relating learning capability to the success of...
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Technological Forecasting & Social Change (will be published in Vol. 69, No.1)
RELATING LEARNING CAPABILITY TO THE SUCCESS OF COMPUTER-INTEGRATED
MANUFACTURING
Jason Z. Yin W. Paul Stillman School of Business
Seton Hall University South Orange, New Jersey 07079
Tel: 973-761-9360 Fax: 973-761-9217
E-mail: [email protected]
JASON Z. YIN is an associate professor of strategic management and international business at Seton Hall University. His current research interests center on technology strategies and strategic change in the new economy. Address reprint requests to Jason Z. Yin, W. Paul Stillman School of Business, Seton Hall University. 400 South Orange Avenue, South Orange, New Jersey 07079-2692, or send email to: [email protected].
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RELATING LEARNING CAPABILITY TO THE SUCCESS OF COMPUTER-INTEGRATED MANUFACTURING
ABSTRACT
With rapid advances in both electronic and mechanical technologies, computer-
integrated manufacturing (CIM) systems increasingly give users greater
flexibility, quality, speed and productivity. However, the exploration and
exploitation of sophisticated CIM systems necessitate organizational learning.
This study empirically relates organizational learning capability to the
performance of CIM firms (or plants). The results of a hierarchical regression
analysis of 124 firms indicates that while learning capability plays a significant
role overall, proper alignment of learning capabilities with CIM techniques will
lead to better performance.
Key Words: CIM, Organizational learning, Learning capability
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RELATING LEARNING CAPABILITY TO THE SUCCESS OF COMPUTER-INTEGRATED MANUFACTURING
Growing market complexity and intense global competition have recently forced manufacturing
firms to seek process-based innovations [1]. Using programmable tools to connect separate
manufacturing functions into a unified system, firms have introduced a radically new type of
automation called computer-integrated manufacturing (CIM). Electronic integration of several
manufacturing functions is believed to provide firms with production flexibility, quality and
responsiveness that are fundamental for meeting diverse market demands. To exploit this
operational strength in competition, scholars have added organizational learning to the list of
critical variables in assessing the CIM-competitive performance relationship [2, 3, 4]. The
underlying assumption is that learning will enhance the learner's effectiveness [5]. Such effect
of learning in CIM processes, however, was not studied rigorously. Most writings intending to
relate learning with CIM were either based on purely hypothetical perspectives or anecdotal
accounts in case studies [6, 7]. Large-scale empirical validation is thus missing.
The purpose of this research project is to fill the gap and enrich our understanding of the
role of organizational learning in the implementation of CIM through an empirical analysis of a
sample of 124 manufacturing firms. Strategic management is built on a search for organizational
intelligence [8]. Better understanding the role of organizational learning in the implementation of
computerized automation clearly has policy implications in strategic planning and decision-
making.
In this article, we first examine the concepts and the three sources of organizational
learning and the three categories of CIM techniques. We then hypothesize that organizational
learning will positively affect CIM performance and each of the three types of learning will have
significant marginal contribution to firm’s the performance over the three types of CIM
techniques respectively. We explain the data and method of research and report the findings of
this empirical study. And finally, we conclude with a discussion of policy implications of the
findings and future study.
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Background ORGANIZATIONAL LEARNING
Organizational learning is consensually defined as a process of improving actions
through better knowledge and understanding [9]. Organizational learning involves acquiring
existing knowledge (cumulating into and retrieving knowledge from organizational memory) and
creating new knowledge [10, 11]. Learning is frequently described as a critical feature of the
behavior of an organization and learning capability is the only source of sustainable competitive
advantage. An organization that commits to learning and development at all levels and has the
ability to adapt to changes is a "learning organization" [5].
Besides the general discussion, some scholars pay special attention to learning behavior
in manufacturing organizations. In a study of beleaguered manufacturing industries, Hayes,
Wheelwright and Clark found that ability to learn and to achieve sustained improvement in
performance over a long period of time is one common feature in high-performance plants [12].
Three basic sources of organizational learning in manufacturing organizations have been
identified in the literature. First, learning in R&D, which explores and creates new technologies.
Second, learning by doing during implementation, which explores and exploits productive
potentials of existing technologies. And third, learning by managing which accumulates both
technical and non-technical knowledge for making decisions and collaboration throughout the
entire R&D, production and marketing processes.
Learning by R&D. Searching, studying and experimenting in R&D are important
learning activities in a manufacturing organization. Rosenberg describes R&D as a learning
process in generation of new technologies [13]. It involves acquiring scientific and engineering
and turning it into useful commercial applications through optimal design of product and
process. While many researchers focus on outcomes of cumulated experience of R&D activities,
some others have looked into the R&D process itself. Cohen and Levithal [14] argued that
"...while R&D obviously generates innovation, it also develops the firm's ability to identify,
assimilate, and exploit knowledge from the environment" (p.569) and they refer this ability as a
firm's learning capacity.
