manufacturing planning and control technology versus ... · manufacturing planning and control...

4 Int. J. Manufacturing Technology and Management, Vol. 13, No. 1, 2008

Copyright © 2008 Inderscience Enterprises Ltd.

Manufacturing planning and control technology versus operational performance: an empirical study of MRP and JIT in China

Zhixiang Chen* School of Business, Sun Yat-Sen University, Guangzhou, Guangdong 510275, PR China Fax: +86-20-8403-6924 E-mail: [email protected] *Corresponding author

Jennifer S. Shang Katz Graduate School of Business, University of Pittsburgh, Pittsburgh, PA 15260, USA Fax: +1-412-648-1681 E-mail: [email protected]

Abstract: Materials Requirements Planning (MRP) and Just-in-Time (JIT) are currently two of the most popular manufacturing technologies. Based on 246 Chinese companies’ survey, we found that the degree of the MRP and JIT implementation and integration has a positive relationship with the manufacturer’s performance. The hybrid MRP and JIT system, which create synergy and attains better performance, is widely accepted in China.

Keywords: materials requirements planning; MRP; just-in-time; JIT; manufacturing technology; operational performance.

Reference to this paper should be made as follows: Chen, Z. and Shang, J.S. (2008) ‘Manufacturing planning and control technology versus operational performance: an empirical study of MRP and JIT in China’, Int. J. Manufacturing Technology and Management, Vol. 13, No. 1, pp.4–29.

Biographical notes: Zhixiang Chen is an Associate Professor at the School of Business, Sun Yat-sen University, China. He received a PhD in Management Science and Engineering from Huazhong University of Science and Technology, Wuhan, China in 2000. His research interests are in the areas of computer-aided production planning and control systems, modelling and optimisation in production and enterprise integration technology and operations management.

Jennifer S. Shang received a PhD in Operations Management from the University of Texas at Austin. She teaches operations management, simulation, statistics and process and quality improvement courses. Her main research interests include multicriteria decision making and its application to the design, planning, scheduling, control and evaluation of production and

Manufacturing planning and control technology 5

service operational systems. She has published in various journals, including Management Science, European Journal of Operational Research, IEEE Transactions on Engineering Management and International Journal of Production Research. She has won the 2005 EMBA Distinguished Teaching Award and several Excellence in Teaching Awards from the MBA/EMBA programs at Katz Business School.

1 Introduction

As the leading emerging market, China’s manufacturing industry is advancing rapidly. 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 recognised 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. Although no official data available regarding the implementation status of different systems, some survey reports that MRP and JIT are the most widely adopted technologies (Chan and Burns, 2002; Zhang, 1998). The former originated in the USA (Orlicky, 1975), while the latter was introduced by Japan.

MRP was first introduced to China following the normalisation of the diplomatic relationship between China and the USA in 1979. The first MRP was installed in 1981 in Beijing No. 1 Machine Tool Plant and Shengyang Bollwing Machine Plant in Liaoning Province (Wang et al., 2005). Since then, it has steadily gained acceptance and a significant number of Chinese enterprises have employed this tool. To speed up industrialisation and enhance competitiveness, the Chinese government has aggressively promoted advanced technology. In March 1986, the 863-program was launched to support research and application of high-tech, including Computer Integrated Manufacturing System (CIMS). MRP since has been extended to Manufacturing Resource Planning (MRP II) and ERP. In this research, the short form, MRP, is used interchangeably for either MRP, MRP II and ERP.

