Section C. The degree of use
measured in 5-point Likert scale (5"high) of
these manufacturing improvement activities for last
three years was used as the measure of practice
variable. Pro"t before tax, sales and inventory
values were selected from Section D as performance
measures.
3.2. Scale reliability
Both the manufacturing strategy variables and
the manufacturing practice variables were classi"ed
into 5 categories for further analysis in terms of
generally accepted manufacturing capabilities: cost,
quality, #exibility, delivery and customer service
(Table 2). Each of the categorized scales was statistically tested to see if it has high enough internal
consistency or reliability. As shown in Table 2, all
of Cronbach's alpha values from the scales exceed
0.5 level which is generally considered adequate for
exploratory work. Each scale was represented by
the average of respective item measures. Performance measures were also converted to two widely
used business performance measures: pro"t to sales
ratio and inventory turnovers.
B.-H. Rho et al. / Int. J. Production Economics 70 (2001) 89}97
93
Table 2
Reliability test
Factors
Manufacturing strategy
Practices
Item
Questionnaire question
Cronbach a
Cost
Lower selling prices
*
Quality
Product design and quality
Manufacturing quality
0.5907
Flexibility
Wider product range
Greater number of new products
Greater order size #exibility
0.6543
Delivery
Faster deliveries,
Dependable deliveries
0.5096
Customer service
Superior customer service
*
Cost
ABC (Activity-based Costing),
TPM (Total Productive Maintenance)
Energy conservation programs
0.5848
Quality
SPC (Statistical Process Control)
Quality function deployment
Quality policy deployment
0.6889
Flexibility
NC/CNC/DNC
AGVs (Automatic Guided Vehicles)
CAM/FMC/FAS
0.6364
Delivery
Just-In-Time manufacturing/Lean production
Just-In-Time deliveries to customers
Pull scheduling (e.g. Kanban)
0.6869
Customer service
Benchmarking
KAIZEN (continuous improvement)
0.5576
3.3. GAP analysis
The absolute value of the di!erence between each
strategic orientation variable and respective practice variable was de"ned as the gap variable.
A company's gap value of cost becomes lower if the
perceived importance of cost leadership and implementation level of cost improvement practices
becomes closer.
Basically, IMSS II targeted the manufacturing
industry of fabricated metal products, machinery
and equipment whose International Standard Industrial Classi"cation Code ranges from 381 to 385.
Thus, there exists a high similarity among country
samples in terms of industrial background.
To analyze the overall e!ect of gap on company's
performance, the whole sampled manufacturers
were divided into two performance groups: superior and inferior. Median values of pro"t to sales
Table 3
Gap comparison between the superior and the inferior groups
based on pro"t-to-sales ratio
Capability
category
Superior
group
Inferior
group
ยน value
(p value)
Cost
Quality
Flexibility
Delivery
Customer service
1.062
1.293
1.801
1.214
1.086
1.531
1.692
2.626
1.108
1.680
!1.96
!2.02
!2.75
!0.64
!2.75
(0.048)
(0.043)
(0.008)
(0.543)
(0.008)
ratio (5.4%) and inventory turnovers (9.926) were
used to divide these two groups since distributions
of them appeared to be a little skewed. Table 3
shows the di!erence between the average gap
values of the superior and the inferior groups in
terms of pro"t to sales performance by manufacturing capability categories. All the gaps except for
94
B.-H. Rho et al. / Int. J. Production Economics 70 (2001) 89}97
Table 4
Gap comparison between the superior and the inferior groups
based on inventory turnovers
Capability
category
Superior
group
Inferior
group
t value
(p value)
Cost
Quality
Flexibility
Delivery
Customer service
1.446
1.532
1.611
1.812
1.157
1.240
1.851
1.469
1.552
1.003
0.32
!0.43
1.05
1.32
0.48
(0.747)
(0.668)
(0.332)
(0.208)
(0.635)
delivery show statistically signi"cant di!erence between these two groups. For instance, gap value of
the superior group in the customer service category
is 1.086 which is signi"cantly lower than that of the
inferior group (1.680). We may be able to say from
this result that the higher the congruence between
the strategic orientation and manufacturing practices a company has, the higher its pro"t to sales
ratio.
Table 4 shows the di!erence between the average
gap values of the superior and the inferior groups
in terms of inventory turnovers by manufacturing
capability categories. All the gaps fail to show a
statistically signi"cant di!erence between these
two groups. One possible explanation for this
somewhat unsatisfactory result may stem from the
relatively high di!erence of average inventory levels
among sampled industries. That is, industry
di!erence may have a higher e!ect on inventory
turnovers than the gap variable.
3.4. Discriminant analysis
To apply discriminant analysis, strategic orientation, practices implementation and the gap between
them are considered as independent variables while
superior and inferior performance groups are
considered as dependent variables. For this
discriminant analysis we di!erentiated groups
by pro"t to sales ratio only since as mentioned
above inventory turnovers seemed to be not appropriate as a performance measure for gap analysis.
From the discriminant analysis, we can assess the
independent variables' relative importance to dependent variables. Discriminant analysis was carried out on the whole sample group in order to see
which independent variable contributes most when
the superior group is discriminated from the
inferior group.