Learning by Doing. In addition to learning by R&D, learning occurs throughout the
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implementation process of innovation and production activities [10, 15, 16]. The learning taking
place during implementation is called as "learning by doing," following Arrow's notion [17].
Learning by doing refers to knowledge gained via production experience, which may result in
manufacturing cost reduction and efficiency improvement over time. Such experience may come
from the firm itself, its suppliers and its users [15]. In studies on learning by doing, more
empirical evidence indicates the importance of user's learning or learning by using [18, 19].
Leonard-Barton, through a study of Chaparal Steel, found that major learning activities in
operating factories result from employee empowerment, creation and control of both internal and
external knowledge, problem solving, innovation and experimentation. She argued that a
manufacturing factory could and should function as a learning laboratory [20].
Learning by Managing. In a manufacturing system, R&D learning is more exploratory
and learning by doing is more exploitatory. Both learning by R&D and learning by doing are
mostly performed by technical and engineering personnel and workers, which have been the
primary focus of the current literature. March [11] argues that maintaining an appropriate
balance between exploration and exploitation is a primary factor in system survival and
prosperity. Understanding the choices and improving the balance between exploration and
exploitation through appropriate resource allocation command a great deal of learning from
managerial practice or learning by managing. March's assertion calls for paying more attention
to manager's learning. Learning by managing involves cross-level planning, relating the firm to
its social context, coordinating inter-functional activities, and formulating strategies to allocate
resources [21, 22, 23]. It requires the capability to link human resource management, strategic
management and the management of information systems as a means to facilitate the flow of
learning [7].
IMPLEMENTATION OF CIM SYSTEMS
CIM is the term used to describe the total automation of a manufacturing system under
the control of computer and digital information. CIM integrates an organization by automating
the flow of information among interrelated processes (such as design, testing, manufacturing,
tooling, and scheduling), and organizational functions (such as R&D, production, marketing,
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inventory control, material handling and quality control). The actual installation of computer
integration systems varies widely from flexible manufacturing machining centers to "automation
islands" (individual departments) with specialized computer technologies. Computerized
manufacturing technologies are often employed for three manufacturing tasks. (1) Computer-
aided design (CAD) and computer-aided engineering (CAE) are used for product development.
(2) Computer-aided manufacturing (CAM) and material requirement planning (MRP) or their
combination is often used to automate the production processes. And (3) manufacturing resource
planning (MRP-II) and computer-automated process planning (CAPP) are often used to assist
managerial planing.
A few case studies have been conducted to explore the learning effect on computerized
automation systems. In an analysis of application-specific integrated circuits (ASICs) in
electronic-systems manufacturers, Callahan and Diedrich found that organizational learning is
the driving force to make CAD and other manufacturing technologies work [24]. They also
found that the design complexity is always higher than the power of CAD tools. Firms have to
bring in specialists with different skills, objectives and even cultures to carry out the product
design, system design and functional specifications of the required ASICs. Instead of
individual's learning, the task force has to learn collectively and to develop standard processes as
a coordination mechanism. Studies on applications of MRP systems also indicate that a great
deal of organizational learning is needed. MRP is technically very well developed and is
commercially feasible for manufacturing companies. The implementation of an MRP system
should be straightforward. But in fact there are many problems that arise in such "proven"
systems and many "failures" in trying to install them. Most of the problems are human factors
and learning related. Whenever poor performance occurs in a MRP system, this is usually
caused by a lack of understanding and a lack of training and discipline from top management
through to functional staff and to workers on the shop floor [20].
THE MODEL AND HYPOTHESES
Except a few case studies as mentioned above, little empirical work has been done in large scale
to validate the learning-CIM relationship. This project intends to conduct a large-scale
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investigation across industry sectors to empirically validate the effect of learning capabilities
over the accomplishment of CIM firms. THE MODEL
We propose that organization learning capability has a positive effect on CIM system and
in addition, each of the three types of learning facilitates each of the three CIM subsystems
respectively for better performance, as shown in Table 1.
Table 1: The Proposed Fit of CIM and Learning Capabilities
Organizational Learning (ORGCAP)
Learning by R&D Capability (R&DCAP)
Learning by Doing Capability (LBDCAP)
Learning by Managing Capability (MAMCAP)
CIM + CAD, CAE + CAM, MRP + MRP-II, CAPP +
When a firm adopts CAD/CAE for product development, the designers and the engineers
may have to learn by studying internal and external information and knowledge to identify their
needs, to search, assess, select and acquire the fitted software. They may also have to learn how
to use the software to design new products and to solve related engineering issues. Learning may
also take place in experimentation through pilot production and testing. We identify these
learning activities as learning by R&D interacting with CAD/CAE processes.
Furthermore, when CAM/MRP are used for production, the engineers, functional staff,
and workers may have to learn how the computer systems work, their strengths and limits and
how to coordinate the material flow and information flow. They learn how to customize
programs to the local environment and how to reengineer the system for improvements. These
activities are categorized as learning by doing during implementation.