Almost at the same time MRP entered China, JIT was also introduced to China by Japanese experts. The first JIT production line was installed in early 1980s 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 1990s did Sino-foreign joint ventures help more Chinese firms realise the benefits of JIT. Since then, JIT has been gaining much 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

6 Z. Chen and J.S. Shang

production technology in China have not been conducted. Our study fills this void in literature by

1 examining manufacturing and operations management practices in China

2 revealing the progress of MRP and JIT application

3 investigating the relationships among MRP/JIT techniques and their impact on operational performance and

4 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, the survey technique and data characteristics. In Section 4, we discuss 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 management philosophy and an approach to minimise waste in manufacturing. Due to this distinction, MRP and JIT have followed separate research streams for decades. Earlier research mainly focused on the principle and comparison of the two from the theoretical perspective (Aggarwal, 1985; Aggarwal and Aggarwal, 1985; Orlicky, 1975; Toni et al., 1988). Since the 1990s, empirical studies of MRP (Hitt and Zhou, 2002; Hunton et al., 2003; Murphy and Simon, 2002; Yusuf and Little, 1998) and JIT (Fullerton and McWatters, 2001; Hum and Ng, 1995; Salaheldin and Francis, 1998; Sriparavastu and Gupta, 1997) start to appear. Although some researchers felt that MRP and JIT should complement each other (Benton and Shin, 1998; Bose and Rao, 1988; Flapper et al., 1991; Sillince and Sykes, 1992; Titone, 1994), no empirical study has so far addressed the integrated JIT and MRP systems (JIT + MRP). We will fill this gap in this study. In Sections 2.1 and 2.2, we examine the MRP and JIT literature independently.

2.1 MRP application and performance

Empirical study of MRP has historically focused on the identification of factors that impact the overall performance (Salaheldin, 2004). The consensus is that management support, market strategy, organisation climate, education and training, project management, vendor support, experiences with IT systems, company size and age are 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 (Lau et al., 2002; Petroni and Braglia, 1999; Rabinovich et al., 2003; Salaheldin and Francis, 1998; Sum et al., 1995; Wilson et al., 1994) showed that MRP can improve quality; shorten lead times; reduce WIP; improve Production Planning (PP), schedule and control; lower inventory level; increase productivity and customer service; reduce operations cost and improve cooperation among department. Hunton et al. (2003) and Murphy and Simon (2002) have also identified the operational and financial performance measures for ERP. Table 1 summarises these measures from various MRP literatures.


Table 1 Various performance measures from MRP implementation

Performance measures Authors

Lower inventory level Salaheldin (1998), Rabinovich et al. (2003) and Lau et al. (2002)

Increase operating efficiency Sum et al. (1995) and Petroni (2002) Lower WIP Yusuf and Little(1998) and Petroni (2002) Improve quality Yusuf and Little (1998), Salaheldin (1998), Petroni

(2002), Lau et al. (2002) and Zhao et al. (2002) Increase productivity Sum et al. (1995), Salaheldin (1998), Petroni

(2002), Lau et al. (2002) and Zhao et al. (2002) Improve planning, schedule and control

Sum et al. (1995), Salheldin (2002), Petroni (2002), Lau et al. (2002) and Zhao et al. (2002)

Improve communication Sum et al. (1995), Salaheldin (1998), Petroni (2002), Lau et al. (2002), Zhao et al. (2002)

Lower operation cost Sum et al. (1995), Yusuf and Little (1998), Salaheldin (1998) and Petroni (2002)

Shorten lead time Yusuf and Little (1998) Improved market forecasting Zhao et al. (2002)

Despite the 20 years’ history of MRP in China, research publications on this subject are rare. The few publications mainly focus on theoretical discussions. In Chinese, only 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) analysed the relationship between MRP benefits and the problems encountered. All utilise a relatively small sample size. Today, many operation 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 and Kusunoki (1977) first reported its implementation and Golhar and Stamm (1991) later identified over 860 journal papers on JIT, many of which focused on the components and success factors of JIT. Ramarapu et al. (1995) considered waste elimination, quality improvement, management commitment, employee participation and vendor/supplier participation has the main JIT components. According to Brox and Fader (1997), the essential JIT success factor is inventory minimisation. Efficiency is derived from frequent deliveries of small quantities to meet immediate demand. The application of Kanban – a ‘pull’ system of production and materials control and employee participation and involvement are keys for eliminating waste and achieving efficiency (Golhar and Stamm, 1991).

