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Manufacturing Planning and Control Technology vs. Operational Performance: An Empirical Study of MRP and JIT in China
Zhixiang Chen School of Business, Sun Yat-Sen University,
Guangzhou, Guangdong 510275, P.R of China Email: [email protected]
Office Tel: 86-20-84114149Fax: 86-20-84036924
Jen S. ShangKatz Graduate School of Business, University of Pittsburgh,
Pittsburgh, PA 15260, USAEmail: [email protected]
Office Tel: 412-648-1681Fax: 412-648-1681
March 31, 2006
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Manufacturing Planning and Control Technology vs. Operational Performance: An Empirical Study of MRP and JIT in China
Abstract
Materials Requirement Planning (MRP) and Just-in-Time (JIT) systems are two of the
most widely adopted manufacturing technologies around the world. These systems have
marched into the Chinese manufacturing environment in quick succession. Based on the
survey responses from 246 companies in China, we applied the reliability test, correlation
analysis, and aggregate and multiple regression models to establish the relationship
between the implementation degree of MRP and JIT, and firms’ operational performance.
The results show that the implementation degree of each MRP, JIT, and the integrated
MRP/JIT system has a positive relationship with the production planning and control
performance. We also found that different components of each system contribute
differently to the production performance, and joint application of MRP and JIT is a
popular trend in China.
Keywords: MRP, JIT, Operational performance
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Manufacturing Planning and Control Technology vs. Operational Performance: An Empirical Study of MRP and JIT in China
1. Introduction
As the leading emerging market, China produces 50% of the world’s cameras, 30% of
air conditioners and TVs, and 60% of all microwave ovens sold in Europe (Pinto, 2005).
Multinational firms continue their strong interest in China, both as an outsourcing base
and as a strategic location for marketplace (Pyke et al, 2002). Due to its manufacturing
focus, China has increasingly recognized the importance of the manufacturing planning
and control technologies. More and more Chinese enterprises are interested in learning
about the advanced manufacturing technologies from developed countries, and absorbing
new management ideas for practices. Over the last two decades, new manufacturing
technologies such as Materials Requirements Planning (MRP), Just-in-Time (JIT),
Optimal Production Technology (OPT), and Enterprise Resource Planning (ERP), have
marched into the Chinese manufacturing environment in quick succession. Among them,
MRP and JIT are the most widely adopted. The former originated in the U.S. (Orliky,
1975), while the latter was introduced by Japan .
MRP was first introduced to China following the normalization of the diplomatic
relationship between China and the U.S. in 1979. The first MRP was installed in Beijing
No.1 Machine Tool Plant and Shengyang Bollwing Machine Plant in Liaoning Province.
Since then, it has steadily gained acceptance; MRP has now been adopted by most of the
large manufacturing firms. To speed up the industrialization and enhance competitiveness,
the Chinese government is avidly promoting the advanced manufacturing technology;
MRP thus has been extended to Manufacturing Resource Planning (MRP II) and
Enterprise Resource Planning (ERP). In this research, the short form, MRP, is used
interchangeably for either MRP, MRP II, or ERP. Almost at the same time MRP entered
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China, JIT was also introduced to China by Japanese experts. The first JIT production line
was installed in the First Automobile Manufacturer of China in Changchun, Jilin Province,
but after that JIT was only partially implemented by a few businesses. Not until the 1990’s
did Sino-foreign joint ventures help more Chinese firms realize the benefits of JIT. Since
then, JIT has been gaining more and more attention in China.
In this research, we uncover the current status of MRP and JIT applications. Do both
production technologies contribute to the operational performance in China? What are the
characteristics of the firms that adopted the MRP and/or JIT in China? Is there a
significant relationship between implementation degree and operational performance?
Extensive literature review, both in English and in Chinese, did not yield answers to these
questions since detailed examinations and empirical studies of the current production
technology in China have not been conducted. Our study fills this void in literature by a)
examining manufacturing and operations management practices in China, b) revealing the
progress of MRP and JIT application, c) investigating the relationships among MRP/JIT
techniques and their impact on operational performance, and d) presenting insightful
recommendations for further efficiency improvement.
In the following sections, we first review the literature of MRP and JIT and use the
literature as theoretical foundation to build the research hypotheses and framework.
Section 3 focuses on research methodologies, data collection method, and data
characteristics, and Section 4 discusses the empirical results and research findings.
Summary and Conclusion are made in Section 5.
2. Literature Review and Research Propositions
An important difference between MRP and JIT is that MRP is a computer-based
planning system, whereas JIT is a manual-based control system. Due to this distinction,
MRP and JIT have followed separate research streams for decades. Earlier research
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mainly focused on the principle and comparison of the two from the theoretical
perspective (Orlicky, 1975; Aggarwal, 1985; Aggarwal and Aggarwal, 1985; Toni et al.
1988). Since the 1990s, empirical studies of MRP (Yusuf, 1998; Hunton, et al, 2003;
Myrphy and Simon, 2002; Hitt, et al, 2002) and JIT (Hum and Ng, 1995; Fullerton, 2001;
Sriparavastu, 1997; Salahedin and Francis, 1998) start to appear. Although some
researchers felt that MRP and JIT should complement each other (Bose and Rao, 1988;
Flapper et al.,1991; Sillince and Sykes, 1992; Titone, 1994; Benton and Shin, 1998), no
empirical study has so far addressed the integrated JIT and MRP systems (JIT+MRP). We
will fill this gap in this study. In the next two subsections, we examine the MRP and JIT
literature independently.