Finally, traditional process planning solely depends on human experience, which is time
consuming, subjective, and inconsistent in quality. When MRP-II /CAPP and other planning
systems are used, significant improvements can be achieved in terms of efficiency and accuracy.
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However, managers may have to learn how to decide when and what type of automation
technology to adopt. They have to decide how to formulate strategies that will cause the
organization to adapt to the new manufacturing environment and to coordinate and collaborate
internal and external activities to reinforce collective effect of the CIM system.
HYPOTHESIS . The primary interest of this study is to determine the marginal contribution of
organizational learning to the performance of CIM systems. The central hypothesis is that higher
learning capability will lead to better performance of a CIM system.
Based on a detailed literature review, eleven items were selected as proxies for the multi-
dimensional performance of a CIM system, the dependent variables in the model. Both CIM
variable(s) and organizational learning variable(s) are employed to explain the variance of the
dependent variables. Therefore, it is necessary to control statistically the variance of dependent
variable due to causally antecedent variables, CIM. Thus, we consider the learning variables as
treatment variables and CIM as covariates. The hypothesized general model of the performance
of a CIM system takes the form:
Y = a + b1X1 (1)
Y = a + b1X1 + b2X2 (2)
where Y is the dependent variable, X1 is the CIM variable (the covariate) and X2 is the learning
variable (the treatment), b1 and b2 are the coefficients to be estimated. To determine the effect of
organizational learning, a "forced" hierarchical regression is designed to serve this purpose. In
this procedure, each of the 11 dependent variables, in turn, regresses against the covariate(s) as
in Equation (1) and then the regression is repeated with the treatment variable added to the
covariate(s) as in Equation 2. The addition of the treatment variable results in an increment in
the variance (R2) of dependent variables. The increment in the variance (∆R2 ) is thus explained
by the learning variables.
Specifically, we propose four hypotheses for testing. The first hypothesis is to relate
overall organizational learning to the performance of CIM firms:
Hypothesis 1: Organizational learning capability will positively affect organizational
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performance over the effect of CIM technology.
After identifying the overall marginal contribution of organizational learning to
performance over and above the effect of CIM technology, we are interested in looking into the
effect of each of the three types of learning with the control of the effect of its correspondent
automation techniques employed.
Hypothesis 2: Learning by R&D capability will positively affect organizational performance
over the effects of CAD/CAE.
Hypothesis 3: Learning by doing capabilities will positively affect organizational performance over the effects of CAM/ MRP.
Hypothesis 4: Learning by managing capability will positively affect organizational
performance over the effect of MRP-II /CAPP. DATA AND METHODS
DATA AND PROCEDURE
The sampling population for this research is defined as plants in manufacturing
organizations that used computerized automation technology. We identify the manufacturing
organizations directly from Ward's Industrial Directory (Vol. IV, 1993). To facilitate an in-depth
analysis, the sample is restricted to medium and large size firms (with 500 or more employees) in
four industry groups: motor vehicles and equipment (SIC 371, equipment only), aircraft and
parts (SIC 372, parts only), medical instruments (SIC 384) and consumer appliances (SIC 364).
We used the total design method outlined in Dillman for our design of the questionnaire,
the implementation and conduct of the survey [25]. We drew the questionnaire items from
previous research and consultation with practitioners and colleagues. Questions were framed on
a five-point Likert scale. We selected the chief manufacturing executives from 290 firms as the
respondents. The selection of the respondents was dictated by a single imperative: the
individual’s expert knowledge of and familiarity with CIM techniques deployed and his/her
ability to assess comprehensively and report accurately. Given the technicality of CIM systems
and the managerial and strategic nature of the survey contents, it was necessary to choose top
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manufacturing executives whose understanding and decision-making pertained to their company.
As informants, they were the most able to recognize and assess the relevant learning and
technical activities and determined their impacts on the firm’s multidimensional performance
objectives. They were also the most qualified to report specific information on their strategic
intents and actions.
The questionnaire was addressed to the chief manufacturing executives directly. In the
cover letter to the respondents, we emphasize that this study is not designed to assess the
performance of any individual company and the filled questionnaires will be coded with names
of companies and respondents removed. We guaranteed complete confidentiality and
anonymity of their response to all respondents. There were 154 chief executives responded.
Since the data were obtained through the respondent’s observation, it might raise
concerns about the reliability of measures because responses might differ from one to another
(individual bias) if different persons are responsible for the questionnaire [26]. We addressed
these concerns by taking second informant from the company responded to our survey. After
filling the questionnaire, we asked the chief executives to forward us the names of chief
managers at the business unit or plant level who had clear knowledge concerning the strategic
decisions on CIM and its implementation. And we sent the same questionnaire to a chief
manager referred to us by the chief executives. We received the second responses from 124
firms.