Billesbach and Hayen (1994), Chakravorty and Atwater (1995) and White et al. (1999) found JIT benefits in throughput time, internal quality, external quality, labour productivity and employee behaviour. They show that company size matters. Upton (1998) surveyed 110 New Zealand manufacturers and found that JIT implementation improves labour efficiency, supplier quality, inventory turnover, supplier on-time


delivery, scrap, machine utilisation and set-up time. Fullerton and McWatters (2001); Fullerton and McWatters (2002) and Fullerton et al., (2003) reported improvement in inventory level, quality cost and responsiveness. Table 2 summarises the benefits of JIT implementation. It is obvious that JIT implementation improves manufacturing performance. However, hitherto few empirical study of JIT application in China has been conducted. We aim to bridge this gap.

Table 2 Summary of the operating benefits from JIT implementation

Operations benefit Authors

1 Reduced throughput time Flynn et al. (1995), White et al. (1999) and Im and Lee (1990)

2 Improved internal quality Chang and Lee (1995), Gravel and Price (1988), Wacker (1987), White et al. (1999), Upton (1998) and Shoal and Egglestone (1994)

3 Improved external quality Chang and Lee (1995), Flynn et al. (1995), Norris et al. (1994), Wacker (1987), Upton (1998) and Chen (1997)

4 Improved labour productivity

Lieberman and Demeester (1999), Brox and Fader (1997), Celley et al. (1986), Norris et al. (1994), Ramarapu et al. (1995) and Shoal and Egglestone (1994)

5 Reduced inventory Chang and Lee (1995), Gilbert (1990), Im and Lee (1989), Ramarapu et al. (1995) and Shoal and Egglestone (1994)

6 Decreased unit cost Brox and Fader (1997), Im and Lee (1989), White et al. (1999) and Zangwill (1987)

7 Increased flexibility Ahmad et al. (2004) and Sohal and Egglestone (1994)

8 Utilisation of machine Upton (1998)

2.3 Research hypothesis and framework

Several researchers have theoretically shown that 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.

H1: Chinese firms with a higher level of JIT Implementation have better operational performance than those with a lower level of JIT Implementation.

H2: Chinese firms with a higher level of MRP implementation have better operational performance than those with a lower level of MRP implementation.

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. H4: When JIT and MRP are implemented concurrently, JIT has more impact on the Production Control (PC) performance than MRP, while MRP has more impact on the PP performance than JIT.


The complete research framework is summarised in Figure 1.

Figure 1 The research framework

Note:

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 control

g. Inventory management

h. Purchasing/supplier management

i. Equipment maintenance management

j. Basic data management

Production performance

Production planning performance measures:

a. Effectiveness of production planning

b. Accuracy of demand forecasting

c. Information sharing degree of cross-function

d. Flexibility of production planning

e. Data accuracy of production planning

JIT Implementation degree is measured by:

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 multifunction employee

g. ‘5S’ activities: workplace organisation and standardisation

h. KANBAN system

i. Scheduling stability

j. Total production maintenance (TPM)

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


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 final part measures the PP and Control (PPC) 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, centre, 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 (Lambert and Harrington, 1990). In most survey studies, the response rates range from 10% (Co et al., 1998) to 40% (Boyer et al., 1997; Dean et al., 1992), however, a number of papers in the operations management field often report a response rate of 20% or less. Because survey study is relatively 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 even lower. Our earlier experience revealed a response rate of 7% when sampling through post 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 Operations Management (OM) professors in these universities were informed of our survey contents, and at their consent, we e-mailed them with the questionnaire. As a part of the OM course activities, the professors distributed the two-page survey form to students. If the student works in operations area, he/she will finish the questionnaires directly, otherwise the questionnaire is taken back to the company’s Operations Manager, Plant Manager, Director of Manufacturing or Vice President of Operations to complete. Students turned in the survey the following class as an extra-credit assignment. Shortly after, the questionnaires were mailed back to the authors.

