The Role of Information Systems on Supply Chain Performance in a Just-In-Time Environment

(1)

The Role of Information Systems on Supply Chain

Performance in a Just-In-Time Environment

Master thesis, MSc, specialization Supply Chain Management University of Groningen, Faculty of Economics and Business

June 24th_{, 2019} Author JUDITH KUIPERS Student number: S3146782 e-mail: j.kuipers.19@student.rug.nl Supervisor/ university

Dr. Ir. Thomas Bortolotti

Co-assessor/ university

Dr. X. (Bruce) Tong

Acknowledgements: This master thesis is the final part of my master’s study in supply chain

(2)

Abstract

During the last decade, consumers have gained significant more options to fulfil their needs, in terms of purchases, than before due to globalization. Globalization increases the competition between companies. Just-In-time and information systems are tools helping the company to become more competitive. Just-In-Time (JIT) enables companies to respond quicker to the market and supply chain changes. Information systems contribute by sharing information with the lead firm’s suppliers to improve its supply chain planning, production processes, and delivery practices. The purpose of this study is to investigate the role of information systems between supply chain actors, conducting JIT, on the performance of manufacturing plants. The data for this study is obtained by an ongoing survey of the study high-performance manufacturing from worldwide manufacturing plants. The results show an antecedent effect of information system to JIT supply on performance. Future research should investigate the effects of the different categories of information systems to JIT.

Keywords: globalization, competition, just-in-time, information systems, high-performance

(3)

Table of Content

1. Introduction ... 1 2. Theoretical background ... 4 2.1. JIT ... 4 2.2. JIT manufacturing ... 5 2.3. JIT supply ... 6 2.4. Information system ... 6

2.5. Relation JIT supply and JIT manufacturing ... 7

2.6. Performance ... 8 2.7. Conceptual model ... 9 3. Method ... 12 3.1. Data collection ... 12 3.2. Survey selection ... 12 3.3. Measurement ... 14 3.4. Data analysis ... 14 3.5. Validity ... 16 3.6. Reliability ... 18 4. Results ... 20

4.1. Moderator effect of information system ... 20

4.2. Individual items of information system as moderator effect ... 23

4.3. Antecedent effect of Information system ... 23

4.4. Individual items of Information system as antecedent effect ... 26

4.5. Additional analysis ... 28

5. Discussion ... 33

5.1. Theoretical implications ... 33

5.2. Practical implications ... 36

5.3. Limitations and future research ... 37

(4)

1

1. Introduction

During the last decade consumers have gained significantly more options to fulfil their needs, in terms of purchases, than before due to globalization. Globalization increases the possibilities for consumers causing more competition between companies (Leidner, 2010). Companies must possess competitive priorities to satisfy customers, and to maintain or gain market share competitive (Krajewski, Ritzman, & Malhotra, 2013). There are four competitive capabilities, which will serve the company to become competitive in the market; cost, quality, time, and flexibility dimensions. The company selects the capabilities that are the most important for its strategy to become competitive (Krajewski, Ritzman, & Malhotra, 2013). However, companies can consider to implement all four capabilities, because all the capabilities influence each other to drive competitiveness (Joshi, Nepal, Rathore, & Sharma, 2013). When all four capabilities are taking into account, the company will get overall competitiveness in the market (Joshi et al., 2013).

Lean philosophy is a practice which pays attention to all four competitive capabilities, and to the whole supply network. One of the most important elements of lean is Just-In-Time (JIT) (Näslund, 2008). JIT focuses on responsiveness and efficiency in the material flow (Teeravaraprug, Kitiwanwong, & Saetong, 2011). Responsiveness and efficiency reduces the lead time of the production process, and reduces the response time to customer’s requests; this improves the company’s competitiveness (Tu, Vonderembse, & Ragu-Nathan, 2001). Furthermore, JIT is continuously making sure if the right materials are produced, of the right quantity at the right place and the right time (Javadian Kootanaee, Babu, & Talari, 2013).

(5)

2 beneficial to a company once it is implemented. The next paragraph discusses this contradiction more in depth.

Research has shown that JIT implementation improves competitive performance, because of the small lot sizes the company has lower inventory level, quality costs and throughput time (Fullerton & McWatters, 2001). However, other research state that there is limited generalizable evidence that JIT leads to improved performance. They show that in one-third of the 54 companies studied JIT has no significant effect on firm performance, due to the lack of the company’s capability (Eroglu & Hofer, 2011). Furthermore, Mia and Winata (2014) state that there is no significant effect of implementing JIT in a low market competition situation; this is because the investment costs are higher than the financial benefits. This study will investigate the effects of JIT to performance, which will serve the theoretical contribution.

(6)

3 Qrunfleh and Tarafdar (2014) mention that there is still research missing showing the fit between the manufacturing plant and its suppliers regarding the capabilities and sophistication of information systems. Furthermore, other research mention that it is still unclear how information system interacts with the supply chain actors and how this affects the performance (Li & Lin, 2006; Youn, Yang, Kim, & Hong, 2014) Therefore, this research investigates how information system affect JIT Supply and JIT manufacturing and its effect on performance. This results into the main research question:

What is the role of information system in relation to JIT manufacturing and JIT supply?

(7)

4

2. Theoretical background

This chapter explains the theoretical groundings for this research. First, the concept of Just-In-Time (JIT) philosophy will be discussed, which can be subdivided in JIT manufacturing and JIT supply (Bortolotti et al., 2012). Second, the definition of information systems will be explained. Third, the relation between JIT supply and JIT manufacturing will be discussed. Lastly, the performance definition will be explained followed by two conceptual models.

2.1. JIT

Just-In-Time (JIT) originates from Japan in the 1950s; the car manufacturer Toyota adopted JIT to raise productivity and eliminate waste in their production processes (Hutchins, Robinson, & Dornfeld, 2013; Kaneko & Nojiri, 2008). Eliminating waste is requiring the minimum amount of equipment, materials and employees (Hutchins et al., 2013). To keep the minimum amount of materials low the materials are produced and purchased when needed, not in advance. Looking at the right materials produced, of the right quantity at the right place and the right time, requires a lot of coordination, to avoid delays in the production schedule (Javadian Kootanaee et al., 2013). Toyota managed to be competitive in the global market, due to its JIT implementation (Hutchins et al., 2013; Kaneko & Nojiri, 2008).

The success of implementing JIT depends on the performance of the whole company, due to the needed cross-functional activities within the company (Chan, Yin, & Chan, 2010). All the different departments, such as marketing, operations and finance, have to pursue company performance instead of pursuing the optimal department performance in order to successful implement JIT (Chan et al., 2010). Toyota already emphasized this, by saying that JIT can only be implemented successful when every individual within the company is committed and involved to JIT implementation, all the resources and processes are fully used, and if the materials from the supplier are delivered on time to the company. The processes in the supply chain are more aligned with each other when conducting JIT, the productivity, and the efficiency are improved and less waste and costs are present in the production process (Javadian Kootanaee et al., 2013). Because of the involvement of the employees, the communication is improved between employees and supply chain actors (Javadian Kootanaee et al., 2013).

(8)

5 actors because sharing information helps to predict future demand and available supply; thereby reducing the inventory costs, which avoids over supply or shortage of demand (Sun & Yen, 2010). The information add into an information system improves the company’s supply chain planning, JIT production and delivery practices. Because, the information systems provide all the information from the different actors in the supply chain. This improves the decision making of manager and improves flexibility performance (Zhou & Benton, 2007). JIT can be split up into JIT manufacturing and JIT supply (Bortolotti et al., 2012). The following paragraphs will explain these two concepts.

