Modeling and analysis of FMS performance variables by ISM, SEM and GTMA approach
Introduction
High expectations of present day customers have become very critical for the manufacturing industries. They need to give prominence to improve the performance of flexible manufacturing system (FMS) to meet the challenges of today’s volatile market (Raj et al., 2012). Flexible manufacturing systems (FMSs) have been developed with the hope that they will be able to tackle new challenges like cost, quality, improve delivery speed and to operate to be more flexible in their operations and to satisfy different market segments.
An FMS can be defined as general-purpose manufacturing machines, coupled with material-handling systems and have the capabilities to perform different types of operations. In these systems, machines and material handling systems are controlled by a central computer system (Jahan et al., 2012). The basic objective of the flexible manufacturing concept is to achieve the efficiency and utilization levels of mass production, while retaining the flexibility of manually operated job shops. The individual machines are quite versatile and capable of performing with many different types of operations (Stecke and Solberg, 1981). Flexibility in manufacturing has been identified as one of the key factors to improve the performance of FMS. A significant challenge for many manufacturers is to achieve flexibility in addition to achieving productivity and quality (Cordero, 1997). FMS is crucial for modern manufacturing to enhance productivity involved with high product proliferation (Dai and Lee, 2012). Jain and Raj (2014b) said that productivity is a key factor in a flexible manufacturing system (FMS) performance, and to improve profitability and the wage earning capacity of employees. Li et al. (2005) proved that a high level of quality leads to high level of performance. FMS promise to provide quality and economies of scope-the ability to achieve productivity and flexibility simultaneously and also to achieve economies of scope by reducing the time and cost of product variety (Cordero, 1997). Jain and Raj (2014a) focused on flexibility to increase the performance of the manufacturing system. Therefore, Manufacturing firms are under constant and intense pressure to improve their operations continuously and efficiently by enhancing quality, productivity, and flexibility. So, performance of manufacturing system can be increased by increasing the quality, productivity, and flexibility of a manufacturing organization. On the basis of the exhaustive literature review and discussions with the industry experts and the academia, 15 variables were identified. These variables are given below with their references.
- 1.
Unit manufacturing cost (D׳Souza and Williams, 2000, Ferdows and De Meyer, 1990, Gupta and Somers, 1992).
- 2.
Unit labor cost (Kazerooni et al., 1997, Oke, 2005, Sriram and Gupta, 1991).
- 3.
Manufacturing lead time (Mehrabi et al., 2000, Öztürk et al., 2006, Sarkis, 1997).
- 4.
Effect of tool life (Buyurgan et al., 2004, Özbayrak and Bell, 2003, Tetzlaff, 1995).
- 5.
Throughput time (Byrne and Chutima, 1997, Chan and Chan, 2004, Özbayrak and Bell, 2003).
- 6.
Set up cost (Das, 1996, Kazerooni et al., 1997, Milgrom and Roberts, 1990).
- 7.
Scrap percentage (Boer et al., 1990, Sharma et al., 2006, Youssef and Al-Ahmady, 2002).
- 8.
Rework percentage (Sharma et al., 2006, Youssef and Al-Ahmady, 2002).
- 9.
Setup time (Boer et al., 1990, Das, 1996, Jain and Raj, 2013b, Kazerooni et al., 1997).
- 10.
Automation (Bessant and Haywood, 1986a, Choe et al., 2015, Gupta, 1988, Martin et al., 1990).
- 11.
Equipment utilization (Banaszak et al., 2000, Kashyap and Khator, 1996, Vosniakos and Mamalis, 1990).
- 12.
Ability of manufacturing of variety of product (Da Silveira, 1998, ElMaraghy, 2005, Jain and Raj, 2013c, Slack, 1987).
- 13.
Capacity to handle new product (Bengtsson and Olhager, 2002, Elkins et al., 2004, Olhager, 1993).
- 14.
Use of automated material handling devices (Beamon, 1998, Kulak, 2005, Vosniakos and Mamalis, 1990).
- 15.
Reduced work in process inventory (Bessant and Haywood, 1986b; Patuwo and Hu, 1998; Shanthikumar and Stecke, 1986; Stecke, 1992).
The main objectives of this paper are as follows:
- •
To identify the variables which affect the Performance of FMS from the literature.
- •
To establish relationship among these variables by using ISM.
- •
To identify the factors/dimensions which affect the Performance of FMS by exploratory factor analysis through SPSS 20.
- •
To confirm the factor structure of the same using confirmatory factor analysis with AMOS 20.
- •
Evaluation of intensity of performance variables of FMS by GTMA.
In the remainder of this paper, in Section 2 an overview of methodology ISM, SEM, and GTMA. Modelling of the variable is analyzed by ISM in Section 3. In Section 4, Model analyses by SEM i.e. EFA and CFA. Evaluation of intensity of performance variables of FMS by GTMA in Section 5. Discussion and conclusion are followed in Section 6.
Section snippets
Methodology
A questionnaire-based survey; ISM approach; EFA; CFA and GTMA approach have been used to accomplish the aims of this research study. These methodologies are separately discussed in the following sections.
ISM model for performance variables of FMS
In this section, the development of the model using ISM is described below.
Model analyses by SEM
Data analysis proceeds in two steps. First the EFA is used to identify the underlying dimensions of performance variables in FMS. Next, CFA to confirm the factor structure of the performance dimensions in FMS.
Evaluation of intensity of variables affecting performance
Analysis of variable takes place by GTMA as given below:
- 1.
After identifying three clear factors through EFA (principal components analysis) and confirming this model by CFA, A digraph is developed for these three factors, as shown in Fig. 6.
- 2.
The digraphs for each category of factors (Fig. 7, Fig. 8, Fig. 9) are developed considering the variables that affect the particular category of factors. The nodes in the digraph represent the variables and their mutual interaction is described by different
Discussion/conclusion
This paper has provided an insight into the modelling and analysis of performance variables of the flexible manufacturing system (FMS). Manufacturing performance is measured by productivity, quality and flexibility. Productivity indicates the efficiency of converting inputs (resources) into outputs. Quality refers to the degree of excellence in making products. Flexibility measures the adaptability to various changes in manufacturing environments (Son and Park, 1987). The ISM model developed in
Limitation and future research directions
The present study applies SEM to a first order three factor structure for fifteen performance variables. SEM could be applied to a more advanced model incorporating a greater number of variables.
Acknowledgment
We would like to thank everyone that participated in this research work, in particular Mr. Pankaj Jain, Mr. Kailash Yadav, Mr. Amarjeet Singh, Mr. Sachin Jain and Mr. Achin Jain from JCB India Limited, New Holland Fiat India Pvt. Limited, Maruti Suzuki India Limited, DENSO Haryana Pvt. Limited, Tata Motors Limited respectively. We thank the all anonymous reviewers of this paper for his or her valuable suggestions, which have helped to improve the quality of this paper.
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