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%. Table 3 summarises the regional distribution, where companies in the centre, east and south China account for more than 80% of the respondents. 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. We believe our survey captures the nature of the current manufacturing industry in China.


Table 3 Sample distribution and response rate

District East China

South China

West China

North China

Centre China

Total

Response 111 68 44 24 150 397

Effective response

62 53 33 7 91 246

Effective response rate

55.85% 77.94% 75% 29.17% 60.77% 61.96%

3.3 Data characteristics

The sample profile given in Table 4 symbolises 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 4 Company characteristics reported by the total sample

Only MRP companies

Only JIT companies

MRP+JIT companies

Overall Characteristics

N % N % N % N %

1.Scale of production (Million Yuan)

<50 2 0.8 5 2.0 27 11.2 34 14.0

50–100 1 0.4 3 1.2 37 15.0 41 16.6

101–500 2 0.8 4 1.6 67 27.2 73 29.6

501–1000 3 1.2 0 0.0 20 8.1 23 9.3

>1000 10 4.1 3 1.2 62 25.2 75 30.5

Total 18 7.3 15 6.1 213 86.6 246 100.0

2. Ownership

(1) State-owned 2 0.8 5 2.0 61 24.9 68 27.7

(2) Private-owned 4 1.6 3 1.2 35 14.2 42 17

(3) Joint-venture 3 1.2 4 1.6 41 16.7 48 19.5

(4) Foreign sole proprietorship

9 3.7 3 1.2 76 30.9 88 35.8

Total 18 7.3 15 6.1 213 86.6 246 100.0


Table 4 Company characteristics reported by the total sample (continued)

Only MRP companies

Only JIT companies

MRP+JIT companies

Overall Characteristics

N % N % N % N %

3. Production type

(1) Make-to-order 4 1.6 9 3.7 71 28.9 84 34.2 (2) Make-to stock 3 1.2 0 0.0 29 11.8 32 13 (3) Mix of MTS

and MTO 11 4.5 6 2.4 113 45.9 130 52.8

Total 18 7.3 15 6.1 213 86.6 246 100.0 4. Industry type

(1) Family apparatus 2 0.8 0 0.0 14 5.9 16 6.7 (2) Chemical industry 3 1.2 2 0.8 24 9.8 29 11.8

(3) Pharmaceutical industry

0 0.0 0 0.0 13 5.3 13 5.3

(4) Textile industry 0 0.0 3 1.2 4 1.6 7 2.8

(5) Metallurgy industry

3 1.2 0 0.0 16 6.5 19 7.7

(6) Electronic industry 0 0.0 2 0.8 28 11.4 30 12.2

(7) Automobile industry

2 0.8 2 0.8 28 11.4 32 13

(8) Mechanical industry

4 1.6 3 1.2 17 6.9 34 9.7

(9) Food industry 1 0.4 2 0.8 20 8.1 23 9.3 (10) Other 3 1.2 1 0.4 49 19.9 53 21.5 Total 18 7.3 15 6.1 213 86.6 246 100.0 5. Batch size

(1) Job shop 1 0.4 0 0.0 9 3.7 10 4.1 (2) Medium size 6 2.4 10 4.1 92 37.4 108 33.9

(3) Large batch size 11 4.5 5 2.0 112 45.5 128 52.0 Total 18 7.3 15 6.1 213 86.6 246 100.0

3.4 The production performance measure

We identified ten response variables for measuring the PPC performance. In Table 5, the first five items, PP01–PP05, correspond to the PP measure, while the last five items, PC01–PC05, correspond to the PC 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, for example, how well PP01–PP05 measure the PP performance. Cronbach’s α will be low if data show a multidimensional structure; this then requires factor analysis to determine which variables load highest on certain dimensions. Since Cronbach’s α is relatively high in Table 5, we believe the ten variables have appropriately formed a single latent construct in measuring the production performance. Table 6 provides additional evidence to show that the variables are measuring the same underlying construct, since the correlations among variables are relatively high.


