015-0395
TITLE
The Sequence of Cumulative Capabilities: A Comparison of Three
Industries in Thailand
Sakun Boon-itt1
Thamasat Business School
2 Prachan, Rd Pranakorn
Bangkok, THAILAND 10200
E-mail: sboonitt@tu.ac.th
Chee Yew Wong
Logistics Institute
Hull Business School
Hull, UK HU6 7RX
E-mail: c.wong@hull.ac.uk
POMS 21st Annual Conference
Vancouver, Canada
May 7 to May 10, 2010
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The Sequence of Cumulative Capabilities: A Comparison of Three
Industries in Thailand
Abstract
Purpose – This research compares models (sequences) of cumulative capabilities
among automotive, electronics and food industries in Thailand, and further explains
how different industrial contexts shape different models of cumulative capabilities.
Design/methodology/approach – Based on survey data, structural equations models
for each of the three industries are examined and compared.
Findings – Quality is the base for all other capabilities for all three industries. The
research found different models (sequences) of cumulative capabilities for the three
industries: delivery becomes the next capability for the automotive industry;
production flexibility for the electronics industry and production cost for the food
industry.
Research limitations/implications – Results confirm the effects of industrial
differences, suggesting further research into industry contexts in terms of the rate new
product introduction (clock-speed), demand characteristics, product life-cycle,
industry maturity, and competitive priorities. The results should not be interpreted in a
prescriptive manner. Similar research replicated in other countries may yield different
results.
Practical implications – Recognise the relative importance of various manufacturing
capabilities in different industries and different sequences of cumulative capabilities
for different industries.
Originality/value – Clarify the effects of industrial contexts in shaping different
models of cumulative capabilities.
Keywords Manufacturing capability, operations strategy, automotive industry, food
industry, electronics industry
Paper type Research paper
1
Corresponding author
2
Introduction
The operations strategy literature has long recognised that various manufacturing
capabilities (i.e. quality, cost, flexibility and delivery) may reinforce each other to
form a certain pattern of cumulative capabilities. Some literature also suggests that the
sequence of cumulative capabilities may differ from one industry to another because
the importance of various capabilities in different industries is not the same (Noble,
1995; Flynn and Flynn, 2004). For example, quality and product introduction speed
may form the basis of the cumulative capabilities for industries competing in more
competitive and rapidly changing industries, where product life cycles are shorter
(Flynn and Flynn, 2004). Furthermore, in a more mature industry, where cost
becomes an order qualifier, cumulative of multiple capabilities is often essential for
success (Hill, 1988; Schmenner and Swink, 1998). To-date different models of
cumulative capabilities have been suggested or discovered but the reasons for their
differences are not adequately explained.
Recognising the potential contextual effects of industry differences, this research
is set out to compare models of cumulative capabilities among automotive,
electronics, and food industries in Thailand. This research posits that quality forms the
base of the models of cumulative capabilities for all these three industries, but the
next capabilities in the models of cumulative capabilities vary depending on industrial
contexts. We hypothesise that delivery, production flexibility, and production cost are
the next capabilities for the automotive, electronics and food industries respectively.
To test these hypotheses, all together 151, 102, and 115 usable responses respectively
from these three industries are analysed in three separate structural equation models.
Path analyses of the three models support the above assumptions, which confirm that
different industries have different models (sequences) of cumulative capabilities.
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This research contributes to the operations management literature in several
aspects. First, unlike most previous cross-sectional studies, which combine data from
various industries, this research builds three separate structural equations models for
each of the three industries. In this research, cross-sectional analysis of dataset from
multiple countries is avoided because it can only provide indirect empirical measures
of dynamic changes of capabilities and it can never indicate chronological sequences
(Gröler and Grübner, 2006). Second, this research applies structural equation
modelling because it is able to conclude a total model of cumulative relationships
between capabilities (Gröler and Grübner, 2006). Other methods (i.e. correlation,
stepwise regression, multiple regression, common method variance, and path analysis)
commonly applied to study cumulative model are not able to establish such a total
model and therefore only provide us with a partial understanding of the sequence of
cumulative capabilities.
Third, though differences between countries (regions) have already been identified
as a potential contingent factor affecting the relationships between manufacturing
capabilities (Hall and Nakane, 1990; Noble, 1995; Flynn and Flynn, 2004) the effects
of the industrial contexts are still unclear. Noble (1995) argued that industrial contexts
may affect the sequences of cumulative capabilities because different types of
industries compete with different sets of competitive priorities. Based on a study of
three North American industries, Noble (1995) concluded that the process industry
performed low on productivity but competed on delivery; the metal fabrication and
assembly industry focused on quality, dependability and cost (high productivity
cluster); and the high-technology industry competed on flexibility and innovation.
Noble (195) found no significant difference among different industries from the
Korean sample. However, in a more recent study of five countries, Flynn and Flynn
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(2004) found limited support for the hypothesis that there are differences in the
pattern of cumulative capabilities between electronics, machinery, and transportation
component manufacturing industries. With these mixed results the effects of industrial
contexts are still inconclusive, and therefore further research is required.
Models of cumulative capabilities
When developing an operations strategy, managers need to decide which
manufacturing capabilities to prioritise for improvement. They may apply the tradeoff model, which argues that one manufacturing capability can only be improved at
the expense of other capabilities (Skinner, 1974). An alternate view to that of the
trade-off model is that one capability would enhance another; they become
cumulative (Ferdows and De Meyer, 1990). Ferdows and De Meyer (1990) further
argue that when capabilities are developed in a cumulative manner, it is likely to be
more lasting and less fragile than if it were developed at the expense of other
capabilities. The operations management literature also suggests that cumulative
capabilities can be achieved by improving various capabilities according to a
particular sequence. For example, a study of Japanese manufacturers concluded that
manufacturers must qualify for a minimum level of abilities on quality, dependability
and cost improvement before they can offer flexibility; failing to achieve these “base”
capabilities can end up with a tragic or chaos condition (Nakane, 1986).
Quality as the base of cumulative capabilities
Even though the operations management literature has reached little consensus about
the sequence of cumulative capabilities, most empirical studies concluded that quality
should be the base of all other capabilities for most manufacturing industries (i.e.
Nakane, 1986; Noble, 1995; Ferdows and De Meyer, 1990; Corbett and van
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Wassenhove, 1993; Swink and Way, 1995; Rosenzweig and Roth, 2004; Gröler and
Grübner, 2006; Amoako-Gympah and Meredith, 2007). In line with the above
literature, this research considers quality as the base of the other cumulative
capabilities. The relationships between quality and other capabilities (delivery,
flexibility and cost) are thus formulated as follows.
In this research, a product is considered to be of high quality when it meets
customers needs (market-based quality) without generating in a lot of rework or waste
during the process of production (conformance or process-based quality).
Conformance or process-based quality is essential because when there is effective
quality control the production process will become reliable, less variable and more
predictable; such a capability is essential to warrant on-time and reliable delivery
(Ferdows and De Meyer, 1990; Noble, 1995; Fawcett et al., 1997). Furthermore,
manufacturers with less quality problems will be able to reduce delivery lead time
because they will spend less time and resources to rework or handle rejects (Flynn et
al., 1995; Milgate, 2000). Several empirical studies have confirmed that an enhanced
product (conformance) quality positively and directly influences improvements in
delivery reliability (Flynn and Flynn, 2004; Gröler and Grübner, 2006). Based on
these arguments, the following hypothesis H1 is formulated.