2.1 MRP application and performance
To successfully implement MRP, Cox et al. (1981) and Thomas and Heyl (1986)
emphasized changing organizational views toward processes, responsibility, employees,
and external environment. On the other hand, Peter et al (1989), Robert and Scott (1989),
Burns and Turnipseed (1991), Roberts and Barrar (1992), Ang et al. (1995), and Alberto
(2002) empirically examined those factors. Recently, Salaheldin (2004) identified
management support, market strategy, organization climate, vendor support, experience
with IT systems, and company size and age as main success factors.
Sponsored by APICS, Anderson et al (1982), Schroeder et al. (1981), and White et
al. (1982) first examined the benefits of MRP. Subsequent studies (Wilson et al. 1994;
Salaheldin and Francis, 1998; and Alberto and Bragila, 1999; Alberto (2002) showed that
MRP can improve quality, lead times, WIP, production planning, scheduling and control,
and organization climate.
Despite MRP’s 20 years of history in China, research publications on this subject
are rare. The published few mainly focus on theoretical discussions. In Chinese, only
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Wang et al. (1998) and Zhang et al (1998) have empirically studied the accuracy of MRP
and non-MRP firms. Overseas, Lau et al. (2002) compared the MRP firms in Hong Kong
and China, while Zhao et al. (2002) analyzed the relationship between MRP benefits and
the problems encountered. Today, many operations managers in China are eager to learn
the how’s and why’s of MRP. Should their company stay with the basic MRP, move to
MRPII, or bear the financial risk and upgrade to ERP? In this paper, we will address this
issue by understanding the relationship between the implementation degree and
performance.
2.2 JIT application and performance
Since adopted by Toyota in early 1970s, JIT has engendered great interest
internationally. Sugimori et al. (1977) first reported its implementation, and Golhat and
Stamm (1991) later identified over 860 journal articles on JIT. Golhar and Stamm (1991)
and Ramarapu et al. (1995) considered waste elimination, quality improvement,
management commitment, employee participation, and vendor/supplier participation as
the main JIT components. Michael and Guide (1993) gave emphasis to production
strategy, vendor strategy, and human relationship strategy. On the other hand, Im and Lee
(1989) and Chong et al. (2001) focused on top management commitment, worker
participation, and education. On a more complete note, Salaheldin (2005) advised
changing management strategy, production line, product design, inventory order policy,
and employee training and education.
In terms of JIT benefits, Billesbach and Hayen (1994), Chakravorty and Atwater
(1995), Fullerton and McWatters (2001), and McWatters (2002, 2003) reported
improvement in inventory level, quality cost, and responsiveness. However, conflicting
results abound. Out of the 30 firms examined, Sohal et al. (1993) found only ten to be
successful. Likewise, Azmi et al. (2004) showed that direct and indirect benefits of JIT on
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financial performance are almost non-existent. Despite rich JIT literature, there is no
empirical study about JIT application in China. We aim to bridge this gap.
2.3. Research Hypothesis and Framework
Several researchers have theoretically shown that a JIT and MRP integrated system is
more effective due to complementary effects (Lee, 1992). Benton and Shin (1998) believe
a combined MRP and JIT system reflects a more effective manufacturing environment.
Based on the literature and interviews with managers and academicians, we developed the
following hypotheses.
Hypothesis H1
Firms with a higher level of JIT Implementation have better operational
performance than those with a lower level of JIT Implementation.
Hypothesis H2
Firms with a higher level of MRP implementation have better operational
performance than those with a lower level of MRP implementation.
Hypothesis H3
Regardless of the firm type, the integrated implementation degree of the combined
MRP and JIT has a positive relationship with the overall operational performance.
Hypothesis H04
When JIT and MRP are implemented concurrently, JIT has more impact on the
production control performance than MRP, while MRP has more impact on the
production planning performance than JIT
The complete research framework is summarized in Figure 1.
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Note that:
MRP Implementation degree is measured by:a. Demand forecasting/order management b. Master Production Scheduling (MPS)c. Rough Cut Capacity Planning (RCCP)d. Materials Requirement Planning (MRP) e. Capacity Requirement Planning (CRP)f. Shop flow scheduling and controlg. Inventory managementh. Purchasing/supplier managementi. Equipment maintenance managementj. Basic data managementJIT Implementation degree is measured by: a. Set-up time reduction b. Small lot sizing c. Quality circle and TQM d. JIT purchasinge. Pull production linef. Cross-training and multi-function employee g. “5S” activities: Workplace organization & Standardization h. KANBAN system i. Scheduling stabilityj. Total production maintenance (TPM)
Production performance
Production planning performance measures:a. Effectiveness of production planning b. Accuracy of demand forecastingc. Information sharing degree of cross-functiond. Flexibility of production planninge. Data accuracy of production planning
Production Control performance measure:f. Accuracy of completing production plan g. level of WIP reduction h. Degree of on time delivery i. Satisfaction degree of quality j. Operations Cost
Figure 1. The Research Framework
MRP implementation
degree
JIT implementation
degreeControl variables
• Scale of firm• Production type• Industry type• Ownership
Production planning performance
Production control performance
Production performance
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3. Research methodology
3.1 Questionnaire
The questionnaire was designed based on extensive literature review and discussion with
managers and researchers, and can be found in the Appendix. The first part of the questionnaire
concerns the basic information of the firms, the second part relates the implementation degree of
production technology, and the last part measures the production planning and control
performance. Except for the questions in part I, all inquiries are to be answered on the 5-point
Likert-scale, corresponding to the degree of agreement with the statement.