2.2. JIT manufacturing

JIT manufacturing focuses on activities within the company: pull system production, set-up time reduction and daily schedules of the production process (Danese et al., 2012; Cua et al., 2001). The pull production system is a material control system, which controls the throughput times by reducing the amount of work on the operation floor (Germs & Riezebos, 2010; Hopp & Spearman, 2004). Setup time does not add value to the material flow, therefore a company wants to have the smallest setup times as possible. Small setup times create the oppurtunity to have smaller batch size, smaller batch sizes create exchange possibilities between different batches without higher costs. The output will remain the same, but the lead time per product will be lower (Cakmakci, 2009). Schedules based on customer demand contribute to keep the lead time, and setup time in the production line as constant and as low as possible (McMullen & Tarasewich, 2005). Another way to decrease lead time is to change the design of the production line of the shop floor. Because the layout of the shop floor determines the productivity of the material flow, the equipment has to be standing in a logical order to process the materials in an efficient way without doing unnecessary movements with the materials (Hemanand, Amutbuselvan, Raja, & Sundararaja, 2012). Having the layout of the shop floor proper organized leads to less bottlenecks in the production process, leading to consistent product quality (Khan & Tidke, 2013).

A smaller setup time realizes quicker switches between different batches based on customer demand which creates an efficient production process and delivery to customers. So, JIT manufacturing has a positive relation with performance (MacKelprang & Nair, 2010). This relates to the hypothesis 1:

(9)

6 2.3. JIT supply

JIT supply is about the suppliers receiving orders based on the pull logic. This means the manufacturing plant’s supplier only starts production when there is a customer order from the manufacturing plant (Bortolotti et al., 2012). The deliveries from the supplier to the manufacturing plant are fast and frequent with small lot sizes (Bortolotti et al., 2012; Sakakibara, Sadao, Flynn, Barbara B. Schroeder, 1993)(Bortolotti et al., 2012; Sakakibara, Sadao, Flynn, Barbara B. Schroeder, 1993). This leads to an reduce in inventory and increases the production flexibility, which is an important attribute to increase efficiency and responsiveness performance to the customer (Aksoy & Öztürk, 2011; Bortolotti, Danese, & Romano, 2013). Furthermore, the materials of small lot sizes are used in the production process as soon as possible, which decreases inventory costs (Aksoy & Öztürk, 2011). Besides the small lot sizes, the supplier tries to delivery consistent conformance to the product specifications (Bayraktar et al., 2010; Canel, C., Rosen, D., & Anderson, 2000; Kros, Falasca, & Nadler, 2006; Mistry, 2005)

When JIT supply in the supply chain is applied, then the performance of the manufacturing plant will be more positive. This is because the manufacturing plant will have small lot sizes from the supplier, which will create more flexibility and lower inventory of the manufacturing plant to fulfil customer demand which positive effects customer delivery performance. This leads to the hypothesis 2:

JIT supply has a positive effect of the manufacturing plant’s performance. 2.4. Information system

An information system is helpful for the continuous information sharing between the supplier and the manufacturing firm (Frohlich & Westbrook, 2001; Green, Inman, Birou, & Whitten, 2014; Schoenherr & Swink, 2012). An information system is a database intended to collect, enter, and process data and report information for a company to achieve its objectives and goals (Salehi, Rostami, & Mogadam, 2014). The more data is added to the information system the better. Having good relationships between the manufacturing plant and the its suppliers creates trust, trust promotes sharing sensitive information between the two actors. When all the available information is included in the information system, companies are more efficient in meeting customer requirements (Banker, Bardhan, Chang, & Lin, 2006).

(10)

7 inventory and lead time. This has a positive influence on the performance of JIT manufacturing and JIT supply. The company has to keep the activities in the information system up to date, to provide the most effective support for decision making in inventory replenishment, capacity activation, and synchronized material flows (Salehi et al., 2010; Soroor, Tarokh, & Keshtgary, 2009). Telecommunication is important to make information sharing between the manufacturing plant’s supplier and the manufacturing plant easier and faster. Internet, intranet, extranet and EDI are examples of telecommunication networks. In this way information systems can be seen as a resource which helps to increase JIT supply and JIT manufacturing efficiency, and this improves performance (Salehi et al., 2010).

The planning of JIT manufacturing is based by collecting information by the manufacturing plant’s supplier (Bortolotti et al., 2012; Kannan & Tan, 2005). This information must be continuously updated in an integrated information system between the supplier and the manufacturing plant. The knowledge of the up to date information of the supplier creates a schedule which is the most efficient for the production process of the manufacturing plant. This thereby creates a constant or low lead time, setup time and lot size. Information system influence the relation between JIT supply and JIT manufacturing. This leads to hypothesis 3.1:

Information systems act as a moderator between JIT supply and JIT manufacturing. Information systems can furthermore be used as a resource to affect performance when conducting JIT supply or when conducting JIT manufacturing. This leads to the following hypothesis 3.2 and 3.3:

Information system act as antecedent for JIT supply.

Information system act as antecedent for JIT manufacturing.

2.5. Relation JIT supply and JIT manufacturing

(11)

8 Naim, 2010). This uncertainty creates longer lead and setup times, which are elements of the JIT manufacturing. Longer lead and setup time means longer time needed per product to produce by a machine, thereby increasing costs which negatively effects performance. Therefore, uncertainty from the suppliers’ side about when and how many materials to provide to the manufacturing plant leads to negative performance of the manufacturing plant.

Collaboration between the manufacturing plant and its supplier is necessary to align each other needs and capabilities. Sometimes external factors influence the material flow between the manufacturing plant and its supplier, for example bad weather, infrastructure or political situations. When the supplier has direct access to the information, the coordination of the supplier’ orders will go faster (Monden, 1983; Danese et al., 2012). To support the access of information between the manufacturing plant and its supplier, information needs to be shared. This creates a link between the manufacturing plant and the manufacturing plant’s suppliers to share information with each other (Mishra, Kumar, & Garg, 2013). This is only done by a small number of the manufacturing plant’s suppliers who have a long-term relationship with the manufacturing plant, with suppliers who the company can trust. (Kaynak & Hartley, 2006) This is because the maintenance and the continuous information updating is costly and time consuming. The main issue of JIT supply is the trade-off between decreasing inventory costs, by having smaller lot sizes, and the increase of the transportation costs because of the more frequent deliveries of resources. (Chen & Sarker, 2010)

So, lead time can be improved when there is a high level of collaboration between the supplier and the manufacturing plant, because of the joint effort to solve problems and planning (Boon-itt & Wong, 2011). The supplier and the manufacturing plant need to share more real time information to create an integrated JIT material flow. By having this continuous information sharing the manufacturing plant knows in advance if their daily production schedule needs to be adjusted or not. From this we can conclude that JIT supply influences JIT manufacturing (planning, schedules), leading to hypothesis 4:

JIT supply has a positive effect on JIT manufacturing. 2.6. Performance

(12)

9 delivery to fulfil quickly a customer’s order, and the on-time delivery to meet the promised delivery-time. Flexibility consists of speeding up or slowing down the volume and product mix of products (Krajewski, Ritzman, & Malhotra, 2013).