H1. Quality has a direct positive impact on delivery capability
The relationship between quality and production flexibility has also been widely
examined. One of the reasons a manufacturer suffers from low production flexibility
is that much of its production capacity and resources are occupied by activities that
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handle poor quality or produce buffer stocks, instead of being allocated to produce the
right products for the right customers whenever they are needed. Theoretically, an
improvement in conformance quality will reduce the uncertainties of customer
requirements and timing of material supply quality (Wacker, 1996). These
improvements will improve the accuracy of production scheduling and subsequently
increase not only delivery reliability but also volume and lead-time flexibilities
(Corbett and van Wassenhove, 1993). Also, poor quality reduces speed (Ferdows and
De Meyer, 1990) and speed is an essential enabler of volume and lead-time flexibility.
Furthermore, in order to offer many product variants to meet different customer
needs, production flexibility (e.g. range or product mix) becomes even more critical.
Many operations managers with a trade-off mindset would argue that offering more
product variants will lead to poorer quality or increased cost. However, for example,
the Yamazaki machine tool factory in the UK was able to offer four times more
models in the third of the time normal to the industry, while the quality of their
products “matched or beat” the high Japanese standard (Jones et al., 1988). One of the
explanations for this success is that lower process variability, as a result of a higher
level of product (conformance) quality, leads to greater flexibility in offering a wider
variety of products. Based on these arguments, as well as existing empirical evidence
(i.e. Gröler and Grübner, 2006) this research formulates the following hypothesis
H2.
H2. Quality has a direct positive impact on production flexibility
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Plenty of previous research has confirmed that enhanced cost competitiveness can
be achieved by investment in a quality programme (Crosby, 1979, Deming, 1982;
Juran et al., 1974; Garvin, 1987; Skinner, 1986; Gupta and Campbell, 1995; Flynn et
al., 1995). Furthermore, Ferdows and De Meyer (1990) argue that the quality-cost
relationship does not work in the opposite direction - an increase in cost efficiency
does not seem to improve quality. This is because any reduction in labour and/or
material cost must be originated from (but not a consequence of) the improvements in
process or innovation in material owing to a quality programme or any similar
improvement initiative (Ferdows and De Meyer, 1990). This argument also further
supports our assumption that quality is the base of other cumulative capabilities.
Essentially, when quality control becomes effective, the production process will
produce less rejects and therefore less rework is required, subsequently resulting in
lower cost of poor quality (Crosby, 1979; Deming, 1982; Gupta and Campbell, 1995;
Flynn et al., 1995). Furthermore, quality management techniques such as the quality
deployment function, Taguchi method, and design for manufacturing design are often
used to improve the features of a product as well as the costs of production (Taguchi
and Clausing, 1990; Lockamy and Khurana, 1995). Based on these arguments, and
existing empirical evidence (i.e. Gröler and Grübner, 2006; Amoako-Gympah and
Meredith, 2007), this research formulates the following hypothesis H3.
H3. Quality has a direct positive impact on production cost
This research posits that the above three hypotheses (H1, H2 and H3) form the
base of the models of cumulative capabilities applicable for most industries. Though
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there is already a lot of empirical evidence which supports this assumption, this
research has not ignored the contradictory results reported by Flynn and Flynn (2004).
They found that quality was the base capability for Korean manufacturers but not for
Italian and German manufacturers. Experience in quality management and the theory
of performance frontier are among the explanations used to explain such a
contradictory finding. As explained by Flynn and Flynn (2004), the Italian and
German manufacturers, in their samples, were operating at a relative high level of
competition and perhaps at performance frontiers; therefore they could not solely rely
on quality to improve other capabilities. Instead, manufacturers from developing
countries such as Ghana, as argued by Amoako-Gympah and Meredith (2007), will
rely heavily on quality improvement to improve other capabilities. Since this research
focuses on relatively less matured industries in Thailand, quality is expected to form
the base for all other capabilities.
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The next capability after quality
There is a need to determine the next capability to be improved after determining
quality as the base. Which capability should be improved next - delivery, flexibility,
or cost? Flynn and Flynn (2004) found that cumulative capabilities may involve more
delivery for some industries but cost for other industries. That means delivery,
production cost and perhaps production flexibility may become the next capability of
the models of cumulative capabilities, depending on industrial and other contexts.
When each of these three capabilities is considered as the base of the next (upper)
level of the models, we will find three possible models of cumulative capabilities.
Figure 1 illustrates the three possible models and their respective supporting
hypotheses.
<< Insert Figure 1 about here >>
Figure 1 also illustrates another primary assumption of this research i.e. the models of
cumulative capabilities do not follow a simple and serial sequence (such as
quality>dependability>flexibility>cost, proposed by some literature), but they follow
a divergent sequence in such a way that quality will have positive impacts on all three
other capabilities (delivery, cost, and flexibility) while one of these three capabilities
will form the base for the other two capabilities. This research also suggests that the
choice of the next capability depends on differences in industrial characteristics.
Based on different characteristics for automotive, electronics and food industries in
Thailand, we further establish hypotheses (H4, H5 and H6) about the relationships
among delivery, cost and flexibility for each of these industries.
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Automotive industry (Model A)
Quality has always been an order qualifier in the automotive industry (Curkovic et al.,
2000). Thus, quality should become the first capability to be improved and form the
base of the model of cumulative capabilities for the automotive industry. Other than
quality, just-in-time (JIT) delivery has been identified as the second (and perhaps
equally critical) competitive weapon of successful automakers (Schonberger, 1982;
Goyal and Deshmukh, 1992). JIT is characterized by small batch-size production with
relatively low defects and process variability (Schonberger, 1982; Ohno, 1988). JIT
cannot exist in manufacturing plants with poor conformance quality; instead, quality
is the prerequisite for implementing JIT (Zipkin, 1991). Furthermore, it is world-class
quality and JIT delivery that allowed Japanese automakers to overtake in the
competitive landscape of automotive markets (Schonberger, 1982; Womack et al.,
1990; Zipkin, 1991). Even facing low-cost competition from China and sophisticated
competitors from Korea and the United States, Japanese automakers have not changed
their strategic focus on quality and JIT (Daniel et al., 2009). Instead, successful
automakers rely heavily on conformance quality and reliable JIT delivery to improve
cost competitiveness and flexibility. Since Japanese automakers are still the leaders of
the industry, many other countries (including Thailand) attempt to imitate them in
many aspects, including engaging in the same sequence of building up cumulative
capabilities i.e. first quality and then JIT delivery (model A in figure 1).
Model A represents the case where quality becomes the base capability and
delivery becomes the base at the next (upper) level of the model of cumulative
capabilities. This is by far the most accepted model in the literature (i.e. Nakane,
1986; Noble, 1995; Ferdows and De Meyer, 1990; Swink and Way, 1995;
Rosenzweig and Roth, 2004; Gröler and Grübner, 2006). As suggested by
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Sakakibara et al. (1997) and Funk (1995), manufacturers operating at a high delivery
speed are able to improve flexibility of their operations because less time is required
to respond to different demands or adjust to changing requirements. Furthermore,
when manufacturers reduce variance in their delivery processes and the predictability
of the production and distribution systems (delivery reliability), production (volume)
flexibility will be enhanced (Flynn and Flynn, 2004; Rosenzweig and Roth, 2004).