We then performed an inter-rater reliability (IRR) test with the data from the 124 firms
(plants), following the procedure prescribed in James, Demaree, and Wolf [27], to verify the
concerns and to validate the measures. The results from the test showed that the IRR scores of
all variables were ranged from .58 to .93 (Column 4 in Table 2), indicating that the observations
and judgments of the respondents were objective and consistent (convergent) and thus the
measures are reliable.
Because the results of inter-rater reliability test were acceptable, we averaged the scores
of each survey item from the two respondents within a firm as the aggregate score of item for
each firm, following James, Demaree & Wolf, 1984. We then performed the data analysis of the
124 firms with two responses and excluded the remaining from this study due to incomplete
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information characteristics of the sample are shown in Table 2.
DEPENDENT VARIABLES
As mentioned above, eleven dependent variables were selected to represent the potential
performance objectives for adopting a CIM system. The first six were considered as internal
operational objectives and the remaining five were in line with competitive strategy. The eleven
dependent variables were defined as follows:
1. To improve operational efficiency and productivity (EFFICI);
2. To achieve effective control of the process (PROCES);
3. To enhance product design and to speed new product introduction (NEWPRD);
4. To increase the level of manufacturing integration (INTEGR);
5. To allocate resources effectively and efficiently (RESOUR);
6. To attain high quality in products and services (QUALIT);
7. To achieve manufacturing flexibility (FLEXIB);
8. To respond quickly to market demand (RESPON);
9. To gain a higher level of customer satisfaction (SATIDF);
10. To have better economic returns (RETURN);
11. To enhance overall competitiveness (COMPET).
The eleven performance objectives were measured using single items by asking the
extent to which the firm had achieved each of the objectives (1=not achieved, 5=achieved
mostly). Chief executives and plant managers responded according to their observations.
Although the internal consistency of single item measures is impossible to test [28], the inter-
rater reliability across respondents within firms indicates a high level of convergence.
INDEPENDENT VARIABLES
There were two composite variables, ORGCAP and CIM to represent organizational
learning capability and the level of computerized integration in a manufacturing system,
respectively. ORGCAP was composed of three learning variables that were employed as
indicators of the three types of organizational learning activities, R&D learning, learning by
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doing and learning by managing. CIM was composed of the six above-mentioned computerized
techniques, CAD, CAE, CAM, MRP, MRP-II and CAPP, adopted in a CIM system.
R&D learning capability (R&DCAP). Learning by R&D capability was measured by a
seven-item scale assessing the extent to which the firm was able to acquire new knowledge
externally, to create new knowledge internally, and to apply this knowledge to product and
process innovations. The items included the training background of R&D personnel, frequency
of seeking ideas from both suppliers and customers for product design and improvement, efforts
in seeking cooperation with industry alliance and outside research groups for technology
development, efforts in engineering and tooling for operating efficiency; speed in innovation
comparing to industry standard. The mean composite score of R&DCAP was 3.50 with an
average inter-item correlation r=.54. The Cronbach's alpha (α ) value, as a measure of internal
consistency reliability of the composite, was .88.
Learning by doing capability (LBDCAP). Learning by doing capability was measured
by an eight-item score that evaluates a wide range of learning activities taking place on the
factory floor. The items included the workers' training and skills, technical personnel skills,
participation of workers and technical personnel in decision-making, autonomous work team,
cross-functional committees for product and process innovation, communication and
coordination among operational functions. The mean composite score of LBDCAP was 3.68,
with an average inter-item correlation r=.49 and α =.79.
Leaning by managing capability (MANCAP). Managers' learning capability was
measured by a six-item score, which estimates the ability of executives in decision making,
resource allocation, and coordination. The items included managers' educational and
experiential background, knowledge of computer and information system, organizational culture,
management style (autocratic to participative) and structural coordination of functional activities
(reactive to planned). The mean score of MANCAP was 3.20 with r=.45 and α=.78.
Organizational learning capability (ORGCAP). As discussed above, ORGCAP was
composed of seven R&D learning items, eight learning by doing, and six learning by managing.
The mean score of ORGCAP was 3.46 with r=.47 and α=.80.
The level of CIM adoption (CIM). The level of computer-integrated manufacturing
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was measured by a composite score of six-item representing the type and the extent of
implementing computer-integrated automation techniques, including CAD, CAE, CAM, MRP,
MRP-II and CAPP, in manufacturing plants (1=not used to 5=extensively used). The mean score
was 3.22 with r=.64 and α=.89.
Because the Cronbach's alpha coefficients are acceptable, we used the composite scores
to measure CIM and the three learning variables, R&DCAP, LBDCAP, and LBMCAP. The
average of the three learning variables was used as the measure of organizational learning
capability (ORGCAP); consequently, the three correspondent correlation coefficients are high
(.74, .79 and .51 respectively). However, multi-collinearity was not of concern because these
four variables were not regressed in same equation.
RESULTS
Table 3 gives the means, standard deviations, inter-rater reliabilities, Cronbach's alpha
coefficients (for independent variables only), and zero-order correlation scores among the
variables.