Table 5 Description of variable of production performance

Serial number

Variable Mean SD Description of variable Cronbach α 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 PP03 3.48 0.812 Information sharing degree of cross-function department 0.846

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 10 Pc05 3.31 0.923 Operation cost 0.841

Cronbach α = 0.859

Table 6 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 (Ahmad et al., 2004; Fullerton and McWatters, 2001; Im and Lee, 1989; White et al., 1999; Zhu and Paul, 1995). 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 multifunction employee

6 pull 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 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, Standardise and Sustain) is widely recognised as an important process for optimising workplace organisation.

Modules employed in MRP vary considerably. Based on MRP software functions and literature (Chan and Burns, 2002; Lau et al., 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

9 equipment maintenance management and

10 basic data management.

Following the same approach taken in Table 5, 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 PPC act as dependent variables, while the degree of JIT and MRP implementation serve as the independent variables.


3.6.1 Testing Hypothesis 1

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:

JIT JIT JIT,PPC ,PPC ,PPCi i i i iY a b X ε= + + (1)

JIT JIT JIT,PP ,PP ,PPi i i i iY a b X ε= + + (2)

JIT JIT JIT,PC ,PC ,PCi i i i iY a b X ε= + + (3)

where Yi,PPC is the expected value of combined PPC performance measure for firm i; whereas Yi,PP and Yi,PC are the expected PP performance measure and expected PC

performance measure, respectively. ( )10

,PPC ,11 /10 ,i i jj

Y Y=

= ∑ ( )5,PP ,PP1

1/ 5 ,=

= ∑ ji ijY Y

( )5,PC ,PC1/ 5 1= =∑ ji iY j Y and Yi,j is the jth performance measure for company i. The

independent variable, ( )10JIT JIT,1

1 /10 ,i i jjX X

== ∑ is the average value of the ten JIT

implementation degree measures for firm i. In addition to the above aggregated regression model, we also analysed the

relationship between the implementation degree of each JIT component and the combined production performance. The following regression models are used:

10JIT JIT JIT

,PPC ,PPC , ,PPC ,1

i i i j i j ij

Y xβ β ε=

= + +∑ (4)

10JIT JIT JIT

,PP ,PP , ,PP ,1

i i i j i j ij

Y xβ β ε=

= + +∑ (5)

10JIT JIT JIT

,PP ,PC , ,PC ,1

i i i j i j ij

Y xβ β ε=

= + +∑ (6)

In the regression models, if the coefficient is positive, we conclude that the higher the implementation degree of JIT, the better the performance, that is, the production performance of the manufacturing system has a positive association with the JIT implementing degree.


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).

MRP MRP MRP,PPC ,PPC ,PPCi i i i iY a b X ε= + + (7)

MRP MRP MRP,PP ,PP ,PPi i i i iY a b X ε= + + (8)

MRP MRP MRP,PC ,PC ,PCi i i i iY a b X ε= + + (9)


10MRP MRP MRP

,PPC ,PPC , ,PPC ,1

i i i j i j ij

Y xβ β ε=

= + +∑ (10)

10MRP MRP MRP

,PP ,PP , ,PP ,1

i i i j i j ij

Y xβ β ε=

= + +∑ (11)

10MRP MRP MRP

,PP ,PC , , ,1

i i i j PC i j ij

Y xβ β ε=

= + +∑ (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:

JIT MRP JIT MRP JIT MRP,PPC ,PPC ,PPCi i i i iY a b X ε+ + += + + (13)

JIT MRP JIT MRP JIT MRP,PP ,PP ,PPi i i i iY a b X ε+ + += + + (14)