The result from applying stepwise discriminant
analysis for all companies regardless of their origin
is summarized in Table 5. From Table 5, the
delivery gap is the only available variable in
di!erentiating the superior from the inferior
performance group. None of the strategy or practice variables are useful for discriminanting between these two groups. Because of sample
limitation it may not be appropriate to generalize
whether only the delivery gap variable indicating
the di!erence between strategic importance and
implementation of delivery capability determines
a manufacturer's group a$liation. But from the
result of this discriminant analysis we may argue
that it is not su$cient to simply place more emphasis on a certain strategic orientation such as lower
price or on a certain manufacturing practice such
as statistical process control for enhancing a "rm's
performance. A more important way to achieve
higher performance is to place more emphasis on
a certain manufacturing practice which seems to be
more appropriate for strengthening that "rm's
strategic orientation.
Table 5
Result of discriminant analysis (total sample)
Dependent
variable
Independent variable
Wilk's
lambda
Signi"cance
Discriminant function
coe$cient
Performance group
Gap (delivery)
0.696
0.051
1.000
Eigen
Val.
Canonical Cor.
Wilk's
lambda
Chi-square
Sig.
Hit-ratio
0.436
0.551
0.696
3.800
0.051
100%
B.-H. Rho et al. / Int. J. Production Economics 70 (2001) 89}97
95
Table 6
Result of discriminant analysis (Korean sample)
Dependent
variable
Independent variable
Wilk's
lambda
Signi"cance
Discriminant function
coe$cient
Performance group
Practices (quality)
Gap (quality)
Gap (#exibility)
0.073
0.020
0.001
0.009
0.020
0.038
5.891
!6.497
!5.098
Eigen Val.
Canonical
Cor.
Wilk's
lambda
Chi-square
Sig.
Hit-ratio
1118.109
1.000
0.001
10.530
0.015
100%
Three consecutive discriminant analyses, each of
which was based on each country's manufacturers
only, were also done to con"rm whether the proposed gap variables consistently outperform other
strategy or practice variables across the nations.
The "rst analysis was done for the Korean sample,
and the result is given in Table 6. There we see that
two gap variables (quality and #exibility) and one
practice variable (quality) are statistically signi"cant for discriminating the superior performance
group from the inferior one. This result implies that
a manufacturer in the Korean sample is more likely
to belong to the superior performance group if its
quality and #exibility focused strategic orientations
and practices are well matched and place more
emphasis on quality improvement practices. Thus,
we can con"rm that gap variables seem to play
a more important role for discriminating performance groups in the Korean sample too.
The result of discriminant analysis for the US
sample is given in Table 7. Only the gap variables
for #exibility and cost are included as a statistically
signi"cant variable in the discriminant function.
That is, a manufacturer with less variation between
strategic orientation and practice implementation
in terms of both #exibility and cost has more probability of belonging to the superior performance
group in the US sample. This result seems to
strongly support our argument that the consistency
between manufacturing strategies and practices
is more important than strategic orientation or
practice implementation itself in achieving better
performance.
The result of discriminant analysis for Japanese
companies is given in Table 8. No gap variable is
included in the discriminant function. Instead, it
includes only practice variables regarding delivery
and customer service. Thus, it seems to be less or
Table 7
Result of discriminant analysis (US sample)
Dependent
variable
Independent variable
Wilk's
lambda
Signi"cance
Discriminant function
coe$cient
Performance group
Gap (#exibility)
0.036
0.018
!4.399
Gap (cost)
0.001
0.027
!4.428
Eigen Val.
Canonical Cor.
Wilk's
lambda
Chi-square
Sig.
Hit-ratio
1335.500
1.000
0.001
7.198
0.027
100%
96
B.-H. Rho et al. / Int. J. Production Economics 70 (2001) 89}97
Table 8
Result of discriminant analysis (Japanese sample)
Dependent
variable
Independent variable
Wilk's
lambda
Signi"cance
Discriminant function
coe$cient
Performance group
Practices (delivery)
0.062
0.032
7.572
Practices (customer service)
0.001
0.033
7.507
Eigen Val.
Canonical Cor.
Wilk's
lambda
Chi-square
Sig.
Hit-ratio
891.167
0.999
0.001
6.794
0.033
62.50%
hardly applicable to the Japanese sample that the
consistency between strategies and practices is
more important for achieving higher performance
than strategic orientation or practice implementation alone. However, we need to be a little cautious
in interpreting this discriminant analysis result because the Japanese sample shows a less satisfactory
discriminating result compared to the other samples. Correctness in estimating a manufacturer's
group membership based on the drawn discriminant function is only 62.50% which may be
considered to be marginally higher than the random estimation. One possible explanation for this
might be that it has a relatively limited number of
respondents.
4. Conclusions
This study has the purpose of empirically testing
the importance of consistency between manufacturing strategies and practices in achieving
better business performances. An empirical test
has been conducted on the data sets from three
di!erent nations, each of which seems to have
quite di!erent manufacturing capabilities and
competitive environments. This international
comparison is to see whether this consistencyperformance relationship can be generalized regardless of the nation speci"c characteristics of
manufacturing systems.
What we have learned from the empirical study
is that the gap variable indicating inconsistency
between manufacturing strategy and implementa-
tion practices plays a more important role than the
strategy or implementation variable in discriminating the superior from the inferior performance
groups. For those data sets from the US and Korea,
the gap variables of #exibility, quality and/or cost
show more signi"cant contribution for discriminating between business performance groups. Even
though this gap variable fails to outperform other
strategy or implementation variables in discriminating between performance groups in Japan, the
overall discriminating power of the proposed gap
variables can be considered to be signi"cant based
on this "nding.
References
[1] J.C. Miller, A.V. Roth, A taxonomy of manufacturing
strategies, Management Science 40 (3) (1994) 285}304.