Often, a reduction in lead time and process variance means higher productivity and
lower inventory, therefore leading to a reduction in production cost. In addition, a
relationship between delivery and cost has been found in the literature by Narasimhan
and Jayaram (1998). Based on these arguments the following two hypotheses are
proposed for the automotive industry:
H4a. Delivery capability has a direct positive impact on production flexibility
H5a. Delivery capability has a direct positive impact on production cost
The next challenge is to explain the relationship between flexibility and cost for
the automotive industry. Theoretically a high level of production flexibility will
enable automotive manufacturers to reduce inventory costs while achieving a high
service level. This is the competitive advantage of JIT. A number of studies have
found that without a high level of flexibility the industry will simultaneously suffer
from a high level of inventory, stock-out, and excessive overtime especially in a
uncertain environment (Gerwin, 1993; Pagell and Krause, 1999; Koste and Malhorta,
2000). With this theoretical argument hypothesis H6a is established as follows.
H6a. Production flexibility has a direct positive impact on production cost
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Hypothesis H6a has actually been tested empirically and remains inconclusive in
the literature. A meta-analysis of previous empirical studies found mixed results on
the flexibility-cost relationship (White, 1996). According to the empirical study of
Gröler and Grübner (2006), flexibility and cost are not mutually exclusive and a
trade-off relationship (negative impact) seems to exist between these two capabilities.
Since this study applied cross-sectional data it is difficult to compare with the
automotive industry. Furthermore, this finding needs to be examined carefully rather
than simply accepting that trade-off occurred. According to Schmenner and Swink
(1998), trade-off becomes necessary when a firm reaches its performance frontier, and
negative relationship between two capabilities may occur due to time-slack between
the effects of improvement of a capability on another capability. Since we do not
expect to find industries with performance frontiers in Thailand, time-slack may
become one of the other potential conditions which might lead to non-significant or
even a negative flexibility-cost (H6a) relationship.
Electronics industry (Model B)
The electronics industry is somewhat different from the automotive industry. In an
attempt to determine the relative importance of competitive factors for the US
electronics and computer industries, Lau (2002) concluded that process-based quality
and cost are the order qualifier while new product introduction, delivery speed,
volume flexibility, and market-based quality are among the order winners. In the
electronics industry, standards for quality and delivery may have been firmly
established. Therefore the industry is relatively mature and already has a lot of
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experience in quality management. Quality becomes the very first capability to be
improved in the electronics industry because electronics products are typically
manufactured in high-speed automatic surface mounting and assembly production
lines, which cannot tolerate poor quality.
After quality, flexibility becomes the next capability for electronics because the
industry is facing a highly unpredictable demand shortening product life cycles
(Calantone et al., 1994; Lau, 1999; Oke, 2003). When taking into account the
volatility of the electronics industry in Thailand, it is very likely that the industry will
bias toward the quality>flexibility->delivery+cost model (model B in figure 1). This
is because the industry frequently needs to introduce new products and there is a need
for a greater extent of flexibility in terms of volume, range, and speed (Lau, 1999;
Rosenzweig and Roth, 2004). Thus, delivery performance is highly dependent on
flexibility. With the use of Surface Mount Technology (SMT) or a Flexible
Production System (FPS), the electronics industry is able to produce a high variety of
new products more frequently at a relatively shorter lead time (Bennett et al., 1992;
Lambert et al., 2006). With this theoretical argument hypothesis H4b is formulated.
H4b. Production flexibility has a direct positive impact on delivery capability
Hypothesis H4b suggests that flexibility has a direct positive impact on delivery
but H4a (for automotive industry) suggests the opposite. Both hypotheses can be
supported by different arguments, but their difference lies in the different sequences
of cumulative capabilities necessary for different industries. Hypothesis H4b is
suitable for industries which require a high level of production flexibility competence
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as well as to improve delivery performance, while hypothesis H4a is suitable for
industries which compete mainly on quality and delivery and improve flexibility
based on delivery speed and reliability.
Also, in many industries production cost becomes the result of flexibility and
quality, but not their determinants (Ferdows and De Meyer, 1990). Cost reduction is
often a by-product of quality initiatives, not the opposite (Deming, 1982). In the
electronics industry, a high level of production flexibility should be able to keep the
production cost down whilst in the mean time achieving a high level of delivery
performance (Lau, 1999). Flexibility should not be costly, according to Upton
(1994:73), “flexibility is the ability to change or react with little penalty in time, effort
cost, and performance”. Instead, without a high level of flexibility the electronics
industry operating in a highly volatile environment will simultaneously suffer from a
high level of inventory, stock-out, and overtime. These arguments form the basis of
the following hypotheses H5b and H6b.
H5b. Delivery capability has a direct positive impact on production cost (=H5a)
H6b. Production flexibility has a direct positive impact on production cost (=H6a)
Food industry (Model C)
The food industry is one business that has been responsive to the TQM movement
(Chaudhry et al., 1997). The production process requires non-stop operation of
automatic production line equipment and therefore quality is the base of every other
capability (Tsarouhas, 2007). However, in a study by Tannock and Krasachol (2000),
Thai industry was still slow in terms of ISO 9000 certification. Subsequently, a lack
15
of understanding of quality standards was reported in a study of the Thai seafood
processing industry (Rohitratana and Boon-itt, 2001). This means quality should still
be an ongoing, improving, capability and form the base of the model of cumulative
capabilities for the Thai food industry.
The next capability for the food industry is different from those of the automotive
and electronics industries. The food industry in Thailand generally requires relatively
less advanced automated assembly technology than the automotive industry and more
manual operations than the electronics industry. The food industry is less volatile than
the electronics industry and therefore delivery and flexibility may not be the next
capability to improve after quality. Instead, food manufacturers often compete on cost
as the order winner. This is because the economic structure of the food industry is
based primarily on competition between producers and between retailers, driving food
prices down, combined with externalisation of many costs (Appleby et al., 2003).
This is especially true for the Thai food industry which relies extensively on export
markets. In order to secure orders, the food industry needs to improve quality and
production cost before attempting to improve delivery and flexibility.
We have, earlier, mentioned that production cost is more likely the by-product of
quality, delivery and flexibility but there are exceptions for some industries. For
example, Amoako-Gyampah and Meredith (2007) found that manufacturing and
service industries in Ghana emphasised cost capabilities after satisfying quality
requirements; unfortunately they based their study on cross-sectional data and
industrial contexts are not examined. However, in the Flynn and Flynn (2004) study,
samples from the machinery industry had more cumulative capabilities involving cost
than electronics and transportation industries. For such industries (and the food
industry), after satisfying quality requirements, cost becomes the next priority because
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the ability to reduce costs will allow manufacturers to invest in other means of
improving flexibility and delivery capability. Therefore, this research suggests that the
food industry in Thailand is more likely to fit with model C (quality>production
cost>delivery+production flexibility). When a food manufacturer is able reduce
production costs, it is then able to continuously receive new orders and be able to
invest in improving production flexibility and delivery. With this argument, the
following hypotheses H5c and H6c are formulated.
H5c. Production cost has a direct positive impact on delivery capability
H6c. Production cost has a direct positive impact on production flexibility
It is notable that hypothesis H5c suggests an opposite direction to the deliverycost relationship of hypotheses H5a and H5b simply because of the differences of
competitive priorities among different industries. Based on the same argument, the
flexibility-cost relationship suggested by H6a and H6b will not necessarily hold in
some industries (White, 1996). Finally, the relationship between flexibility and
delivery (hypothesis H4c) remains the same as hypothesis H4b based on the argument
that flexibility in production volume, range, and speed will theoretically contribute to
better delivery performance in terms of delivery speed and reliability.
H4c. Production flexibility has a direct positive impact on delivery capability
(=H4b)
Methodology
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Sampling and data collection
Data for this research were obtained from a survey distributed across three
independent samples: the automotive industry, the electronics industry, and the food
industry in Thailand. These three industries are highly diverse and heterogeneous
spanning manufacturers of structural characteristics and maturity. In addition, these
three industries are likely to influence a country’s gross domestic product (GDP).