3.2 Survey Technique
Because China is a huge country, the geographic dispersion brings about different economic
development pace. In order to make certain that the survey results accurately represent the
manufacturing practice in China, we divided the nation into five survey districts: north, east,
center, south, and west China. These regions cover the entire population and correspond to the
industry distribution in mainland China.
Response rate and non-response bias is always a concern in survey research. The most
common protection against non-response bias is to increase the response rate (Douglas and
Thomas, 1990). In most survey studies, the response rates range from 10% (Co et al., 1998) to
40% (Dean et al. 1992, and Boyer et al. 1997), however, a number of articles in the operations
management field often report a response rate of 20% or less. Because survey study is relatively
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new in China, and most of the practitioners are not willing to reveal information if they are not
acquainted with the surveyor, the response rate is very low. Our earlier experience revealed a
response rate of 7% when sampling through mail. In order to increase the response rate, we
tested a new survey technique and collected data from several Chinese MBA classrooms.
In each region, we first identified major universities that offered part-time MBA programs.
The MBA professors in these universities were informed of our survey contents, and at their
consent, we e-mailed them with the questionnaire. Third, as a part of the OM course activities,
the professors distributed the two-page survey form to students and asked them to work with the
Operations Manager, Plant Manager, Director of Manufacturing, or Vice President of Operations
in their own companies. Students turned in the survey the following class as an extra-credit
assignment. Shortly after, the questionnaires are mailed back to us.
The survey work was started in late September of 2005 and ended in early December of
2005 with 397 questionnaires received. Among them, 246 were complete, giving an effective
response rate of 62%. The responding data shows that where companies in the center, east and
south China account for more than 80% of the samples. Note that the samples distribution is
consistent with the true dispersion of the Chinese manufacturing industry. In fact, these three
regions are China’s industrial bases, and are the most economically developed zones. By
consulting with other experts, we believe that our survey captures the nature of the current
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manufacturing industry in China.
3.3 Data Characteristics
The sample profile given in Table 1 symbolizes China’s manufacturing industry, in which we
can find the following characteristics. State-owned and foreign sole proprietorship companies
account for the majority of the ownership. Seventy percent of the companies are sized from
medium to large. While respondents are well distributed across industrial sectors, automobile,
electronic, chemical, and machine industry make up nearly 50% of the group. Then again, 87%
of the companies adopt make to order (MTO), or a mix of MTO and make-to-stock (MTS)
strategy. This suggests that the current Chinese economy is market-oriented, not the planned
economy typically seen in communist societies. Finally, most companies employ medium- to
large-batch size production.
Table 1. Company characteristics reported by the total sample
Characteristics Only MRP companies
Only JIT companies
MRP+JIT companies
Overall
(N) (%) (N)
(%) (N) (%) (N)
(%)
1.Scale of production (Million
Yuan)
(1) <50
(2) 50-100
(3) 101-500
(4) 501-1000
(5) >1000
2
1
2
3
10
18
0.8%
0.4%
0.8%
1.2%
4.1%
7.3%
5
3
4
0
3
15
2.0%
1.2%
1.6%
0.0%
1.2%
6.1%
27
37
67
20
62
213
11.2%
15.0%
27.2%
8.1%
25.2%
86.6%
34
41
73
23
75
246
14.0%
16.6%
29.6%
9.3%
30.5%
100.0
33
Total
2. Ownership
(1) state-owned
(2) private-owned
(3) joint-venture
(4) Foreign sole proprietorship
Total
3. Production type
(1) Make-to-order
(2) Make-to stock
(3) Mix of MTS and MTO
Total
4. Industry type
(1) Family Apparatus
(2) Chemical Industry
(3) Pharmaceutical Industry
(4) Textile industry
(5) Metallurgy industry
(6) Electronic industry
(7) Automobile industry
(8) Mechanical industry
(9) Food industry
(10) Other
Total
5. Batch size
(1) Job shop
(2) Medium size
(3) Large batch size
Total
2
4
3
9
18
4
3
11
18
2
3
0
0
3
0
2
4
1
3
18
1
6
11
18
0.8%
1.6%
1.2%
3.7%
7.3%
1.6%
1.2%
4.5%
7.3%
0.8%
1.2%
0.0%
0.0%
1.2%
0.0%
0.8%
1.6%
0.4%
1.2%
7.3%
0.4%
2.4%
4.5%
7.3%
5
3
4
3
15
9
0
6
15
0
2
0
3
0
2
2
3
2
1
15
0
10
5
15
2.0%
1.2%
1.6%
1.2%
6.1%
3.7%
0.0%
2.4%
6.1%
0.0%
0.8%
0.0%
1.2%
0.0%
0.8%
0.8%
1.2%
0.8%
0.4%
6.1%
0.0%
4.1%
2.0%
6.1%
61
35
41
76
213
71
29
113
213
14
24
13
4
16
28
28
17
20
49
213
9
92
112
213
24.9%
14.2%
16.7%
30.9%
86.6%
28.9%
11.8%
45.9%
86.6%
5.9%
9.8%
5.3%
1.6%
6.5%
11.4%
11.4%
6.9%
8.1%
19.9%
86.6%
3.7%
37.4%
45.5%
86.6%
68
42
48
88
246
84
32
130
246
16
29
13
7
19
30
32
34
23
53
246
10
108
128
246
%
27.7%
17%
19.5%
35.8%
100.0
%
34.2%
13%
52.8%
100.0
%
6.7%
11.8%
5.3%
2.8%
7.7%
12.2%
13%
9.7%
9.3%
21.5%
100.0
%
4.1%
33.9%
52.0%
100.0
%
3.4 The Production Performance Measure
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We identified ten response variables for measuring the production planning and control
performance. In Table 2, the first five items, PP01~PP05, correspond to the production planning
measure, while the last five items, PC01~PC05, correspond to the production control measure.