[2] W. Skinner, Manufacturing in the Corporate Strategy,
Wiley, New York, 1998.
[3] R.H. Hayes, S.C. Wheelwright, Restoring our competitive
edge: competing through manufacturing, Wiley, New
York, 1984.
[4] J.R. Dixon, A.J. Nanni, Jr., T.E. Vollmann, The New
Performance Challenge: Measuring Operations for
World-Class Competition, R.D. Irwin, Homewood, IL,
1990.
[5] W. Skinner, Manufacturing}missing link in corporate
strategy, Harvard Business Review 47 (1969) 136}145.
[6] W. Skinner, The focused factory, Harvard Business Review
52 (1974) 113}21.
[7] S.C. Wheelwright, Manufacturing strategy: De"ning the
missing link, Strategic Management Journal 5 (1984)
77}91.
[8] P. Swamidass, W. Newell, Manufacturing strategy, environmental uncertainty, and performance: A path analytic
model, Management Science 33 (1987) 509}524.
B.-H. Rho et al. / Int. J. Production Economics 70 (2001) 89}97
[9] T. Hill, Manufacturing Strategy: Text and Cases, R.D.
Irwin, Homewood, IL, 1989.
[10] P. McDougall, R. Dean, D. D'Souza, Manufacturing strategy and business origin of ventures "rms in the computer
and communications equipment industries, Production
and Operations Management 1 (1) (1992) 53}69.
[11] J.S. Kim, P. Arnold, Manufacturing competence and business performance: A framework and empirical analysis,
International Journal of Operations and Production Management 13 (10) (1993) 4}25.
[12] C. Tunalv, Manufacturing strategy plans and business
performance, International Journal of Operations and
Production Management 12 (3) (1992) 4}24.
[13] M.A. Nobel, Manufacturing competitive priorities and
productivity: An empirical study, International Journal of
Operations and Production Management 17 (1) (1997)
85}99.
[14] F.P. Williams et al., Manufacturing strategy, business
strategy and "rm performance in a mature industry, Journal of Operations Management 13 (1995) 19}33.
[15] M.T. Sweeney, M. Szwejczewski, Manufacturing strategy
and performance: A study of the UK engineering industry,
International Journal of Operations and Production
Management 16 (5) (1996) 25}40.
[16] P. Wright, M. Kroll, B. Pray, A. Lado, Strategic orientations, competitive advantage, and business performance,
Journal of Business Research 33 (1995) 143}151.
[17] G. Taninecz, Best practices and performances, Industry
Week 246 (22) (1997a) 28}43.
[18] G. Taninecz, World-class manufacturers, Industry Week
246 (22) (1997b) 44}47.
[19] N.B. Beaumont, R.M. Schroder, Technology, manufacturing performance and business performance amongst
Australian manufacturers, Technovation 17 (6) (1997)
297}307.
[20] J. Lowe, R. Delbridge, N. Oliver, High-performance
manufacturing: Evidence from the automotive components industry, Organization Studies 18 (5) (1997) 783}798.
97
[21] J.G. Wacker, M. Hanson, Some practical advice for manufacturing managers: Empirical results from the Global
Manufacturing Research Group, Production and Inventory Management Journal 38 (3) (1997) 64}71.
[22] N.U. Ahmed, R.V. Montagno, R.J. Firenze, Operations
strategy and organizational performance: An empirical
study, International Journal of Operations and Production Management 16 (5) (1996) 41}53.
[23] R.H. Chenhall, S.K. Lang"eld, The relationship between
strategic priorities, management techniques and management accounting: An empirical investigation using a systems approach, Accounting, Organizations and Society
23 (3) (1998) 243}264.
[24] J.W. Dean, S.A. Snell, The strategic use of integrated
manufacturing: An empirical examination, Strategic Management Journal 17 (6) (1996) 459}480.
[25] L. Gelders, P. Mannaerts, J. Maes, Manufacturing strategy, performance indicators and improvement programs,
International Journal of Production Research 32 (4) (1994)
797}805.
[26] G. Cleveland, R.G. Schroeder, J.C. Anderson, A theory of
production competence, Decision Sciences 20 (1989)
655}668.
[27] S.K. Vickery, A theory of production competence revisited,
Decision Sciences 22 (1991) 635}643.
[28] S.K. Vickery, C. Droge, R.E. Markland, Production
competence and business strategy: Do they a!ect business
performance? Decision Sciences 24 (2) (1993) 435}455.
[29] Y. Kim, J. Lee, Manufacturing strategy and production
systems: An integrated framework, Journal of Operations
Management 11 (1) (1993) 3}15.
[30] L.M. Corbett, 1995, Manufacturing competence and business performance: the New Zealand experience 1988}1992,
Proceedings of Pan-Paci"c Conference, Dunedin Queenstown, New Zealand, pp. 73}75.
[31] P. Lindberg, C.A. Voss, K.L. Blackmon, International
Manufacturing Strategies: Context, Content and Change,
Kluwer Academic Publishers, Dordrecht, 1998.