JIT MRP JIT MRP JIT MRP,PC ,PC ,PCi i i i iY a b X ε+ + += + + (15)

where, JIT MRPiX + is the average implementation degree of the JIT + MRP system for firm i

and ( )10 10JIT MRP JIT MRP, ,1 1

1/ 20 .i i j i jj jX X X+

= == +∑ ∑ If the regression coefficient turned out to

be positive, we concluded that the higher the implementation degree of JIT + MRP, the better the PPC 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 PPC environment, JIT acts as a control system while MRP as a planning system. In other words, does MRP influence more on PP performance? Will JIT contribute more to the PC performance? We construct the following models to test this hypothesis:

JIT MRP JIT JIT MRP MRP,PPC ,PPC ,PPC ,PPCi i i i i i iY X Xβ β β ε+= + + + (16)

JIT MRP JIT JIT MRP MRP,PP ,PP ,PP ,PPi i i i i i iY X Xβ β β ε+= + + + (17)

JIT MTP JIT JIT MRP MRP,PP ,PC ,PC ,PCi i i i i i iY X Xβ β β ε+= + + + (18)

4 Empirical results and research findings

The results of the regression analyses are shown in Tables 7–9. Details are discussed below.


4.1 JIT implementation degree versus operational performance

Table 7 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 PPC, PP and PC, respectively. As seen on the bottom of Table 7, they are all significant at α = 0.05. This indicates that the PP, PC and PPC performances all have significant association with the implementation degree of JIT system. Hypothesis 1 is thus supported, that is, the more thoroughly the JIT is implemented, the better the performance of the production system.

Table 7 Regression results for the relationship between production performance and JIT implementation degree

PP performance PC performance PPC performance Dependent variable Beta T Sig. Beta T Sig. Beta T Sig.

Cross-training

0.203 3.048 0.003* 0.180 2.648 0.009* 0.173 2.556 0.011*

Set-up time reduction

0.185 2.887 0.004* 0.153 2.347 0.020* 0.208 3.297 0.001*

‘5S ‘ activities

0.065 0.911 0.363 0.233 3.477 0.001* 0.156 2.311 0.022*

Small lot sizing

−0.113 −1.860 0.064 −0.157 −2.612 0.010* −0.169 −2.880 0.004*

JIT purchasing

0.113 1.712 0.088 0.134 2.094 0.037* 0.144 2.302 0.022*

TQM 0.129 1.967 0.050* 0.097 1.443 0.150 0.131 1.995 0.047* Pull production

−0.118 −1.661 0.098 −0.012 −0.172 0.863 −0.052 −0.779 0.437

KANBAN system

−0.003 −0.048 0.962 −0.131 −1.744 0.083 −0.090 −1.238 0.217

Scheduling stability

0.125 1.692 0.092 0.012 0.174 0.862 0.064 0.922 0.357

TPM 0.229 3.279 0.001* 0.037 0.504 0.615 0.104 1.454 0.147 R2 0.223 0.250 0.300

∆R2 0.029 0.019 0.013

F 21.386 14.829 15.783 Sig. F 0.000 0.000 0.000 na 228 228 228 JIT system 0.434 7.232 0.000* 0.406 6.728 0.000* 0.463 7.863 0.000* R2 0.188 0.167 0.215

∆R2 0.188 0.167 0.215

F 52.306 45.269 61.829 Sig. F 0.000 0.000 0.000 na 228 228 228

aNumber of enterprises that have implemented JIT system.

*p < 0.05.

Note: PP = performance of production planning; PC = performance of production control; PPC = total performance of production planning and control.