The reliability of these variables was tested using Cronbach’s α, which shows how well a set of
variables measure a single uni-dimensional latent construct, e.g. how well PP01~PP05 measure
the production planning performance. Cronbach's α will be low if data show a multi-dimensional
structure; this then requires factor analysis to determine which variables load highest on certain
dimensions. Since Cronbach's α is relatively high in Table 2, we believe the ten variables have
appropriately formed a single latent construct in measuring the production performance. Table 3
provides additional evidence to show that the variables are measuring the same underlying
construct, since the correlations among variables are relatively high.
Table2 Description of variable of production performance
Serial
number
Variable Mean Standard
Deviation
Description of variable Cronbach
Alpha if
item deleted
1 PP01 3.50 0.733 Effectiveness of production planning 0.841
2 PP02 3.25 0.761 Accuracy of demand forecasting 0.851
3 PP033.48 0.812
Information sharing degree of cross-
function department0.846
4 PP04 3.46 0.816 Flexibility of production planning 0.849
5 PP05 3.54 0.865 Data accuracy of production planning 0.846
6 PC01 3.75 0.722 Rate of completing production plan 0.843
7 PC02 3.42 0.899 level of WIP 0.844
8 PC03 3.78 0.849 Degree of on time delivery 0.847
9 PC04 3.89 0.772 Satisfactory degree of quality 0.850
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10 Pc05 3.31 0.923 Operation cost 0.841
Cronbach Alpha=0.859
Table 3. Correlations among dependent variables
Variable PP01 PP02 PP03 PP04 PP05 PC01 PC02 PC03 PC04 PC05
PP01 1
PP02 0.461** 1
PP03 0.496** 0.454** 1
PP04 0.435** 0.285** 0.445** 1
PP05 0.398** 0.383** 0.515** 0.401** 1
PC01 0.438** 0.315** 0.336** 0.374** 0.384** 1
PC02 0.406** 0.332** 0.289** 0.326** 0.359** 0.324** 1
PC03 0.421** 0.266** 0.297** 0.411** 0.369** 0.414** 0.424** 1
PC04 0.272** 0.192** 0.168** 0.449** 0.365** 0.374** 0.337** 0.390** 1
PC05 0.365** 0.422** 0.318** 0.421** 0.490** 0.432** 0.437** 0.436** 0.531** 1
**p<0.01
3.5 Measuring the Degree of JIT and MRP Implementation
The components of JIT are often perceived differently among academicians and practitioners
(Im and Lee, 1989; Richard et al, 1999; Zhu and Paul, 1995; Fullerton, 2001; Azmi et al. 2004).
Based on the literature review and interview with managers, we have chosen the following ten
factors to measure the JIT implementation degree. They are 1) set-up time reduction, 2) small lot
size, 3) quality circle and TQM, 4) JIT purchasing, 5) cross-training and multi-function
employee, 6) push production line, 7) “5S” and improvement activities, 8) KANBAN system, 9)
scheduling stability, and 10) total production maintenance. Note that we do not include “focused
factory” or “group technology” (Chase et al, 2004) because the two technologies are rarely
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employed in China. On the contrary, we bring in the “5S” since it is often practiced in Chinese
firms when they implement JIT. “5S” originated within Toyota; nowadays it has become one of
the first step companies take to implement lean manufacturing or six sigma. The “5S” (Sort, Set
in Order, Shine, Standardize, and Sustain) is widely recognized as an important process for
optimizing workplace organization.
Modules employed in MRP vary considerably. Based on MRP software functions and
literature (Lau, et al, 2002; Chan and Burns, 2002; Zhao, 2002), we chose ten variables to
measure MRP implementation degree. They are 1) demand forecasting/order management, 2)
master production scheduling, 3) rough-cut capacity planning, 4) materials requirement planning,
5) capacity requirements planning, 6) shop flow scheduling and control, 7) inventory
management, 8) purchasing/supplier management 8) equipment maintenance management, and
10) basic data management.
Following the same approach taken in Table 3, we found the Cronbach's α reliability
measures for MRP and JIT implementation degree are 0.92 and 0.89 respectively. The
correlation analysis also demonstrates that all variables within each set are highly correlated.
This indicates that the chosen MRP and JIT variables are reliable and reasonable.
3.6 Data Analysis Methods
Our hypotheses were tested through multiple regression models. The ten performance
measures of production planning and control act as dependent variables, while the degree of JIT
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and MRP implementation serve as the independent variables
(1) Testing Hypothsis1
Hypothesis 1 assumes that the higher the implementation degree of JIT, the better the
production performance. In order to test this assumption, we use the following models:
(1)
(2)
(3)
where is the expected value of combined production planning and control performance
measure for firm i; whereas and are the expected production planning performance measure
and expected production control performance measure respectively.