2.7. Conceptual model

As mentioned before there are two ways on how information systems can affect performance, as a moderator or as an antecedent. That is why this study has two conceptual models to analyse the moderator effect (Figure 2.7.1), and the antecedent effect (Figure 2.7.2). The moderator effect is about the interaction of information systems between JIT supply and JIT manufacturing. The antecedent effect is about using information system as resource to implement JIT supply or JIT manufacturing. Hypothesis 3.1 refers to the moderator (interaction) effect of information system and is presented in Figure 2.7.1. Hypothesis 3.2 and 3.3 refers to the antecedent (prior) effect of information system and is presented in Figure 2.7.2.

Figure 2.7.1

Conceptual model for moderator effect

(13)

10

Figure 2.7.2

Conceptual model for antecedent effect

The hypotheses in the conceptual model are based upon literature discussed in the theoretical background. Conducting JIT manufacturing has a positive relation on performance (H1). Conducting JIT supply positive affects performance (H2). Information system act as antecedent to JIT Supply (H3.2) and IS act as antecedent to JIT manufacturing (H3.3). JIT supply positive affects JIT manufacturing (H4).

(14)

11

Figure 2.7.3

Relations JIT supply and JIT manufacturing to the performance indicators (moderator effect)

Figure 2.7.3 shows the sub hypotheses of the moderator effect. The sub hypothesis 1.A states; JIT manufacturing has a positive effect on time/flexibility. Sub hypothesis 1.B states; JIT manufacturing has a positive effect on efficiency (cost). Sub hypothesis 1.C states; JIT manufacturing has a positive effect on quality. Sub hypothesis 2.A states; JIT supply has a positive effect on time/flexibility. Sub hypothesis 2.B states; JIT supply has a positive effect on efficiency (cost). Sub hypothesis 2.C states; JIT supply has a positive effect on quality.

Figure 2.7.4

Relations JIT supply and JIT manufacturing to the performance indicators (antecedent effect)

(15)

12

3. Method

This chapter explains the methodology for this study. First, the chapter explains the data selection and sample study. Then the measurements, and the data analysis is discussed. Thereafter, the validity and reliability of the data is examined.

3.1. Data collection

The study uses secondary data obtained from an international project called the high-performance manufacturing (HPM) study. The HPM study contains surveys answered by a variety of employees of each plant. The questionnaires contain objective and subjective data on performance and company features (MacHuca, Ortega Jiménez, Garrido-Vega, & De Los Ríos, 2011). The questions used in this study are answered by the plant manager, production control manager, inventory manger, supervisor, information systems manager and quality manager. The dataset of the HPM study is chosen because of the different amount of data from over 300 plants worldwide. Among the amount of data, the four variables discussed in this study are included in the HPM study; JIT supply, JIT manufacturing, information system, and performance. A case study is not sufficient for this study, because of the lack of generalizable (Jensen & Rodgers, 2007). A case study would only contain information about a few companies, most likely located in the same country. In this way very specific information can be collected, but this study focusses on the interaction of information system to supply chain actors. This can be different per company, so a survey study must be conducted to give more generalizable results.

3.2. Survey selection

(16)

13

Table 3.2.1

Overview sample of the different types of industries Industry

Total Electronics Machinery Transportation

country Austria 10 7 4 21 China 21 16 14 51 Finland 14 6 10 30 Germany 9 13 19 41 Italy 10 10 7 27 Japan 10 12 13 35 South Korea 10 10 11 31 Spain 9 9 10 28 Sweden 7 10 7 24 United States 9 11 9 29 Total 109 104 104 317

Table 3.2.1 shows that all three industries are equally represented for each country. The different types of plants of the different countries make the results more generalizable, than when only one industry in one country is represented in the dataset.

Table 3.2.2

Overview sample of the different types of companies type

Total Unknown Traditional World-class

country Austria 0 16 5 21 China 45 0 6 51 Finland 0 20 10 30 Germany 41 0 0 41 Italy 0 14 13 27 Japan 1 17 17 35 South Korea 0 13 18 31 Spain 0 14 14 28 Sweden 24 0 0 24 United States 0 14 15 29 Total 111 108 98 317

(17)

14 they are labelled as "Unknown" in Table 3.2.2. All the plants of Germany, Sweden, and a lot of China are labelled as unknown. That is because these plants could not be categorized in either traditional or world class and would be situated in the middle between traditional and world class plants.

3.3. Measurement

The questions, named the items in this study, use Likert scales as measurement in the international High-Performance Manufacturing study. The items selected in this study for the different variables are generated from literature on the practice of manufacturing. There are four variables in this study: JIT supply, JIT manufacturing, information system and performance, as discussed in the theoretical background. In section 3.5, more details are provided on the specific variables. The items for each variable was first tested for the reliability by statistics, the inter-correlation matrices, Cronbach’s alpha, and factor analysis (Machuca, Jiménez, & Garrido-Vega, 2011). Invalid scales were eliminated.

3.4. Data analysis

The data is analysed in SPSS 24 to test the different hypothesis mentioned in the theoretical background. In SPSS model 6 and 7 of the program PROCESS by Hayes are applied to analyse significant effects between information system and JIT. Thereafter, linear regression analysis is conducted to get more details for the specific relation of information system and JIT supply and JIT manufacturing. The standardized beta coefficient are used to determine the effect size between the variables (Menard, 2004). The questions that are used to analyse the different relations of the variables are listed in Appendix A. The descriptive statistics in Table 3.4.1 is used to describe the basic features of the data of the study.

(18)

15

Table 3.4.1

Descriptive statistics of the individual items

Mean Std. Deviation

JIT Supply Sup1 4,24 1,16

Sup2 4,61 1,04

Sup3 4,98 ,88

Sup4 4,28 ,98

JIT Manufacturing Man1 5,32 ,82

Man2 5,01 ,97 Man3 5,28 ,89 Man4 4,77 ,93 Man5 4,63 ,93 Man6 4,68 1,05 Man7 5,00 ,88

Performance

Efficiency/cost Eff1 3,35 ,80 Eff2 3,45 ,75 Flexibility Flex1 3,85 ,73 Flex2 3,80 ,77 Flex3 3,74 ,79 Flex4 3,83 ,81 Quality Qual1 3,88 ,66 Qual2 3,88 ,71

(19)

16 3.5. Validity

The research consists of one dependent variable, two independent variables and a moderator or antecedent. The paragraph discusses how the variables are constructed in this study, to ensure content validity.

3.5.1. JIT supply

The first independent variable is JIT supply with a seven-point Likert scale from one, strongly disagree, to seven, strongly agree, with the items: JIT delivery, on-time delivery, frequent delivery, small lot sizes (Appendix A). The three aforementioned items are used in the paper of Phan and Matsui (2010) to determine whether or not suppliers use JIT. Furthermore, one more item, small lot sizes, is added to get a reliable variable.

3.5.2. JIT manufacturing

The second independent variable is JIT manufacturing with a seven-point Likert scale from one, strongly disagree, to seven, strongly agree with the items: daily schedule, layout shop floor, low setup times, production schedule, balanced capacity, manufacturing capacity, bottleneck (Appendix A). The first three aforementioned are used in the paper of Danese et al. (2012) to determine if JIT manufacturing is applied by the company. More items are added to get reliable data.