An international comparison of the e!ect of manufacturing
strategy-implementation gap on business performanceq
Boo-Ho Rho!, Kwangtae Park",*, Yung-Mok Yu#
!Sogang University, Seoul, South Korea
"Department of Management, Korea University, 1, 5Ka, Anam-dong, Sungbuk-ku, Seoul, South Korea
#Dankook University, Seoul, South Korea
Received 28 January 1999; accepted 12 April 2000
Abstract
This study has the purpose of empirically testing the importance of consistency between manufacturing strategies and
practices in achieving better business performances. An empirical test has been conducted and compared on the data sets
from three di!erent nations, each of which seems to have quite di!erent manufacturing capabilities and competitive
environments. The empirical test result implies that the gap variable indicating inconsistency between manufacturing
strategy and implementation practices plays a more important role than the strategy or implementation variable in
discriminating the superior from the inferior performance groups. For those data sets from the US and Korea, the gap
variables of #exibility, quality and/or cost show more signi"cant contribution in discriminating business performance
groups. But none of the gap variables outperform other strategy or implementation variables in discriminating
performance groups in Japan. ( 2001 Published by Elsevier Science B.V. All rights reserved.
Keywords: Manufacturing strategy; Empirical study; Comparative study; Manufacturing practices; Business performance; Gap analysis;
Discriminant analysis
1. Introduction
What will be the ultimate goal of manufacturing
strategies or practices? Why are we compelled to
adopt so many up-to-date three-letter manufacturing innovation approaches such as BPR (Business
Process Reengineering), ERP (Enterprise Resource
Planning), TQM (Total Quality Management), JIT
q
This paper was supported by NONDIRECTED RESEARCH FUND, Korea Research Foundation, 1996.
* Corresponding author. Tel.: #82-2-3290-1944; fax: #822-922-7220.
E-mail address: ktpark@kuccnx.korea.ac.kr (K. Park).
(Just-In-Time), TPM (Total Productive Maintenance), CIM (Computer Integrated Manufacturing),
QFD (Quality Function Deployment), DFM
(Design For Manufacturer), FMS (Flexible Manufacturing System), CAD (Computer-Aided Manufacturing) and CAE (Computer-Aided Engineering)?
The answer is simply: in order to be more competitive and pro"table. But how we can be so is not that
simply answered. A "rm usually can use only limited resources for implementing its strategies
and/or practices. Because of this limitation, it has
to seek more cost-e!ective as well as goal-achievable ways of allocating its resources. A commonly
suggested approach to prioritize resource allocations among manufacturing activities is to take into
0925-5273/01/$ - see front matter ( 2001 Published by Elsevier Science B.V. All rights reserved.
PII: S 0 9 2 5 - 5 2 7 3 ( 0 0 ) 0 0 0 4 9 - 9
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B.-H. Rho et al. / Int. J. Production Economics 70 (2001) 89}97
account their relative contributions toward achieving the "rm's strategic goals pursued in its manufacturing strategy. In this context, manufacturing
strategy consists of two core elements, as Miller
and Roth [1] pointed out: the manufacturing task
and the pattern of manufacturing choices. The "rst
states what the manufacturing function must accomplish [2], the so-called competitive priorities
such as quality, cost, delivery or #exibility, while
the second is concerned with the major decisions on
manufacturing structure and infrastructure that
a company makes to achieve its addressed manufacturing tasks [3].
There have been a large number of theoretical
and empirical research studies designed to investigate the relationship between manufacturing strategies, practices and performances. Even though
some of the most recent will be brie#y discussed
later, most of these research studies are focused on
a one-to-one relationship between strategic orientation and performance or between manufacturing practices and performances. Very few empirical
studies directly address the appropriateness of
manufacturing practices for a certain strategic orientation. And most of them also fail to extend their
"ndings to show how this appropriateness can affect business performance. As Dixon et al. [4] point
out, however, strategically important manufacturing activities should be given more attention and
resources than those that are not important. Overor under-allocation of manufacturing resources
should be avoided to achieve a more balanced and
cost-e!ective use of them.
This study aims to empirically test the importance of consistency between manufacturing strategies and practices in achieving better business
performances. An empirical test has been conducted on the data sets from three di!erent nations,
each of which seems to have quite di!erent manufacturing capabilities and competitive environments. This international comparison is to see
whether a consistency}performance relationship
can be generalized regardless of the nation's speci"c
characteristics of manufacturing systems. A consistency measure is de"ned in terms of the gap between
the perceived importance of a competitive priority
and thought-to-be-important manufacturing practices to achieve this priority. Consistency measures
along with other strategy and implementation
measures are then compared to investigate which
measures are more e!ective to di!erentiate high
performance business units from low performance
ones.
2. Literature review
2.1. Manufacturing strategies and performances
The notion of manufacturing strategy as an
important functional component of business strategy was initiated by Skinner [5,6] Skinner emphasized that manufacturing has the potential to
strengthen or weaken a company's competitive
ability. Wheelwright [7] articulated how manufacturing can support a "rm's competitiveness by de"ning four basic competitive priorities of manufacturing: cost e$ciency, quality, #exibility and dependability. This framework has greatly in#uenced
the terminology and direction of manufacturing
strategy research. Hayes and Wheelwright [3] provided more speci"c descriptions about how manufacturing capabilities can help a business attain
a desired competitive advantage. Since then many
researchers, for example Swamidass and Newell
[8], Hill [9], McDougall et al. [10] and Kim and
Lee [11], have explored the role of manufacturing
strategy in the strategy formulation and the strategy implementation process of a business unit.
Many empirical studies have also reported that
well-formulated and e!ective manufacturing strategies, aligned with business strategies and goals,
can produce better performances. For example,
there is a study that the business units with a formulated manufacturing strategy outperformed the
business units without one in terms of business
performance such as return on sales [12]. Another
study states that high-productive "rms are more
likely to have more clearly de"ned competitive
strategies [13]. This study helps to highlight the
importance of manufacturing strategy to productivity and other measures of performance.