, , and Yi,j is the jth performance measure for company
i. The independent variable, , is the average value of the ten JIT
implementation degree measures for firm i.
In addition to the above aggregated regression model, we also analyzed the relationship
between the implementation degree of each JIT component and the combined production
performance. The following regression models are used:
(4)
(5)
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(6)
In the regression models, if the coefficient was positive, we concluded that the higher the
implementation degree of JIT, the better the performance, i.e. the production performance of the
manufacturing system has a positive association with the JIT implementing degree.
(2) Testing Hypothesis 2
Hypothesis 2 assumes that the production performance has a positive relationship with the
implementation degree of MRP system. Similar to the testing models proposed for Hypothesis 1,
we construct the regression models as follows. The variables below are defined similarly to
those in equations (1)~(6).
(7)
(8)
(9)
(10)
(11)
(12)
(3) Testing Hypothesis 3
Hypothesis 3 assumes that for any firm type, the implementation degree of the combined
MRP and JIT system has a positive relationship with the operational performance. To test this
hypothesis, we used the model below:
(13)
(14)
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(15)
where, is the average implementation degree of the JIT+MRP system for firm i, and
. If the regression coefficient turned out to be positive, we
concluded that the higher the implementation degree of JIT+MRP, the better the production
planning and control performance, or the operational performance has a positive association with
the aggregated activities of JIT and MRP.
(4) Testing Hypothesis 4
The aim of Hypothesis 4 is to test the popular belief that, in a JIT+MRP combined production
planning and control environment, JIT acts as a control system while MRP as a planning system.
In other words, does MRP influence more on production planning performance? Will JIT
contribute more to the production control performance? We construct the following models to
test this hypothesis:
(16)
(17)
(18)
4. Empirical Results and Research Findings
The results of the regression analyses are shown in Tables 4, 5, and 6. Details regarding the
impact of implementation degree of MRP and JIT on the operational performance are discussed
below.
4.1 JIT implementation degree vs. operational performance
Table 4 shows the results of testing Hypothesis 1 using models (1)-(6). For models (1)-(3), we
obtained the regression coefficients of 0.463, 0.434, and 0.406 for the combined production
planning and control (PPC), production planning (PP), and production control (PC) respectively.
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As seen on the bottom of Table 5, they are all significant at α<0.05. This indicates that the PP
performance, PC performance, and PPC performance all have significant association with the
implementation degree of JIT system. Hypothesis 1 is thus supported, i.e. the more thoroughly
the JIT is implemented, the better the performance of the production system.
Table 4. Regression results for the relationship between production performance and JIT implementation degree
Dependent variable PP performance PC performance PPC performance
Beta T Sig. Beta T Sig. Beta T Sig.
Cross-training
Set-up time reduction
"5S " activities
Small lot sizing
JIT purchasing
TQM
Pull production
KANBAN system
Scheduling stability
TPM
R2
△R2
F
Sig. F
na
JIT system
R2
△R2
F
0.203
0.185
0.065
-0.113
0.113
0.129
-0.118
-0.003
0.125
0.229
0.223
0.029
21.386
0.000
228
0.434
0.188
0.188
52.306
3.048
2.887
0.911
-1.860
1.712
1.967
-1.661
-0.048
1.692
3.279
7.232
0.003*
0.004*
0.363
0.064
0.088
0.050*
0.098
0.962
0.092
0.001*
0.000*
0.1800.153
0.233
-0.157
0.134
0.097
-0.012
-0.131
0.012
0.037
0.250
0.019
14.829
0.000
228
0.406
0.167
0.167
45.269
2.648
2.347
3.477
-2.612
2.094
1.443
-0.172
-1.744
0.174
0.504
6.728
0.009*
0.020*
0.001*
0.010*
0.037*
0.150
0.863
0.083
0.862
0.615
0.000*
0.173
0.208
0.156
-0.169
0.144
0.131
-0.052
-0.090
0.064
0.104
0.300
0.013
15.783
0.000
228
0.463
0.215
0.215
61.829
2.556
3.297
2.311
-2.880
2.302
1.995
-0.779
-1.238
0.922
1.454
7.863
0.011*
0.001*
0.022*
0.004*
0.022*
0.047*
0.437
0.217
0.357
0.147
0.000*
41
Sig. F
na
0.000
228
0.000
228
0.000
228
Notes: *P<0.05, PP=performance of production planning, PC=performance of production control, PPC=total performance of
production planning and control. a number of enterprises that have implemented JIT system.
Multiple regression models (4)-(6) are used to analyze the impact of each element of JIT on
the performance. Before estimating the models, tests of potential multicollineartiy among the set
of independent variables were conducted. We found that all three variance inflation factor (VIF)
values are less than 2.0, falling below the conventional critical value of 10, at which point
multicollinearity becomes problematic (Neter et al. 1983). Examination of the tolerance of the
variables and the condition indices associated with the eigenvalues also support the lack of
collinearity. Therefore, the multiple linear regression models are effective.