3.5.3. Information system

Finally, the moderator or antecedent between JIT Manufacturing and JIT supply of information system and has five dummy variables, with the items: integrated scheduling, information transmission, supply chain management, supplier relationship management, and customer relationship management. The five items are summed together, for which the dummy variable is reversed, so one is no and two is yes, for which the higher sum of information system, the more information systems are used in the company. Thereby is assumed that applying a lot of different types of information systems into the company, the bigger the effect on JIT supply and JIT manufacturing. The sum of the five items are used for the variable information system, to see the moderator and antecedent effects of information system. Thereafter the items are measured separately to see how the individual items of information systems affect JIT.

3.5.4. Performance

(20)

(21)

18 3.6. Reliability

This paragraph discusses the procedures made to get reliable data for the four variables. First, the items were selected for the variables: JIT Manufacturing, JIT Supply, and Performance based on literature. Thereafter, the items were analysed by frequency tables to see if there are missing values. The missing variables were replaced with the mean of the selected item, which is called mean substitution according to Tsikriktsis (2005).

Internal validity is warranted, by having the same scale for all items for each variable, when not, proper action was taken to create same scale items. Then the Cronbach’s alpha is used, for each of the four variables, as a measurement to check the reliability of the items. The value of Cronbach’s alpha has to be higher than ,7 (α >,7) to have acceptable reliable data (Tavakol & Dennick, 2011). Based on the Cronbach’s alpha some items had to be deleted in order to get reliable data for each variable. In Table 3.6.1 the Cronbach’s alpha is given.

Thereafter, the convergent validity and discriminant validity are conducted. The convergent validity makes sure that the different items of the variables are related to each other, and are measuring the same construct (Cunningham, Preacher, & Banaji, 2001). All items from the convergent validity have an item loading greater than ,70 (Karlsson, 2016). The discriminant validity ensures for a empirically unique construct measure (Henseler, Ringle, & Sarstedt, 2014). For the discriminant validity all items have a factor loading lower than ,40. Furthermore unidimensional is tested by the principal component analysis (PCA), using the varimax rotation. Items were deleted when causing issues when not complying to the boundaries. In appendix A are the items listed which are used for the study.

The variables are conducted in SPSS 24 in a versatile modelling tool called PROCESS by Hayes to check if there is an moderator or/and antecedent effect of IS to JIT supply and JIT manufacturing (Andrew F. Hayes, 2012). Model 6 of PROCESS is used to analyse the antecedent effect of information system. Model 7 of PROCESS is used to analyse the moderator effect of information system. The variable of JIT supply is measured by taking the overall mean of the items related to JIT supply. Same is done for the variable JIT manufacturing and performance. For the variable information system are the five items summed up. The higher the sum, the more different kind of information systems are conducting in their company.

(22)

19

Table 3.6.1

Reliability construct

First-order construct Cronbach's alpha Item Factor loading JIT Manufacturing ,822 Man1 ,785 Man2 ,696 Man3 ,685 Man4 ,809 Man5 ,760 Man6 ,772 Man7 ,604 JIT Supply ,702 Sup1 ,828 Sup2 ,711 Sup3 ,665 Sup4 ,694

Performance

Efficiency/cost Eff1 ,828 Eff2 ,831 Eff3 ,621 Flexibility Flex1 ,710 Flex2 ,791 Flex3 ,764 Flex4 ,797 Quality Qual1 ,856 Qual2 ,856

(23)

20

4. Results

In this chapter the results of the analysis of the dataset are presented. First, the results of the moderator effect of information system between JIT supply and JIT manufacturing are shown. Second, the individual items of information systems are presented as moderator. Third, the results of the antecedent effect of information system are presented. Fourth, the individual items of information system as antecedent effect are presented. Thereafter, additional analysis is presented of the effects of JIT to the three performance indicators. Finally, the direct effect of the individual items of information system to performance and the three performance indicators are presented.

4.1. Moderator effect of information systems

In this paragraph the results of the moderator effect of information system are shown. In Table 4.1.1 the specific results of the hypothesis are given. In Figure 4.1.1 the standardized beta coefficients of the outcomes from JIT supply and JIT manufacturing to performance are shows. The standardized beta coefficients display the effect size between the two variables. The higher the value, the higher the effect between the two variables (Peterson & Brown, 2005). Table 4.1.2 states if the hypothesis is confirmed or not based on the results of Table 4.1.1. Table 4.1.1 Results hypotheses Hypothesis Unstandardized coefficient B Standard Error (B) Standardized Coefficient Beta R Square Significant 1. ,179 ,044 ,265 ,143 ,000** 2. ,095 ,040 ,154 ,143 ,019* 3.1 -.045 ,035 -,531 ,366 ,208 4 ,035 ,027 ,058 ,363 ,000**

**. Correlation is significant at the ,01 level (2-tailed). *. Correlation is significant at the ,05 level (2-tailed).

(24)

21

Figure 4.1.1

Standardized beta coefficients of moderator effect

Hypothesis 1 states a positive relation between JIT manufacturing and performance. In Table 4.1.1, H1 has a significant level lower than ,01. This indicates that there is a relation between JIT manufacturing and performance. The coefficient level in Figure 4.1.1 shows a positive effect size of ,179 between JIT manufacturing and performance. This indicates the increase of JIT manufacturing with one unit, leads to an increase of performance with ,179. The R square of ,143 indicates that 14,3 percent explains the variance of the dependent variable which can be explained by the independent variable (Israeli, 2007). This means that JIT Manufacturing explains 14,3 percent variance of performance.

Hypothesis 2 states a positive relation between JIT supply and performance with an effect size of ,154 and a significance level lower than ,05 (Table 4.1.1).

Hypothesis 3.1 states a moderation effect of information system between JIT supply and JIT manufacturing. Table 4.1.1 shows no significance level for H3.1. This indicates that there is no moderation effect of information system between JIT supply and JIT manufacturing.

(25)

22

Table 4.1.2

Overview hypotheses

Hypothesis Results

1. JIT manufacturing has a positive relation with the manufacturing plant’s performance. Confirmed

2. JIT supply has a positive effect of the manufacturing plant’ performance. Confirmed

3.1 Information system act as a moderator between JIT supply and JIT manufacturing. Not confirmed

4. JIT supply has a positive effect on JIT manufacturing. Confirmed

(26)

23 4.2. Individual items of information systems as moderator effect

In Table 4.2.1 the different outcomes for each item of information system acting as moderator is presented. Based on the results described in section 4.1, hypothesis 1, 2, and 4 are confirmed. Hypothesis 3.1 is not confirmed for the sum of all information system items (IS1, IS2, IS3, IS4, and IS5, listed in Appendix A) acting as moderator. This paragraph analyses the effect of the separate items (IS1, IS2, IS3, IS4, and IS5) of information system acting as moderator. In Appendix A the full descriptions of the items of the variable Information system are listed. The results are presented in Table 4.2.1.

Table 4.2.1

Moderator effect of the individual information systems between JIT supply and JIT manufacturing It em U ns ta nda rdi ze d coe ffi ci ent B S ta nda rd E rror (B) Sta nda rdi ze d Coe ffi ci ent Be ta R S qua re S igni fi ca nt IS1 -,008 ,110 -,025 ,359 ,942 IS2 -,132 ,083 -,479 ,380 ,111 IS3 ,058 ,099 ,198 ,360 ,560 IS4 ,056 ,111 -,169 ,360 ,616 IS5 -,185 ,082 -,717 ,370 ,026*

The results in Table 4.2.1 show a negative moderator effect of IS5 between JIT supply and JIT manufacturing of a negative effect size of ,717. Item IS5 contains the information system of the customer relationship management.