---------------------------
Insert Table 3 about Here
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We used hierarchical regression analysis to test the hypotheses. Each of the 11
dependent variables was regressed against the independent variables that include the paired
learning variables (the treatment) and the CIM variables (the paired covariates) proposed. Our
interest was to observe the increment in variance due to the addition of a learning variable.
Organizational Learning and Performance. Table 4 shows the results of hierarchical
regression analysis for CIM and overall learning capability (ORGCAP). The regression
coefficients of CIM are all positive and significant for all regression equations. It confirms that
CIM is a valid strategy to achieve organizational objectives. The highest coefficient of CIM
(b=1.35) is for dependent variable, INTEGR, indicating that CIM technology is an effective tool
for achieving manufacturing integration. The increment in variance (∆R2) due to the addition of
ORGCAP varied from .01 to .27. Among the six operational objectives, organizational learning
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capability is significantly related to efficiency and productivity enhancement (b=.42), effective
process control (b=.47), new product introduction (b=.77), and effective resource allocation
(b=.44). The regression coefficients for manufacturing integration (b=-.05) and for quality
(b=.28) are not significant. Furthermore, the variance of the five strategic objectives (FLEXIB,
RESPON, SATIDF, RETURN, and COMET) can be significantly explained by organizational
learning capabilities. These findings provided support for Hypothesis 1.
----------------------------
Insert Table 4 about Here
-----------------------------
Learning by R&D and Performance. The results in Table 5 shows that CAD mainly
contributed to manufacturing integration and new product introduction whereas CAE almost
made significant contribution to almost all performance objectives. The addition of R&DCAP
results in major increments in variance for product introduction (b=.34), quick response to
market demand (b=.32), effective process control (b=.24), customer satisfaction (b=.22) and
efficiency and productivity enhancement (b=.18). The findings partially support Hypothesis 2.
--------------------------
Insert Table 5 about Here
--------------------------
Learning by Doing and Performance. Table 6 shows the results of regression on three
covariates, CAM, MRP and LBDCAP. CAM contributes more to manufacturing integration
(INTEGR) whereas MRP associates more with resource allocation (RESOUR) and economic
return (RETURN). Learning by doing capability contributes to the increments of variance (∆R2
) higher in effective process control (PROCESS, b=.33), product development (NEWPRD,
b=.24), customer satisfaction (SATISF, b=.23) and efficiency and productivity enhancement
(EFFICI, b=.20) than other objectives. The findings partially support Hypothesis 3.
-------------------------------
Insert Tables 5 & 6 about Here
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Learning by Managing and Performance. Lastly, Table 7 summarizes the analysis for
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MRP-II and CAPP and learning by managing. Both manufacturing resources planning (MRP-II)
and computer-aided process planning (CAPP) techniques make major contributions to
manufacturing integration (INTEGR) and minor contributions to market response (RESPON)
and customer satisfaction (SATISF). More importantly, learning by managing contributes to the
achievement of all dimensions of the 11 objectives in relatively large scales. Eight regression
coefficients are equal and greater than .55. In contrast to the other two types of learning,
learning by managing plays a more significant role in enhancing CIM strategy. Thus the findings
provide strong evidence to support Hypothesis 4.
DISCUSSION AN OVERVIEW OF THE FINDINGS
The results from this study indicate that organizational learning capability plays an
important role for CIM firms in achieving their organizational objectives. However, the
contribution varies from each type of learning towards various organizational objectives. Several
strategic implications can be drawn from the variations.
The findings show that learning by R&D corresponds highly to product design and new
product introduction, quick response to market demand, customer satisfaction and manufacturing
flexibility. The high correspondence demonstrates that R&D learning magnifies the effect of
computer-aided design and engineering. R&D learning explores the market demands and
discovers technological availability, which can be factored into a CAD/CAE system for optimal
design in product characteristics desired for customers. Without R&D learning, the flexibility
and responsiveness of a CAD/CAE system would not be fully utilized. To fully utilize
CAD/CAE technological resources in product development to meet market demand necessitates
strong R&D learning capabilities.
The findings also show a significant marginal contribution of learning by doing
capability to effective process control and operating efficiency and productivity, additional to the
variance contribution due to CAM and MRP. The results confirm the findings in leading
learning by doing studies, e.g. Dutton & Thomas [17] and Leonard-Barton [20]. Learning
activities during implementation enhance the effectiveness of CAM and MRP by coordinating
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the mechanical motions and electronic control, by adjusting product and information flows, and
by coping with operating bottlenecks and pitfalls.
The effect of learning by doing is not limited to manufacturing efficiency and
productivity. Similar to learning by R&D, learning by doing co-varies strongly with new
product development and customer satisfaction. The results indicate that product design and
speed of new product introduction should not be isolated in R&D labs, new ideas on product
improvement and refinement may come from factory floor through learning by doing. The
findings suggest that it would be beneficial to have greater involvement of operating crews such
as engineers and technicians in the product development process.