The survey instrument was developed in three stages. In the first stage, all the
items from the literature review were identified and used to draft the questionnaire,
along with questions where respondents were asked to provide demographic
information relating to their firms. For most items, a five-point Likert scale ranging
from “strongly disagree” to “strongly agree” was used. An English version of the
questionnaire was developed and tested to determine content validity. In the second
stage, the questionnaire was translated into Thai language. A bilingual Thai native
proofread the English version and corrected ambiguities that could cause confusion in
translation. Following these revisions, the questionnaire was reviewed by several Thai
practitioners and academics with expertise in supply chain management in the
automotive industry. This group was asked to examine the questionnaire for clarity
and to ensure that it conveyed the adequate meaning of each item. The comments
primarily focused upon clarification of the instructions and refinement of item
wording. There were no major problems detected and minor modifications were made
to the instructions and wording of some items. In the final stage, the questionnaire
was pre-tested by industry representatives and academics familiar with competitive
capability and operations strategy. The pre-test was conducted to ensure the items
were clear, providing face validity for the construct examined. Based on the pre-test,
minor amendment was made and the instrument was then sent out for data collection.
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In order to include a wide range of respondents, a mailing list was obtained from
three sources: (1) the Directory of the Society of Automotive Engineering of
Thailand, (2) the Thailand Industry Directory, and (3) the Export-Import Bank of
Thailand. Respondents were plant managers as well as CEOs, presidents, vice
presidents, and directors. Potential respondents were contacted first by telephone to
confirm contact information for mail delivery. The survey was sent to 746 potential
respondents from the automotive industry; 426 potential respondents from the
electronics industry; and 536 potential from the food industry during early 2007. The
final number of completed and usable responses from the automotive industry was
151, indicating a response rate of 20.85%. The electronics industry survey yielded
102 usable responses (24% response rate). The final survey from the food industry
also yielded 115 usable responses (21% response rate). This is close to the
recommended minimum of 20% for empirical studies in operations management
(Malhotra and Grover, 1998).
To assess non-respondent bias, we compared the responses of early and late
respondents to test for their significant difference (Armstrong and Overton, 1977).
The first 75% of the responses for each industry were classified as “early
respondents”. The last 25% of them for each industry were classified as “late
respondents” and they are considered as firms that did not respond to the survey. At
0.05 significance level, analysis of variance (ANOVA) tests indicated no differences
between the early and late respondents for all industries, suggesting that non-response
bias was not a problem with regard to data collection.
Construct measures
This research focuses on manufacturing capability, not manufacturing priority.
Manufacturing priorities are intended capabilities, or the capabilities the managers
19
want to have in the future (Ward et al., 1996; Woods et al., 1990; Roth and van der
Velde, 1991). Instead, we are interested in the manufacturing capabilities which
currently exist within the organisations of manufacturers.
The measures and scales for capability constructs applied in this study were
developed based on the well-established procedures suggested by Churchill (1979),
i.e. to clearly define the domain of each construct in terms of which item will be
included. Several previous studies suggested that the use of different definitions and
the choice of dimensions for various capabilities may be one of the reasons (in
addition to other contingent factors such as country and industry) which prevent the
theoretical advancement required to explain the discoveries of many different models
of cumulative capabilities (White, 1996; Flynn and Flynn, 2004). Capabilities such as
quality, delivery, cost and flexibility are considered as multidimensional constructs
and therefore there is a need to clearly define each of these constructs and their
respective dimensions. In this research, the manufacturing capabilities being
considered are quality, delivery, production flexibility and production cost. All
measures in this study were adopted from previous literature, below.
In terms of quality, previous literature has suggested the inclusion of both processbased quality and market-based quality (Flynn and Flynn, 2004; Amoako-Gympah
and Meredith, 2007). Process-based quality focuses on achieving the conformance of
specification (Flynn and Flynn, 2004). Process-based quality has been operationalised
based on abilities in terms of quality control and waste reduction (Noble, 1995).
Ultimately, such capabilities will lead to the consistent conformance of specification
(Vickery et al., 1997). In this research, we have included one item i.e. “producing
consistent quality products with low defects” as the dimension of process-based
quality. Since quality conformance is only one of the eight dimensions of quality
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(Garvin, 1987), there is a need to consider other market-based quality dimensions
(Flynn and Flynn, 2004) such as functionality, features, reliability, durability,
serviceability, aesthetics and perceived quality. Market-based quality ensures that the
products are “fit for use” (Tracey et al., 1999). Unlike process-based quality, marketbased quality is visible to customers and therefore it is essential to fulfil customer
needs (Koufteros et al., 2002). In this research, we have included three items for
measuring market-based quality; these are “performance” (which includes
functionality, features, durability, aesthetics, and serviceability), “reliability”, and
“high quality products (perceived quality) that meet customer needs”.
Delivery performance has become the focal point of many firms’ competitive
strategies in recent years (Fawcett et al., 1997). Delivery capability is often related to
time-based performance such as delivery speed (Li, 2000; Thun et al., 2000) and
reduction of production lead time (Vickery et al., 1997; Jayaram et al., 1999). Noble
(1995) measures delivery speed as the ability to deliver a product quickly or in a short
lead time. Some authors suggest that the speed of delivery should include both current
and new products (Wacker, 1996; Li, 2000). Therefore, in this research, we have
included three items i.e. “delivery of products quickly or short lead time”, “provide
on-time delivery” and “reduce customer order taking time”. Some literature argues
that time-based performance is inadequate to represent delivery capability. For
example, Wacker (1996) argues that the reliability or dependability of delivery is also
equally importance. Those who excel in delivery capability will be able to deliver ontime according to promise (Fawcett et al., 1997) The combined effects of delivery
reliability and delivery speed are essential to ensure a high level of customer service
(Ward and Duray, 2000). Therefore we have added two more items “correct quantity
of the right kind of products” and “reliable delivery to customers”.
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Flexibility is an essential manufacturing capability because of the need to respond
to rapid technological shift, higher level of demand, supply uncertainty, and greater
needs to provide mass customization (Narasimhan and Das, 1999). In general,
production flexibility is described as the ability of a manufacturing system to cope
with environmental uncertainties (Narasimhan and Das, 1999). In a narrow sense,
production flexibility is the ability of an organization to manage production resources
and uncertainty to meet various customer requests (Zhang et al., 2003). Flexibility is
also the ability to respond to changes and to accommodate the unique needs of each
customer.
Similar to quality and delivery, flexibility is also a multidimensional and complex
construct (Koste et al., 2004). This research study chooses to consider production
flexibility at plant level because it reflects one of the major manufacturing
capabilities. Dimensions such as operational flexibility, product and process
flexibility, volume flexibility, and market flexibility have been considered in the
literature (Hyun and Ahn, 1992). Another way to categorise different types of
flexibility is to refer to the sources of flexibility i.e. machine, labour, material
handling, mix, new product and modification flexibility (Koste et al., 2004). Taking
the above types of flexibility into consideration, four items of product flexibility are
included i.e. “able to rapidly change production volume”, “make rapid product mix
changes”, “produce customized products features”, and “produce broad product
specifications within same facility”. The above plant-level flexibility dimensions have
also included the flexibility of suppliers.
Production cost is the last capability construct considered in this research.
Competing in the marketplace based on cost efficiency requires low-cost production.