4.3. Antecedent effect of information systems

(27)

24 confirmed or not based on the results of Table 4.3.1. Figure 4.3.1 presents an overview of the results. Table 4.3.1 Results hypotheses Hypothesis Unstandardized coefficient B Standard Error (B) Standardized Coefficient Beta R Square Significant 1. ,175 ,044 ,259 ,147 ,000** 2. ,092 ,040 ,148 ,147 ,024* 3.2 ,097 ,036 ,148 ,022 ,008** 3.3 ,035 ,027 ,058 ,363 ,200 4 ,541 ,042 ,591 ,363 ,000**

Table 4.3.1 shows the significant effects (p < ,01) of JIT manufacturing to performance (H1). JIT supply significant effects (p < ,05) performance (H2). The table show an antecedent effect (p < ,01) of information system to JIT supply (H3.1). There is no antecedent effect of information system to JIT manufacturing. There is a significant effect (p < ,01) of JIT supply to JIT manufacturing (H4).

Figure 4.3.1

(28)

25 **. Correlation is significant at the ,01 level (2-tailed).

*. Correlation is significant at the ,05 level (2-tailed).

Hypothesis 1 states a positive relation between JIT manufacturing and performance. In Table 4.3.1, H1 has a significant level lower than ,01. This indicates that there is a relation between JIT manufacturing and performance. The standardized coefficient level shows a positive effect size of ,259 between JIT manufacturing and performance. This indicates the increase of JIT manufacturing with one unit, leads to an increase of performance with ,259. The R square of ,147 indicates that 14,7 percent explains the variance of the dependent variable which can be explained by the independent variable (Israeli, 2007). This means that JIT Manufacturing explains 14,7 percent variance of performance.

Hypothesis 2 states a positive relation between JIT supply and performance. In Table 4.3.1, JIT supply has a significant effect on performance with an effect size of ,148.

Hypothesis 3.2 states an antecedent effect of information system to JIT supply. Table 4.3.1 show a significant antecedent effect of an effect size of ,148. Hypothesis 3.3 states an antecedent effect of information system to JIT manufacturing. Table 4.3.1 show no significant antecedent effect.

Hypothesis 4 states a positive relation between JIT supply and JIT manufacturing. Table 4.3.1 shows a significance effect between JIT supply and JIT manufacturing with an effect size of ,591.

Table 4.3.2

Overview hypotheses

Hypothesis Results

1. JIT manufacturing has a positive relation with the manufacturing plant’s performance. Confirmed

2. JIT supply has a positive effect of the manufacturing plant’ performance. Confirmed

3.2 Information system act as antecedent for JIT supply. Confirmed

3.3 Information system act as antecedent for JIT manufacturing. Not confirmed

4. JIT supply has a positive effect on JIT manufacturing. Confirmed

(29)

26 4.4. Individual items of information systems as antecedent effect

Fist, this paragraph shows the results of the different items of information system acting as antecedent to JIT supply (Table 4.4.1). Second, the paragraph presents the results of the separate items of information system, acting as antecedent to JIT manufacturing. In Appendix A the items of the variable information system are listed.

Table 4.4.1

Antecedent effect of the individual items of information systems to JIT supply.

It em U ns ta nda rdi ze d coe ffi ci ent B S ta nda rd E rror (B) Sta nda rdi ze d Coe ffi ci ent Be ta R S qua re S igni fi ca nt IS1 ,397 ,116 ,189 ,036 ,001** IS2 ,036 ,092 ,022 ,000 ,693 IS3 ,135 ,098 ,077 ,006 ,170 IS4 ,096 ,108 ,050 ,003 ,373 IS5 ,117 ,083 ,079 ,006 ,159

(30)

27

Table 4.4.1

Antecedent effect of the individual items of information systems to JIT manufacturing.

It em U ns ta nda rdi ze d coe ffi ci ent B S ta nda rd E rror (B) Sta nda rdi ze d Coe ffi ci ent Be ta R S qua re S igni fi ca nt IS1 ,019 ,088 ,010 ,359 ,830 IS2 ,185 ,067 ,123 ,375 ,006** IS3 ,028 ,073 ,017 ,360 ,701 IS4 -,038 ,079 -,022 ,360 ,634 IS5 ,019 ,061 ,014 ,359 ,758

(31)

28 4.5. Additional analysis of sub hypotheses

Additional analysis is done on JIT supply and JIT manufacturing affecting the three different performance indicators in Table 4.5.1 and Table 4.5.2. Furthermore, the direct effect of the individual information system items to performance are presented. To see the impact of the individual types of information systems to performance. If there is one type of information system affecting performance, then the company can choose to implement one information system instead of five. This would be less costly and time consuming.

Table 4.5.1

Results sub hypotheses (moderator effect)

H ypot he si s U ns ta nda rdi ze d coe ffi ci ent B S ta nda rd E rror (B) S ta nda rdi ze d Coe ffi ci ent Be ta R S qua re S igni fi ca nt

1.A JIT man à Flexibility ,130 ,059 ,149 ,086 ,028*

1.B JIT man à Efficiency ,283 ,059 ,313 ,134 ,000**

1.C JIT man à Quality ,125 ,063 ,140 ,053 ,049*

2.A JIT supply à Flexibility ,143 ,054 ,178 ,086 ,009* 2.B JIT supply à Efficiency ,066 ,054 ,079 ,134 ,226

2.C JIT supply à Quality ,099 ,059 ,140 ,053 ,093

**. Correlation is significant at the ,01 level (2-tailed). **. Correlation is significant at the ,05 level (2-tailed).

(32)

29

Figure 4.5.1

Standardized beta coefficients of the three performance indicators (moderator effect)

(33)

30

Table 4.5.2

Results sub hypotheses (antecedent effect)

H ypot he si s U ns ta nda rdi ze d coe ffi ci ent B S ta nda rd E rror (B) S ta nda rdi ze d Coe ffi ci ent Be ta R S qua re S igni fi ca nt

1.A JIT man à Flexibility ,129 ,059 ,148 ,086 ,030*

1.B JIT man à Efficiency ,278 ,059 ,307 ,138 ,000**

1.C JIT man à Quality ,119 ,064 ,134 ,057 ,061

2.A JIT supply à Flexibility ,142 ,054 ,177 ,086 ,009* 2.B JIT supply à Efficiency ,061 ,054 ,074 ,138 ,265

2.C JIT supply à Quality ,093 ,059 ,113 ,057 ,114

(34)

31

Figure 4.5.2

Standardized beta coefficients of the three performance indicators (antecedent effect)

Figure 4.5.2 shows an overview of the results of JIT supply and JIT manufacturing to the performance indicators, of the moderator effect. JIT manufacturing has for the performance sections flexibility, and efficiency a significant positive effect. JIT manufacturing has for the performance section quality no significant effect. JIT supply has for a positive significant effect the performance section flexibility. JIT supply has no significant effect for the performance sections efficiency, and quality.

Table 4.5.3

Standard coefficients Item/Performance indicator

Flexibility Efficiency Quality Performance (all three indicators) IS1 ,070 ,068 ,086 ,096 IS2 -,054 ,111* ,053 ,052 IS3 ,004 -,015 -,041 -,023 IS4 ,060 ,032 ,024 ,050 IS5 ,093 ,110* ,141* ,146**

(35)

(36)

33

5. Discussion

This chapter explains the theoretical and practical implications. Thereafter, the limitations and future research are explained.