The most interesting finding of this study is that manager's learning plays an
extraordinarily important role in achieving the diversified objectives of CIM strategy. While
manufacturing resources planning and computer-aided process planning play a minor role in
achieving objectives in all dimensions except manufacturing integration, learning by managing is
a dominant factor in extending the achievements for CIM firms. It is well accepted that
computerized manufacturing resource planning and computer aided planning programs are
efficient tools for tightly structured quantitative problems, but they are much less efficient in
unstructured and qualitative problems. Unfortunately, most strategic decision-making issues and
many operating problems are unstructured and qualitative, such as the change of customer
demand, human motivation and performance, and bottlenecks and downtime in operations.
Computerized programs can capture only a fraction of the reality of planning [29]. Solutions to
those problems heavily depend on learning in management teams and in-group thinking [23].
Furthermore, The findings in this study substantiate the argument that CIM is a powerful
technique to improve the level of manufacturing integration. However, in addition to CIM
systems, learning by managing is also an important source for manufacturing integration. The
results suggest that, among many other factors having been discussed in the literature, learning
by managing is an important strategic variable in achieving manufacturing integration.
POLICY IMPLICATIONS
A step further from the conventional perspectives that organizational learning is
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important, the findings in this study illustrate the extent to which learning capability may affect
the performance of CIM firms. Several policy implications can be drawn from such improved
understanding.
First, different manufacturing automation techniques necessitate different learning
capabilities. When a firm is interested in product design and engineering automation, it should
allocate more resources to fostering learning by R&D. Such learning capability will enable the
firm to explore new possibilities, new alternatives, and to make better use of CAD/CAE by
simulating possibilities and alternatives for feasible and optimal solutions. The joint efforts of
CAD/CAE and learning by R&D will lead to better and speedy product development. A firm can
gain its R&D learning capability not only by in-house research but also from interaction with
outside resources, as indicated in many R&D writings. Such capability can be accumulated
through mostly human capital investment.
Furthermore, as revealed in the literature, the importance of learning by doing is often
neglected and under-invested. Quite often, while millions of dollars were invested in CAM and
MRP techniques, fewer dollars were invested to enhance learning capability and skill level at the
work place. Workers and technicians may either not have sufficient education and skill level or
not have proper technical training to work in a CIM environment. Although computerized
automation may eliminate the chances of ‘human error,’ it is not a substitute for human resources
at the work place. As the complexity of CIM systems increase, the human factors play an
increasingly important role and it may demand more learning by doing to cope with problems in
the systems. Adequate resources should be allocated to learning by doing to exploit existing
competence, efficiency and productivity of the process. In addition, operation crews should be
encouraged to participate in design and engineering activities.
Finally, it is a challenge for managers to manage the learning activities for themselves.
Development of managerial skills, as revealed in this study, is critical to the successful
implementation of CIM strategy. Several aspects of management learning warrant consideration.
The most important part of the learning is strategic learning through which to develop a growth
strategy to master opportunities from the competitive and technological environments for full
exploitation of internal technological competence. Another dimension of manager's learning is
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to gain technical knowledge in order to make insightful judgment regarding technological choice
and provide effective supervision. The third aspect is learning how to allocate resources to
maintain an appropriate balance between exploration and exploitation in a CIM system.
CONCLUSIONS
This study focused on the marginal contribution of organizational learning capabilities as a
whole and the three facets of learning to the objectives that are usually set by manufacturing
firms when they decide to adopt and implement CIM systems. The findings indicate that the
proper alignment of organizational learning with CIM will lead to higher performance of CIM
firms. The results suggest giving more emphasis on learning in a firm's CIM strategy. The
results also suggest paying particular attention to learning by managing and to differentiate
learning by R&D and learning by doing to accomplish different operational and strategic
objectives. This study recommends that organizational learning should be managed as a strategic
resource to enhance a firm's CIM strategy for better performance.
As it becomes increasingly imperative for manufacturing firms to adopt computer
automation into their corporate strategy, one challenge for managers and researchers is to find
ways to facilitate the progress through organizational learning and other contingent factors. Our
intention in this study was to develop a model and to empirically validate the marginal
contributions of various organizational learning capabilities to a CIM system. The results
provide useful information to enrich our understanding the complementary nature of
organizational learning to CIM system and might help managers to gain competitive advantage
through proper alignment of learning activities with CIM techniques. However, this research
might be subject to the biases associated with the subjective judgment of respondents. It would
be useful for future research to apply this model with more robust empirical data to assess the
model’s predictability.
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Table 2 Characteristics of the Sample
(N=124) ------------------------------------------------------------------------------ No. of Mean Sales Means Sales Mean CIM SIC Industry Cases ($ millions) Growth (%) Level* ------------------------------------------------------------------------------ 363 Consumer Appliance 27 241 10.0 2.51 371 Automobile Parts 38 598 5.7 3.62 372 Aircraft Parts 30 448 4.9 3.40 384 Medical Instruments 29 321 9.4 3.10 ------------------------------------------------------------------------------ * The mean level of computer-integrated manufacturing, which is measured by the mean of six items composite on Likert scale (1=not integrated, to 5=highly integrated).