Specifically, inventories have been the focus of cost reduction for manufacturers and
22
are one of the justifications for the just-in-time (JIT) system. To keep manufacturing
competitive, firms also have to emphasize materials, labour, overheads, and other
costs (Li, 2000). Noble (1995) suggests that cost-efficiency is associated with a lowcost product, low work-in-process inventories, production flow, reduced overheads,
and so forth. We have, therefore, included “low-cost production”, “low inventory
cost” and “low overhead cost” as three main items for production cost. It is also
necessarily to consider the cost borne by the customers compared with other
competitors. Thus, Swink and Hegarty (1998) suggest including both production cost
and “transfer” cost, which consist of costs to make and deliver products plus costs of
return or replacement if necessary. For this reason, we have included one more item
i.e. “offer low price”.
Scale assessment
As noted by Tan (2001), the measurement model is concerned with the reliability and
validity of the constructs in measuring the latent variables, while the structural model
deals with the direct and indirect relations among the latent variables. In order to test
the measurement model, confirmatory factor analysis (CFA) has been recognized as a
robust method for establishing unidimensionality and testing the measurement model
(Li et al., 2005). CFA involves an estimation of a measurement model, wherein the
observed variables are mapped onto the latent construct. CFA assesses a set of
measures by specifying the expected relationships between the observed indicators
and the latent constructs. In addition, assessing unidimensionality means determining
whether indicators reflect one, as opposed to more than one, construct (Gerbing and
Anderson, 1988). In this study, unidimensionality was established using CFA. Table
1a, 1b and 1c show the summary results of the measurement models, along with the
goodness-of-fit indices for the automotive, electronics and food industries
23
respectively. Several measures of overall fit such as: The root mean square error of
approximation (RMSEA) and the confirmatory fit index (CFI) are assessed. As
Shown in Table 1a, 1b and 1c, the fit indices exceed the recommended threshold
values (Bollen, 1989; Bryne, 2006) providing good fit for the data. These results show
that all the measurement models have acceptable fit indices, which prove the
unidimensionality of the constructs.
<< Insert Table 1a, 1b and 1c about here >>
Moreover, we assessed the convergent and discriminate validity of the scales using
the method suggested by Fornell and Larcker (1981). Convergent validity measures
the similarity or convergence between individual items measuring the same construct;
it can be tested by examining whether each individual item’s standardized coefficient
from the measurement model is significant i.e. greater than twice its standard error
(Anderson and Gerbing, 1988). Additionally, the larger the t-values or the
standardized coefficients become, the stronger the evidence that the individual items
represent the underlying factor. Referring to Tables 1a, b and c, the standardized
coefficients for all items were more than twice their standard errors. Furthermore, the
standardized coefficients for all variables were large (≥ 0.6) and significant (all tvalues are larger than 2). Therefore, all items were significantly related to their
underlying theoretical constructs.
Discriminate validity can be evaluated by fixing the correlation between any pair
of related constructs at 1.0, prior to re-estimating the modified model. A significant
difference in Chi-square values for the fixed and free solutions indicates the
24
distinctiveness of the two constructs (Bagozzi et al., 1991; DeVellis, 1991). In this
study, discriminate validity is established using CFA. For each of supply chain
integration, environmental uncertainty, and competitive capability, discriminate
validity checks were conducted. The results confirm the discriminate validity among
constructs because all the three Chi-square differences between the fixed and free
solutions in Chi-square are statistically significant at p < 0.01 level.
Finally, a test of internal consistency reliability was also performed utilizing scale
composite reliability. The range of scale composite reliability was from 0.69-0.92
which is greater than the suggested 0.70 (Nunnally, 1978). Therefore, all scales were
considered reliable. Thus, the above results support the overall reliability and validity
of the scale items used to measure the hypothesized constructs.
25
Hypothesis testing and results
Path analyses are used to examine the hypothesized structural relationships (using
AMOS 6). Three separate structural models are tested: data from the automotive
industry for model A; data from the electronics industry for model B; and data from
the food industry for model C. As shown in Table 2, it is observed that, for instance,
the overall fit indices of the automotive industry data has χ2 equalling (χ2 = 92.01, p
> 0.05). A thorough examination of alternative goodness-of-fit indices yields the
following results: automotive industry (χ2/d.f. = 0.92; RMSEA = 0.02; GFI=0.94;
NFI=0.94; AGFI=0.90; CFI=0.93), electronics industry (χ2/d.f. = 0.82; RMSEA =
0.01; GFI=0.90; NFI=0.91; AGFI=0.95; CFI=0.94), and food industry (χ2/d.f. = 0.92;
RMSEA = 0.02; GFI=0.92; NFI=0.92; AGFI=0.98; CFI=0.98). It is commonly
accepted within the literature that a model fit is achieved when the GFI, AGFI, CFI
and NFI are all larger than 0.90 and the RMSEA is greater than 0.08 (Hu and Bentler,
1999). Thus, we conclude good model fits for all the three models. To cross-validate
these findings, each of the three structural models is tested with data from two other
industries and no satisfactory model fit was found.
<< Insert Table 2 about here >>
The above model fit indicators confirm that quality is the base capability for all
three industries while delivery, flexibility and cost are the next levels of capabilities
for the automotive, electronics and food industries respectively. Since the model fits
for all the structural models are satisfactory, they can be served as the basis of the
evaluation of our hypotheses. We first examine the three hypotheses (H1, H2 and H3)
26
that suggest quality as the bases of all three models of cumulative capabilities. The
results of the path analyses and the conclusion about hypotheses H1, H2 and H3 are
summarised in table 2.
Hypothesis H1 suggests that quality has significant positive impacts on delivery
for all the three industries. The path analyses show significant and positive
relationships for all three industries (all with p <0.01). Therefore hypothesis H1 is
supported. In terms of the impact of quality on flexibility (H2), the path analyses
show that quality has significant and positive impacts on production flexibility for the
automotive and electronics industries (both with p<0.01). Though less significant (at
p<0.05), a similar path for the food industry is found. Thus, hypothesis H2 is
supported. For hypothesis H3, the path analyses show that quality has significant (at
p<0.01) and positive impacts on production cost for the automotive and food
industries. However, there is no significant quality-cost path for the electronics
industry. Thus hypothesis H3 is partially supported.
To assess the relationships among delivery, production flexibility, and production
cost constructs as the next level of the models of cumulative capabilities for the
automotive, electronics and food industries respectively, three main hypotheses (H4,
H5, and H6) are investigated and their results are shown in table 2. For the automotive
industry, hypothesis H4a suggests a direct positive relationship between delivery and
production flexibility while H5a suggests a direct positive relationship between
delivery and production cost. Hypotheses H4a and H5a are supported by the
following significant positive paths: delivery-production flexibility (r = 0.36, t = 3.11,
p < 0.01) and delivery-production cost (r = 0.26, t = 2.55, p < 0.01). However, the
result shows that there is no significant path for the flexibility-cost relationship and
therefore hypothesis H6a is not supported.
27
<< Insert Figure 2 about here >>
For the electronics industry, the path analyses indicate a significant positive
relationship between production flexibility and delivery (r = 0.35, t = 2.92, p < 0.01).
Thus, hypothesis H4b is supported. In addition, there is also a significant positive
relationship between production flexibility and production cost (r = 0.53, t = 3.51, p <
0.01). Thus, hypothesis H6b is also supported. However, there is no significant
relationship between delivery and production cost; thus hypothesis H5b is not
supported.
For the food industry, hypothesis H4c posits a positive effect of production
flexibility on delivery. However, the path analysis fails to support this prediction.