Several studies deal with the orientations or
types of business strategies and/or manufacturing
strategies and relevant performance levels. The relationship between business strategic orientation
B.-H. Rho et al. / Int. J. Production Economics 70 (2001) 89}97
and manufacturing strategic orientation as well as
the relationship between manufacturing strategic
orientation and business performance has been
analyzed [14]. The manufacturing practices and
performances of the "rms in the four strategic
groups that were formed based on manufacturing
scope and production engineering resource commitment have also been discussed and showed statistically signi"cant di!erences in the performance of
each of the strategic groups formed [15]. From the
results of the empirical investigation, one study
reports that internally as well as externally oriented
businesses have achieved the competitive advantage with higher returns on investment and lower
business risk components [16]. Based on eleven
competitive priorities, three distinct clusters of
manufacturing strategy groups have also been formed and compared in terms of the business context,
manufacturing activities, and manufacturing
performance measures [1].
2.2. Manufacturing practices and performances
Recently, the Industry Week Census of Manufacturers has reported the practices currently most in
favor among US manufacturers, and the practices
most likely to produce the best performances [17].
This Census also shows that facilities deemed at or
nearest to world-class level typically do attain the
best manufacturing metrics, and they are doing it
by voraciously pursuing the best practices [18].
More empirical studies on the relationship between manufacturing practices and performances
have been reported that restrict their focuses
to more speci"c operational areas such as AMT
(Advanced Manufacturing Technology), quality
improvement, production planning and control,
JIT/lean production, and so on. From the 1994
survey of Australian manufacturers, a statistical
link between the use of AMT, manufacturing
performance and business performance has been
reported [19]. One paper examines the manufacturing performance and management practices of
71 automotive components suppliers located in
Europe, Japan, and North America. This paper
reports on the extent to which these plants have
adopted &lean production' practices and tests the
proposition that such techniques are linked with
91
high manufacturing performance [20]. Using the
Global Manufacturing Research Group survey
data, the e!ects that manufacturing planning and
control practices have on manufacturing competitiveness de"ned in terms of manufacturing
goals have been statistically estimated [21]. Also,
a survey result of US "rms examining the e!ect
of commonly adopted new manufacturing practices such as TQM, JIT, FMS, and CE on various
organizational performance measures has been
reported. This result suggests that companies,
which are using a few appropriate and e!ective
manufacturing approaches, have a higher performance [22].
2.3. Manufacturing strategies, practices and
performances
There have been some e!orts to "gure out how
manufacturing strategies, practices and performances can be related to each other. These e!orts
may be categorized into two directions: analytical
and predictive. The "rst one is interested in investigating under what conditions manufacturing
strategy or practices can be more e!ective in attaining higher performance. A study examines how
combinations of management techniques and
management accounting practices enhance the
performance of organizations, under particular
strategic orientations such as emphasizing
product di!erentiation, low price strategies or a
combination of both [23]. Another empirical
study of a large sample of manufacturing organizations con"rms that the use of integrated manufacturing techniques-particularly total quality
management } in#uences performance, and that
these e!ects are magni"ed or diminished by
both the competitive environment and manufacturing strategy. It also shows that, in some cases,
"rms are missing opportunities to combine
integrated manufacturing techniques and manufacturing strategy in ways that would substantially
a!ect performance [24]. Based on the 1991 survey
of 60 manufacturing companies in Belgium, a
study has compared top priorities of competitive
strategy, performance measures, and improvement
programs. It reveals that in many companies
there is still a lack of consistency between business
92
B.-H. Rho et al. / Int. J. Production Economics 70 (2001) 89}97
strategy, performance measurement systems and
improvement actions [25].
The second category aims to predict business
performance based on strategic importance of competitive priorities and their implementation levels.
That is, it attempts to evaluate the appropriateness
of the relationship between strategic orientation,
implementation levels and business performance
through the construction of manufacturing competence models [26}30]. These studies mainly focus
on a "rm's strategic orientation and its relative
strength to derive manufacturing competence. Even
though they seem to implicitly assume that more
strength in important competitive priorities comes
from better implementation of the appropriate
practices, they do not directly address the consistency between strategic orientation and manufacturing practices to evaluate the manufacturing
competence.
3. Empirical study
3.1. The sample
The questionnaire survey for this research was
conducted as a part of the International Manufacturing Strategy Survey II originally initiated by Per
Lindberg, Chalmers University of Technology in
Sweden and C.A. Voss, London School of Business
in England. For this second round world-wide survey, researchers from more than 20 countries including the US, Japan, Korea, and Western and
Eastern European nations have been involved in
establishing a common manufacturing database in
order to compare di!erent manufacturing strategies among the nations. One of the major objectives of this survey is to suggest a direction for
developing an appropriate country speci"c manufacturing strategy and implementation program.
More detailed explanations on IMSS researches
can be found in a recent publication by participating researchers [31].
This research focuses on how di!erent manufacturing strategies and practices a!ect company
performance. The sample used for this research
consists of "fty Korean, forty-one American and
twenty-nine Japanese manufacturing companies se-
Table 1
IMSS questionnaire used for this study
Section
Contents
Section A
Section C
Section D
Strategies, goals and costs
Past and planned activities in manufacturing
Performance measures
lected from the IMSS (International Manufacturing Strategy Survey) II database. A set of survey
items were also selected from Sections A, C and
D of IMSS II questionnaire. They include items
such as strategic orientation or manufacturing
goals to achieve, manufacturing practices or improvement programs and performance measures as
shown in Table 1. Nine strategic orientation items,
measured in 5-point Likert scale (5"very important), were selected from Section A. Each of
these items refers to the relative perceived importance of the respective manufacturing goal.