The observed levels of significance at the center of Table 4 are all zeros, indicting that each
of the multiple linear regression model about PPC, PP, and PC is significant. The regression
coefficients demonstrate that the elements of JIT have different degrees of impact on the
performance. For example, for PPC performance, activities such as set-up time reduction
(0.208), cross-training and multi-function employee (1.173), “5S” and improvement activities
(0.156), JIT purchasing (0.144) and TQM (0.131) have significant positive impacts. Scheduling
stability (0.064) and TPM (0.104), though positive, are not significant. For PP performance,
however, TPM (0.229), cross-training and multifunction employee (0.203), set-up time reduction
42
(0.185), TQM (0.129), have significant positive impact. In terms of PC performance, there are
four elements that have significant positive relationship, i.e. “5S” activities (0.223), cross
training (0.180), set-up time reduction (0.153), and JIT purchasing (0.134). The three elements,
TQM (0.09), TPM (0.037) and scheduling stability (0.012) do not have significant relationship
with PC performance. Noticeably, cross-training and set-up time reduction are the only two
elements having positive relationship with all performance with regards to production planning,
production control, and total operational performance. These two elements are therefore the most
important JIT practices implemented by Chinese enterprises.
The exception here is that small lot size has a negative relationship with operational
performance with regards to PPC, PP and PC; this indicates Chinese firms in fact worse off when
implementing this JIT element. Further investigation is warranted. Two elements of JIT,
KANBAN and the Pull Production Line, are not significant. In fact our survey revealed (not
shown here) the average implementation degrees of KANBAN and the Pull Production Line are
much lower than the total average level of implementation degree of the JIT system. This
demonstrates that Chinese enterprises do not entirely “copy” JIT techniques. They selectively
implement the JIT components that are the most useful and adequate for Chinese enterprises
4.2 MRP Implementation Degree vs. Operational Performance
Hypothesis 2 assumes that the implementation degree of the MRP has a positive association
43
with the production performance. Models (7)-(9) were used for such test, and the results are
shown at the bottom of Table 5. Based on the 231 firms that have implemented MRP, we
obtained the regression coefficients of 0.581, 0.529, and 0.536 for PPC, PP and PC respectively.
All are significant at α=0.05. Thus, the hypothesis was supported.
Table 5 Regression result for the relationship between production performance and MRP
implementation degree
Dependent variable PP performance PC performance PPC performance
Beta T Sig. Beta T Sig. Beta T Sig.
Inventory management
Demand forecasting
Equipment management
Basic data management
MPS
RCCP
MRP
CRP
Shop flow scheduling
Purchasing management
R2
△R2
F
Sig. F
na
MRP system
R2
△R2
F
Sig. F
na
0.146
0.159
0.164
0.176
0.012
-0.112
0.009
0.144
0.033
-0.042
0.317
0.017
20.921
0.000
231
0.529
0.280
0.280
88.87
0.000
231
1.998
2.393
2.651
2.461
0.153
-1.570
0.109
2.104
0.450
-0.502
9.427
0.047*
0.018*
0.009*
0.015*
0.879
0.118
0.914
0.036
0.653
0.616
0.000*
0.086
0.118
0.186
0.153
0.301
-0.134
0.091
0.001
-0.004
0.225
0.349
0.011
24.102
0.000
231
0.536
0.288
0.288
92.464
0.000
1.076
1.563
3.026
2.265
4.112
-1.989
1.133
0.010
-0.049
3.217
9.616
0.283
0.119
0.003*
0.024*
0.000*
0.048*
0.259
0.992
0.961
0.001*
0.000*
0.178
0.241
0.208
0.209
0.120
-0.058
0.093
0.050
0.051
0.074
0.370
0.026
33.229
0.000
231
0.581
0.337
0.337
116.649
0.000
231
2.548
4.011
3.718
3.078
1.595
-0.958
1.277
0.756
0.745
0.932
10.800
0.012*
0.000*
0.000*
0.002*
0.112
0.339
0.203
0.450
0.457
0.352
0.000*
44
Notes: *P<0.05, PP=performance of production planning, PC=performance of production control, PPC=total performance of
production planning and control. a number of enterprises that have implemented MRP system.
Models (10)-(12) were used to test the impact of each element of MRP on performance. We
checked VIF, examined the tolerance of the variables and the condition indices associated with
the eigenvalues. All supported the lack of collinearity. Therefore, the multiple linear regression
models were effective. Based on the observed significance levels, we found that each of the
multiple linear regression models about PPC, PP and PC are statistically significant at α=0.05.
The regression coefficients revealed that demand and order management (0.241), basic data
management (0.209), equipment management (0.208), and inventory management (0.178) had
significant positive relationships with PPC. Master production scheduling (MPS) (0.120),
materials requirements planning (MRP) (0.093), capacity requirement planning (CRP) (0.050)
and shop flow scheduling and control (0.050), purchasing management (0.074) modules are not
significant.
For production planning, the regression coefficient of inventory management (0.146), demand
management (0.159), equipment management (0.164) and basic data management (0.176)
revealed that these elements have significant positive relationships with PP. For production
control performance, the regression coefficients of MPS (0.301), purchasing management
(0.225), equipment management (0.186) and basic data management (0.153) revealed significant
positive relationships with the performance.
45
It is noticeable that data management and equipment management are two elements which
are significant in all three performance measures. This confirms the popular belief that basic
infrastructure management is the most important success factor in implementing MRP, fitting the
saying that “MRP/ERP is three technology, seven management and twelve data”.