Thus, hypothesis H4c is not supported. Instead, the production significant and
positive cost-delivery path (r = 0.26, t = 2.38, p < 0.01) indicates that production cost
has significant positive impacts on delivery (H5c) while the production costproduction flexibility path (r = 0.38, t = 3.02, p < 0.01) indicates that production cost
has significant positive impacts on production flexibility (H6c). Therefore, hypotheses
H5c and H6c are supported.
Discussion and practical implications
With path analyses of the three separate structural equation models for the three
industries respectively this research has generated many interesting findings which
may lead to profound implications to theory and practice. Firstly, the findings confirm
that quality is the base for all other capabilities for the automotive, electronics and
food industries in Thailand. In other words, it means that manufacturers from these
28
industries need to first satisfy quality requirements and use quality improvement
initiatives as the foundations for enhancing performance of production flexibility,
cost, and delivery to its customers. It appears that manufacturers with less quality
problems will be able to reduce delivery lead time because they will spend less time
and resource to solve the quality problem (Flynn et al., 1995). In the same manner,
providing products with minimal defects will also allow the firm to reduce the cost of
its production (Ferdows and De Meyer, 1990; Noble, 1995). Several previous studies
of various Thai industries indicate there is still plenty of room for improving quality
management and Just-in-time practices in Thailand (Tannock and Krasachol, 2000;
Rohitratana and Boon-itt, 2001; Phusavat and Kanchana, 2007). Similar arguments
and empirical evidence have already been widely accepted in the operations literature,
especially for industries from developing countries such as Thailand or Ghana
(Amoako-Gyampah and Meredith, 2007), but not necessarily for industries operating
at their performance frontiers (Flynn and Flynn, 2004).
The second significant finding of this research is that, even though quality forms
the base of all other capabilities, it does not necessarily have direct impact on
delivery, production flexibility and production cost for all three industries in Thailand.
For example, quality has significant direct impact on production cost for the
automotive and food industries, but not for the electronics industry. However, for the
electronics industry the effect of quality on production cost is generated via
production flexibility. In fact, similar indirect relationships between quality and
production cost have been revealed earlier (Rosenzweig and Roth, 2004; AmoakoGyampah and Acquaah, 2008).
This finding gives rise to the importance of
recognising the roles of industrial (contingent) contexts, in this case the environmental
uncertainty. Since the electronics industry is operating in a highly volatile
29
environment, flexibility becomes as importance as quality. For the electronics
industry, the impact of quality on production cost may not be significant because
higher production and inventory costs are generated by a higher level of demand and
supply uncertainty. Quality control under such circumstances cost money but it is
useful for improving production flexibility. Therefore, it is production flexibility that
has the direct impact on reducing production cost in this instance.
Another significant finding of this research allows us to clarify the relevant
industrial contexts which affect the sequences of cumulative capabilities. Based on
three separate structural models, each with acceptable goodness-of-fit indices, we
found that the three industries are engaging in different sequences of cumulative
capability, after satisfying quality requirements i.e. delivery capability for the
automotive industry (model A), flexibility for the electronics industry (model B), and
production cost for the food industry (model C) as shown in figure 2.
Our results confirm that the automotive industry in Thailand relies on quality as
the base, and delivery capability as the next capability in the sequence of cumulative
capabilities (quality>delivery>flexibility+cost). This is in fact among the most
discussed sequence of cumulative capabilities. This sequence also reflects the
competitive nature of the Thai automotive industry i.e. the emphasis of quality and
JIT delivery to improve flexibility and cost (Funk, 1995; Sakakibara et al., 1997;
Liker and Wu, 2000; Phusavat and Kanchana, 2007). Delivery naturally becomes the
next capability because delivery capability (on-time and reliable delivery) is required
to reduce variance in production and delivery processes and increase the predictability
of the production and distribution systems (delivery reliability) in such a way that
production flexibility (especially volume) can be enhanced (Flynn and Flynn, 2004;
Rosenzweig and Roth, 2004; Gröβler and Grübner, 2006). Our results also confirm
30
direct and positive delivery capability on production cost, similar to the finding of
Gröβler and Grübner’s (2006) study using cross-sectional data. Often, a reduction in
delivery lead-time and an improvement of delivery reliability will lower the inventory
level, leading to lower production cost (Ferdows and De Meyer, 1990).
The only surprising result for the automotive industry is that production flexibility
has no direct impact on production cost. One possible explanation to this finding is
that the Thai automotive industry has invested considerably in improving production
flexibility but more time is needed to have a true impact on production cost (saving in
inventory). We explain this result by referring the time-slack effect suggested by
Schmenner and Swink (1998). Alternately, Gröβler and Grübner (2006) found the
existence of a trade-off (negative) relationship between flexibility and cost. Instead,
another study by Sarmiento et al. (2007) found that there is an indirect effect between
production flexibility and production cost via delivery. These contradictory findings
need further explanation; further study is required to examine the industry contexts or
other factors which generate the direct effects, indirect effects, or trade-off
relationships between flexibility and cost.
The structural model for the electronics industry suggests that production
flexibility is the next capability after quality; furthermore production flexibility will
positively
impact
production
cost
and
delivery
capability
(quality>flexibility>delivery+cost). This reminds operations managers that the
knowledge of the “better” sequence of cumulative capabilities applicable for the
automotive industry may not be simply be transferred to the electronics industry. One
has to recognise the roles of environmental uncertainty. The electronics industry is
similar to the automotive industry in many aspects but the significant volatile nature
of demand and the significant higher rate of new production introduction lead to a
31
greater emphasis on flexibility (Rosenzweig and Roth, 2004). Numerous studies have
confirmed that the electronics industry, competing under a high level volatility (Lau,
1999; Rosenzweig and Roth, 2004) and such an environmental condition, makes a
difference between the electronics and automotive industries. We argue that, in a
highly volatile condition it is flexibility that leads to the improvement of delivery and
production cost, not the other way round. This explains why our results confirm that
the delivery-flexibility relationship for the electronics industry is in the opposite
direction of that of the automotive industry.
Another insightful finding is that production flexibility is found to have a
significant impact on production cost for the electronics industry, but no significant
path is found for the automotive industry. We use time-slack to explain the
insignificant path of the automotive industry; and environmental uncertainty to
explain the significant path for the electronics industry. The Thai electronics industry
has more manufacturing experience than the automotive industry. Therefore, while
the automotive industry is facing time-slack in realising cost saving from the
investment in flexibility, electronics seems to have reaped the cost benefits of
flexibility. Theoretically, under greater environmental uncertainty, when electronics
manufacturers are able to respond quickly to changes in demand there will be fewer
wasteful activities and inventory and therefore productivity will rise (Liker, 1997).
Upton (1994) suggested that flexibility should not lead to penalty in time, effort cost,
and performance. This finding clarifies that the ambiguous relationship between
flexibility and cost (Gröβler and Grübner, 2006) may be originated from differences
among industries. Several previous attempts found no support for the proposition that
firms that respond to increased uncertainty with increased flexibility will experience
increased performance (Pagell and Krause, 1999 & 2004). Our finding helps to
32
explain such a result. The Pagell and Krause (1999 & 2004) studies relied either on a
small sample-size or cross-sectional data and therefore the effects of an industrial
context were not revealed. When we analysed each industry separately, we found
different flexibility-cost relationships and therefore are able to explain the difference.
The findings for the food industry further reveal the multiplicity of the sequences
of cumulative capabilities. The results confirm that quality is the base of all other
capabilities for the food industry in Thailand, and instead of delivery or production
flexibility, production cost becomes the next capability. This is perhaps the least
commonly accepted model of cumulative capabilities. So far only Amoako-Gyampah
and Meredith (2007) found such a quality-cost sequence from the Ghana dataset. Our
explanations are similar to those offered by Amoako-Gyampah and Meredith (2007).