5.1. Theoretical implications

The objective of this study is to investigate the role of information system to JIT supply and JIT manufacturing affecting performance. Furthermore, the study investigates, the relations between JIT supply, JIT manufacturing and performance.

The results show a positive effect of JIT supply to JIT manufacturing. This is in line with hypothesis 4 and previous literature about joint effort solving problems and planning of the company (Boon-itt & Wong, 2011). Furthermore, sharing information makes the coordination of the material flow go faster (Monden, 1983; Danese et al., 2012). This highlights the importance of companies persuading its supplier to conduct JIT, because of the positive effect for the processes of JIT manufacturing as for the performance of the company.

The result show that JIT manufacturing positively affects performance. This is consistent with previous studies and hypothesis 1, having smaller setup time does create an efficient delivery process to positively affect efficiency performance (MacKelprang & Nair, 2010). Furthermore it confirms the influence of the shop floor design, and schedules, to decrease the lead time of products positively influencing the flexibility performance (McMullen & Tarasewich, 2005; Hemanand, Amutbuselvan, Raja, & Sundararaja, 2012). Furthermore, the right design of the shop floor leads to less bottlenecks in the production process, leading to consistent product quality. This highlights the importance of implementing an efficient shop floor design and schedule to improve the performance of the company.

(37)

34 country diverse than the 317 worldwide plants in the High-Performance manufacturing study. This study can state that there is a significant effect of JIT to performance, this highlights the importance of suppliers applying information system to their daily routine.

The results show no moderation effect of information system between JIT supply and JIT manufacturing. This is not supporting to hypothesis 3.1 and previous literature, on having useful information in a database is beneficial for meeting customer requirements (Banker et al., 2006). Whereas, analysing the moderator effect of the individual items of information system, a negative moderation effect was discovered from the information system item ‘customer relationship management’ (CRM). CRM is known to collect data from the company’s customer, this information about the customer’s preferences leads to lower costs and gaining higher customer value in the production process (Pai & Tu, 2011). The result in this study show a negative moderator effect of CRM, this is because of the overload of information. The manufacturing plants cannot determine which information is important and which not, this leads to negative outcomes (Villena, Revilla, & Choi, 2011).

(38)

35 implementation, but some aspects of that area would not perform well. So the supply chain management could be too broad to analyse every aspect of the supply chain (Zhu et al., 2008). The item supplier relationship management does not act as moderator because, buyer-supplier relationship can have bright and dark sides. The bright side is that shared visions are creates, which increases their commitment. But this leads to group thinking and collective blindness (Villena et al., 2011).

The results show an antecedent effect of information system to JIT supply. This is in line with hypothesis 3.2 and previous literature on information system acting as resource to increase the efficiency of JIT supply (Salehi et al., 2010). The results show that the item "Real-time integrated scheduling, shipping and warehouse management" has an antecedent effect to JIT supply. Literature says that real-time information from the second-tier suppliers can serve the manager of the first-tier supplier to monitor and control the total process. The first supplier can update the progress in real time, and can change the schedule of the production and assemblage (Wang, Lin, & Lin, 2007). So, when suppliers have an overview of the real-time material, then the process of JIT supply increases in efficiency. One single item does not act as antecedent effect, but together with the four other items information system has an antecedent effect. This indicates that using a lot of information systems is good for implementing JIT supply.

The item information transmitting is not acting as antecedent to JIT supply because, second tier suppliers do not have enough time to give the information to the first tier supplier, or information is given at a certain point (every week, every month, or at the end/beginning of the day), to save on transaction costs (Qi & Zhang, 2008). Furthermore, some suppliers have closely related products, which makes the suppliers competitors. The supplier is not willing to share important information to the other supplier, to create an advantages to itself (Qi & Zhang, 2008). The item supplier relationship management do not act as antecedent to JIT supply, because the buyer-supplier relationship can reduce monitoring costs and create willingness to collaborate beyond their contract (Villena et al., 2011). But because of the reduction of monitoring, opportunistic behaviour exists from the supplier side (Villena et al., 2011). The item customer relationship management do not act as antecedent to JIT supply, because of the issue concerning giving the customer a unified face. The customer is showed all the capabilities of marketing, sales, purchasing and collaborations, which is hard to implement in practice (Piercy, 2009).

(39)

36 states that information system provides effective support for decision making in inventory replenishment, capacity activation, and synchronized material flows (Salehi et al., 2010; Soroor et al., 2009). However, the results show a significant antecedent effect of the item "Information transmission" to JIT manufacturing. This is in line with research from Williamson, Harrison and Jordan (2004), stating that the information system provides customers to notify quickly to the company. The database can process the orders very quick, which leads to a reduction in the order cycle time by 50 percent (Williamson et al., 2004). This indicates that there are different categories in information system, that one category affects JIT manufacturing and the other category does not. Some categories of information systems are affecting each other and others not. This highlights the importance to look deeper into the different categories of information systems.

The item integrated scheduling does not act as antecedent to JIT manufacturing, because the scheduling models, which use algorithms, are not able to apply the real time information. The company has to make general measures of utility and stability, in order to determine what changes has to be made in the production schedule (Cowling & Johansson, 2002). Buyer-supplier relationship creates frequent interactions and promotes to share information, this increases the speed to resolve problems (Villena et al., 2011). But there could be an overload of information which leads to negative outcomes (Villena et al., 2011). Which is why customer relationship management does not act as antecedent.

5.2.Practical implications

(40)

37 Furthermore, practical implications are derived from this study regarding information system to JIT supply and JIT manufacturing. These results are beneficial for companies, because the implementation of information systems takes a lot of time and is costly. Information system has an antecedent effect on JIT supply towards a positive effect on performance. This presents the importance of suppliers using information systems, and information system contributes to implementing JIT supply to its customers. The usage of the information system item "integrated scheduling" displays a positive antecedent effect to JIT manufacturing. IT can be very costly to use multiple information systems; in this case the supplier can choose to have only one information system; integrated scheduling. Even though there is no antecedent effect of all the five different information systems together, there is an antecedent effect of one type of information system, called information transmission. Manufacturing plants can implement information transmission, and this will have a positive impact to the company’s its production process.

When the company wants to increase the efficiency performance, then the company can use the information system information transmission or customer relationship management. Both systems have the same impact, so the company can choose one of the two systems to save on costs. To increase the quality performance, the company can choose the information system customer relationship management. As this system will furthermore increase efficiency it is best to choose customer relationship management system over information transmission system. Furthermore, customer relationship management system increases overall performance. Meaning all three indicators of performance, flexibility, efficiency, and quality, are positive effected by the information system customer relationship management.

5.3.Limitations and future research

(41)

38

different categories of information system. So, this study chooses the information system which would likely influence JIT supply and JIT manufacturing.

(42)

39

6. Conclusion

This study intends to contribute to confirm the positive impact of applying JIT in a supply chain to performance, and the effect of implementing JIT to the supplier and the manufacturing plant. The results show a positively effects from both JIT supply and JIT manufacturing to performance, and JIT supply positively influence JIT manufacturing.

Furthermore, the study objective is to get more insight into the role of information systems to supply chain actors conducting JIT. The results show no moderator effect of information systems between JIT supply and JIT manufacturing, whereas one type of information system, customer relationship management, does act as moderator. There is a positive antecedent effect of information system to JIT supply. In contradiction to no antecedent effect of information system to JIT manufacturing, whereas one type of information system, information transmission, does act as antecedent to JIT manufacturing.