Rough cut capacity planning (RCCP) (-0.058) is an exception that reveals a negative
relationship with production control performance. Our interviews with manufacturing firms and
ERP software companies indicate that RCCP is a module not often used by manufacturers. Its
implementation probably exacerbated the control performance due to the firms’ unfamiliarity
with the technique.
4.3 The Joint JIT and MRP Implementation vs. Operational Performance
Compatibility of JIT to the existing MRP systems is an issue that has inspired heated debate
among practitioners and researchers (Benton and Shin, 1998). For some who conducted
comparison studies, MRP and JIT are mutually exclusive. To others, JIT and MRP are
complementary. Regardless of the viewpoints, all such studies have been from industrialized
countries, and there is no empirical study regarding the performance of the joint MRP+JIT
systems in China.
Hypothesis 3 conjectures that for firms jointly implementing JIT and MRP, the aggregated
implementation degree of JIT+MRP has a positive association with the production performance.
This hypothesis is supported by our data. The results of models (13)-(15) are shown at the center
46
of Table 6. Among the 213 firms which have currently implemented both MRP and JIT systems,
the implementation degree of MRP+JIT significantly affects the three performances − PPC, PP
and PC, with coefficients of 0.589, 0.541 and 0.536 respectively. All are statistically significant
with p-values equal to zero. This suggests that the greater the implementation degree of
JIT+MRP system, the higher the operational performance.
Our study adds evidence to support the argument that there is growing trend of embedding
JIT into the MRP system. Future success most likely depends on both concepts. In fact, MRP and
JIT can, and must, be applied together as a hybrid manufacturing system (Lee, 1993). The
resulting problem is the type of roles MRP and JIT should play in a hybrid system. This can be
answered by the discussion of hypothesis 4 below.
Table 6.Regression result for the relationship between production performance and integrated system of MRP & JIT
Dependent variable PP performance PC performance PPC performance
Beta T Sig. Beta T Sig. Beta T Sig.
JIT
MRP
R2
△R2
F
Sig. F
na
JIT+MRP systems
R2
△R2
F
0.196
0.407
0.301
0.301
45.157
0.000
213
0.541
0.293
0.293
87.282
2.703
5.615
9.342
0.007*
0.000*
0.000*
0.138
0.459
0.306
0.306
46.350
0.000
213
0.536
0.287
0.287
84.878
1.905
6.358
9.213
0.058
0.000*
0.000*
0.182
0.474
0.362
0.362
59.690
0.000
213
0.589
0.347
0.347
111.936
2.629
6.849
10.580
0.009*
0.000*
0.000*
47
Sig. F
na
0.000
213
0.000
213
0.000
213
Notes: *P<0.05, PP=performance of production planning, PC=performance of production control, PPC=total performance of
production planning and control, a number of enterprises in which JIT and MRP coexist .
4.4. Role Comparison of JIT and MRP in an Integrated System
Our work differs from the literature in that we base our study on examining both MRP and
JIT simultaneously. For this, Hypothesis 4 assumes that in joint JIT+MRP system, MRP plays a
more important role in planning function, while JIT contributes more to process control. Models
(14)-(16) were used for testing this hypothesis. Surprisingly, our analysis results did not support
this hypothesis. Table 6 shows that in an integrated system, implementation degree of MRP
contributed more to both planning and control than JIT. The nature of our JIT data does not
support the theoretical arguments.
The result is reasonable and justifiable. For firms that employ pure JIT strategy, JIT would be
the only “guru” leading their production control environment. But in a combined MRP+JIT
environment, which represents 85% of our surveyed firms, JIT effectively becomes the base for
implementing MRP (including MRPII or ERP). It lays the foundation to ready the MRP
implementation. Therefore, it does not necessarily lead in production control. This does not
imply that JIT is inferior. An empirical study by Rabimovich and Evers (2002) actually suggests
that MRP and JIT are substitutes for each other at the enterprise level. Nowadays, since MRP
requires significant financial investment, it has become more prominent in the Chinese
manufacturing environment.
5. Conclusion
48
5.1 Main Findings and Managerial Implications
The purpose of this research is to investigate the current application of advanced
manufacturing planning and control technologies in China and to provide empirical evidence
about the impacts of the implementation degree of JIT/MRP on the operational performance. The
results and managerial implications are summarized below.
1) Advanced manufacturing planning and control technologies, such as JIT and MRP, have been
widely accepted by Chinese enterprises. The manufacturers in China benefit from the
effective implementation of the JIT and MRP systems.
2) The MRP system is the most adopted planning method in any type of firms. MRP performs
well in both the planning and control areas. The implementation degree of the MRP system
has a positive association with operational performance.
3) JIT philosophy has been applied by Chinese enterprises for more than two decades. Firms
can benefit from an in-depth implementation of the JIT technology.
4) Individual elements of JIT play different roles in improving operational performance. The
JIT components that have a notable positive influence on performance are a) set-up time
reduction, b) cross-training and multi-function employee, c) “5S” and improvement
activities, and d) TQM. But KABAN and the Pull Production Line are rarely applied by
Chinese enterprise, and therefore do not play significant roles.
5) Modules of the MRP system also function differently in improving performance. Those that
49
have a positive influence on all performance measures are a) basic data management, b)
equipment management, c) demand forecasting and order management, and d) inventory
management. Data management and equipment management are the important success
factor in implementing MRP.