The Thai food industry is characterised as the less-mature industry with tremendous
opportunities for improvement. A similar finding was discovered for the
manufacturing and service industries in Ghana (Amoako-Gyampah and Meredith,
2007). Respondents from the Thai food industry for this research are mainly involved
in packaging of agricultural products in the form of commodities and therefore cost is
the key order winner. Also, the Thai food industry is considered low-cost suppliers of
food products to the global market and therefore capabilities in continuous cost
reduction become essential. Only upon receiving a high level of customer orders can
food manufacturers afford to invest in flexibility and delivery.
Another intriguing finding is the positive impact of production cost on delivery
and production flexibility for the food industry. The impact of flexibility or delivery
capabilities on production costs for the electronics and automotive industries are
easier to explain, but the relationships in the opposite direction appear to be rather
unusual. For the automotive and electronics industries production cost becomes the
33
product of flexibility and quality. Again we found explanations from industrial
contexts. The food industry needs to work very hard to control their production costs.
That is why they need to manage their production capacity very well to compete in
this “low-cost production” environment. Under such a low-cost environment there
will be lower inventory, higher capacity and equipment utilisation, leading to
enhanced delivery and increased production flexibility (Ward and Duray, 2000).
Further, there is often lower environment uncertainty in the food industry because
they produce mostly commodities with a stable demand, long product life-cycle, low
variety, and often with a make-to-stock policy.
On a final note for the food industry, we found no significant relationship between
production flexibility and delivery. Interestingly we found positive impacts of
production flexibility on delivery for the electronics industry, and a similar
relationship in an opposite direction for the automotive industry. We used
environmental uncertainty to explain the difference between the automotive and
electronics industries. For the food industry, quality and production costs are often the
more critical enablers for improving delivery capabilities but production flexibility
provides less impact because the industry is operating in a more stable and low-cost
environment.
Finally, this research found that differences in competitive priorities are the main
explanations for the different sequences of cumulative capabilities among the three
industries. Such effects of industrial contexts on the sequences of cumulative
capabilities were first suggested by Noble (1995). The findings of this research allow
us to further relate and explain specific industrial characteristics that influence the
sequences of cumulative capabilities for each industry, as shown in table 3. Other than
competitive priorities, characteristics such as demand patterns, rate of new product
34
introduction, product characteristics, and price competition are external competitive
environments that can be used to explain differences in the sequences of cumulative
capabilities. Instead, the type of production processes that may form the constraint can
actually be used to explain the competitive capabilities of the industry. These
industrial characteristics have already been briefly mentioned by Noble (1995) and
Flynn and Flynn (2004).
<< Insert Table 3 about here >>
The findings of this research to suggest that industries (e.g. automotive industry) with
relatively constant and predictable demand, less frequent new product introduction
and competing with mainly quality and JIT delivery will first focus on meeting
quality requirements and then use quality initiatives to improve delivery, flexibility
and cost. For industries (e.g. the food industry) which produce commodities with even
more stable and predictable demand and relative no need for new product
introduction, but competing with main price, an emphasis on quality and cost is
essential, before embarking in the improvement of delivery and flexibility. In
Thailand, flexibility and delivery capabilities are also constrained by the use of
relatively low-cost and manual workers. However, for more volatile industries (e.g.
the electronics industry) with relatively unstable and unpredictable demand, short
product life-cycle but very frequent new product introduction, quality and flexibility
become the most important capabilities which are then used to improve delivery and
cost.
35
The above interpretation is by no means an attempt to generalise a prescription for
operations managers. Wheelwright (1984) suggests that different firms can develop
different sets of capabilities, and still be equally competitive. Porter (1980) calls it a
differentiation strategy. Even within the same industry, firms can opt to compete on a
unique set of capabilities. With these arguments it is very likely to find that the
strengths of the relationships among quality and other capabilities differ from one
industry to another, from one state of maturity to another, and from one country to
another. This research is somehow an attempt to provoke and initiate further study of
the various possible contextual (contingent) factors which influence the sequences of
cumulative capabilities.
Conclusion
This research attempts to compare models (sequences) of cumulative capabilities
among the automotive, electronics, and food industries in Thailand, and further
explains how different industrial contexts may shape the different models of
cumulative capabilities. The findings confirm quality as the base for all other
capabilities for all three industries, but upon satisfying the quality requirement,
different industries rely on different sequences of cumulative capabilities: delivery
becomes the next capability for the automotive industry; production flexibility for the
electronics industry and production cost for the food industry. This means different
industries may proceed with the improvement of different capabilities depending on
some specific industrial context such as competitive priorities, demand characteristics,
rate of new product innovation, and type of manufacturing process.
One of the unique contributions of this research is the use of three separate
structural equation models for the three industries. This approach allows for the
analysis of the total models of cumulative capabilities and reveals the multiplicity of
36
the sequences of developing cumulative capabilities. With such an approach we also
managed to clarify and explain the relationships among delivery, flexibility, and cost
based on the contexts of each industry. The ambiguity on the relationships among
delivery, flexibility, and cost in the literature is now clarified. However, one of the
weaknesses is that business performance has not been included in the structural
models. Kim and Arnold (1993) found that the manufacturing capabilities
(operationalised as competence index) appears to have more significant statistical
relationships with some performance measures but not equally to all the financial and
market performance. They also found that manufacturing capabilities of different
industries do not have the same influence on business performance. Thus, further
research to relate manufacturing capabilities with business performance for different
industries is desirable.
Furthermore, this research also generates several unexpected findings which
further provoke and initiate more studies of the various possible contextual
(contingent) factors which may influence the sequences of cumulative capabilities.
Competitive priorities and environmental uncertainty appear to be contextual factors
but further examination is required. Swink and Way (1995) suggest the identification
of the environmental contingencies which favour cumulative capabilities as one of the
important challenges for future research in operations strategy.