(43)

I

7. Reference

Aksoy, A., & Öztürk, N. (2011). Supplier selection and performance evaluation in just-in-time production environments. Expert Systems with Applications, 38(5), 6351–6359. https://doi.org/10.1016/j.eswa.2010.11.104

Andrew F. Hayes. (2012). PROCESS: A Versatile Computational Tool for Observed Variable Mediation, Moderation, and Conditional Process Modeling1 Andrew. https://doi.org/10.1088/1751-8113/44/8/085201

Banker, Bardhan, Chang, & Lin. (2006). Plant Information Systems, Manufacturing Capabilities, and Plant Performance. MIS Quarterly, 30(2), 315. https://doi.org/10.2307/25148733

Bayraktar, E., Gunasekaran, A., Koh, S. C. L., Tatoglu, E., Demirbag, M., & Zaim, S. (2010). An efficiency comparison of supply chain management and information systems practices: A study of Turkish and Bulgarian small- and medium-sized enterprises in food products and beverages. International Journal of Production Research, 48(2), 425–451. https://doi.org/10.1080/00207540903174957

Boon-itt, S., & Wong, C. Y. (2011). The moderating effects of technological and demand uncertainties on the relationship between supply chain integration and customer delivery performance. International Journal of Physical Distribution and Logistics Management, 41(3), 253–276. https://doi.org/10.1108/09600031111123787

Bortolotti, T., Danese, P., & Romano, P. (2012). JIT production, JIT supply and performance: Investigating the moderating effects. Industrial Management and Data Systems, 112(3), 441–465. https://doi.org/10.1108/02635571211210068

Bortolotti, T., Danese, P., & Romano, P. (2013). Assessing the impact of just-in-time on operational performance at varying degrees of repetitiveness. International Journal of Production Research, 51(4), 1117–1130. https://doi.org/10.1080/00207543.2012.678403 Cakmakci, M. (2009). Process improvement: Performance analysis of the setup time reduction-SMED in the automobile industry. International Journal of Advanced Manufacturing Technology, 41(1–2), 168–179. https://doi.org/10.1007/s00170-008-1434-4

(44)

II Chan, H. K., Yin, S., & Chan, F. T. S. (2010). Implementing just-in-time philosophy to reverse logistics systems: A review. International Journal of Production Research, 48(21), 6293– 6313. https://doi.org/10.1080/00207540903225213

Chen, Z. X., & Sarker, B. R. (2010). Multi-vendor integrated procurement-production system under shared transportation and just-in-time delivery system. Journal of the Operational Research Society, 61(11), 1654–1666. https://doi.org/10.1057/jors.2009.115

Cowling, P., & Johansson, M. (2002). Using real time information for effective dynamic scheduling. European Journal of Operational Research, 139(2), 230–244. https://doi.org/10.1016/S0377-2217(01)00355-1

Cunningham, W. A., Preacher, K. J., & Banaji, M. R. (2001). Research Article Consistency , Stability , and Convergent Validity. Psychological Science, 12(2), 163–170.

Eroglu, C., & Hofer, C. (2011). Lean, leaner, too lean? the inventory-performance link revisited.

Journal of Operations Management, 29(4), 356–369.

https://doi.org/10.1016/j.jom.2010.05.002

Farh, J. L., Hackett, R. D., & Liang, J. (2007). Individual-level cultural values as moderators of perceived organizational support-employee outcome relationships in China: Comparing the effects of power distance and traditionality. Academy of Management Journal, 50(3), 715–729.

Flynn, B. B., Huo, B., & Zhao, X. (2010). The impact of supply chain integration on performance: A contingency and configuration approach. Journal of Operations Management, 28(1), 58–71. https://doi.org/10.1016/j.jom.2009.06.001

Frohlich, M. T., & Westbrook, R. (2001). Arcs of integration: An international study of supply chain strategies. Journal of Operations Management, 19(2), 185–200. https://doi.org/10.1016/S0272-6963(00)00055-3

Fullerton, R. R., & McWatters, C. S. (2001). Production performance benefits from JIT implementation. Journal of Operations Management, 19(1), 81–96. https://doi.org/10.1016/S0272-6963(00)00051-6

(45)

III https://doi.org/10.1016/j.ijpe.2010.07.043

Germs, R., & Riezebos, J. (2010). Workload balancing capability of pull systems in MTO production. International Journal of Production Research, 48(8), 2345–2360. https://doi.org/10.1080/00207540902814314

Green, K. W., Inman, R. A., Birou, L. M., & Whitten, D. (2014). Total JIT (T-JIT) and its impact on supply chain competency and organizational performance. International Journal of Production Economics, 147(PART A), 125–135. https://doi.org/10.1016/j.ijpe.2013.08.026

Hemanand, K., Amutbuselvan, D., Raja, S. C., & Sundararaja, G. (2012). Improving productivity of manufacturing division using lean concepts and development of material gravity feeder - a case study. International Journal of Lean Thinking, 3(2), 102–116. Henseler, J., Ringle, C. M., & Sarstedt, M. (2014). A new criterion for assessing discriminant

validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science, 43(1), 115–135. https://doi.org/10.1007/s11747-014-0403-8

Hopp, W. J., & Spearman, M. L. (2004). To Pull or Not to Pull: What Is the Question? Manufacturing & Service Operations Management, 6(2), 133–148. https://doi.org/10.1287/msom.1030.0028

Hutchins, M. J., Robinson, S. L., & Dornfeld, D. (2013). Understanding life cycle social impacts in manufacturing: A processed-based approach. Journal of Manufacturing Systems, 32(4), 536–542. https://doi.org/10.1016/j.jmsy.2013.05.008

Israeli, O. (2007). A Shapley-based decomposition of the R-Square of a linear regression. Journal of Economic Inequality, 5(2), 199–212. https://doi.org/10.1007/s10888-006-9036-6

Javadian Kootanaee, A., Babu, K. N., & Talari, H. (2013). Just-In-Time Manufacturing System: From Introduction to Implement. Ssrn, 1(2), 7–25. https://doi.org/10.2139/ssrn.2253243 Jensen, J. L., & Rodgers, R. (2007). Cumulating the Intellectual Gold of. Public Administration,

61(2), 235–246.

(46)

IV Karlsson, C. (Ed.). (2016). Research methods for operations management. Routledge.