Items about manufacturing practices or activities
were selected from Section C. The degree of use
measured in 5-point Likert scale (5"high) of
these manufacturing improvement activities for last
three years was used as the measure of practice
variable. Pro"t before tax, sales and inventory
values were selected from Section D as performance
measures.
3.2. Scale reliability
Both the manufacturing strategy variables and
the manufacturing practice variables were classi"ed
into 5 categories for further analysis in terms of
generally accepted manufacturing capabilities: cost,
quality, #exibility, delivery and customer service
(Table 2). Each of the categorized scales was statistically tested to see if it has high enough internal
consistency or reliability. As shown in Table 2, all
of Cronbach's alpha values from the scales exceed
0.5 level which is generally considered adequate for
exploratory work. Each scale was represented by
the average of respective item measures. Performance measures were also converted to two widely
used business performance measures: pro"t to sales
ratio and inventory turnovers.
B.-H. Rho et al. / Int. J. Production Economics 70 (2001) 89}97
93
Table 2
Reliability test
Factors
Manufacturing strategy
Practices
Item
Questionnaire question
Cronbach a
Cost
Lower selling prices
*
Quality
Product design and quality
Manufacturing quality
0.5907
Flexibility
Wider product range
Greater number of new products
Greater order size #exibility
0.6543
Delivery
Faster deliveries,
Dependable deliveries
0.5096
Customer service
Superior customer service
*
Cost
ABC (Activity-based Costing),
TPM (Total Productive Maintenance)
Energy conservation programs
0.5848
Quality
SPC (Statistical Process Control)
Quality function deployment
Quality policy deployment
0.6889
Flexibility
NC/CNC/DNC
AGVs (Automatic Guided Vehicles)
CAM/FMC/FAS
0.6364
Delivery
Just-In-Time manufacturing/Lean production
Just-In-Time deliveries to customers
Pull scheduling (e.g. Kanban)
0.6869
Customer service
Benchmarking
KAIZEN (continuous improvement)
0.5576
3.3. GAP analysis
The absolute value of the di!erence between each
strategic orientation variable and respective practice variable was de"ned as the gap variable.
A company's gap value of cost becomes lower if the
perceived importance of cost leadership and implementation level of cost improvement practices
becomes closer.
Basically, IMSS II targeted the manufacturing
industry of fabricated metal products, machinery
and equipment whose International Standard Industrial Classi"cation Code ranges from 381 to 385.
Thus, there exists a high similarity among country
samples in terms of industrial background.
To analyze the overall e!ect of gap on company's
performance, the whole sampled manufacturers
were divided into two performance groups: superior and inferior. Median values of pro"t to sales
Table 3
Gap comparison between the superior and the inferior groups
based on pro"t-to-sales ratio
Capability
category
Superior
group
Inferior
group
ยน value
(p value)
Cost
Quality
Flexibility
Delivery
Customer service
1.062
1.293
1.801
1.214
1.086
1.531
1.692
2.626
1.108
1.680
!1.96
!2.02
!2.75
!0.64
!2.75
(0.048)
(0.043)
(0.008)
(0.543)
(0.008)
ratio (5.4%) and inventory turnovers (9.926) were
used to divide these two groups since distributions
of them appeared to be a little skewed. Table 3
shows the di!erence between the average gap
values of the superior and the inferior groups in
terms of pro"t to sales performance by manufacturing capability categories. All the gaps except for
94
B.-H. Rho et al. / Int. J. Production Economics 70 (2001) 89}97
Table 4
Gap comparison between the superior and the inferior groups
based on inventory turnovers
Capability
category
Superior
group
Inferior
group
t value
(p value)
Cost
Quality
Flexibility
Delivery
Customer service
1.446
1.532
1.611
1.812
1.157
1.240
1.851
1.469
1.552
1.003
0.32
!0.43
1.05
1.32
0.48
(0.747)
(0.668)
(0.332)
(0.208)
(0.635)
delivery show statistically signi"cant di!erence between these two groups. For instance, gap value of
the superior group in the customer service category
is 1.086 which is signi"cantly lower than that of the
inferior group (1.680). We may be able to say from
this result that the higher the congruence between
the strategic orientation and manufacturing practices a company has, the higher its pro"t to sales
ratio.
Table 4 shows the di!erence between the average
gap values of the superior and the inferior groups
in terms of inventory turnovers by manufacturing
capability categories. All the gaps fail to show a
statistically signi"cant di!erence between these
two groups. One possible explanation for this
somewhat unsatisfactory result may stem from the
relatively high di!erence of average inventory levels
among sampled industries. That is, industry
di!erence may have a higher e!ect on inventory
turnovers than the gap variable.
3.4. Discriminant analysis
To apply discriminant analysis, strategic orientation, practices implementation and the gap between
them are considered as independent variables while
superior and inferior performance groups are
considered as dependent variables. For this
discriminant analysis we di!erentiated groups
by pro"t to sales ratio only since as mentioned
above inventory turnovers seemed to be not appropriate as a performance measure for gap analysis.