37
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Factor and Scale items
Product Quality (ρ = .77)
High performance products that meet customer needs
Produce consistent quality products with low defects
Offer high reliable products that meet customer needs
High quality products that meet our customer needs
Delivery ( ρ = .81)
Correct quantity with the right kind of products
Delivery products quickly or short lead-time
Provide on-time delivery to our customers
Provide reliable delivery to our customers
Reduce customer order taking time
Production Cost ( ρ = .86)
Produce products with low costs
Produce products with low inventory costs
Produce products with low overhead costs
Offer price as low or lower than our competitors
Production Flexibility ( ρ = .75)
Able to rapidly change production volume
Produce customized product features
Produce broad product specifications within same facility
The capability to make rapid product mix changes
Standardized
Coefficient
Standard
Error
t-value
0.75
0.75
0.91
0.84
__a
0.08
0.09
0.10
__a
13.82
10.46
10.49
0.75
0.88
0.88
0.70
0.68
__a
0.11
0.11
0.09
0.13
__a
10.97
10.98
9.26
8.31
0.83
0.78
0.82
0.56
__a
0.11
0.10
0.11
__a
10.30
10.90
7.17
0.72
0.39
0.94
0.67
__a
0.12
0.19
0.16
__a
4.15
7.09
6.04
Notes: Loadings are common metric completely standardized estimate: all t-value
significant at p < .01. ρ = scale composite reliability. CFA fit statistics: χ2 = 92.012;
d.f. = 99; p > .05: RMSEA = 0.02: CFI = 0.93; __a = fixed loading
Table 1a Measurement model and confirmatory factor analysis results for automotive
industry
47
Factor and Scale items
Product Quality (ρ = .84)
High performance products that meet customer needs
Produce consistent quality products with low defects
Offer high reliable products that meet customer needs
High quality products that meet our customer needs
Delivery ( ρ = .92)
Correct quantity with the right kind of products
Delivery products quickly or short lead-time
Provide on-time delivery to our customers
Provide reliable delivery to our customers
Reduce customer order taking time
Production Cost ( ρ = .76)
Produce products with low costs
Produce products with low inventory costs
Produce products with low overhead costs
Offer price as low or lower than our competitors
Production Flexibility ( ρ = .74)
Able to rapidly change production volume
Produce customized product features
Produce broad product specifications within same facility
The capability to make rapid product mix changes
Standardized
Coefficient
Standard
Error
t-value
0.90
0.86
0.75
0.79
__a
0.09
0.13
0.10
__a
10.18
6.96
9.20
0.80
0.85
0.91
0.93
0.67
__a
0.12
0.13
0.12
0.16
__a
9.03
9.80
10.03
6.62
0.78
0.89
0.83
0.76
__a
0.14
0.13
0.15
__a
8.42
7.97
6.13
0.81
0.73
0.63
0.76
__a
0.13
0.17
0.16
__a
6.12
5.34
4.68
Notes: Loadings are common metric completely standardized estimate: all t-value
significant at p < .01. ρ = scale composite reliability. CFA fit statistics: χ2 = 84.596;
d.f. = 103; p > .05: RMSEA = 0.01: CFI = 0.94; __a = fixed loading
Table 1b Measurement model and confirmatory factor analysis results for electronic
industry
48
Factor and Scale items
Product Quality (ρ = .82)
High performance products that meet customer needs
Produce consistent quality products with low defects
Offer high reliable products that meet customer needs
High quality products that meet our customer needs
Delivery ( ρ = .71)
Correct quantity with the right kind of products
Delivery products quickly or short lead-time
Provide on-time delivery to our customers
Provide reliable delivery to our customers
Reduce customer order taking time
Production Cost ( ρ = .69)
Produce products with low costs
Produce products with low inventory costs
Produce products with low overhead costs
Offer price as low or lower than our competitors
Production Flexibility ( ρ = .77)
Able to rapidly change production volume
Produce customized product features
Produce broad product specifications within same facility
The capability to make rapid product mix changes
Standardized
Coefficient
Standard
Error
t-value
0.89
0.99
0.70
0.53
__a
0.07
0.09
0.12
__a
16.18
9.54
5.73
0.82
0.77
0.88
0.90
0.65
__a
0.09
0.12
0.11
0.16
__a
12.10
11.12
11.54
6.80
0.79
0.73
0.75
0.69
__a
0.12
0.13
0.14
__a
7.72
7.80
7.20
0.63
0.41
0.59
0.86
__a
0.16
0.19
0.24
__a
3.69
5.20
5.95
Notes: Loadings are common metric completely standardized estimate: all t-value
significant at p < .01. ρ = scale composite reliability. CFA fit statistics: χ2 = 90.921;
d.f. = 103; p > .05: RMSEA = 0.02: CFI = 0.98; __a = fixed loading
Table 1c Measurement model and confirmatory factor analysis results for food
industry
49
Cumulative capabilities
Industry and its characteristics
Quality>Delivery>
Automotive industry in Thailand
Flexibility+Cost
 Competitive priorities = quality + JIT delivery
 Relatively constant and predictable demand
 Less frequent new product introduction
 Semi-manual assembly line
Quality>Flexibility>
Electronic industry in Thailand
Delivery+Cost
 Competitive priorities = quality + flexibility
 Unstable, unpredictable demand, short life cycle
 Frequent new product introduction, price for innovation
 High speed and automated production line
Quality>Cost>
Food industry in Thailand
Delivery+Flexibility
 Competitive priorities = quality + cost
 Relatively constant and predictable demand
 Less frequent product introduction, but frequent price war
 Highly-manual assembly line
Quality as the base for all
capabilities
Automotive, electronic, and food industries in Thailand
 Still inexperience in quality management
 Not yet achieve performance frontier
Table 3 Industrial contexts and cumulative capabilities
50
Base level
Upper level
Production
Cost
H3
Product
Quality
Delivery
H1
H5a
H6a
H4a
Production
Flexibility
H2
Model A
Production
Cost
H3
Product
Quality
Production
Flexibility
H2
H6b=H6a
H5b=H5a
H4b
Delivery
H1
Model B
Delivery
H1
Product
Quality
Production
Cost
H3
H5c
H4c
H6c
Production
Flexibility
H2
Model C
Figure 1 Three possible models of cumulative capabilities
51
Base level
Upper level
Production
Cost
0.34**
Product
Quality
Delivery
0.52**
0.26*
NS
0.36**
Production
Flexibility
0.22**
Automotive†
Production
Cost
NS
Product
Quality
Production
Flexibility
0.40**
0.53**
NS
0.35**
Delivery
0.62**
Electronic‡
Delivery
0.39**
Product
Quality
0.36**
0.22*
*
Production
Cost
0.26*
NS
0.38**
Production
Flexibility
Food §
** p < 0.01, * p < 0.05, NS = Not significance
† Fit Statistic: χ2/d.f. = 0.92, RMSEA = 0.02, GFI=0.94, NFI=0.94, AGFI= 0.90, CFI = 0.93
‡ Fit Statistic: χ2/d.f. = 0.82, RMSEA= 0.01, GFI=0.90, NFI=0.91, AGFI= 0.95, CFI =0.94
§ Fit Statistic: χ2/d.f. = 0.92, RMSEA= 0.02, GFI=0.92, NFI=0.92, AGFI= 0.98, CFI =0.98
Figure 2 Results for the three industries
52
H1
H2
H3
H4a
H5a
H6a
H4b
H5b
H6b
H4c
H5c
H6c
Proposed path
Base Level
Product quality to delivery
Product quality to production flexibility
Product quality to production cost
Upper Level
Delivery to production flexibility
Delivery to production cost
Production flexibility to production cost
Production flexibility to delivery
Delivery to production cost
Production flexibility to production cost
Production flexibility to delivery
Production cost to delivery
Production cost to production flexibility
Automotive†
Estimate
t-value
0.52
5.73**
0.22
3.51**
0.34
3.06**
0.36
0.26
ns
X
X
X
X
X
X
3.11**
2.55**
ns
X
X
X
X
X
X
Electronic‡
Estimate
t-value
0.62
4.99**
0.40
3.14**
ns
ns
X
X
X
0.35
ns
0.53
X
X
X
X
X
X
2.92**
ns
3.51**
X
X
X
Food§
Estimate t-value
0.39
4.13**
0.22
2.14*
0.36
3.63**
X
X
X
X
X
X
ns
0.26
0.38
X
X
X
X
X
X
ns
2.38**
3.02**
Note
Supported
Supported
Partial Supported
Supported
Supported
Not Supported
Supported
Not Supported
Supported
Not Supported
Supported
Supported
Notes: † Fit Statistic: χ2/d.f. = 0.92, RMSEA = 0.02, GFI=0.94, NFI=0.94, AGFI= 0.90, CFI = 0.93
‡ Fit Statistic: χ2/d.f. = 0.82, RMSEA= 0.01, GFI=0.90, NFI=0.91, AGFI= 0.95, CFI = 0.94
§ Fit Statistic: χ2/d.f. = 0.92, RMSEA= 0.02, GFI=0.92, NFI=0.92, AGFI= 0.98, CFI = 0.98
* p < 0.05, ** p < 0.01, ns = not significant path, X = No path in the model
Table 2 Path analyses and testing of hypotheses
53