Recently scalability in a manufacturing system is considered as an area of research for enhancing the techniques and methodologies to meet the challenges of the emerging manufacturing paradigm. In other words, manufacturing systems scalability can further enhance the manufacturing systems operations by providing further optimization. According to the nature of the undertaken problem, we have considered scalability as “the design of a manufacturing system and its machines with adjustable structure that enable system adjustment in response to market demand changes,” (Koren 2010). However, most of the existing manufacturing organizations still exhibit rigid organizational structures and their deterministic approach cannot support the above mentioned requirements. Researchers’ attention to a large extent have been focused on an alternative to the traditional manufacturing system which can meet high flexible manufacturing operations. Several next generation manufacturing systems such as holonic manufacturing systems (Valckenaers et al. 1994), fractal factory (Okino 1993), networked manufacturing systems and bionic manufacturing systems (Ueda 1993; Liu et al. 2002) can be conceptualized as a network of elements that are adaptable to environmental changes in particular when market demand causes turbulent fluctuations. Over the past few years, many studies focused on the newly emerged manufacturing paradigm, networked manufacturing or network based manufacturing, which has the capability to achieve the requirements and functionalities of global manufacturing (Zhou et al. 2010).
Networked manufacturing encapsulates the information and knowledge from product design to manufacturing which enables resource sharing between geographically distributed enterprises to achieve competitive advantages that would be difficult to attain with an individual enterprise (Wiendahl et al. 2007). Networked manufacturing has the ability to change its production mode from make-to-stock to make-to-order. Due to the customized manufacturing environment and competition of delivery times between different manufacturing enterprises, the objective of resource scalability and its effect on the manufacturing system is becoming a critical task. Many manufacturers nowadays try to further push optimizing the performance of the manufacturing system by implementing the scalability function. Different issues related to scalability and its relationship with some of the critical features of recent manufacturing systems are adaptability, flexibility, reconfigurability, etc. (Putnik et al. 2013). A detailed literature review on scalability as an area of research on manufacturing systems is detailed in (Wasserman 1994).
Although many approaches and models have been developed in the recent past, we have identified that there is still a need to address some more issues particularly relevant to the networked manufacturing systems. First, in networked manufacturing systems (NMS) the structure of the network does not affect the performance of the manufacturing system. In other words, the network structure has no influence on analyzing the performance of the manufacturing system. With the obtained networks from social network analysis method (SNAM) on manufacturing systems, there is a possibility to analyze the scalability and its performance on the manufacturing system (Newman 2002). Second, in NMS, the size, scope, and complexity of the network are not defined. On the contrary, with the SNAM on NMS, there is a possibility to define the network size and its functional properties such as centrality measures and network complexity in a much better way (Mendes et al. 2004). Moreover, there is hardly any information regarding the communication flow inside the network structure and the descriptive statistics that can be used to extract some information about the speed/nature of the structure. Various types of topologies and how these topologies affect the search space for exploiting the desired solution is discussed in (Neukum and Ivanov 1994). In their work, authors have presented the descriptive statistics such as average distance, diameter, and distribution sequence of various topologies and found that the series of statistics directly affects the performance of the topologies.
However, much work has been done on a wide range of problems ranging from natural phenomena to military (Lu and Hamilton 1991; Crovella and Bestavros 1996; Roberts and Turcotte 1998; Zhang et al. 2013). A framework to predict the missing quality of service values of the manufacturing services by combining social network and collaborative filtering techniques is presented (Newman and Park 2003). However, there is limited work that has applied a social network kind of analysis on manufacturing problems, in particular NM-problems. In order to give voice to the challenge, in this paper we have analyzed the existing NMS with social network method (SNM) to find the reconfiguration effect of various performance measures of the system. The detailed description of the analysis, method, and framework is presented in later sections.
Since efficiency is a significant part of networked manufacturing problems, the proposed methodology and its characteristics better serves the existing traditional networked manufacturing approach in many ways. The fundamental difference between social networks and non-social networked systems with two important properties are discussed in (Newman 2005). First, the degrees of adjacent vertices in networks are positively correlated in social networks but negatively correlated in most other networks (Watts and Strogatz 1998). Second, high levels of clustering are possible with social networks, whereas in many non-social networks clustering would be expected on the basis of pure chance (Heddaya 2002). In this paper, a case in the context of networked manufacturing is taken. Later, we have shown how a manufacturing execution system data can be extracted and viewed as a network connected with a number of nodes. Later, we map the attributes of the manufacturing system as elements of connecting nodes and the connections between the elements act as interactions where the actual material flows on different resources. Moreover, a framework has been developed and a social network analysis method has been conducted to find the effect of resource scalability and its effect on networked manufacturing system.
The remainder of this paper is organized as follows. In section “Problem Description,” we give a detailed description of a case with the basic assumptions. In section “Framework of the Proposed SNAM Approach,” we presented a framework and the logical steps of the execution of a case with proposed SNM. The detailed SNAM to find the functional properties of the network has been discussed in section “Social Network Analysis Model.” The scalability feature to the networked manufacturing system has been introduced and with the help of clique based social network algorithm the time scale has been measured and its results are presented in section “Scalability with Social Network Analysis Algorithm.” The paper concludes with section “Conclusion and Future Work” which suggests the directions of the future work.