From the discriminant analysis, we can assess the
independent variables' relative importance to dependent variables. Discriminant analysis was carried out on the whole sample group in order to see
which independent variable contributes most when
the superior group is discriminated from the
inferior group.
The result from applying stepwise discriminant
analysis for all companies regardless of their origin
is summarized in Table 5. From Table 5, the
delivery gap is the only available variable in
di!erentiating the superior from the inferior
performance group. None of the strategy or practice variables are useful for discriminanting between these two groups. Because of sample
limitation it may not be appropriate to generalize
whether only the delivery gap variable indicating
the di!erence between strategic importance and
implementation of delivery capability determines
a manufacturer's group a$liation. But from the
result of this discriminant analysis we may argue
that it is not su$cient to simply place more emphasis on a certain strategic orientation such as lower
price or on a certain manufacturing practice such
as statistical process control for enhancing a "rm's
performance. A more important way to achieve
higher performance is to place more emphasis on
a certain manufacturing practice which seems to be
more appropriate for strengthening that "rm's
strategic orientation.
Table 5
Result of discriminant analysis (total sample)
Dependent
variable
Independent variable
Wilk's
lambda
Signi"cance
Discriminant function
coe$cient
Performance group
Gap (delivery)
0.696
0.051
1.000
Eigen
Val.
Canonical Cor.
Wilk's
lambda
Chi-square
Sig.
Hit-ratio
0.436
0.551
0.696
3.800
0.051
100%
B.-H. Rho et al. / Int. J. Production Economics 70 (2001) 89}97
95
Table 6
Result of discriminant analysis (Korean sample)
Dependent
variable
Independent variable
Wilk's
lambda
Signi"cance
Discriminant function
coe$cient
Performance group
Practices (quality)
Gap (quality)
Gap (#exibility)
0.073
0.020
0.001
0.009
0.020
0.038
5.891
!6.497
!5.098
Eigen Val.
Canonical
Cor.
Wilk's
lambda
Chi-square
Sig.
Hit-ratio
1118.109
1.000
0.001
10.530
0.015
100%
Three consecutive discriminant analyses, each of
which was based on each country's manufacturers
only, were also done to con"rm whether the proposed gap variables consistently outperform other
strategy or practice variables across the nations.
The "rst analysis was done for the Korean sample,
and the result is given in Table 6. There we see that
two gap variables (quality and #exibility) and one
practice variable (quality) are statistically signi"cant for discriminating the superior performance
group from the inferior one. This result implies that
a manufacturer in the Korean sample is more likely
to belong to the superior performance group if its
quality and #exibility focused strategic orientations
and practices are well matched and place more
emphasis on quality improvement practices. Thus,
we can con"rm that gap variables seem to play
a more important role for discriminating performance groups in the Korean sample too.
The result of discriminant analysis for the US
sample is given in Table 7. Only the gap variables
for #exibility and cost are included as a statistically
signi"cant variable in the discriminant function.
That is, a manufacturer with less variation between
strategic orientation and practice implementation
in terms of both #exibility and cost has more probability of belonging to the superior performance
group in the US sample. This result seems to
strongly support our argument that the consistency
between manufacturing strategies and practices
is more important than strategic orientation or
practice implementation itself in achieving better
performance.
The result of discriminant analysis for Japanese
companies is given in Table 8. No gap variable is
included in the discriminant function. Instead, it
includes only practice variables regarding delivery
and customer service. Thus, it seems to be less or
Table 7
Result of discriminant analysis (US sample)
Dependent
variable
Independent variable
Wilk's
lambda
Signi"cance
Discriminant function
coe$cient
Performance group
Gap (#exibility)
0.036
0.018
!4.399
Gap (cost)
0.001
0.027
!4.428
Eigen Val.
Canonical Cor.
Wilk's
lambda
Chi-square
Sig.
Hit-ratio
1335.500
1.000
0.001
7.198
0.027
100%
96
B.-H. Rho et al. / Int. J. Production Economics 70 (2001) 89}97
Table 8
Result of discriminant analysis (Japanese sample)
Dependent
variable
Independent variable
Wilk's
lambda
Signi"cance
Discriminant function
coe$cient
Performance group
Practices (delivery)
0.062
0.032
7.572
Practices (customer service)
0.001
0.033
7.507
Eigen Val.
Canonical Cor.
Wilk's
lambda
Chi-square
Sig.
Hit-ratio
891.167
0.999
0.001
6.794
0.033
62.50%
hardly applicable to the Japanese sample that the
consistency between strategies and practices is
more important for achieving higher performance
than strategic orientation or practice implementation alone. However, we need to be a little cautious
in interpreting this discriminant analysis result because the Japanese sample shows a less satisfactory
discriminating result compared to the other samples. Correctness in estimating a manufacturer's
group membership based on the drawn discriminant function is only 62.50% which may be
considered to be marginally higher than the random estimation. One possible explanation for this
might be that it has a relatively limited number of
respondents.
4. Conclusions
This study has the purpose of empirically testing
the importance of consistency between manufacturing strategies and practices in achieving
better business performances. An empirical test
has been conducted on the data sets from three
di!erent nations, each of which seems to have
quite di!erent manufacturing capabilities and
competitive environments. This international
comparison is to see whether this consistencyperformance relationship can be generalized regardless of the nation speci"c characteristics of
manufacturing systems.