Multiple regression models (4)–(6) are used to analyse 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 centre of Table 7 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 multifunction 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, we found TPM (0.229), cross-training and multifunction employee (0.203), set-up time reduction (0.185), TQM (0.129), have significant positive impact. In terms of PC performance, ‘5S’ activities (0.223), cross training (0.180), set-up time reduction (0.153) and JIT purchasing (0.134) are significant, while TQM (0.09), TPM (0.037) and scheduling stability (0.012) are not. Noticeably, cross-training and set-up time reduction are the two elements having positive relationship in all three models, indicating they are the most important JIT practices implemented by Chinese enterprises.

The exception here is that small lot sizing has a negative relationship with operational performance with regards to PPC, PP and PC; this suggests firm’s performance deteriorates when implementing this JIT element. Further investigation is warranted. Two elements, 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 appropriate for Chinese environment.

4.2 MRP implementation degree versus operational performance

Hypothesis 2 assumes that the implementation degree of the MRP has a positive association with the production performance. Models (7)–(9) were used for such test and the results are shown at the bottom of Table 8. 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 2 is supported.

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 significant 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), 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.

Table 8 Regression result for the relationship between production performance and MRP implementation degree


Inventory management

0.146 1.998 0.047* 0.086 1.076 0.283 0.178 2.548 0.012*

Demand forecasting

0.159 2.393 0.018* 0.118 1.563 0.119 0.241 4.011 0.000*

Equipment management

0.164 2.651 0.009* 0.186 3.026 0.003* 0.208 3.718 0.000*

Basic data management

0.176 2.461 0.015* 0.153 2.265 0.024* 0.209 3.078 0.002*

MPS 0.012 0.153 0.879 0.301 4.112 0.000* 0.120 1.595 0.112

RCCP −0.112 −1.570 0.118 −0.134 −1.989 0.048* −0.058 −0.958 0.339

MRP 0.009 0.109 0.914 0.091 1.133 0.259 0.093 1.277 0.203

CRP 0.144 2.104 0.036 0.001 0.010 0.992 0.050 0.756 0.450

Shop flow scheduling

0.033 0.450 0.653 −0.004 −0.049 0.961 0.051 0.745 0.457

Purchasing management

−0.042 −0.502 0.616 0.225 3.217 0.001* 0.074 0.932 0.352

R2 0.317 0.349 0.370

∆R2 0.017 0.011 0.026

F 20.921 24.102 33.229

Sig. F 0.000 0.000 0.000

na 231 231 231

MRP system 0.529 9.427 0.000* 0.536 9.616 0.000* 0.581 10.800 0.000*

R2 0.280 0.288 0.337

∆R2 0.280 0.288 0.337

F 88.87 92.464 116.649

Sig. F 0.000 0.000 0.000

na 231 231 231

aNumber of enterprises that have implemented MRP system.

*p < 0.05.



For PP, 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 PC 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.

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 PC 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 versus 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 industrialised countries and there is no earlier 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 centre of Table 9. 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 less than 0.0001. This suggests that the greater the implementation degree of JIT + MRP system, the better the operational performance.

Table 9 Regression result for the relationship between production performance and integrated system of MRP & JIT


JIT 0.196 2.703 0.007* 0.138 1.905 0.058 0.182 2.629 0.009*

MRP 0.407 5.615 0.000* 0.459 6.358 0.000* 0.474 6.849 0.000*

R2 0.301 0.306 0.362

∆R2 0.301 0.306 0.362

F 45.157 46.350 59.690

Sig. F 0.000 0.000 0.000

na 213 213 213


Table 9 Regression result for the relationship between production performance and integrated system of MRP & JIT (continued)


JIT + MRP systems

0.541 9.342 0.000* 0.536 9.213 0.000* 0.589 10.580 0.000*

R2 0.293 0.287 0.347

∆R2 0.293 0.287 0.347

F 87.282 84.878 111.936

Sig. F 0.000 0.000 0.000

na 213 213 213

aNumber of enterprises in which JIT and MRP coexist.

*p < 0.05.


