Advance Operations Management Technology Application in ... · Web viewZhixiang Chen . School of. Business, Sun Yat-Sen. University, Guangzhou, Guangdong 510275, P.R of . China

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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mailto:[email protected]

mailto:[email protected]


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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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

6

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

37

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)


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)

39

(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.


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.

40

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



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


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



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


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

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Advance Operations Management Technology Application in ... · Web viewZhixiang Chen . School of. Business, Sun Yat-Sen. University, Guangzhou, Guangdong 510275, P.R of . China