What we have learned from the empirical study
is that the gap variable indicating inconsistency
between manufacturing strategy and implementa-
tion practices plays a more important role than the
strategy or implementation variable in discriminating the superior from the inferior performance
groups. For those data sets from the US and Korea,
the gap variables of #exibility, quality and/or cost
show more signi"cant contribution for discriminating between business performance groups. Even
though this gap variable fails to outperform other
strategy or implementation variables in discriminating between performance groups in Japan, the
overall discriminating power of the proposed gap
variables can be considered to be signi"cant based
on this "nding.
References
[1] J.C. Miller, A.V. Roth, A taxonomy of manufacturing
strategies, Management Science 40 (3) (1994) 285}304.
[2] W. Skinner, Manufacturing in the Corporate Strategy,
Wiley, New York, 1998.
[3] R.H. Hayes, S.C. Wheelwright, Restoring our competitive
edge: competing through manufacturing, Wiley, New
York, 1984.
[4] J.R. Dixon, A.J. Nanni, Jr., T.E. Vollmann, The New
Performance Challenge: Measuring Operations for
World-Class Competition, R.D. Irwin, Homewood, IL,
1990.
[5] W. Skinner, Manufacturing}missing link in corporate
strategy, Harvard Business Review 47 (1969) 136}145.
[6] W. Skinner, The focused factory, Harvard Business Review
52 (1974) 113}21.
[7] S.C. Wheelwright, Manufacturing strategy: De"ning the
missing link, Strategic Management Journal 5 (1984)
77}91.
[8] P. Swamidass, W. Newell, Manufacturing strategy, environmental uncertainty, and performance: A path analytic
model, Management Science 33 (1987) 509}524.
B.-H. Rho et al. / Int. J. Production Economics 70 (2001) 89}97
[9] T. Hill, Manufacturing Strategy: Text and Cases, R.D.
Irwin, Homewood, IL, 1989.
[10] P. McDougall, R. Dean, D. D'Souza, Manufacturing strategy and business origin of ventures "rms in the computer
and communications equipment industries, Production
and Operations Management 1 (1) (1992) 53}69.
[11] J.S. Kim, P. Arnold, Manufacturing competence and business performance: A framework and empirical analysis,
International Journal of Operations and Production Management 13 (10) (1993) 4}25.
[12] C. Tunalv, Manufacturing strategy plans and business
performance, International Journal of Operations and
Production Management 12 (3) (1992) 4}24.
[13] M.A. Nobel, Manufacturing competitive priorities and
productivity: An empirical study, International Journal of
Operations and Production Management 17 (1) (1997)
85}99.
[14] F.P. Williams et al., Manufacturing strategy, business
strategy and "rm performance in a mature industry, Journal of Operations Management 13 (1995) 19}33.
[15] M.T. Sweeney, M. Szwejczewski, Manufacturing strategy
and performance: A study of the UK engineering industry,
International Journal of Operations and Production
Management 16 (5) (1996) 25}40.
[16] P. Wright, M. Kroll, B. Pray, A. Lado, Strategic orientations, competitive advantage, and business performance,
Journal of Business Research 33 (1995) 143}151.
[17] G. Taninecz, Best practices and performances, Industry
Week 246 (22) (1997a) 28}43.
[18] G. Taninecz, World-class manufacturers, Industry Week
246 (22) (1997b) 44}47.
[19] N.B. Beaumont, R.M. Schroder, Technology, manufacturing performance and business performance amongst
Australian manufacturers, Technovation 17 (6) (1997)
297}307.
[20] J. Lowe, R. Delbridge, N. Oliver, High-performance
manufacturing: Evidence from the automotive components industry, Organization Studies 18 (5) (1997) 783}798.
97
[21] J.G. Wacker, M. Hanson, Some practical advice for manufacturing managers: Empirical results from the Global
Manufacturing Research Group, Production and Inventory Management Journal 38 (3) (1997) 64}71.
[22] N.U. Ahmed, R.V. Montagno, R.J. Firenze, Operations
strategy and organizational performance: An empirical
study, International Journal of Operations and Production Management 16 (5) (1996) 41}53.
[23] R.H. Chenhall, S.K. Lang"eld, The relationship between
strategic priorities, management techniques and management accounting: An empirical investigation using a systems approach, Accounting, Organizations and Society
23 (3) (1998) 243}264.
[24] J.W. Dean, S.A. Snell, The strategic use of integrated
manufacturing: An empirical examination, Strategic Management Journal 17 (6) (1996) 459}480.
[25] L. Gelders, P. Mannaerts, J. Maes, Manufacturing strategy, performance indicators and improvement programs,
International Journal of Production Research 32 (4) (1994)
797}805.
[26] G. Cleveland, R.G. Schroeder, J.C. Anderson, A theory of
production competence, Decision Sciences 20 (1989)
655}668.
[27] S.K. Vickery, A theory of production competence revisited,
Decision Sciences 22 (1991) 635}643.
[28] S.K. Vickery, C. Droge, R.E. Markland, Production
competence and business strategy: Do they a!ect business
performance? Decision Sciences 24 (2) (1993) 435}455.
[29] Y. Kim, J. Lee, Manufacturing strategy and production
systems: An integrated framework, Journal of Operations
Management 11 (1) (1993) 3}15.
[30] L.M. Corbett, 1995, Manufacturing competence and business performance: the New Zealand experience 1988}1992,
Proceedings of Pan-Paci"c Conference, Dunedin Queenstown, New Zealand, pp. 73}75.
[31] P. Lindberg, C.A. Voss, K.L. Blackmon, International
Manufacturing Strategies: Context, Content and Change,
Kluwer Academic Publishers, Dordrecht, 1998.