Kaneko, J., & Nojiri, W. (2008). The logistics of Just-in-Time between parts suppliers and car assemblers in Japan. Journal of Transport Geography, 16(3), 155–173. https://doi.org/10.1016/j.jtrangeo.2007.06.001

Kannan, V. R., & Tan, K. C. (2005). Just in time, total quality management, and supply chain management: Understanding their linkages and impact on business performance. Omega, 33(2), 153–162. https://doi.org/10.1016/j.omega.2004.03.012

Kaynak, H., & Hartley, J. L. (2006). Using replication research for just-in-time purchasing construct development. Journal of Operations Management, 24(6), 868–892. https://doi.org/10.1016/j.jom.2005.11.006

Krajewski, L. J., Ritzman, L. P., & Malhotra, M. K. (2013). Operations management. Pearson,. Khan, A. J., & Tidke, D. J. (2013). Designing Facilities Layout for Small and Medium Enterprises. International Journal of Engineering Research and General Science, 1(2). Retrieved from www.ijergs.org

Konecny, P. A., & Thun, J. H. (2011). Do it separately or simultaneously - An empirical analysis of a conjoint implementation of TQM and TPM on plant performance. International Journal of Production Economics, 133(2), 496–507. https://doi.org/10.1016/j.ijpe.2010.12.009

Kros, J. F., Falasca, M., & Nadler, S. S. (2006). Impact of just-in-time inventory systems on OEM suppliers. Industrial Management and Data Systems, 106(2), 224–241. https://doi.org/10.1108/02635570610649871

Leidner, D. E. (2010). Globalization, culture, and information: Towards global knowledge transparency. Journal of Strategic Information Systems, 19(2), 69–77. https://doi.org/10.1016/j.jsis.2010.02.006

Li, S., & Lin, B. (2006). Accessing information sharing and information quality in supply chain management. Decision Support Systems, 42(3), 1641–1656. https://doi.org/10.1016/j.dss.2006.02.011

(47)

V Liu, G., McKone-Sweet, K., & Shah, R. (2009). Assessing the performance impact of supply chain planning in net-enhanced organizations. Operations Management Research, 2(1), 33–43. https://doi.org/10.1007/s12063-009-0022-6

MacHuca, J. A. D., Ortega Jiménez, C. H., Garrido-Vega, P., & De Los Ríos, J. L. P. D. (2011). Do technology and manufacturing strategy links enhance operational performance? Empirical research in the auto supplier sector. International Journal of Production Economics, 133(2), 541–550. https://doi.org/10.1016/j.ijpe.2010.12.010

MacKelprang, A. W., & Nair, A. (2010). Relationship between just-in-time manufacturing practices and performance: A meta-analytic investigation. Journal of Operations Management, 28(4), 283–302. https://doi.org/10.1016/j.jom.2009.10.002

Matson, J. E., & Matson, J. O. (2007). Just-in-time implementation issues among automotive suppliers in the southern USA. Supply Chain Management, 12(6), 432–443. https://doi.org/10.1108/13598540710826362

McMullen, P. R., & Tarasewich, P. (2005). A beam search heuristic method for mixed-model scheduling with setups. International Journal of Production Economics, 96(2), 273–283. https://doi.org/10.1016/j.ijpe.2003.12.010

Menard, S. (2004). Six approaches to calculating standardized logistic regression coefficients. American Statistician, 58(3), 218–223. https://doi.org/10.1198/000313004X946

Mia, L., & Winata, L. (2014). Manufacturing strategy and organisational performance. Journal of Accounting & Organizational Change, 10(1), 83–115. https://doi.org/10.1108/jaoc-10-2011-0048

Mishra, O. P., Kumar, V., & Garg, D. (2013). JIT supply chain; an investigation through general system theory. Management Science Letters, 3(3), 743–752. https://doi.org/10.5267/j.msl.2013.01.011

Mistry, J. J. (2005). Origins of profitability through JIT processes in the supply chain. Industrial

Management and Data Systems, 105(6), 752–768.

https://doi.org/10.1108/02635570510606987

Monden, Y. (1983). Toyota production system. An Integrated Apprpach to Just-In-Time. Näslund, D. (2008). Lean, six sigma and lean sigma: Fads or real process improvement

(48)

VI https://doi.org/10.1108/14637150810876634

Pai, J. C., & Tu, F. M. (2011). The acceptance and use of customer relationship management (CRM) systems: An empirical study of distribution service industry in Taiwan. Expert Systems with Applications, 38(1), 579–584. https://doi.org/10.1016/j.eswa.2010.07.005 Peterson, R. A., & Brown, S. P. (2005). On the use of beta coefficients in meta-analysis. Journal

of Applied Psychology, 90(1), 175–181. https://doi.org/10.1037/0021-9010.90.1.175 Phan, C. A., & Matsui, Y. (2010). Comparative study on the relationship between just-in-time

production practices and operational performance in manufacturing plants. Operations Management Research, 3(3–4), 184–198. https://doi.org/10.1007/s12063-010-0040-4 Piercy, N. F. (2009). Strategic relationships between boundary-spanning functions: Aligning

customer relationship management with supplier relationship management. Industrial

Marketing Management, 38(8), 857–864.

https://doi.org/10.1016/j.indmarman.2009.03.015

Qi, Y., & Zhang, Q. (2008). Research on information sharing risk in supply chain management. 2008 International Conference on Wireless Communications, Networking and Mobile Computing, WiCOM 2008, 1–6. https://doi.org/10.1109/WiCom.2008.1618

Qrunfleh, S., & Tarafdar, M. (2014). Supply chain information systems strategy: Impacts on supply chain performance and firm performance. International Journal of Production Economics, 147(PART B), 340–350. https://doi.org/10.1016/j.ijpe.2012.09.018

Reichhart, A., & Holweg, M. (2007). Creating the customer-responsive supply chain: A reconciliation of concepts. International Journal of Operations and Production Management, 27(11), 1144–1172. https://doi.org/10.1108/01443570710830575

Rose, A. M. N., Deros, B. M., Rahman, M. N. A., & Nordin, N. (2011). Lean manufacturing best practices in SMEs. International Conference on Industrial Engineering and Operation Management, 1(1), 872–877.

Sakakibara, Sadao, Flynn, Barbara B. Schroeder, R. G. (1993). a Framework and Measurement Instrument for Just-in-Time Manufacturing. Production and Operations Management, 2(3), 177–194. https://doi.org/10.1111/j.1937-5956.1993.tb00097.x

(49)

VII and Finance, 2(2), 186–195. https://doi.org/10.5539/ijef.v2n2p186

Sanchez-Rodrigues, V., Potter, A., & Naim, M. M. (2010). The impact of logistics uncertainty on sustainable transport operations. International Journal of Physical Distribution and Logistics Management, 40(1–2), 61–83. https://doi.org/10.1108/09600031011018046 Schoenherr, T., & Swink, M. (2012). Revisiting the arcs of integration: Cross-validations and

extensions. Journal of Operations Management, 30(1–2), 99–115. https://doi.org/10.1016/j.jom.2011.09.001

Sezen, B. (2008). Relative effects of design, integration and information sharing on supply chain performance. Supply Chain Management, 13(3), 233–240. https://doi.org/10.1108/13598540810871271

Soroor, J., Tarokh, M. J., & Keshtgary, M. (2009). Preventing failure in IT-enabled systems for supply chain management. International Journal of Production Research, 47(23), 6543– 6557. https://doi.org/10.1080/00207540802314837

Sun, S., & Yen, J. (2010). Information Supply Chain: A Unified Framework for Information-Sharing, 422–428. https://doi.org/10.1007/11427995_38

Tavakol, M., & Dennick, R. (2011). Making sense of Cronbach’s alpha. International Journal of Medical Education, 2, 53–55. https://doi.org/10.5116/ijme.4dfb.8dfd

Teeravaraprug, J., Kitiwanwong, K., & Saetong, N. (2011). Relationship model and supporting activities of JIT, TQM and TPM. Songklanakarin Journal of Science and Technology, 33(1), 101–106.

Tsikriktsis, N. (2005). A review of techniques for treating missing data in OM survey research.

Journal of Operations Management, 24(1), 53–62.

https://doi.org/10.1016/j.jom.2005.03.001

Tu, Q., Vonderembse, M. A., & Ragu-Nathan, T. S. (2001). Impact of time-based manufacturing practices on mass customization and value to customer. Journal of Operations Management, 19(2), 201–217. https://doi.org/10.1016/S0272-6963(00)00056-5