Our study adds evidence to support the argument that there is a 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, 1992). The ensuing problem is what type of roles MRP and JIT should play in a hybrid system. This can be answered by the discussion of Hypothesis 4 below.

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 a 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 9 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.

This result seems reasonable and justifiable. For firms that employ pure JIT strategy, JIT would be the only ‘guru’ leading the firm’s operation. 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 show dominance in PC. This does not imply that JIT is inferior. Rabinovich and Evers (2002) suggests that MRP and JIT are substitutes for each other; we found JIT fulfils MRP. A JIT imbedded MRP system is a much more efficient system as evidenced by our results in Table 9.


5 Conclusion

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 summarised 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 multifunction 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 have a positive influence on 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 most important success factors 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 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 PC. However, a JIT imbedded MRP system is a much more efficient system and combining the MRP and JIT philosophies helps create synergy and attain a performance better than implementing any 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. Firstly, the samples in effect were collected from economic developed zones and information from west and north China is limited. Secondly, the survey results were collected from different university’s par-time MBA students who work in the adjacent areas, thus the sample though representative is not complete random. Our work may have provided sufficient empirical insights to the current JIT and MRP practice in China, but a more comprehensive understanding of the advanced manufacturing of planning and control technologies employed in China may be warranted.

5.3 Future research

To enhance our knowledge about the PPC technologies employed in modern China, we propose the following future research.

1 Although we have obtained some valuable insights and broadened our knowledge of JIT (Lean Production) and MRP (MRPII and ERP) practice in China, we may replicate this study by employing different sampling approaches, increasing the sample size and collecting wider-ranging manufacturing firms to derive more information and higher reliability.

2 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.

3 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.

4 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 they be implemented sequentially? Moreover, when and how should they be integrated together?

5 Since it is not clear which PPC 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.

Acknowledgements

The authors would like to thank the anonymous referees and the editor for their helpful and invaluable comments. This research was partially supported by the National Natural Science Foundation of China (# 70271023, #70672078).


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Appendix

Questionnaire

1 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) The industry my company belongs to

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 used.

Function of JIT Degree of implementation

a. Set-up time reduction 1 2 3 4 5 b. Small lot sizing 1 2 3 4 5 c. Quality circle and TQM 1 2 3 4 5 d. JIT purchasing 1 2 3 4 5 e. Pull production line 1 2 3 4 5 f. Cross-training and multifunction employee 1 2 3 4 5 g. ‘5S’ activities: workplace organisation and standardisation 1 2 3 4 5 h. KANBAN system 1 2 3 4 5 i. Scheduling stability 1 2 3 4 5 j. Total production maintenance (TPM) 1 2 3 4 5


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 1 2 3 4 5

b. Master Production Scheduling (MPS) 1 2 3 4 5

c. Rough Cut Capacity Planning (RCCP) 1 2 3 4 5

d. Materials Requirements Planning (MRP) 1 2 3 4 5

e. Capacity Requirement Planning (CRP) 1 2 3 4 5

f. Shop flow scheduling and control 1 2 3 4 5

g. Inventory management 1 2 3 4 5

h. Purchasing/supplier management 1 2 3 4 5

i. Equipment maintenance management 1 2 3 4 5

j. Basic data management 1 2 3 4 5

3 Production performance

Please indicate the perceived 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.

Perceived production performance Degree

a. Effectiveness of production planning 1 2 3 4 5

b. Accuracy of demand forecasting 1 2 3 4 5

c. Information sharing degree of cross-function 1 2 3 4 5

d. Flexibility of production planning 1 2 3 4 5

e. Data accuracy of production planning 1 2 3 4 5

f. Accuracy of completing production plan 1 2 3 4 5

g. level of WIP reduction 1 2 3 4 5

h. Degree of on-time delivery 1 2 3 4 5

i. Satisfaction degree of quality 1 2 3 4 5

j. Operations cost 1 2 3 4 5

manufacturing planning and control technology versus ... · manufacturing planning and control...

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