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Table 3 Descriptive Statistics and Correlation
------------------------------------------------------------------------------------------------------------ Variables Means s.d. IRR1 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 ------------------------------------------------------------------------------------------------------------ Independent Variables 1 R&DCAP 3.50 .91 .85 (.88)2
2 LBDCAP 3.68 1.08 .73 .25* (.79) 3 MANCAP 3.20 .48 .77 .25* .18 (.78) 4 ORGCAP 3.46 .47 .78 .74**.79**.51** (.80) 5 CIM 3.22 .79 .91 .19 .34**.33** .38** (.89) Dependent Variables 6 FLEXIB 3.50 .72 .64 .30* .29* .23 .39** .32** - 7 RESPON 3.75 .78 .77 .41**.11 .20 .39** .26* .47** - 8 SATISF 3.69 .90 .82 .30* .40**.32** .37** .48**.47** .33** - 9 PRPCES 3.46 .95 .65 .28* .26* .45** .42** .41**.34** .24* .47** - 10 NEWPRD 3.51 .75 .88 .47**.36**.45** .54** .26* .38** .31* .42** .54** - 11 INTEGR 3.90 1.57 .93 .20 .21 .31* .31** .68**.34** .29* .34** .34** .31* - 12 EFFICI 3.47 .68 .61 .24* .31**.18 .37** .17 .31* .20 .36** .21 .13 .11 - 13 RESOUR 3.54 .81 .58 .21 .33**.54** .46** .44**.33** .19 .49** .45** .44**.27* .16 - 14 RETURN 3.68 1.15 .54 .10 .24* .38** .30* .37**.25* .16 .24* .43** .06 .22 .32**.57** - 15 COMPET 3.62 .95 .69 .11 .26* .48** .35** .39**.29* .21 .48** .52** .46**.26* .30* .59** .42** 16 QUALIT 3.48 .67 .74 .16 .22 .35** .31* .38**.37** .37**.61** .50** .30* .37**..13 .37** .38**.42** ----------------------------------------------------------------------------------------------------------- Notes: 1. IRR, inter-rater reliability; 2. Cronbach’s alpha value. One-tailed significance: * p<.01; ** P<.001. R&DCAP= learning by R&D capability; LBDCAP= learning by doing capability; MANCAP= learning by managing capability; ORGCAP= organizational learning capability (the sum of RBDCAP,LDCAP and MANCAP); CIM= level of computer-integrated manufacturing; FLEXIB= manufacturing flexibility improvement; RESPON= responsiveness to market demand; SATISF= customer satisfaction; EFFICI= efficiency and productivity enhancement; PROCES= effective process control; NEWPRD= speed of new product introduction; INTEGR= manufacturing integration; RESOUR= effective resource allocation; RETURN= return on CIM investment and profitability; COMPET= overall competitiveness; QUALIT= quality of product and services.
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Table 4 Results of Hierarchical Regression Analyses for Overall
Performance and Organizational Learning Capability (ORGCAP) ------------------------------------------------------------------------------ Independent Variables ----------------------------------------------------------------------- Step 1 Step 2 CIM ORGCAP ------------------------------------------------------------------------------ Dependent F- F- Variables b R2 •R2 Sig. b R2 ∆R2 Sig. ----------------------------------------------------------------------------- EFFICI .15** .03 .03 .092 .42*** .13 .10 .001 PROCES .49*** .16 .16 .000 .47** .23 .07 .000 NEWPRD .24* .07 .07 .000 .77*** .34 .27 .000 INTEGR 1.35*** .46 .46 .000 -.05 .47 .01 .000 RESOUR .45*** .20 .20 .000 .44** .27 .07 .000 QUALIT .50*** .15 .15 .000 .28 .17 .02 .000 FLEXIB .29** .10 .10 .001 .38** .18 .08 .000 RESPON .26 .07 .07 .010 .45** .16 .06 .000 SATISF .55*** .23 .23 .000 .98*** .55 .22 .000 RETURN .55*** .13 .13 .000 .32# .15 .02 .000 COMPET .47*** .15 .15 .000 .33* .19 .04 .000 ------------------------------------------------------------------------------ # p<.1; * p<.05; ** p<.01; *** p<.001 CIM= level of computer-integrated manufacturing; ORGCAP= organizational learning capability (the sum of RBDCAP,LDCAP and MANCAP); EFFICI= efficiency and productivity enhancement; PROCES= effective process control; NEWPRD= product design and new product introduction; INTEGR= level of manufacturing integration; RESOUR= effective resource allocation; QUALIT= quality of product and services. RETURN= return on CIM investment and profitability; FLEXIB= manufacturing flexibility improvement; RESPON= responsiveness to market demand; SATISF= customer satisfaction; COMPET= overall competitiveness.