6) Integrated application of MRP and JIT is a popular trend in China. We found that the
implementation degree of the joint JIT+MRP system has a positive influence on operational
performance. Effectively applying both manufacturing planning and control technologies will
give firms a competitive edge.
7) The results of our research do not validate the notion that, in the joint JIT+MRP system, JIT
is superior to MRP in production control. However, it is generally accepted that combining
the MRP and JIT philosophies helps create synergy and attain a performance better than
implementing either one individually.
5.2 Limitations of the Research
Although this research was successfully carried out and meaningful results were derived, there
exist some limitations that make further research necessary. The samples in effect were collected
from economic developed zones, but information from west and north China is limited. Our
work provided sufficient empirical insights to the current JIT and MRP practice in China,
although more comprehensive understanding of the advanced manufacturing planning and
control technologies employed in China may be warranted.
50
5.3 Future Research
To enhance our knowledge about the production planning and control technologies
employed in modern China, further research work may be conducted as follows.
a) Although our research findings have provided valuable insights and broadened our
knowledge of JIT (Lean Production) and MRP (MRPII and ERP) applications in China, we
may replicate this study by employing different sampling approaches, increasing the sample
size, and collecting wider-ranging manufacturing firms to obtain more insights and higher
reliability.
b) Take into account the moderator effect of enterprise characteristics and business
environment, such as ownership, industry type, scale of production, etc. Will these factors
change the relationship between the implementation degree and the operational performance?
Such study helps understand the different facets of the JIT and MRP implementation in
Chinese enterprises.
c) Explore the relationship between implementation degree of JIT/MRP and enterprise-wide
financial performance. This helps draw the financial insight regarding investment in the
advanced manufacturing planning and control technologies in China.
d) Study the implementation preference issues. For example, among the different advanced
manufacturing technologies, when, in terms of the company development stage and
environment, should JIT and MRP be applied simultaneously? Under what condition should
51
they be implemented sequentially? Moreover, when and how should they be integrated
together?
e) Since it is not clear which production planning and control system dominates the others, and
there is no single perfect system suited for all types of production environment,
benchmarking becomes necessary when production system improvement is required. Case
studies are therefore needed to detail JIT and MRP implementation processes and examine
the problems encountered during the implementation.
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Appendix : Questionnaire1. Firm’s Information
(1) Production scale (annual sales in million Yuan RMB)
a) < 50 b) 50-100 c) 101-500 d) 501-1000 e) > 1000
(2) Company ownership
a) State-owned
b) Private-owned
c) Joint-venture
d) Foreign sole proprietorship
(3) Production strategy
a) Make to Order (MTO) b) Make to Stock (MTS) c) Mix of MTO & MTS
(4) Industry types:
a) Family Apparatus
b) Chemical Industry
c) Pharmaceutical Industry
d) Textile industry
e) Metallurgy industry
f) Electronic industry
g) Automobile industry
h) Mechanical industry
i) Food industry
j) Other
(5) Batch size
a) Job shop b) Medium size c) Large batch size
2. Implementation degree of manufacturing technology
2.1 JIT
If your company has implemented JIT system, please indicate the degree of implementation in your
company using five scales. 1) Not used, 2) seldom used,3) sometime,4) often used,5) always
56
used
Function of JIT Degree of implementation
a. Set-up time reduction
b. Small lot sizing
c. Quality circle and TQM
d. JIT purchasing
e. Pull production line
f. Cross-training and multi-function employee
g. “5S” activities: Workplace organization & Standardization
h. KANBAN system
i. Scheduling stability
j. Total production maintenance (TPM)
1 2 3 4 5
1 2 3 4 5
1 2 3 4 5
1 2 3 4 5
1 2 3 4 5
1 2 3 4 5
1 2 3 4 5
1 2 3 4 5
1 2 3 4 6
1 2 3 4 7
2.2 MRP
If your company has implemented MRP system, please indicate the degree of implementation in your
company using five scale 1).Not used,2) seldom used,3) sometime used,4) often used,5) always used
Function of MRPII Degree of implementation
a. Demand forecasting/order management
b. Master Production Scheduling (MPS)
c. Rough Cut Capacity Planning (RCCP)
d. Materials Requirements Planning (MRP)
e. Capacity Requirement Planning (CRP)
f. Shop flow scheduling and control
g. Inventory management
h. Purchasing/supplier management
i. Equipment maintenance management
j. Basic data management
1 2 3 4 5
1 2 3 4 5
1 2 3 4 5
1 2 3 4 5
1 2 3 4 5
1 2 3 4 5
1 2 3 4 5
1 2 3 4 5
1 2 3 4 6
1 2 3 4 7
3. Production performance
Please indicate the performance satisfaction degree your company has gained in each of the following
production planning and control criteria. 1) very low, 2) low, 3) average, 4) high, 5) very high
Production performance Degree
a. Effectiveness of production planning
b. Accuracy of demand forecasting
1 2 3 4 5
1 2 3 4 5
57
c. Information sharing degree of cross-function
d. Flexibility of production planning
e. Data accuracy of production planning
1 2 3 4 5
1 2 3 4 5
1 2 3 4 5
f. Accuracy of completing production plan
g. level of WIP reduction
h. Degree of on time delivery
i. Satisfaction degree of quality
j. Operations Cost
1 2 3 4 5
1 2 3 4 5
1 2 3 4 5
1 2 3 4 5
1 2 3 4 5
58