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Table 5 Results of Hierarchical Regression Analyses for Overall
Performance and Learning by R&D Capability (R&DCAP) ------------------------------------------------------------------------------ Independent Variables -------------------------------------------------------- Step 1 Step 2 CAD CAE R&DCAP ------------------------------------------------------------------------------ Dependent F- F- Variables b1 b2 R2 R2 Sig. b R2 ∆R2 Sig. ------------------------------------------------------------------------------ EFFICI -.01 .15** .04 .04 .137 .18* .10 .06 .025 PROCES .07 .25* .10 .10 .007 .24* .15 .05 .001 NEWPRD .14* .12 .11 .11 .004 .34*** .27 .16 .000 INTEGR .84*** .30** .64 .64 .000 .01 .64 .00 .000 RESOUR .15* .25** .20 .20 .000 .11 .22 .02 .000 QUALIT .13≅ .34** .18 .20 .000 .09 .18 .00 .000 FLEXIB .08 .15* .09 .09 .011 .20** .15 .06 .002 RESPON .09 .18* .10 .10 .007 .32*** .23 .13 .000 SATISF .07 .42*** .26 .26 .000 .22** .31 .05 .000 RETURN .08 .28* .08 .08 .017 .07 .09 .01 .039 COMPET .07 .23* .09 .09 .012 .07 .09 .00 .026 ------------------------------------------------------------------------------ # p<.1; * p<.05; ** p<.01; *** p<.001 CAD= computer-aided design; CAE= computer-aided engineering; R&DCAP= learning by R&D capability; The definition of each dependent variable, sees the notes to Table 4.
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Table 6. Results of Hierarchical Regression Analyses for Overall Performance and Learning by doing Capability (LBDCAP)
------------------------------------------------------------------------------ Independent Variables ------------------------------------------------------------------- Step 1 Step 2 CAM MRP LBDCAP ------------------------------------------------------------------------------ Dependent F- F- Variables b1 b2 R2 •R2 Sig. b R2 ∆R2 Sig. ------------------------------------------------------------------------------ EFFICI .03 .15** .01 .01 .699 .20** .10 .09 .041 PROCES .29** .07 .20 .20 .000 .33*** .34 .14 .000 NEWPRD .15* .06 .07 .07 .074 .24** .17 .10 .002 INTEGR .47***.20* .46 .46 .000 -.07 .46 .00 .000 RESOUR .02 .26*** .16 .16 .001 .19* .22 .06 .000 QUALIT .08 .05 .08 .08 .056 .13 .09 .01 .060 FLEXIB .07 .06 .07 .07 .011 .15* .11 .04 .023 RESPON .08 .00 .08 .08 .056 .10 .09 .01 .061 SATISF .11 .18* .17 .17 .001 .23** .26 .09 .000 RETURN .09 .34*** .17 .17 .001 .12 .18 .01 .0001 COMPET .11 .23** .16 .16 .001 .11 .18 .02 .001 ------------------------------------------------------------------------------ # p<.1; * p<.05; ** p<.01; *** p<.001 CAM= computer-aided manufacturing; MRP= material requirement planning; MRP-II= manufacturing resources planning; LBDCAP= learning by doing capability. See the notes to Table 4 for definition of each dependent variable.
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Table 7
Results of Hierarchical Regression Analyses for Overall Performance and Learning By Managing Capability (MANCAP)
------------------------------------------------------------------------------ Independent Variables ---------------------------------------------------------------- Step 1 Step 2 MRP-II CAPP MANCAP ------------------------------------------------------------------------------ Dependent F- F- Variables b1 b2 R2 •R2 Sig. b R2 ∆R2 Sig. ------------------------------------------------------------------------------ EFFICI .01 -.05 .01 .01 .427 .27≅ .04 .13 .132 PROCES .08 .16≅ .04 .04 .058 .84*** .22 .18 .000 NEWPRD -.02 .10 .02 .02 .140 .68*** .21 .19 .000 INTEGR .48*** .52*** .14 .14 .000 .87*** .21 .07 .000 RESOUR -.03 .18* .06 .06 .013 .87*** .32 .26 .000 QUALIT .12 .14 .02 .02 .134 .71*** .13 .11 .001 FLEXIB .08 .10 .02 .02 .129 .32** .07 .05 .035 RESPON .13* .16* .05 .05 .025 .29# .08 .03 .018 SATISF .12* .20* .06 .06 .015 .55** .15 .09 .001 RETURN -.01 .12 .01 .01 .271 .89*** .15 .14 .001 COMPET .04 .16≅ .04 .04 .058 .92*** .25 .21 .000 -----------------------------------------------------------------------------
# p<.1; * p<.05; ** p<.01; *** p<.001 CAPP= computer-aided process planning; MANCAP= learning by managing capability; The definition of each dependent variable, see notes to Table 4.